lit.bib

% Encoding: UTF-8

@Article{Abramowitz2019,
  author       = {Gab Abramowitz and Nadja Herger and Ethan Gutmann and Dorit Hammerling and Reto Knutti and Martin Leduc and Ruth Lorenz and Robert Pincus and Gavin A. Schmidt},
  date         = {2019-02},
  journaltitle = {Earth System Dynamics},
  title        = {{ESD} Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing},
  doi          = {10.5194/esd-10-91-2019},
  number       = {1},
  pages        = {91--105},
  volume       = {10},
  file         = {:Abramowitz2019.pdf:PDF},
  groups       = {Reviews, Climate Model Weighting / Reducing uncertainties},
  publisher    = {Copernicus {GmbH}},
}

@Article{Adler2003,
  author       = {Adler, R. F. and Huffman, G. J. and Chang, A. and Ferraro, R. and Xie, P. P. and Janowiak, J. and Rudolf, B. and Schneider, U. and Curtis, S. and Bolvin, D. and Gruber, A. and Susskind, J. and Arkin, P. and Nelkin, E.},
  date         = {2003},
  journaltitle = {Journal of Hydrometeorology},
  title        = {The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present)},
  doi          = {10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2},
  number       = {6},
  pages        = {1147--1167},
  volume       = {4},
  abstract     = {The Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5degrees latitude x 2.5degrees longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The dataset is extended back into the premicrowave era (before mid-1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the rain gauge analysis. The dataset archive also contains the individual input fields, a combined satellite estimate, and error estimates for each field. This monthly analysis is the foundation for the GPCP suite of products, including those at finer temporal resolution. The 23-yr GPCP climatology is characterized, along with time and space variations of precipitation.},
  file         = {:Adler2003.pdf:PDF},
  groups       = {OBS},
  keywords     = {low-orbit microwave, monthly rainfall, gauge observations, geosynchronous ir, satellite, patterns, variability, algorithms, retrieval, anomalies},
}

@InBook{Albritton2001,
  author       = {D. L. Albritton and L. G. Meira Filho and U. Cubasch and X. Dai and Y. Ding and D. J. Griggs and B. Hewitson and J. T. Houghton and I. Isaksen and T. Karl and M. McFarland and V. P. Meleshko and J. F. B. Mitchell and M. Noguer and B. S. Nyenzi and M. Oppenheimer and J. E. Penner and S. Pollonais and T. Stocker and K. E. Trenberth},
  date         = {2001},
  title        = {Technical Summary},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {21--83},
  publisher    = {Cambridge University Press},
  url          = {https://archive.ipcc.ch/ipccreports/tar/wg1/pdf/WG1_TAR-FRONT.PDF},
  file         = {:Albritton2001.pdf:PDF},
  groups       = {IPCC},
  journaltitle = {Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Article{Allen2002,
  author       = {Allen, M. R. and Ingram, W. J.},
  date         = {2002},
  journaltitle = {Nature},
  title        = {Constraints on future changes in climate and the hydrologic cycle},
  doi          = {10.1038/nature01092},
  number       = {6903},
  pages        = {224--232},
  volume       = {419},
  abstract     = {What can we say about changes in the hydrologic cycle on 50-year timescales when we cannot predict rainfall next week? Eventually, perhaps, a great deal: the overall climate response to increasing atmospheric concentrations of greenhouse gases may prove much simpler and more predictable than the chaos of short-term weather. Quantifying the diversity of possible responses is essential for any objective, probability-based climate forecast, and this task will require a new generation of climate modelling experiments, systematically exploring the range of model behaviour that is consistent with observations. It will be substantially harder to quantify the range of possible changes in the hydrologic cycle than in global-mean temperature, both because the observations are less complete and because the physical constraints are weaker.},
  comment      = {1st emergent constraint},
  file         = {:Allen2002.pdf:PDF},
  groups       = {Emergent Constraints},
  keywords     = {atlantic thermohaline circulation, north-atlantic, global precipitation, southern oscillations, water-vapor, atmosphere, model, variability, trends, frequency},
}

@Article{Amos2020,
  author       = {Matt Amos and Paul J. Young and J. Scott Hosking and Jean-François Lamarque and N. Luke Abraham and Hideharu Akiyoshi and Alexander T. Archibald and Slimane Bekki and Makoto Deushi and Patrick Jöckel and Douglas Kinnison and Ole Kirner and Markus Kunze and Marion Marchand and David A. Plummer and David Saint-Martin and Kengo Sudo and Simone Tilmes and Yousuke Yamashita},
  date         = {2020-08},
  journaltitle = {Atmospheric Chemistry and Physics},
  title        = {Projecting ozone hole recovery using an ensemble of chemistry-climate models weighted by model performance and independence},
  doi          = {10.5194/acp-20-9961-2020},
  number       = {16},
  pages        = {9961--9977},
  volume       = {20},
  file         = {:Amos2020.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  publisher    = {Copernicus {GmbH}},
}

@Article{AMSRE2011,
  author       = {AMSR-E},
  date         = {2011},
  journaltitle = {Remote Sensing Systems},
  title        = {AMSR-E Level 3 Sea Surface Temperature for Climate Model Comparison. Ver. 1. PO.DAAC, CA, USA},
  doi          = {10.5067/SST00-1D1M1},
  groups       = {OBS},
}

@Article{Anav2013,
  author       = {Anav, A. and Friedlingstein, P. and Kidston, M. and Bopp, L. and Ciais, P. and Cox, P. and Jones, C. and Jung, M. and Myneni, R. and Zhu, Z.},
  date         = {2013},
  journaltitle = {Journal of Climate},
  title        = {Evaluating the Land and Ocean Components of the Global Carbon Cycle in the {CMIP}5 Earth System Models},
  doi          = {10.1175/Jcli-D-12-00417.1},
  number       = {18},
  pages        = {6801--6843},
  volume       = {26},
  abstract     = {The authors assess the ability of 18 Earth system models to simulate the land and ocean carbon cycle for the present climate. These models will be used in the next Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) for climate projections, and such evaluation allows identification of the strengths and weaknesses of individual coupled carbon-climate models as well as identification of systematic biases of the models. Results show that models correctly reproduce the main climatic variables controlling the spatial and temporal characteristics of the carbon cycle. The seasonal evolution of the variables under examination is well captured. However, weaknesses appear when reproducing specific fields: in particular, considering the land carbon cycle, a general overestimation of photosynthesis and leaf area index is found for most of the models, while the ocean evaluation shows that quite a few models underestimate the primary production. The authors also propose climate and carbon cycle performance metrics in order to assess whether there is a set of consistently better models for reproducing the carbon cycle. Averaged seasonal cycles and probability density functions (PDFs) calculated from model simulations are compared with the corresponding seasonal cycles and PDFs from different observed datasets. Although the metrics used in this study allow identification of some models as better or worse than the average, the ranking of this study is partially subjective because of the choice of the variables under examination and also can be sensitive to the choice of reference data. In addition, it was found that the model performances show significant regional variations.},
  file         = {:Anav2013.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {ranking methods, coupled models, land surface model, model comparison, model evaluation, performance, biosphere-atmosphere interaction, leaf-area index, ar4 climate models, interannual variability, atmospheric co2, maximum temperature, minimum temperature, part i, vegetation, validation, transport, read},
  readstatus   = {read},
}

@Misc{Andela2020,
  author    = {Andela, Bouwe and Broetz, Bjoern and de Mora, Lee and Drost, Niels and Eyring, Veronika and Koldunov, Nikolay and Lauer, Axel and Mueller, Benjamin and Predoi, Valeriu and Righi, Mattia and Schlund, Manuel and Vegas-Regidor, Javier and Zimmermann, Klaus and Adeniyi, Kemisola and Arnone, Enrico and Bellprat, Omar and Berg, Peter and Bock, Lisa and Caron, Louis-Philippe and Carvalhais, Nuno and Cionni, Irene and Cortesi, Nicola and Corti, Susanna and Crezee, Bas and Davin, Edouard Leopold and Davini, Paolo and Deser, Clara and Diblen, Faruk and Docquier, David and Dreyer, Laura and Ehbrecht, Carsten and Earnshaw, Paul and Gier, Bettina and Gonzalez-Reviriego, Nube and Goodman, Paul and Hagemann, Stefan and von Hardenberg, Jost and Hassler, Birgit and Hunter, Alasdair and Kadow, Christopher and Kindermann, Stephan and Koirala, Sujan and Lledó, Llorenç and Lejeune, Quentin and Lembo, Valerio and Little, Bill and Loosveldt-Tomas, Saskia and Lorenz, Ruth and Lovato, Tomas and Lucarini, Valerio and Massonnet, François and Mohr, Christian Wilhelm and Amarjiit, Pandde and Pérez-Zanón, Núria and Phillips, Adam and Russell, Joellen and Sandstad, Marit and Sellar, Alistair and Senftleben, Daniel and Serva, Federico and Sillmann, Jana and Stacke, Tobias and Swaminathan, Ranjini and Torralba, Verónica and Weigel, Katja},
  date      = {2020},
  title     = {{ESMValTool}},
  doi       = {10.5281/ZENODO.3401363},
  copyright = {Apache License 2.0},
  groups    = {Code, ESMValTool},
  publisher = {Zenodo},
}

@Misc{Andela2020a,
  author    = {Andela, Bouwe and Broetz, Bjoern and de Mora, Lee and Drost, Niels and Eyring, Veronika and Koldunov, Nikolay and Lauer, Axel and Predoi, Valeriu and Righi, Mattia and Schlund, Manuel and Vegas-Regidor, Javier and Zimmermann, Klaus and Bock, Lisa and Diblen, Faruk and Dreyer, Laura and Earnshaw, Paul and Hassler, Birgit and Little, Bill and Loosveldt-Tomas, Saskia and Smeets, Stef and Camphuijsen, Jaro and Gier, Bettina K. and Weigel, Katja and Hauser, Mathias and Kalverla, Peter and Galytska, Evgenia and Cos-Espuña, Pep and Pelupessy, Inti and Koirala, Sujan and Stacke, Tobias and Alidoost, Sarah and Jury, Martin},
  date      = {2020},
  title     = {{ESMValCore}},
  doi       = {10.5281/ZENODO.3387139},
  copyright = {Apache License 2.0},
  groups    = {Code, ESMValTool},
  publisher = {Zenodo},
}

@Article{Andrews2012,
  author       = {Andrews, T. and Gregory, J. M. and Webb, M. J. and Taylor, K. E.},
  date         = {2012},
  journaltitle = {Geophysical Research Letters},
  title        = {Forcing, feedbacks and climate sensitivity in {CMIP}5 coupled atmosphere-ocean climate models},
  doi          = {10.1029/2012gl051607},
  number       = {9},
  pages        = {L09712},
  volume       = {39},
  abstract     = {We quantify forcing and feedbacks across available CMIP5 coupled atmosphere-ocean general circulation models (AOGCMs) by analysing simulations forced by an abrupt quadrupling of atmospheric carbon dioxide concentration. This is the first application of the linear forcing-feedback regression analysis of Gregory et al. (2004) to an ensemble of AOGCMs. The range of equilibrium climate sensitivity is 2.1-4.7 K. Differences in cloud feedbacks continue to be important contributors to this range. Some models show small deviations from a linear dependence of top-of-atmosphere radiative fluxes on global surface temperature change. We show that this phenomenon largely arises from shortwave cloud radiative effects over the ocean and is consistent with independent estimates of forcing using fixed sea-surface temperature methods. We suggest that future research should focus more on understanding transient climate change, including any time-scale dependence of the forcing and/or feedback, rather than on the equilibrium response to large instantaneous forcing. Citation: Andrews, T., J. M. Gregory, M. J. Webb, and K. E. Taylor (2012), Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models, Geophys. Res. Lett., 39, L09712, doi: 10.1029/2012GL051607.},
  comment      = {ECS for CMIP5 models},
  file         = {:Andrews2012.pdf:PDF},
  groups       = {Feedbacks, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {mechanisms, printed, read},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Andrews2019,
  author       = {Timothy Andrews and Martin B. Andrews and Alejandro Bodas-Salcedo and Gareth S. Jones and Till Kuhlbrodt and James Manners and Matthew B. Menary and Jeff Ridley and Mark A. Ringer and Alistair A. Sellar and Catherine A. Senior and Yongming Tang},
  date         = {2019-12},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {Forcings, Feedbacks, and Climate Sensitivity in {HadGEM}3-{GC}3.1 and {UKESM}1},
  doi          = {10.1029/2019ms001866},
  number       = {12},
  pages        = {4377--4394},
  volume       = {11},
  file         = {:Andrews2019.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming, Feedbacks},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Annan2020,
  author       = {Annan, J. D. and Hargreaves, J. C. and Mauritsen, T. and Stevens, B.},
  date         = {2020},
  journaltitle = {Earth System Dynamics},
  title        = {What could we learn about climate sensitivity from variability in the surface temperature record?},
  doi          = {10.5194/esd-11-709-2020},
  number       = {3},
  pages        = {709--719},
  volume       = {11},
  abstract     = {We examine what can be learnt about climate sensitivity from variability in the surface air temperature record over the instrumental period, from around 1880 to the present. While many previous studies have used trends in observational time series to constrain equilibrium climate sensitivity, it has also been argued that temporal variability may also be a powerful constraint. We explore this question in the context of a simple widely used energy balance model of the climate system. We consider two recently proposed summary measures of variability and also show how the full information content can be optimally used in this idealised scenario. We find that the constraint provided by variability is inherently skewed, and its power is inversely related to the sensitivity itself, discriminating most strongly between low sensitivity values and weakening substantially for higher values. It is only when the sensitivity is very low that the variability can provide a tight constraint. Our investigations take the form of "perfect model" experiments, in which we make the optimistic assumption that the model is structurally perfect and all uncertainties (including the true parameter values and nature of internal variability noise) are correctly characterised. Therefore the results might be interpreted as a best-case scenario for what we can learn from variability, rather than a realistic estimate of this. In these experiments, we find that for a moderate sensitivity of 2.5 degrees C, a 150-year time series of pure internal variability will typically support an estimate with a 5 \%-95\% range of around 5 degrees C (e.g. 1.9-6.8 degrees C). Total variability including that due to the forced response, as inferred from the detrended observational record, can provide a stronger constraint with an equivalent 5 \%-95 \% posterior range of around 4 degrees C (e.g. 1.8-6.0 degrees C) even when uncertainty in aerosol forcing is considered. Using a statistical summary of variability based on autocorrelation and the magnitude of residuals after detrending proves somewhat less powerful as a constraint than the full time series in both situations. Our results support the analysis of variability as a potentially useful tool in helping to constrain equilibrium climate sensitivity but suggest caution in the interpretation of precise results.},
  file         = {:Annan2020.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {constraints, models},
}

@Article{Arora2011,
  author       = {Arora, V. K. and Scinocca, J. F. and Boer, G. J. and Christian, J. R. and Denman, K. L. and Flato, G. M. and Kharin, V. V. and Lee, W. G. and Merryfield, W. J.},
  date         = {2011},
  journaltitle = {Geophysical Research Letters},
  title        = {Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases},
  doi          = {10.1029/2010gl046270},
  number       = {5},
  pages        = {L05805},
  volume       = {38},
  abstract     = {The response of the second-generation Canadian earth system model (CanESM2) to historical (1850-2005) and future (2006-2100) natural and anthropogenic forcing is assessed using the newly-developed representative concentration pathways (RCPs) of greenhouse gases (GHGs) and aerosols. Allowable emissions required to achieve the future atmospheric CO2 concentration pathways, are reported for the RCP 2.6, 4.5 and 8.5 scenarios. For the historical 1850-2005 period, cumulative land plus ocean carbon uptake and, consequently, cumulative diagnosed emissions compare well with observation-based estimates. The simulated historical carbon uptake is somewhat weaker for the ocean and stronger for the land relative to their observation-based estimates. The simulated historical warming of 0.9 degrees C compares well with the observation-based estimate of 0.76 +/- 0.19 degrees C. The RCP 2.6, 4.5 and 8.5 scenarios respectively yield warmings of 1.4, 2.3, and 4.9 degrees C and cumulative diagnosed fossil fuel emissions of 182, 643 and 1617 Pg C over the 2006-2100 period. The simulated warming of 2.3 degrees C over the 1850-2100 period in the RCP 2.6 scenario, with the lowest concentration of GHGs, is slightly larger than the 2 degrees C warming target set to avoid dangerous climate change by the 2009 UN Copenhagen Accord. The results of this study suggest that limiting warming to roughly 2 degrees C by the end of this century is unlikely since it requires an immediate ramp down of emissions followed by ongoing carbon sequestration in the second half of this century. Citation: Arora, V. K., J. F. Scinocca, G. J. Boer, J. R. Christian, K. L. Denman, G. M. Flato, V. V. Kharin, W. G. Lee, and W. J. Merryfield (2011), Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases, Geophys. Res. Lett., 38, L05805, doi:10.1029/2010GL046270.},
  file         = {:Arora2011.pdf:PDF},
  groups       = {Carbon Cycle, TCRE and Remaining Carbon Budgets, CMIP5 models},
  keywords     = {land},
}

@Article{Arora2013,
  author       = {Arora, V. K. and Boer, G. J. and Friedlingstein, P. and Eby, M. and Jones, C. D. and Christian, J. R. and Bonan, G. and Bopp, L. and Brovkin, V. and Cadule, P. and Hajima, T. and Ilyina, T. and Lindsay, K. and Tjiputra, J. F. and Wu, T.},
  date         = {2013},
  journaltitle = {Journal of Climate},
  title        = {Carbon-Concentration and Carbon-Climate Feedbacks in {CMIP}5 Earth System Models},
  doi          = {10.1175/Jcli-D-12-00494.1},
  number       = {15},
  pages        = {5289--5314},
  volume       = {26},
  abstract     = {The magnitude and evolution of parameters that characterize feedbacks in the coupled carbon-climate system are compared across nine Earth system models (ESMs). The analysis is based on results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1\% yr(-1). These simulations are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CO2 fluxes between the atmosphere and underlying land and ocean respond to changes in atmospheric CO2 concentration and to changes in temperature and other climate variables. The carbon-concentration and carbon-climate feedback parameters characterize the response of the CO2 flux between the atmosphere and the underlying surface to these changes. Feedback parameters are calculated using two different approaches. The two approaches are equivalent and either may be used to calculate the contribution of the feedback terms to diagnosed cumulative emissions. The contribution of carbon-concentration feedback to diagnosed cumulative emissions that are consistent with the 1\% increasing CO2 concentration scenario is about 4.5 times larger than the carbon-climate feedback. Differences in the modeled responses of the carbon budget to changes in CO2 and temperature are seen to be 3-4 times larger for the land components compared to the ocean components of participating models. The feedback parameters depend on the state of the system as well the forcing scenario but nevertheless provide insight into the behavior of the coupled carbon-climate system and a useful common framework for comparing models.},
  file         = {:Arora2013.pdf:PDF},
  groups       = {Carbon Cycle, Feedbacks},
  keywords     = {carbon cycle, carbon dioxide, ecosystem effects, general-circulation model, global vegetation model, ocean-circulation, cycle feedback, sensitivity, atmosphere, ecosystem, photosynthesis, simulation, dynamics, printed, read},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Arora2020,
  author       = {Vivek K. Arora and Anna Katavouta and Richard G. Williams and Chris D. Jones and Victor Brovkin and Pierre Friedlingstein and Jörg Schwinger and Laurent Bopp and Olivier Boucher and Patricia Cadule and Matthew A. Chamberlain and James R. Christian and Christine Delire and Rosie A. Fisher and Tomohiro Hajima and Tatiana Ilyina and Emilie Joetzjer and Michio Kawamiya and Charles D. Koven and John P. Krasting and Rachel M. Law and David M. Lawrence and Andrew Lenton and Keith Lindsay and Julia Pongratz and Thomas Raddatz and Roland Séférian and Kaoru Tachiiri and Jerry F. Tjiputra and Andy Wiltshire and Tongwen Wu and Tilo Ziehn},
  date         = {2020-08},
  journaltitle = {Biogeosciences},
  title        = {Carbon-concentration and carbon-climate feedbacks in {CMIP}6 models and their comparison to {CMIP}5 models},
  doi          = {10.5194/bg-17-4173-2020},
  number       = {16},
  pages        = {4173--4222},
  volume       = {17},
  file         = {:Arora2020.pdf:PDF},
  groups       = {Carbon Cycle, Feedbacks},
  publisher    = {Copernicus {GmbH}},
}

@Article{Aumann2003,
  author       = {Aumann, H. H. and Chahine, M. T. and Gautier, C. and Goldberg, M. D. and Kalnay, E. and McMillin, L. M. and Revercomb, H. and Rosenkranz, P. W. and Smith, W. L. and Staelin, D. H. and Strow, L. L. and Susskind, J.},
  date         = {2003},
  journaltitle = {Ieee Transactions on Geoscience and Remote Sensing},
  title        = {AIRS/AMSU/HSB on the aqua mission: Design, science objectives, data products, and processing systems},
  doi          = {10.1109/Tgrs.2002.808356},
  number       = {2},
  pages        = {253--264},
  volume       = {41},
  abstract     = {The Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) form an integrated cross-track scanning temperature and humidity sounding system on the Aqua satellite of the Earth Observing System (EOS). AIRS is an infrared spectrometer/radiometer that covers the 3.7-15.4-tim spectral range with 2378 spectral channels. AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz. HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz. In addition to supporting the National Aeronautics and Space Administration's interest in process study and climate research, AIRS is the first hyperspectral infrared radiometer designed to support the operational requirements for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers. AIRS, together with the AMSU and HSB microwave radiometers, will achieve global retrieval accuracy of better than 1 K in the lower troposphere under clear and partly cloudy conditions. This paper presents an overview of the science objectives, AIRS/AMSU/HSB data products, retrieval algorithms, and the ground-data processing concepts. The EOS Aqua was launched on May 4, 2002 from Vandenberg AFB, CA, into a 705-km-high, sun-synchronous orbit. Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, which has by now been verified during on-orbit testing, we expect the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements.},
  file         = {:Aumann2003.pdf:PDF},
  groups       = {OBS},
  keywords     = {climate, greenhouse gases, humidity sounder, hyperspectral, infrared, microwave, temperature sounder, weather forecasting, satellite},
}

@Article{Balaji2018,
  author       = {Venkatramani Balaji and Karl E. Taylor and Martin Juckes and Bryan N. Lawrence and Paul J. Durack and Michael Lautenschlager and Chris Blanton and Luca Cinquini and Sébastien Denvil and Mark Elkington and Francesca Guglielmo and Eric Guilyardi and David Hassell and Slava Kharin and Stefan Kindermann and Sergey Nikonov and Aparna Radhakrishnan and Martina Stockhause and Tobias Weigel and Dean Williams},
  date         = {2018-09},
  journaltitle = {Geoscientific Model Development},
  title        = {Requirements for a global data infrastructure in support of {CMIP}6},
  doi          = {10.5194/gmd-11-3659-2018},
  number       = {9},
  pages        = {3659--3680},
  volume       = {11},
  file         = {:Balaji2018.pdf:PDF},
  groups       = {CMIP and other MIPs},
  publisher    = {Copernicus {GmbH}},
}

@Article{Barnes2019,
  author       = {Elizabeth A. Barnes and James W. Hurrell and Imme Ebert-Uphoff and Chuck Anderson and David Anderson},
  date         = {2019-11},
  journaltitle = {Geophysical Research Letters},
  title        = {Viewing Forced Climate Patterns Through an {AI} Lens},
  doi          = {10.1029/2019gl084944},
  number       = {22},
  pages        = {13389--13398},
  volume       = {46},
  file         = {:Barnes2019.pdf:PDF},
  groups       = {ML in Climate Sciences},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Bastos2019,
  author       = {Bastos, A. and Ciais, P. and Chevallier, F. and Rodenbeck, C. and Ballantyne, A. P. and Maignan, F. and Yin, Y. and Fernandez-Martinez, M. and Friedlingstein, P. and Penuelas, J. and Piao, S. L. and Sitch, S. and Smith, W. K. and Wang, X. H. and Zhu, Z. C. and Haverd, V. and Kato, E. and Jain, A. K. and Lienert, S. and Lombardozzi, D. and Nabel, J. E. M. S. and Peylin, P. and Poulter, B. and Zhu, D.},
  date         = {2019},
  journaltitle = {Atmospheric Chemistry and Physics},
  title        = {Contrasting effects of {CO}$_2$ fertilization, land-use change and warming on seasonal amplitude of Northern Hemisphere {CO}$_2$ exchange},
  doi          = {10.5194/acp-19-12361-2019},
  number       = {19},
  pages        = {12361--12375},
  volume       = {19},
  abstract     = {Continuous atmospheric CO2 monitoring data indicate an increase in the amplitude of seasonal CO2-cycle exchange (SCA(NBP)) in northern high latitudes. The major drivers of enhanced SCA(NBP) remain unclear and intensely debated, with land-use change, CO2 fertilization and warming being identified as likely contributors. We integrated CO2-flux data from two atmospheric inversions (consistent with atmospheric records) and from 11 state-of-the-art land-surface models (LSMs) to evaluate the relative importance of individual contributors to trends and drivers of the SCA(NBP) of CO2 fluxes for 1980-2015. The LSMs generally reproduce the latitudinal increase in SCA(NBP) trends within the inversions range. Inversions and LSMs attribute SCA(NBP) increase to boreal Asia and Europe due to enhanced vegetation productivity (in LSMs) and point to contrasting effects of CO2 fertilization (positive) and warming (negative) on SCA(NBP). Our results do not support land-use change as a key contributor to the increase in SCA(NBP). The sensitivity of simulated microbial respiration to temperature in LSMs explained biases in SCA(NBP) trends, which suggests that SCA(NBP) could help to constrain model turnover times.},
  file         = {:Bastos2019.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {winter respiration, earth system, vegetation, trends, temperature, ecosystems, model, satellite, dynamics, growth},
}

@Article{Beer2006,
  author       = {Beer, R.},
  date         = {2006},
  journaltitle = {Ieee Transactions on Geoscience and Remote Sensing},
  title        = {TES on the Aura mission: Scientific objectives, measurements, and analysis overview},
  doi          = {10.1109/Tgrs.2005.863716},
  number       = {5},
  pages        = {1102--1105},
  volume       = {44},
  abstract     = {The Tropospheric Emission Spectrometer (TES) is a high-resolution infrared imaging Fourier transform spectrometer specifically aimed at determining the chemical state of the Earth's lower atmosphere (the troposphere). In particular, TES produces vertical profiles 0-32 km of important pollutant and greenhouse gases such as carbon monoxide, ozone, methane, and water vapor on a global scale every other day.},
  file         = {:Beer2006.pdf:PDF},
  groups       = {OBS},
  keywords     = {chemistry, fourier spectroscopy, infrared spectroscopy, ozone, remote sensing, terrestrial atmosphere, tropospheric emission spectrometer},
}

@Article{Bellucci2010,
  author       = {A. Bellucci and S. Gualdi and A. Navarra},
  date         = {2010-03},
  journaltitle = {Journal of Climate},
  title        = {The Double-{ITCZ} Syndrome in Coupled General Circulation Models: The Role of Large-Scale Vertical Circulation Regimes},
  doi          = {10.1175/2009jcli3002.1},
  number       = {5},
  pages        = {1127--1145},
  volume       = {23},
  file         = {:Bellucci2010.pdf:PDF},
  groups       = {Clouds / Aerosols},
  publisher    = {American Meteorological Society},
}

@Article{Bentsen2013,
  author       = {Bentsen, M. and Bethke, I. and Debernard, J. B. and Iversen, T. and Kirkevag, A. and Seland, O. and Drange, H. and Roelandt, C. and Seierstad, I. A. and Hoose, C. and Kristjansson, J. E.},
  date         = {2013},
  journaltitle = {Geoscientific Model Development},
  title        = {The Norwegian Earth System Model, NorESM1-M - Part 1: Description and basic evaluation of the physical climate},
  doi          = {10.5194/gmd-6-687-2013},
  number       = {3},
  pages        = {687--720},
  volume       = {6},
  abstract     = {The core version of the Norwegian Climate Center's Earth System Model, named NorESM1-M, is presented. The NorESM family of models are based on the Community Climate System Model version 4 (CCSM4) of the University Corporation for Atmospheric Research, but differs from the latter by, in particular, an isopycnic coordinate ocean model and advanced chemistry-aerosol-cloud-radiation interaction schemes. NorESM1-M has a horizontal resolution of approximately 2A degrees for the atmosphere and land components and 1A degrees for the ocean and ice components. NorESM is also available in a lower resolution version (NorESM1-L) and a version that includes prognostic biogeochemical cycling (NorESM1-ME). The latter two model configurations are not part of this paper. Here, a first-order assessment of the model stability, the mean model state and the internal variability based on the model experiments made available to CMIP5 are presented. Further analysis of the model performance is provided in an accompanying paper (Iversen et al., 2013), presenting the corresponding climate response and scenario projections made with NorESM1-M.},
  file         = {:Bentsen2013.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {community atmosphere model, madden-julian oscillation, isopycnic coordinate model, general-circulation model, sea-ice, intraseasonal variability, multidecadal variability, atlantic circulation, decadal variability, natural variability},
}

@Article{Bi2013,
  author       = {Bi, D. H. and Dix, M. and Marsland, S. J. and O'Farrell, S. and Rashid, H. A. and Uotila, P. and Hirst, A. C. and Kowalczyk, E. and Golebiewski, M. and Sullivan, A. and Yan, H. L. and Hannah, N. and Franklin, C. and Sun, Z. A. and Vohralik, P. and Watterson, I. and Zhou, X. B. and Fiedler, R. and Collier, M. and Ma, Y. M. and Noonan, J. and Stevens, L. and Uhe, P. and Zhu, H. Y. and Griffies, S. M. and Hill, R. and Harris, C. and Puri, K.},
  date         = {2013},
  journaltitle = {Australian Meteorological and Oceanographic Journal},
  title        = {The ACCESS coupled model: description, control climate and evaluation},
  doi          = {10.22499/2.6301.004},
  number       = {1},
  pages        = {41--64},
  volume       = {63},
  abstract     = {The Australian Community Climate and Earth System Simulator coupled model (ACCESS-CM) has been developed at the Centre for Australian Weather and Climate Research (CAWCR), a partnership between CSIRO1 and the Bureau of Meteorology. It is built by coupling the UK Met Office atmospheric unified model (UM), and other sub-models as required, to the ACCESS ocean model, which consists of the NOAA/GFDL(2) ocean model MOM4p1 and the LANL(3) sea-ice model CICE4.1, under the CERFACS(4) OASIS3.2-5 coupling framework. The primary goal of the ACCESS-CM development is to provide the Australian climate community with a new generation fully coupled climate model for climate research, and to participate in phase five of the Coupled Model Inter-comparison Project (CMIP5). This paper describes the ACCESS-CM framework and components, and presents the control climates from two versions of the ACCESS-CM, ACCESS1.0 and ACCESS1.3, together with some fields from the 20th century historical experiments, as part of model evaluation. While sharing the same ocean sea-ice model (except different setups for a few parameters), ACCESS1.0 and ACCESS1.3 differ from each other in their atmospheric and land surface components: the former is configured with the UK Met Office HadGEM2 (r1.1) atmospheric physics and the Met Office Surface Exchange Scheme land surface model version 2, and the latter with atmospheric physics similar to the UK Met Office Global Atmosphere 1.0 including modifications performed at CAWCR and the CSIRO Community Atmosphere Biosphere Land Exchange land surface model version 1.8. The global average annual mean surface air temperature across the 500-year preindustrial control integrations show a warming drift of 0.35 degrees C in ACCESS1.0 and 0.04 degrees C in ACCESS1.3. The overall skills of ACCESS-CM in simulating a set of key climatic fields both globally and over Australia significantly surpass those from the preceding CSIRO Mk3.5 model delivered to the previous coupled model inter-comparison. However, ACCESS-CM, like other CMIP5 models, has deficiencies in various aspects, and these are also discussed.},
  file         = {:Bi2013.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {convective momentum transport, sea-ice, part i, ocean model, simulation, circulation, scheme, parameterization, representation, surface},
}

@InBook{Bindoff2013,
  author       = {Bindoff, Nathaniel L. and Stott, Peter A. and AchutaRao, Krishna Mirle and Allen, Myles R. and Gillett, Nathan and Gutzler, David and Hansingo, Kabumbwe and Hegerl, G. and Hu, Yongyun and Jain, Suman and Mokhov, Igor I. and Overland, James and Perlwitz, Judith and Sebbari, Rachid and Zhang, Xuebin},
  date         = {2013},
  title        = {Detection and Attribution of Climate Change: from Global to Regional},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {867--952},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter10_FINAL.pdf},
  file         = {:Bindoff2013.pdf:PDF},
  groups       = {IPCC, Detection and Attribution},
  journaltitle = {Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Book{Bishop2006,
  author     = {Bishop, Christopher M.},
  date       = {2006},
  title      = {Pattern recognition and machine learning},
  isbn       = {0387310738},
  file       = {:Bishop2006.pdf:PDF},
  groups     = {ML basics},
  keywords   = {skimmed},
  readstatus = {skimmed},
}

@Article{Bock2020,
  author       = {L. Bock and A. Lauer and M. Schlund and M. Barreiro and N. Bellouin and C. Jones and G. A. Meehl and V. Predoi and M. J. Roberts and V. Eyring},
  date         = {2020-10},
  journaltitle = {Journal of Geophysical Research: Atmospheres},
  title        = {Quantifying Progress Across Different {CMIP} Phases With the {ESMValTool}},
  doi          = {10.1029/2019jd032321},
  number       = {21},
  pages        = {e2019JD032321},
  volume       = {125},
  file         = {:Bock2020.pdf:PDF},
  groups       = {ESMValTool, CMIP and other MIPs},
  keywords     = {own},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{BodasSalcedo2016,
  author       = {A. Bodas-Salcedo and P. G. Hill and K. Furtado and K. D. Williams and P. R. Field and J. C. Manners and P. Hyder and S. Kato},
  date         = {2016-05},
  journaltitle = {Journal of Climate},
  title        = {Large Contribution of Supercooled Liquid Clouds to the Solar Radiation Budget of the Southern Ocean},
  doi          = {10.1175/jcli-d-15-0564.1},
  number       = {11},
  pages        = {4213--4228},
  volume       = {29},
  file         = {:BodasSalcedo2016.pdf:PDF},
  groups       = {Clouds / Aerosols},
  publisher    = {American Meteorological Society},
}

@Article{BodasSalcedo2019,
  author       = {Bodas-Salcedo, A. and Mulcahy, J. P. and Andrews, T. and Williams, K. D. and Ringer, M. A. and Field, P. R. and Elsaesser, G. S.},
  date         = {2019},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {Strong Dependence of Atmospheric Feedbacks on Mixed-Phase Microphysics and Aerosol-Cloud Interactions in HadGEM3},
  doi          = {10.1029/2019ms001688},
  number       = {6},
  pages        = {1735--1758},
  volume       = {11},
  abstract     = {We analyze the atmospheric processes that explain the large changes in radiative feedbacks between the two latest climate configurations of the Hadley Centre Global Environmental model. We use a large set of atmosphere-only climate change simulations (amip and amip-p4K) to separate the contributions to the differences in feedback parameter from all the atmospheric model developments between the two latest model configurations. We show that the differences are mostly driven by changes in the shortwave cloud radiative feedback in the midlatitudes, mainly over the Southern Ocean. Two new schemes explain most of the differences: the introduction of a new aerosol scheme and the development of a new mixed-phase cloud scheme. Both schemes reduce the strength of the preexisting shortwave negative cloud feedback in the midlatitudes. The new aerosol scheme dampens a strong aerosol-cloud interaction, and it also suppresses a negative clear-sky shortwave feedback. The mixed-phase scheme increases the amount of cloud liquid water path (LWP) in the present day and reduces the increase in LWP with warming. Both changes contribute to reducing the negative radiative feedback of the increase of LWP in the warmer climate. The mixed-phase scheme also enhances a strong, preexisting, positive cloud fraction feedback. We assess the realism of the changes by comparing present-day simulations against observations and discuss avenues that could help constrain the relevant processes.},
  file         = {:BodasSalcedo2019.pdf:PDF},
  groups       = {Feedbacks, ECS / TCR / Climate Sensitivity / Warming, Clouds / Aerosols},
  keywords     = {optical depth feedback, liquid water path, emergent constraints, gas-exchange, radiative feedbacks, climate sensitivity, southern-ocean, hadley-center, glomap-mode, wind-speed, skimmed},
  readstatus   = {skimmed},
}

@Article{Bodman2013,
  author       = {Bodman, R. and Rayner, P. J. and Karoly, D. J.},
  date         = {2013},
  journaltitle = {Nature Climate Change},
  title        = {Uncertainty in temperature projections reduced using carbon cycle and climate observations},
  doi          = {10.1038/Nclimate1903},
  number       = {8},
  pages        = {725--729},
  volume       = {3},
  abstract     = {The future behaviour of the carbon cycle is a major contributor to uncertainty in temperature projections for the twenty-first century(1,2). Using a simplified climate model(3), we show that, for a given emission scenario, it is the second most important contributor to this uncertainty after climate sensitivity, followed by aerosol impacts. Historical measurements of carbon dioxide concentrations(4) have been used along with global temperature observations(5) to help reduce this uncertainty. This results in an increased probability of exceeding a 2 degrees C global-mean temperature increase by 2100 while reducing the probability of surpassing a 6 degrees C threshold for non-mitigation scenarios such as the Special Report on Emissions Scenarios A1B and A1FI scenarios(6), as compared with projections from the Fourth Assessment Report(7) of the Intergovernmental Panel on Climate Change. Climate sensitivity, the response of the carbon cycle and aerosol effects remain highly uncertain but historical observations of temperature and carbon dioxide imply a trade-off between them so that temperature projections are more certain than they would be considering each factor in isolation. As well as pointing out the promise from the formal use of observational constraints in climate projection, this also highlights the need for an holistic view of uncertainty.},
  file         = {:Bodman2013.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {model, ocean, skimmed},
  readstatus   = {skimmed},
}

@Article{Booth2012,
  author       = {Booth, B. B. B. and Jones, C. D. and Collins, M. and Totterdell, I. J. and Cox, P. M. and Sitch, S. and Huntingford, C. and Betts, R. A. and Harris, G. R. and Lloyd, J.},
  date         = {2012},
  journaltitle = {Environmental Research Letters},
  title        = {High sensitivity of future global warming to land carbon cycle processes},
  doi          = {10.1088/1748-9326/7/2/024002},
  number       = {2},
  pages        = {024002},
  volume       = {7},
  abstract     = {Unknowns in future global warming are usually assumed to arise from uncertainties either in the amount of anthropogenic greenhouse gas emissions or in the sensitivity of the climate to changes in greenhouse gas concentrations. Characterizing the additional uncertainty in relating CO2 emissions to atmospheric concentrations has relied on either a small number of complex models with diversity in process representations, or simple models. To date, these models indicate that the relevant carbon cycle uncertainties are smaller than the uncertainties in physical climate feedbacks and emissions. Here, for a single emissions scenario, we use a full coupled climate-carbon cycle model and a systematic method to explore uncertainties in the land carbon cycle feedback. We find a plausible range of climate-carbon cycle feedbacks significantly larger than previously estimated. Indeed the range of CO2 concentrations arising from our single emissions scenario is greater than that previously estimated across the full range of IPCC SRES emissions scenarios with carbon cycle uncertainties ignored. The sensitivity of photosynthetic metabolism to temperature emerges as the most important uncertainty. This highlights an aspect of current land carbon modelling where there are open questions about the potential role of plant acclimation to increasing temperatures. There is an urgent need for better understanding of plant photosynthetic responses to high temperature, as these responses are shown here to be key contributors to the magnitude of future change.},
  file         = {:Booth2012.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {carbon cycle, uncertainty, climate change, plant physiology, climate-change, physics, temperatures, emissions, feedback, model, skimmed},
  readstatus   = {skimmed},
}

@InBook{Boucher2013,
  author       = {Boucher, Olivier and Randall, David and Artaxo, Paulo and Bretherton, Christopher S. and Feingold, Gragam and Forster, Piers and Kerminen, V.-M. and Kondo, Yutaka and Liao, Hong and Lohmann, Ulrike},
  date         = {2013},
  title        = {Clouds and Aerosols},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {571--657},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter07_FINAL-1.pdf},
  file         = {:Boucher2013.pdf:PDF},
  groups       = {IPCC, Clouds / Aerosols},
  journaltitle = {Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Article{Boucher2020,
  author       = {Boucher, O. and Servonnat, J. and Albright, A. L. and Aumont, O. and Balkanski, Y. and Bastrikov, V. and Bekki, S. and Bonnet, R. and Bony, S. and Bopp, L. and Braconnot, P. and Brockmann, P. and Cadule, P. and Caubel, A. and Cheruy, F. and Codron, F. and Cozic, A. and Cugnet, D. and D'Andrea, F. and Davini, P. and de Lavergne, C. and Denvil, S. and Deshayes, J. and Devilliers, M. and Ducharne, A. and Dufresne, J. L. and Dupont, E. and Ethe, C. and Fairhead, L. and Falletti, L. and Flavoni, S. and Foujols, M. A. and Gardoll, S. and Gastineau, G. and Ghattas, J. and Grandpeix, J. Y. and Guenet, B. and Guez, L. E. and Guilyardi, E. and Guimberteau, M. and Hauglustaine, D. and Hourdin, F. and Idelkadi, A. and Joussaume, S. and Kageyama, M. and Khodri, M. and Krinner, G. and Lebas, N. and Levavasseur, G. and Levy, C. and Li, L. and Lott, F. and Lurton, T. and Luyssaert, S. and Madec, G. and Madeleine, J. B. and Maignan, F. and Marchand, M. and Marti, O. and Mellul, L. and Meurdesoif, Y. and Mignot, J. and Musat, I. and Ottle, C. and Peylin, P. and Planton, Y. and Polcher, J. and Rio, C. and Rochetin, N. and Rousset, C. and Sepulchre, P. and Sima, A. and Swingedouw, D. and Thieblemont, R. and Traore, A. K. and Vancoppenolle, M. and Vial, J. and Vialard, J. and Viovy, N. and Vuichard, N.},
  date         = {2020},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {Presentation and Evaluation of the IPSL-CM6A-LR Climate Model},
  doi          = {10.1029/2019MS002010},
  number       = {7},
  pages        = {e2019MS002010},
  volume       = {12},
  abstract     = {This study presents the global climate model IPSL-CM6A-LR developed at Institut Pierre-Simon Laplace (IPSL) to study natural climate variability and climate response to natural and anthropogenic forcings as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This article describes the different model components, their coupling, and the simulated climate in comparison to previous model versions. We focus here on the representation of the physical climate along with the main characteristics of the global carbon cycle. The model's climatology, as assessed from a range of metrics (related in particular to radiation, temperature, precipitation, and wind), is strongly improved in comparison to previous model versions. Although they are reduced, a number of known biases and shortcomings (e.g., double Intertropical Convergence Zone [ITCZ], frequency of midlatitude wintertime blockings, and El Nino-Southern Oscillation [ENSO] dynamics) persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL-CM5A-LR used in CMIP5. A large ensemble of more than 30 members for the historical period (1850-2018) and a smaller ensemble for a range of emissions scenarios (until 2100 and 2300) are also presented and discussed.},
  file         = {:Boucher2020.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {ipsl-cm6a-lr</author_keyword>, climate model</author_keyword>, climate metrics</author_keyword>, cmip6</author_keyword>, climate sensitivity</author_keyword>, hemisphere atmospheric blocking, convective boundary-layer, general-circulation model, sea-ice, thickness distribution, mixed-layer, part i, surface-temperature, cumulus convection, soil-moisture},
}

@InBook{Bretherton1990,
  author       = {Bretherton, F. P. and Bryan, K. and Woods, J. D.},
  date         = {1990},
  title        = {Time-Dependent Greenhouse-Gas-Induced Climate Change},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {173--193},
  publisher    = {Cambridge University Press},
  url          = {https://archive.ipcc.ch/ipccreports/far/wg_I/ipcc_far_wg_I_chapter_06.pdf},
  file         = {:Bretherton1990.pdf:PDF},
  groups       = {IPCC, ECS / TCR / Climate Sensitivity / Warming},
  journaltitle = {Climate Change: The IPCC Scientific Assessment},
}

@Article{Bretherton2020,
  author       = {Bretherton, Christopher S. and Caldwell, Peter M.},
  date         = {2020},
  journaltitle = {Journal of Climate},
  title        = {Combining Emergent Constraints for Climate Sensitivity},
  doi          = {10.1175/JCLI-D-19-0911.1},
  number       = {17},
  pages        = {7413--7430},
  volume       = {33},
  abstract     = {A method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy Y such as equilibrium climate sensitivity (ECS). The method is based on fitting a multivariate Gaussian PDF for Y and the emergent constraints using an ensemble of global climate models (GCMs); it can be viewed as a form of multiple linear regression of Y on the constraints. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Its general form (Method C) accounts for correlations between the constraints. Method C becomes less robust when some constraints are too strongly related to each other; this can be mitigated using regularization approaches such as ridge regression. An illuminating special case, Method U, neglects any correlations between constraints except through their mutual relationship to the climate proxy; it is more robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and the climate feedback parameter using a previously published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). The ±2σ posterior range of ECS for Method C with no overconfidence adjustment is 4.3 ± 0.7 K. For Method U with a large overconfidence adjustment, it is 4.0 ± 1.3 K. This study adds confidence to past findings that most constraints predict higher climate sensitivity than the CMIP mean.},
  file         = {:Bretherton2020.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
}

@Article{Brient2015,
  author       = {Florent Brient and Tapio Schneider and Zhihong Tan and Sandrine Bony and Xin Qu and Alex Hall},
  date         = {2015-10},
  journaltitle = {Climate Dynamics},
  title        = {Shallowness of tropical low clouds as a predictor of climate models' response to warming},
  doi          = {10.1007/s00382-015-2846-0},
  number       = {1-2},
  pages        = {433--449},
  volume       = {47},
  file         = {:Brient2015.pdf:PDF},
  groups       = {Emergent Constraints, Clouds / Aerosols},
  keywords     = {printed, read},
  printed      = {printed},
  publisher    = {Springer Science and Business Media {LLC}},
  readstatus   = {read},
}

@Article{Brient2016,
  author       = {Brient, F. and Schneider, T.},
  date         = {2016},
  journaltitle = {Journal of Climate},
  title        = {Constraints on Climate Sensitivity from Space-Based Measurements of Low-Cloud Reflection},
  doi          = {10.1175/Jcli-D-15-0897.1},
  number       = {16},
  pages        = {5821--5835},
  volume       = {29},
  abstract     = {Physical uncertainties in global-warming projections are dominated by uncertainties about how the fraction of incoming shortwave radiation that clouds reflect will change as greenhouse gas concentrations rise. Differences in the shortwave reflection by low clouds over tropical oceans alone account for more than half of the variance of the equilibrium climate sensitivity (ECS) among climate models, which ranges from 2.1 to 4.7 K. Space-based measurements now provide an opportunity to assess how well models reproduce temporal variations of this shortwave reflection on seasonal to interannual time scales. Here such space-based measurements are used to show that shortwave reflection by low clouds over tropical oceans decreases robustly when the underlying surface warms, for example, by -(0.96 +/- 0.22)\% K-1 (90\% confidence level) for de-seasonalized variations. Additionally, the temporal covariance of low-cloud reflection with temperature in historical simulations with current climate models correlates strongly (r = -0.67) with the models' ECS. Therefore, measurements of temporal low-cloud variations can be used to constrain ECS estimates based on climate models. An information-theoretic weighting of climate models by how well they reproduce the measured deseasonalized covariance of shortwave cloud reflection with temperature yields a most likely ECS estimate around 4.0 K; an ECS below 2.3K becomes very unlikely (90\% confidence).},
  file         = {:Brient2016.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming, Clouds / Aerosols},
  keywords     = {feedback, spread, temperature, future, cover, skimmed, printed},
  printed      = {printed},
  readstatus   = {skimmed},
}

@Article{Brient2020,
  author       = {Brient, F.},
  date         = {2020},
  journaltitle = {Advances in Atmospheric Sciences},
  title        = {Reducing Uncertainties in Climate Projections with Emergent Constraints: Concepts, Examples and Prospects},
  doi          = {10.1007/s00376-019-9140-8},
  number       = {1},
  pages        = {1--15},
  volume       = {37},
  abstract     = {Models disagree on a significant number of responses to climate change, such as climate feedback, regional changes, or the strength of equilibrium climate sensitivity. Emergent constraints aim to reduce these uncertainties by finding links between the inter-model spread in an observable predictor and climate projections. In this paper, the concepts underlying this framework are recalled with an emphasis on the statistical inference used for narrowing uncertainties, and a review of emergent constraints found in the last two decades. Potential links between highlighted predictors are explored, especially those targeting uncertainty reductions in climate sensitivity, cloud feedback, and changes of the hydrological cycle. Yet the disagreement across emergent constraints suggests that the spread in climate sensitivity can not be significantly narrowed. This calls for weighting the realism of emergent constraints by quantifying the level of physical understanding explaining the relationship. This would also permit more efficient model evaluation and better targeted model development. In the context of the upcoming CMIP6 model intercomparison a growing number of new predictors and uncertainty reductions is expected, which call for robust statistical inferences that allow cross-validation of more likely estimates.},
  file         = {:Brient2020.pdf:PDF},
  groups       = {Emergent Constraints, Mathematical basics},
  keywords     = {climate modeling, emergent constraint, climate change, climate sensitivity, double-itcz bias, low-cloud cover, carbon-dioxide, seasonal cycle, future changes, sensitivity, feedback, model, spread, cmip5, skimmed},
  readstatus   = {skimmed},
}

@Article{Brown2018,
  author       = {Brown, P. T. and Stolpe, M. B. and Caldeira, K.},
  date         = {2018},
  journaltitle = {Nature},
  title        = {Assumptions for emergent constraints},
  doi          = {10.1038/s41586-018-0638-5},
  number       = {7729},
  pages        = {E1-E3},
  volume       = {563},
  comment      = {Answer to Cox et al. (2018)},
  file         = {:Brown2018.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {variability},
}

@Article{Brunner2019,
  author       = {Lukas Brunner and Ruth Lorenz and Marius Zumwald and Reto Knutti},
  date         = {2019-11},
  journaltitle = {Environmental Research Letters},
  title        = {Quantifying uncertainty in European climate projections using combined performance-independence weighting},
  doi          = {10.1088/1748-9326/ab492f},
  number       = {12},
  pages        = {124010},
  volume       = {14},
  file         = {:Brunner2019.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming, Precipitation, Climate Model Weighting / Reducing uncertainties},
  publisher    = {{IOP} Publishing},
}

@Article{Brunner2020,
  author       = {Lukas Brunner and Angeline G. Pendergrass and Flavio Lehner and Anna L. Merrifield and Ruth Lorenz and Reto Knutti},
  date         = {2020-11},
  journaltitle = {Earth System Dynamics},
  title        = {Reduced global warming from {CMIP}6 projections when weighting models by performance and independence},
  doi          = {10.5194/esd-11-995-2020},
  number       = {4},
  pages        = {995--1012},
  volume       = {11},
  file         = {:Brunner2020.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties, ECS / TCR / Climate Sensitivity / Warming},
  publisher    = {Copernicus {GmbH}},
}

@Article{Caldwell2014,
  author       = {Caldwell, P. M. and Bretherton, C. S. and Zelinka, M. D. and Klein, S. A. and Santer, B. D. and Sanderson, B. M.},
  date         = {2014},
  journaltitle = {Geophysical Research Letters},
  title        = {Statistical significance of climate sensitivity predictors obtained by data mining},
  doi          = {10.1002/2014gl059205},
  number       = {5},
  pages        = {1803--1808},
  volume       = {41},
  abstract     = {Several recent efforts to estimate Earth's equilibrium climate sensitivity (ECS) focus on identifying quantities in the current climate which are skillful predictors of ECS yet can be constrained by observations. This study automates the search for observable predictors using data from phase 5 of the Coupled Model Intercomparison Project. The primary focus of this paper is assessing statistical significance of the resulting predictive relationships. Failure to account for dependence between models, variables, locations, and seasons is shown to yield misleading results. A new technique for testing the field significance of data-mined correlations which avoids these problems is presented. Using this new approach, all 41,741 relationships we tested were found to be explainable by chance. This leads us to conclude that data mining is best used to identify potential relationships which are then validated or discarded using physically based hypothesis testing.
Key Points <list list-type="bulleted"> <list-item id="grl51462-li-0001">Correlation magnitude is not sufficient proof of predictive skill <list-item id="grl51462-li-0002">Significance testing is complicated by model nonindependence in ensembles <list-item id="grl51462-li-0003">The best predictors of climate change are related to the Southern Ocean},
  file         = {:Caldwell2014.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {data mining, climate sensitivity, cmip, intercomparison, ensemble, carbon-dioxide, field significance, seasonal cycle, temperature, circulation, models, skimmed},
  readstatus   = {skimmed},
}

@Article{Caldwell2018,
  author       = {Caldwell, P. M. and Zelinka, M. D. and Klein, S. A.},
  date         = {2018},
  journaltitle = {Journal of Climate},
  title        = {Evaluating Emergent Constraints on Equilibrium Climate Sensitivity},
  doi          = {10.1175/Jcli-D-17-0631.1},
  number       = {10},
  pages        = {3921--3942},
  volume       = {31},
  abstract     = {Emergent constraints are quantities that are observable from current measurements and have skill predicting future climate. This study explores 19 previously proposed emergent constraints related to equilibrium climate sensitivity (ECS; the global-average equilibrium surface temperature response to CO2 doubling). Several constraints are shown to be closely related, emphasizing the importance for careful understanding of proposed constraints. A new method is presented for decomposing correlation between an emergent constraint and ECS into terms related to physical processes and geographical regions. Using this decomposition, one can determine whether the processes and regions explaining correlation with ECS correspond to the physical explanation offered for the constraint. Shortwave cloud feedback is generally found to be the dominant contributor to correlations with ECS because it is the largest source of intermodel spread in ECS. In all cases, correlation results from interaction between a variety of terms, reflecting the complex nature of ECS and the fact that feedback terms and forcing are themselves correlated with each other. For 4 of the 19 constraints, the originally proposed explanation for correlation is borne out by our analysis. These four constraints all predict relatively high climate sensitivity. The credibility of six other constraints is called into question owing to correlation with ECS coming mainly from unexpected sources and/or lack of robustness to changes in ensembles. Another six constraints lack a testable explanation and hence cannot be confirmed. The fact that this study casts doubt upon more constraints than it confirms highlights the need for caution when identifying emergent constraints from small ensembles.},
  file         = {:Caldwell2018.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {climate change, climate sensitivity, feedback, statistics, climate models, cloud feedback, surface-temperature, radiative feedbacks, model simulations, spatial-pattern, carbon-dioxide, seasonal cycle, southern-ocean, spread, variability, read, printed},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Cao2018,
  author       = {Cao, J. and Wang, B. and Yang, Y. M. and Ma, L. B. and Li, J. and Sun, B. and Bao, Y. and He, J. and Zhou, X. and Wu, L. G.},
  date         = {2018},
  journaltitle = {Geoscientific Model Development},
  title        = {The NUIST Earth System Model (NESM) version 3: description and preliminary evaluation},
  doi          = {10.5194/gmd-11-2975-2018},
  number       = {7},
  pages        = {2975--2993},
  volume       = {11},
  abstract     = {The Nanjing University of Information Science and Technology Earth System Model version 3 (NESM v3) has been developed, aiming to provide a numerical modeling platform for cross-disciplinary Earth system studies, project future Earth climate and environment changes, and conduct subseasonal-to-seasonal prediction. While the previous model version NESM v1 simulates the internal modes of climate variability well, it has no vegetation dynamics and suffers considerable radiative energy imbalance at the top of the atmosphere and surface, resulting in large biases in the global mean surface air temperature, which limits its utility to simulate past and project future climate changes. The NESM v3 has upgraded atmospheric and land surface model components and improved physical parameterization and conservation of coupling variables. Here we describe the new version's basic features and how the major improvements were made. We demonstrate the v3 model's fidelity and suitability to address global climate variability and change issues. The 500-year preindustrial (PI) experiment shows negligible trends in the net heat flux at the top of atmosphere and the Earth surface. Consistently, the simulated global mean surface air temperature, land surface temperature, and sea surface temperature (SST) are all in a quasi-equilibrium state. The conservation of global water is demonstrated by the stable evolution of the global mean precipitation, sea surface salinity (SSS), and sea water salinity. The sea ice extents (SIEs), as a major indication of high-latitude climate, also maintain a balanced state. The simulated spatial patterns of the energy states, SST, precipitation, and SSS fields are realistic, but the model suffers from a cold bias in the North Atlantic, a warm bias in the Southern Ocean, and associated deficient Antarctic sea ice area, as well as a delicate sign of the double ITCZ syndrome. The estimated radiative forcing of quadrupling carbon dioxide is about 7.24 W m(-2), yielding a climate sensitivity feedback parameter of -0.98 W m(-2) K-1 , and the equilibrium climate sensitivity is 3.69 K. The transient climate response from the 1 \% yr(-1) CO2 (1pctCO(2)) increase experiment is 2.16 K. The model's performance on internal modes and responses to external forcing during the historical period will be documented in an accompanying paper.},
  file         = {:Cao2018.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {meridional overturning circulation, enso phase-locking, sea-ice, global precipitation, double itcz, parameterization, surface, representation, temperature, sensitivity},
}

@Article{Ceppi2017,
  author       = {Ceppi, P. and Brient, F. and Zelinka, M. D. and Hartmann, D. L.},
  date         = {2017},
  journaltitle = {Wiley Interdisciplinary Reviews-Climate Change},
  title        = {Cloud feedback mechanisms and their representation in global climate models},
  doi          = {10.1002/wcc.465},
  number       = {4},
  pages        = {e465},
  volume       = {8},
  abstract     = {Cloud feedbackthe change in top-of-atmosphere radiative flux resulting from the cloud response to warmingconstitutes by far the largest source of uncertainty in the climate response to CO2 forcing simulated by global climate models (GCMs). We review the main mechanisms for cloud feedbacks, and discuss their representation in climate models and the sources of intermodel spread. Global-mean cloud feedback in GCMs results from three main effects: (1) rising free-tropospheric clouds (a positive longwave effect); (2) decreasing tropical low cloud amount (a positive shortwave [SW] effect); (3) increasing high-latitude low cloud optical depth (a negative SW effect). These cloud responses simulated by GCMs are qualitatively supported by theory, high-resolution modeling, and observations. Rising high clouds are consistent with the fixed anvil temperature (FAT) hypothesis, whereby enhanced upper-tropospheric radiative cooling causes anvil cloud tops to remain at a nearly fixed temperature as the atmosphere warms. Tropical low cloud amount decreases are driven by a delicate balance between the effects of vertical turbulent fluxes, radiative cooling, large-scale subsidence, and lower-tropospheric stability on the boundary-layer moisture budget. High-latitude low cloud optical depth increases are dominated by phase changes in mixed-phase clouds. The causes of intermodel spread in cloud feedback are discussed, focusing particularly on the role of unresolved parameterized processes such as cloud microphysics, turbulence, and convection. (C) 2017 Wiley Periodicals, Inc.},
  file         = {:Ceppi2017.pdf:PDF},
  groups       = {Feedbacks, Reviews, Clouds / Aerosols},
  keywords     = {general-circulation model, sea-surface temperature, optical depth feedback, large-eddy simulation, 1998 el-nino, self-aggregation, tropospheric adjustment, statistical-analyses, thermal-equilibrium, physical-mechanisms},
}

@Book{Charney1979,
  author = {Charney, Jule G. and Arakawa, Akio and Baker, D. James and Bolin, Bert and Dickinson, Robert E. and Goody, Richard M. and Leith, Cecil E. and Stommel, Henry M. and Wunsch, Carl I.},
  date   = {1979},
  title  = {Carbon dioxide and climate: a scientific assessment},
  groups = {Policy},
}

@Article{Cherchi2019,
  author       = {Cherchi, A. and Fogli, P. G. and Lovato, T. and Peano, D. and Iovino, D. and Gualdi, S. and Masina, S. and Scoccimarro, E. and Materia, S. and Bellucci, A. and Navarra, A.},
  date         = {2019},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled Model},
  doi          = {10.1029/2018ms001369},
  number       = {1},
  pages        = {185--209},
  volume       = {11},
  abstract     = {Euro-Mediterranean Centre on Climate Change coupled climate model (CMCC-CM2) represents the new family of the global coupled climate models developed and used at CMCC. It is based on the atmospheric, land and sea ice components from the Community Earth System Model coupled with the global ocean model Nucleus for European Modeling of the Ocean. This study documents the model components, the coupling strategy, particularly for the oceanic, atmospheric, and sea ice components, and the overall model ability in reproducing the observed mean climate and main patterns of interannual variability. As a first step toward a more comprehensive, process-oriented, validation of the model, this work analyzes a 200-year simulation performed under constant forcing corresponding to present-day climate conditions. In terms of mean climate, the model is able to realistically reproduce the main patterns of temperature, precipitation, and winds. Specifically, we report improvements in the representation of the sea surface temperature with respect to the previous version of the model. In terms of mean atmospheric circulation features, we notice a realistic simulation of upper tropospheric winds and midtroposphere geopotential eddies. The oceanic heat transport and the Atlantic meridional overturning circulation satisfactorily compare with present-day observations and estimates from global ocean reanalyses. The sea ice patterns and associated seasonal variations are realistically reproduced in both hemispheres, with a better skill in winter. Main weaknesses of the simulated climate are related with the precipitation patterns, specifically in the tropical regions with large dry biases over the Amazon basin. Similarly, the seasonal precipitation associated with the monsoons, mostly over Asia, is weaker than observed. The main patterns of interannual variability in terms of dominant empirical orthogonal functions are faithfully reproduced, mostly in the Northern Hemisphere winter. In the tropics the main teleconnection patterns associated with El Nino-Southern Oscillation and with the Indian Ocean Dipole are also in good agreement with observations.},
  file         = {:Cherchi2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {community atmosphere model, meridional overturning circulation, upwelling annual cycle, indian-ocean dipole, earth system, tropical pacific, seasonal cycle, sea-ice, surface-temperature, simulated climate},
}

@InBook{Ciais2013,
  author       = {Ciais, Philippe and Sabine, Christopher and Bala, Govindasamy and Bopp, Laurent and Brovkin, Victor and Canadell, Josep and Chhabra, Abha and DeFries, Ruth and Galloway, James and Heimann, Martin},
  date         = {2013},
  title        = {Carbon and Other Biogeochemical Cycles},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {465--570},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter06_FINAL.pdf},
  file         = {:Ciais2013.pdf:PDF},
  groups       = {IPCC, Carbon Cycle},
  journaltitle = {Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Article{Collatz1992,
  author       = {G. J. Collatz and M. Ribas-Carbo and J. A. Berry},
  date         = {1992},
  journaltitle = {Functional Plant Biology},
  title        = {Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants},
  doi          = {10.1071/pp9920519},
  number       = {5},
  pages        = {519},
  volume       = {19},
  groups       = {Carbon Cycle},
  publisher    = {{CSIRO} Publishing},
}

@Article{Collins2011,
  author       = {Collins, W. J. and Bellouin, N. and Doutriaux-Boucher, M. and Gedney, N. and Halloran, P. and Hinton, T. and Hughes, J. and Jones, C. D. and Joshi, M. and Liddicoat, S. and Martin, G. and O'Connor, F. and Rae, J. and Senior, C. and Sitch, S. and Totterdell, I. and Wiltshire, A. and Woodward, S.},
  date         = {2011},
  journaltitle = {Geoscientific Model Development},
  title        = {Development and evaluation of an Earth-System model-HadGEM2},
  doi          = {10.5194/gmd-4-1051-2011},
  number       = {4},
  pages        = {1051--1075},
  volume       = {4},
  abstract     = {We describe here the development and evaluation of an Earth system model suitable for centennial-scale climate prediction. The principal new components added to the physical climate model are the terrestrial and ocean ecosystems and gas-phase tropospheric chemistry, along with their coupled interactions.
The individual Earth system components are described briefly and the relevant interactions between the components are explained. Because the multiple interactions could lead to unstable feedbacks, we go through a careful process of model spin up to ensure that all components are stable and the interactions balanced. This spun-up configuration is evaluated against observed data for the Earth system components and is generally found to perform very satisfactorily. The reason for the evaluation phase is that the model is to be used for the core climate simulations carried out by the Met Office Hadley Centre for the Coupled Model Intercomparison Project (CMIP5), so it is essential that addition of the extra complexity does not detract substantially from its climate performance. Localised changes in some specific meteorological variables can be identified, but the impacts on the overall simulation of present day climate are slight.
This model is proving valuable both for climate predictions, and for investigating the strengths of biogeochemical feedbacks.},
  file         = {:Collins2011.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {carbon-cycle feedbacks, environment simulator jules, net primary productivity, fresh-water discharge, center climate model, seasonal-variations, tropospheric ozone, methane emissions, dimethyl sulfide, plant geography},
}

@InBook{Collins2013,
  author       = {Collins, Matthew and Knutti, Reto and Arblaster, Julie and Dufresne, J.-L. and Fichefet, Thierry and Friedlingstein, Pierre and Gao, Xuejie and Gutowski, William J. and Johns, Tim and Krinner, Gerhard},
  date         = {2013},
  title        = {Long-term Climate Change: Projections, Commitments and Irreversibility},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {1029--1136},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter12_FINAL.pdf},
  file         = {:Collins2013.pdf:PDF},
  groups       = {IPCC},
  journaltitle = {Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Article{Collins2017,
  author       = {William J. Collins and Jean-François Lamarque and Michael Schulz and Olivier Boucher and Veronika Eyring and Michaela I. Hegglin and Amanda Maycock and Gunnar Myhre and Michael Prather and Drew Shindell and Steven J. Smith},
  date         = {2017-02},
  journaltitle = {Geoscientific Model Development},
  title        = {{AerChemMIP}: quantifying the effects of chemistry and aerosols in {CMIP}6},
  doi          = {10.5194/gmd-10-585-2017},
  number       = {2},
  pages        = {585--607},
  volume       = {10},
  file         = {:Collins2017.pdf:PDF},
  groups       = {CMIP and other MIPs},
  publisher    = {Copernicus {GmbH}},
}

@Article{Counillon2016,
  author       = {Counillon, F. and Keenlyside, N. and Bethke, I. and Wang, Y. G. and Billeau, S. and Shen, M. L. and Bentsen, M.},
  date         = {2016},
  journaltitle = {Tellus Series a-Dynamic Meteorology and Oceanography},
  title        = {Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model},
  doi          = {10.3402/tellusa.v68.32437},
  number       = {1},
  pages        = {32437},
  volume       = {68},
  abstract     = {We document a pilot stochastic re-analysis computed by assimilating sea surface temperature (SST) anomalies into the ocean component of the coupled Norwegian Climate Prediction Model (NorCPM) for the period 1950-2010 (doi: 10.11582/2016.00002). NorCPM is based on the Norwegian Earth System Model and uses the ensemble Kalman filter for data assimilation (DA). Here, we assimilate SST from the stochastic HadISST2 historical reconstruction. The accuracy, reliability and drift are investigated using both assimilated and independent observations. NorCPM is slightly overdispersive against assimilated observations but shows stable performance through the analysis period. It demonstrates skills against independent measurements: sea surface height, heat and salt content, in particular in the Equatorial and North Pacific, the North Atlantic Subpolar Gyre (SPG) region and the Nordic Seas. Furthermore, NorCPM provides a reliable monitoring of the SPG index and represents the vertical temperature variability there, in good agreement with observations. The monitoring of the Atlantic meridional overturning circulation is also encouraging. The benefit of using a flow-dependent assimilation method and constructing the covariance in isopycnal coordinates are investigated in the SPG region. Isopycnal coordinates discretisation is found to better capture the vertical structure than standard depth-coordinate discretisation, because it leads to a deeper influence of the assimilated surface observations. The vertical covariance shows a pronounced seasonal and decadal variability that highlights the benefit of flow-dependent DA method. This study demonstrates the potential of NorCPM to compute an ocean re-analysis for the 19th and 20th centuries when SST observations are available.},
  file         = {:Counillon2016.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {ocean re-analysis, enkf, isopycnal ocean model, sst coupled re-analysis, weakly coupled data assimilation, norcpm, flow dependent assimilation, north-atlantic ocean, meridional overturning circulation, earth system model, thermohaline circulation, global ocean, heat-content, reanalysis, salinity, ice, variability},
}

@Article{Cox2013,
  author       = {Cox, P. M. and Pearson, D. and Booth, B. B. and Friedlingstein, P. and Huntingford, C. and Jones, C. D. and Luke, C. M.},
  date         = {2013},
  journaltitle = {Nature},
  title        = {Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability},
  doi          = {10.1038/nature11882},
  number       = {7437},
  pages        = {341--344},
  volume       = {494},
  abstract     = {The release of carbon from tropical forests may exacerbate future climate change(1), but the magnitude of the effect in climate models remains uncertain(2). Coupled climate-carbon-cycle models generally agree that carbon storage on land will increase as a result of the simultaneous enhancement of plant photosynthesis and water use efficiency under higher atmospheric CO2 concentrations, but will decrease owing to higher soil and plant respiration rates associated with warming temperatures(3). At present, the balance between these effects varies markedly among coupled climate-carbon-cycle models, leading to a range of 330 gigatonnes in the projected change in the amount of carbon stored on tropical land by 2100. Explanations for this large uncertainty include differences in the predicted change in rainfall in Amazonia(4,5) and variations in the responses of alternative vegetation models to warming(6). Here we identify an emergent linear relationship, across an ensemble of models(7), between the sensitivity of tropical land carbon storage to warming and the sensitivity of the annual growth rate of atmospheric CO2 to tropical temperature anomalies(8). Combined with contemporary observations of atmospheric CO2 concentration and tropical temperature, this relationship provides a tight constraint on the sensitivity of tropical land carbon to climate change. We estimate that over tropical land from latitude 30 degrees north to 30 degrees south, warming alone will release 53 +/- 17 gigatonnes of carbon per kelvin. Compared with the unconstrained ensemble of climate-carbon-cycle projections, this indicates a much lower risk of Amazon forest die-back under CO2-induced climate change if CO2 fertilization effects are as large as suggested by current models(9). Our study, however, also implies greater certainty that carbon will be lost from tropical land if warming arises from reductions in aerosols(10) or increases in other greenhouse gases(11).},
  file         = {:Cox2013.pdf:PDF},
  groups       = {Carbon Cycle, Emergent Constraints},
  keywords     = {cycle, feedback, drought, biomass, risk, read, printed},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Cox2018,
  author       = {Cox, P. M. and Huntingford, C. and Williamson, M. S.},
  date         = {2018},
  journaltitle = {Nature},
  title        = {Emergent constraint on equilibrium climate sensitivity from global temperature variability},
  doi          = {10.1038/nature25450},
  number       = {7688},
  pages        = {319--322},
  volume       = {553},
  abstract     = {Equilibrium climate sensitivity (ECS) remains one of the most important unknowns in climate change science. ECS is defined as the global mean warming that would occur if the atmospheric carbon dioxide (CO2) concentration were instantly doubled and the climate were then brought to equilibrium with that new level of CO2. Despite its rather idealized definition, ECS has continuing relevance for international climate change agreements, which are often framed in terms of stabilization of global warming relative to the pre-industrial climate. However, the 'likely' range of ECS as stated by the Intergovernmental Panel on Climate Change (IPCC) has remained at 1.5-4.5 degrees Celsius for more than 25 years(1). The possibility of a value of ECS towards the upper end of this range reduces the feasibility of avoiding 2 degrees Celsius of global warming, as required by the Paris Agreement. Here we present a new emergent constraint on ECS that yields a central estimate of 2.8 degrees Celsius with 66 per cent confidence limits (equivalent to the IPCC 'likely' range) of 2.2-3.4 degrees Celsius. Our approach is to focus on the variability of temperature about long-term historical warming, rather than on the warming trend itself. We use an ensemble of climate models to define an emergent relationship(2) between ECS and a theoretically informed metric of global temperature variability. This metric of variability can also be calculated from observational records of global warming(3), which enables tighter constraints to be placed on ECS, reducing the probability of ECS being less than 1.5 degrees Celsius to less than 3 per cent, and the probability of ECS exceeding 4.5 degrees Celsius to less than 1 per cent.},
  file         = {:Cox2018.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {energy budget constraints, read, printed},
  printed      = {printed},
  readstatus   = {read},
}

@Article{CraftsBrandner2000,
  author       = {Crafts-Brandner, S. J. and Salvucci, M. E.},
  date         = {2000},
  journaltitle = {Proceedings of the National Academy of Sciences of the United States of America},
  title        = {Rubisco activase constrains the photosynthetic potential of leaves at high temperature and {CO}$_2$},
  doi          = {10.1073/pnas.230451497},
  number       = {24},
  pages        = {13430--13435},
  volume       = {97},
  abstract     = {Net photosynthesis (Pn) is inhibited by moderate heat stress. To elucidate the mechanism of inhibition, we examined the effects of temperature on gas exchange and ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco) activation in cotton and tobacco leaves and compared the responses to those of the isolated enzymes. Depending on the CO2 concentration, Pn decreased when temperatures exceeded 35-40 degreesC. This response was inconsistent with the response predicted from the properties of fully activated Rubisco, Rubisco deactivated in leaves when temperature was increased and also in response to high CO2 or low O-2. The decrease in Rubisco activation occurred when leaf temperatures exceeded 35 degreesC, whereas the activities of isolated activase and Rubisco were highest at 42 degreesC and >50 degreesC, respectively. In the absence of activase, isolated Rubisco deactivated under catalytic conditions and the rate of deactivation increased with temperature but not with CO2, The ability of activase to maintain or promote Rubisco activation in vitro also decreased with temperature but was not affected by CO2, Increasing the activase/Rubisco ratio reduced Rubisco deactivation at higher temperatures. The results indicate that, as temperature increases, the rate of Rubisco deactivation exceeds the capacity of activase to promote activation. The decrease in Rubisco activation that occurred in leaves at high CO2 was not caused by a faster rate of deactivation, but by reduced activase activity possibly in response to unfavorable ATP/ADP ratios. When adjustments were made for changes in activation state, the kinetic properties of Rubisco predicted the response of Pn at high temperature and CO2.},
  file         = {:CraftsBrandner2000.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {ribulose-bisphosphate carboxylase, ribulose-1,5-bisphosphate carboxylase, phaseolus-vulgaris, intact leaves, elevated co2, light, chloroplasts, sensitivity, inhibition, dependence},
}

@InBook{Cubasch2001,
  author       = {Cubasch, Ulrich and Meehl, G. A. and Boer, G. J. and Stouffer, R. J. and Dix, M. and Noda, A. and Senior, C. A. and Raper, S. and Yap, K. S.},
  date         = {2001},
  title        = {Projections of Future Climate Change},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {525--585},
  publisher    = {Cambridge University Press},
  url          = {https://archive.ipcc.ch/ipccreports/tar/wg1/pdf/TAR-09.PDF},
  file         = {:Cubasch2001.pdf:PDF},
  groups       = {IPCC, ECS / TCR / Climate Sensitivity / Warming},
  journaltitle = {Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change},
}

@InBook{Cubasch2013,
  author       = {Cubasch, Ulrich and Wuebbles, Donald and Chen, Deliang and Facchini, Maria Cristina and Frame, David and Mahowald, N. and Winther, Jan-Gunnar},
  date         = {2013},
  title        = {Introduction},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {119--158},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2017/09/WG1AR5_Chapter01_FINAL.pdf},
  file         = {:Cubasch2013.pdf:PDF},
  groups       = {IPCC, ESM basics},
  journaltitle = {Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Article{Danabasoglu2020,
  author       = {Danabasoglu, G. and Lamarque, J. F. and Bacmeister, J. and Bailey, D. A. and DuVivier, A. K. and Edwards, J. and Emmons, L. K. and Fasullo, J. and Garcia, R. and Gettelman, A. and Hannay, C. and Holland, M. M. and Large, W. G. and Lauritzen, P. H. and Lawrence, D. M. and Lenaerts, J. T. M. and Lindsay, K. and Lipscomb, W. H. and Mills, M. J. and Neale, R. and Oleson, K. W. and Otto-Bliesner, B. and Phillips, A. S. and Sacks, W. and Tilmes, S. and van Kampenhout, L. and Vertenstein, M. and Bertini, A. and Dennis, J. and Deser, C. and Fischer, C. and Fox-Kemper, B. and Kay, J. E. and Kinnison, D. and Kushner, P. J. and Larson, V. E. and Long, M. C. and Mickelson, S. and Moore, J. K. and Nienhouse, E. and Polvani, L. and Rasch, P. J. and Strand, W. G.},
  date         = {2020},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {The Community Earth System Model Version 2 (CESM2)},
  doi          = {10.1029/2019MS001916},
  number       = {2},
  pages        = {e2019MS001916},
  volume       = {12},
  abstract     = {An overview of the Community Earth System Model Version 2 (CESM2) is provided, including a discussion of the challenges encountered during its development and how they were addressed. In addition, an evaluation of a pair of CESM2 long preindustrial control and historical ensemble simulations is presented. These simulations were performed using the nominal 1 degrees horizontal resolution configuration of the coupled model with both the "low-top" (40 km, with limited chemistry) and "high-top" (130 km, with comprehensive chemistry) versions of the atmospheric component. CESM2 contains many substantial science and infrastructure improvements and new capabilities since its previous major release, CESM1, resulting in improved historical simulations in comparison to CESM1 and available observations. These include major reductions in low-latitude precipitation and shortwave cloud forcing biases; better representation of the Madden-Julian Oscillation; better El Nino-Southern Oscillation-related teleconnections; and a global land carbon accumulation trend that agrees well with observationally based estimates. Most tropospheric and surface features of the low- and high-top simulations are very similar to each other, so these improvements are present in both configurations. CESM2 has an equilibrium climate sensitivity of 5.1-5.3 degrees C, larger than in CESM1, primarily due to a combination of relatively small changes to cloud microphysics and boundary layer parameters. In contrast, CESM2's transient climate response of 1.9-2.0 degrees C is comparable to that of CESM1. The model outputs from these and many other simulations are available to the research community, and they represent CESM2's contributions to the Coupled Model Intercomparison Project Phase 6.},
  file         = {:Danabasoglu2020.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {community earth system model (cesm), global coupled earth system modeling, preindustrial and historical simulations, coupled model development and evaluation, surface mass-balance, heterogeneous ice nucleation, climate sensitivity, atmosphere model, land model, part i, sheet model, ocean, temperature, impact},
}

@Article{Death2007,
  author       = {De'ath, G.},
  date         = {2007},
  journaltitle = {Ecology},
  title        = {Boosted trees for ecological modeling and prediction},
  doi          = {10.1890/0012-9658(2007)88[243:Btfema]2.0.Co;2},
  number       = {1},
  pages        = {243--251},
  volume       = {88},
  abstract     = {Accurate prediction and explanation are fundamental objectives of statistical analysis, yet they seldom coincide. Boosted trees are a statistical learning method that attains both of these objectives for regression and classification analyses. They can deal with many types of response variables ( numeric, categorical, and censored), loss functions ( Gaussian, binomial, Poisson, and robust), and predictors ( numeric, categorical). Interactions between predictors can also be quantified and visualized. The theory underpinning boosted trees is presented, together with interpretive techniques. A new form of boosted trees, namely, "aggregated boosted trees'' (ABT), is proposed and, in a simulation study, is shown to reduce prediction error relative to boosted trees. A regression data set is analyzed using ABT to illustrate the technique and to compare it with other methods, including boosted trees, bagged trees, random forests, and generalized additive models. A software package for ABT analysis using the R software environment is included in the Appendices together with worked examples.},
  file         = {:Death2007.pdf:PDF},
  groups       = {ML basics},
  keywords     = {aggregated boosted trees, bagging, boosting, classification, cross-validation, prediction, regression, regression trees, read},
  readstatus   = {read},
}

@Article{Dee2011,
  author       = {Dee, D. P. and Uppala, S. M. and Simmons, A. J. and Berrisford, P. and Poli, P. and Kobayashi, S. and Andrae, U. and Balmaseda, M. A. and Balsamo, G. and Bauer, P. and Bechtold, P. and Beljaars, A. C. M. and van de Berg, L. and Bidlot, J. and Bormann, N. and Delsol, C. and Dragani, R. and Fuentes, M. and Geer, A. J. and Haimberger, L. and Healy, S. B. and Hersbach, H. and Holm, E. V. and Isaksen, L. and Kallberg, P. and Kohler, M. and Matricardi, M. and McNally, A. P. and Monge-Sanz, B. M. and Morcrette, J. J. and Park, B. K. and Peubey, C. and de Rosnay, P. and Tavolato, C. and Thepaut, J. N. and Vitart, F.},
  date         = {2011},
  journaltitle = {Quarterly Journal of the Royal Meteorological Society},
  title        = {The ERA-Interim reanalysis: configuration and performance of the data assimilation system},
  doi          = {10.1002/qj.828},
  number       = {656},
  pages        = {553--597},
  volume       = {137},
  abstract     = {ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright (C) 2011 Royal Meteorological Society},
  file         = {:Dee2011.pdf:PDF},
  groups       = {OBS},
  keywords     = {era-40, 4d-var, hydrological cycle, stratospheric circulation, observations, forecast model, 4-dimensional variational assimilation, madden-julian oscillation, special sensor microwave/imager, ecmwf forecasting system, radiative-transfer model, column water-vapor, long-term trends, 1d+4d-var assimilation, operational implementation, incremental approach},
}

@Article{Delworth2002,
  author       = {Delworth, T. L. and Stouffer, R. J. and Dixon, K. W. and Spelman, M. J. and Knutson, T. R. and Broccoli, A. J. and Kushner, P. J. and Wetherald, R. T.},
  date         = {2002},
  journaltitle = {Climate Dynamics},
  title        = {Review of simulations of climate variability and change with the GFDL R30 coupled climate model},
  doi          = {10.1007/s00382-002-0249-5},
  number       = {7},
  pages        = {555--574},
  volume       = {19},
  abstract     = {A review is presented of the development and simulation characteristics of the most recent version of a global coupled model for climate variability and change studies at the Geophysical Fluid Dynamics Laboratory, as well as a review of the climate change experiments performed with the model. The atmospheric portion of the coupled model uses a spectral technique with rhomboidal 30 truncation, which corresponds to a transform grid with a resolution of approximately 3.75degrees longitude by 2.25degrees latitude. The ocean component has a resolution of approximately 1.875degrees longitude by 2.25degrees latitude. Relatively simple formulations of river routing, sea ice, and land surface processes are included. Two primary versions of the coupled model are described, differing in their initialization techniques and in the specification of sub-grid scale oceanic mixing of heat and salt. For each model a stable control integration of near millennial scale duration has been conducted, and the characteristics of both the time-mean and variability are described and compared to observations. A review is presented of a suite of climate change experiments conducted with these models using both idealized and realistic estimates of time-varying radiative forcing. Some experiments include estimates of forcing from past changes in volcanic aerosols and solar irradiance. The experiments performed are described, and some of the central findings are highlighted. In particular, the observed increase in global mean surface temperature is largely contained within the spread of simulated global mean temperatures from an ensemble of experiments using observationally-derived estimates of the changes in radiative forcing from increasing greenhouse gases and sulfate aerosols.},
  file         = {:Delworth2002.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {ocean-atmosphere model, surface air-temperature, hurricane intensities, global precipitation, northern-hemisphere, transient responses, arctic oscillation, gradual changes, carbon-dioxide, circulation},
}

@Article{Dittus2020,
  author       = {Andrea J. Dittus and Ed Hawkins and Laura J. Wilcox and Rowan T. Sutton and Christopher J. Smith and Martin B. Andrews and Piers M. Forster},
  date         = {2020-06},
  journaltitle = {Geophysical Research Letters},
  title        = {Sensitivity of Historical Climate Simulations to Uncertain Aerosol Forcing},
  doi          = {10.1029/2019gl085806},
  number       = {13},
  pages        = {e2019GL085806},
  volume       = {47},
  file         = {:Dittus2020.pdf:PDF},
  groups       = {Clouds / Aerosols, Forcing},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Dix2013,
  author       = {Dix, M. and Vohralik, P. and Bi, D. H. and Rashid, H. and Marsland, S. and O'Farrell, S. and Uotila, P. and Hirst, T. and Kowalczyk, E. and Sullivan, A. and Yan, H. L. and Franklin, C. and Sun, Z. A. and Watterson, I. and Collier, M. and Noonan, J. and Rotstayn, L. and Stevens, L. and Uhe, P. and Puri, K.},
  date         = {2013},
  journaltitle = {Australian Meteorological and Oceanographic Journal},
  title        = {The ACCESS coupled model: documentation of core {CMIP}5 simulations and initial results},
  doi          = {10.22499/2.6301.006},
  number       = {1},
  pages        = {83--99},
  volume       = {63},
  abstract     = {There are two versions of global coupled climate models developed at the Centre for Australian Weather and Climate Research (CAWCR) participating in phase 5 of the Coupled Model Inter-comparison Project (CMIP5), namely ACCESS1.0 and ACCESS1.3. This paper describes the CMIP5 experimental configuration of the ACCESS models and the climate forcings for the historical and future scenario runs.
We also present an initial analysis of model results, concentrating on changes in surface air temperature and the hydrologic cycle, and on climate sensitivity. Both models somewhat underestimate the observed 20th century warming, particularly in mid century, though recent warming rates match those observed. Mean warming for 2081-2100 relative to 1986-2005 under the RCP8.5 scenario is 3.61 K and 3.56 K for ACCESS1.0 and ACCESS1.3 respectively, and under RCP4.5 it is 2.34 K and 2.12 K.
Climate sensitivity from idealised simulations is 10-15 per cent larger in ACCESS1.0 than ACCESS1.3 and both models are above the median of the range of CMIP3 and published CMIP5 results.},
  file         = {:Dix2013.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {tropospheric adjustment, next-generation, climate model, temperature, cycle, implementation, sensitivity, scenarios, component, aerosols},
}

@Article{Donner2011,
  author       = {Donner, L. J. and Wyman, B. L. and Hemler, R. S. and Horowitz, L. W. and Ming, Y. and Zhao, M. and Golaz, J. C. and Ginoux, P. and Lin, S. J. and Schwarzkopf, M. D. and Austin, J. and Alaka, G. and Cooke, W. F. and Delworth, T. L. and Freidenreich, S. M. and Gordon, C. T. and Griffies, S. M. and Held, I. M. and Hurlin, W. J. and Klein, S. A. and Knutson, T. R. and Langenhorst, A. R. and Lee, H. C. and Lin, Y. L. and Magi, B. I. and Malyshev, S. L. and Milly, P. C. D. and Naik, V. and Nath, M. J. and Pincus, R. and Ploshay, J. J. and Ramaswamy, V. and Seman, C. J. and Shevliakova, E. and Sirutis, J. J. and Stern, W. F. and Stouffer, R. J. and Wilson, R. J. and Winton, M. and Wittenberg, A. T. and Zeng, F. R.},
  date         = {2011},
  journaltitle = {Journal of Climate},
  title        = {The Dynamical Core, Physical Parameterizations, and Basic Simulation Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled Model CM3},
  doi          = {10.1175/2011jcli3955.1},
  number       = {13},
  pages        = {3484--3519},
  volume       = {24},
  abstract     = {The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for the atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol-cloud interactions, chemistry-climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical system component of earth system models and models for decadal prediction in the near-term future-for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model. Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud droplet activation by aerosols, subgrid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. Its horizontal resolution is approximately 200 km, and its vertical resolution ranges approximately from 70 m near the earth's surface to 1 to 1.5 km near the tropopause and 3 to 4 km in much of the stratosphere. Most basic circulation features in AM3 are simulated as realistically, or more so, as in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks remains problematic, as in AM2. The most intense 0.2\% of precipitation rates occur less frequently in AM3 than observed. The last two decades of the twentieth century warm in CM3 by 0.32 degrees C relative to 1881-1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of 0.56 degrees and 0.52 degrees C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol-cloud interactions, and its warming by the late twentieth century is somewhat less realistic than in CM2.1, which warmed 0.66 degrees C but did not include aerosol-cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud-aerosol interactions to limit greenhouse gas warming.},
  file         = {:Donner2011.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {general-circulation models, large-scale models, shallow cumulus convection, cloud droplet activation, sea-surface temperature, including mass fluxes, ar4 climate models, part i, radiative properties, stratiform clouds},
}

@Article{Du2020,
  author       = {Du, E. Z. and Terrer, C. and Pellegrini, A. F. A. and Ahlstrom, A. and van Lissa, C. J. and Zhao, X. and Xia, N. and Wu, X. H. and Jackson, R. B.},
  date         = {2020},
  journaltitle = {Nature Geoscience},
  title        = {Global patterns of terrestrial nitrogen and phosphorus limitation},
  doi          = {10.1038/s41561-019-0530-4},
  number       = {3},
  pages        = {221--226},
  volume       = {13},
  abstract     = {Spatial patterns in the phosphorus and nitrogen limitation in natural terrestrial ecosystems are reported from analysis of a global database of the resorption efficiency of nutrients by leaves.
Nitrogen (N) and phosphorus (P) limitation constrains the magnitude of terrestrial carbon uptake in response to elevated carbon dioxide and climate change. However, global maps of nutrient limitation are still lacking. Here we examined global N and P limitation using the ratio of site-averaged leaf N and P resorption efficiencies of the dominant species across 171 sites. We evaluated our predictions using a global database of N- and P-limitation experiments based on nutrient additions at 106 and 53 sites, respectively. Globally, we found a shift from relative P to N limitation for both higher latitudes and precipitation seasonality and lower mean annual temperature, temperature seasonality, mean annual precipitation and soil clay fraction. Excluding cropland, urban and glacial areas, we estimate that 18\% of the natural terrestrial land area is significantly limited by N, whereas 43\% is relatively P limited. The remaining 39\% of the natural terrestrial land area could be co-limited by N and P or weakly limited by either nutrient alone. This work provides both a new framework for testing nutrient limitation and a benchmark of N and P limitation for models to constrain predictions of the terrestrial carbon sink.},
  file         = {:Du2020.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {nutrient limitation, co2 fertilization, resorption, models, carbon, productivity, package, ecosystems, deposition, responses},
}

@Article{Dufresne2008,
  author       = {Jean-Louis Dufresne and Sandrine Bony},
  date         = {2008-10},
  journaltitle = {Journal of Climate},
  title        = {An Assessment of the Primary Sources of Spread of Global Warming Estimates from Coupled Atmosphere-Ocean Models},
  doi          = {10.1175/2008jcli2239.1},
  number       = {19},
  pages        = {5135--5144},
  volume       = {21},
  file         = {:Dufresne2008.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming, Feedbacks},
  publisher    = {American Meteorological Society},
}

@Article{Dufresne2013,
  author       = {Dufresne, J.-L. and Foujols, M. A. and Denvil, S. and Caubel, A. and Marti, O. and Aumont, O. and Balkanski, Y. and Bekki, S. and Bellenger, H. and Benshila, R. and Bony, S. and Bopp, L. and Braconnot, P. and Brockmann, P. and Cadule, P. and Cheruy, F. and Codron, F. and Cozic, A. and Cugnet, D. and de Noblet, N. and Duvel, J. P. and Ethe, C. and Fairhead, L. and Fichefet, T. and Flavoni, S. and Friedlingstein, P. and Grandpeix, J. Y. and Guez, L. and Guilyardi, E. and Hauglustaine, D. and Hourdin, F. and Idelkadi, A. and Ghattas, J. and Joussaume, S. and Kageyama, M. and Krinner, G. and Labetoulle, S. and Lahellec, A. and Lefebvre, M. P. and Lefevre, F. and Levy, C. and Li, Z. X. and Lloyd, J. and Lott, F. and Madec, G. and Mancip, M. and Marchand, M. and Masson, S. and Meurdesoif, Y. and Mignot, J. and Musat, I. and Parouty, S. and Polcher, J. and Rio, C. and Schulz, M. and Swingedouw, D. and Szopa, S. and Talandier, C. and Terray, P. and Viovy, N. and Vuichard, N.},
  date         = {2013},
  journaltitle = {Climate Dynamics},
  title        = {Climate change projections using the IPSL-CM5 Earth System Model: from {CMIP}3 to {CMIP}5},
  doi          = {10.1007/s00382-012-1636-1},
  number       = {9-10},
  pages        = {2123--2165},
  volume       = {40},
  abstract     = {We present the global general circulation model IPSL-CM5 developed to study the long-term response of the climate system to natural and anthropogenic forcings as part of the 5th Phase of the Coupled Model Intercomparison Project (CMIP5). This model includes an interactive carbon cycle, a representation of tropospheric and stratospheric chemistry, and a comprehensive representation of aerosols. As it represents the principal dynamical, physical, and bio-geochemical processes relevant to the climate system, it may be referred to as an Earth System Model. However, the IPSL-CM5 model may be used in a multitude of configurations associated with different boundary conditions and with a range of complexities in terms of processes and interactions. This paper presents an overview of the different model components and explains how they were coupled and used to simulate historical climate changes over the past 150 years and different scenarios of future climate change. A single version of the IPSL-CM5 model (IPSL-CM5A-LR) was used to provide climate projections associated with different socio-economic scenarios, including the different Representative Concentration Pathways considered by CMIP5 and several scenarios from the Special Report on Emission Scenarios considered by CMIP3. Results suggest that the magnitude of global warming projections primarily depends on the socio-economic scenario considered, that there is potential for an aggressive mitigation policy to limit global warming to about two degrees, and that the behavior of some components of the climate system such as the Arctic sea ice and the Atlantic Meridional Overturning Circulation may change drastically by the end of the twenty-first century in the case of a no climate policy scenario. Although the magnitude of regional temperature and precipitation changes depends fairly linearly on the magnitude of the projected global warming (and thus on the scenario considered), the geographical pattern of these changes is strikingly similar for the different scenarios. The representation of atmospheric physical processes in the model is shown to strongly influence the simulated climate variability and both the magnitude and pattern of the projected climate changes.},
  file         = {:Dufresne2013.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {climate, climate change, climate projections, earth system model, cmip5, cmip3, greenhouse gases, aerosols, carbon cycle, allowable emissions, rcp scenarios, land use changes, general-circulation model, convective boundary-layer, north-atlantic, cumulus convection, hydrological cycle, vegetation model, sulfate aerosol, seasonal cycle, ocean model, part i},
}

@Article{Dunne2012,
  author       = {Dunne, J. P. and John, J. G. and Adcroft, A. J. and Griffies, S. M. and Hallberg, R. W. and Shevliakova, E. and Stouffer, R. J. and Cooke, W. and Dunne, K. A. and Harrison, M. J. and Krasting, J. P. and Malyshev, S. L. and Milly, P. C. D. and Phillipps, P. J. and Sentman, L. T. and Samuels, B. L. and Spelman, M. J. and Winton, M. and Wittenberg, A. T. and Zadeh, N.},
  date         = {2012},
  journaltitle = {Journal of Climate},
  title        = {GFDL's ESM2 Global Coupled Climate-Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics},
  doi          = {10.1175/Jcli-D-11-00560.1},
  number       = {19},
  pages        = {6646--6665},
  volume       = {25},
  abstract     = {The physical climate formulation and simulation characteristics of two new global coupled carbon-climate Earth System Models, ESM2M and ESM2G, are described. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory's previous Climate Model version 2.1 (CM2.1) while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4p1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in El Nino-Southern Oscillation being overly strong in ESM2M and overly weak in ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to total heat content variability given its lack of long-term drift, gyre circulation, and ventilation in the North Pacific, tropical Atlantic, and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to surface circulation given its superior surface temperature, salinity, and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. The overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon-climate models.},
  file         = {:Dunne2012.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {ocean circulation models, world ocean, general-circulation, initial conditions, tracer transports, atmosphere model, numerical-model, southern-ocean, north-atlantic, z-coordinate},
}

@Article{Elia2002,
  author       = {de Elia, R. and Laprise, R. and Denis, B.},
  date         = {2002},
  journaltitle = {Monthly Weather Review},
  title        = {Forecasting skill limits of nested, limited-area models: A perfect-model approach},
  doi          = {10.1175/1520-0493(2002)130<2006:Fslonl>2.0.Co;2},
  number       = {8},
  pages        = {2006--2023},
  volume       = {130},
  abstract     = {The fundamental hypothesis underlying the use of limited-area models (LAMs) is their ability to generate meaningful small-scale features from low-resolution information, provided as initial conditions and at their lateral boundaries by a model or by objective analyses. This hypothesis has never been seriously challenged in spite of some reservations expressed by the scientific community. In order to study this hypothesis, a perfect-model approach is followed. A high-resolution large-domain LAM driven by global analyses is used to generate a "reference run.'' These fields are filtered afterward to remove small scales in order to mimic a low-resolution run. The same high-resolution LAM, but in a small-domain grid, is nested within these filtered fields and run for several days. Comparison of both runs over the same region allows for the estimation of the ability of the LAM to regenerate the removed small scales.
Results show that the small-domain LAM recreates the right amount of small-scale variability but is incapable of reproducing it with the precision required by a root-mean-square (rms) measure of error. Some success is attained, however, during the first hours of integration. This suggests that LAMs are not very efficient in accurately downscaling information, even in a perfect-model context. On the other hand, when the initial conditions used in the small-domain LAM include the small-scale features that are still absent in the lateral boundary conditions, results improve dramatically. This suggests that lack of high-resolution information in the boundary conditions has a small impact on the performance.
Results of this study also show that predictability timescales of different wavelengths exhibit a behavior similar to those of a global autonomous model.},
  file         = {:Elia2002.pdf:PDF},
  groups       = {Mathematical basics},
  keywords     = {regional-climate model, lateral boundary-conditions, numerical-models, atmospheric flow, mesoscale-model, predictability, sensitivity, resolution, validation, error},
}

@Article{Elith2008,
  author       = {Elith, J. and Leathwick, J. R. and Hastie, T.},
  date         = {2008},
  journaltitle = {Journal of Animal Ecology},
  title        = {A working guide to boosted regression trees},
  doi          = {10.1111/j.1365-2656.2008.01390.x},
  number       = {4},
  pages        = {802--813},
  volume       = {77},
  abstract     = {1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions.
2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion.
3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods.
4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.},
  file         = {:Elith2008.pdf:PDF},
  groups       = {ML basics},
  keywords     = {data mining, machine learning, model averaging, random forests, species distributions, generalized additive-models, new-zealand, prediction, fish, classification, read},
  readstatus   = {read},
}

@Article{Eyring2016,
  author       = {Eyring, V. and Bony, S. and Meehl, G. A. and Senior, C. A. and Stevens, B. and Stouffer, R. J. and Taylor, K. E.},
  date         = {2016},
  journaltitle = {Geoscientific Model Development},
  title        = {Overview of the Coupled Model Intercomparison Project Phase 6 ({CMIP}6) experimental design and organization},
  doi          = {10.5194/gmd-9-1937-2016},
  number       = {5},
  pages        = {1937--1958},
  volume       = {9},
  abstract     = {By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850-near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions:
- How does the Earth system respond to forcing?
- What are the origins and consequences of systematic model biases?
- How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios?
This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.},
  file         = {:Eyring2016.pdf:PDF},
  groups       = {CMIP and other MIPs},
  keywords     = {climate-change research, carbon-dioxide, circulation, scenarios, co2},
}

@Article{Eyring2016a,
  author       = {Eyring, V. and Righi, M. and Lauer, A. and Evaldsson, M. and Wenzel, S. and Jones, C. and Anav, A. and Andrews, O. and Cionni, I. and Davin, E. L. and Deser, C. and Ehbrecht, C. and Friedlingstein, P. and Gleckler, P. and Gottschaldt, K. D. and Hagemann, S. and Juckes, M. and Kindermann, S. and Krasting, J. and Kunert, D. and Levine, R. and Loew, A. and Makela, J. and Martin, G. and Mason, E. and Phillips, A. S. and Read, S. and Rio, C. and Roehrig, R. and Senftleben, D. and Sterl, A. and van Ulft, L. H. and Walton, J. and Wang, S. Y. and Williams, K. D.},
  date         = {2016},
  journaltitle = {Geoscientific Model Development},
  title        = {{ESMValTool} (v1.0) --- a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP},
  doi          = {10.5194/gmd-9-1747-2016},
  number       = {5},
  pages        = {1747--1802},
  volume       = {9},
  abstract     = {A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected essential climate variables (ECVs), a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases, and soil hydrology-climate interactions, as well as atmospheric CO2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard namelists for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The Earth System Model Evaluation Tool (ESMValTool) is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the Coupled Model Intercomparison Project (CMIP) ensemble. This will facilitate and improve ESM evaluation beyond the state-of-the-art and aims at supporting such activities within CMIP and at individual modelling centres. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user.},
  file         = {:Eyring2016a.pdf:PDF},
  groups       = {ESMValTool},
  keywords     = {asian summer monsoon, outgoing longwave radiation, west-african monsoon, indian-ocean dipole, climate model, global precipitation, el-nino, part i, atmospheric chemistry, aerosol microphysics, skimmed},
  readstatus   = {skimmed},
}

@Article{Eyring2016b,
  author       = {Veronika Eyring and Peter J. Gleckler and Christoph Heinze and Ronald J. Stouffer and Karl E. Taylor and V. Balaji and Eric Guilyardi and Sylvie Joussaume and Stephan Kindermann and Bryan N. Lawrence and Gerald A. Meehl and Mattia Righi and Dean N. Williams},
  date         = {2016-11},
  journaltitle = {Earth System Dynamics},
  title        = {Towards improved and more routine Earth system model evaluation in {CMIP}},
  doi          = {10.5194/esd-7-813-2016},
  number       = {4},
  pages        = {813--830},
  volume       = {7},
  file         = {:Eyring2016b.pdf:PDF},
  groups       = {CMIP and other MIPs},
  publisher    = {Copernicus {GmbH}},
}

@Article{Eyring2019,
  author       = {Eyring, V. and Cox, P. M. and Flato, G. M. and Gleckler, P. J. and Abramowitz, G. and Caldwell, P. and Collins, W. D. and Gier, B. K. and Hall, A. D. and Hoffman, F. M. and Hurtt, G. C. and Jahn, A. and Jones, C. D. and Klein, S. A. and Krasting, J. P. and Kwiatkowski, L. and Lorenz, R. and Maloney, E. and Meehl, G. A. and Pendergrass, A. G. and Pincus, R. and Ruane, A. C. and Russell, J. L. and Sanderson, B. M. and Santer, B. D. and Sherwood, S. C. and Simpson, I. R. and Stouffer, R. J. and Williamson, M. S.},
  date         = {2019},
  journaltitle = {Nature Climate Change},
  title        = {Taking climate model evaluation to the next level},
  doi          = {10.1038/s41558-018-0355-y},
  number       = {2},
  pages        = {102--110},
  volume       = {9},
  abstract     = {Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.},
  file         = {:Eyring2019.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties, Emergent Constraints},
  keywords     = {emergent constraints, performance metrics, cmip5 models, sensitivity, projections, cycle, temperature, extremes, impact, carbon, skimmed},
  readstatus   = {skimmed},
}

@Article{Eyring2020,
  author       = {Veronika Eyring and Lisa Bock and Axel Lauer and Mattia Righi and Manuel Schlund and Bouwe Andela and Enrico Arnone and Omar Bellprat and Björn Brötz and Louis-Philippe Caron and Nuno Carvalhais and Irene Cionni and Nicola Cortesi and Bas Crezee and Edouard L. Davin and Paolo Davini and Kevin Debeire and Lee de Mora and Clara Deser and David Docquier and Paul Earnshaw and Carsten Ehbrecht and Bettina K. Gier and Nube Gonzalez-Reviriego and Paul Goodman and Stefan Hagemann and Steven Hardiman and Birgit Hassler and Alasdair Hunter and Christopher Kadow and Stephan Kindermann and Sujan Koirala and Nikolay Koldunov and Quentin Lejeune and Valerio Lembo and Tomas Lovato and Valerio Lucarini and François Massonnet and Benjamin Müller and Amarjiit Pandde and Núria Pérez-Zanón and Adam Phillips and Valeriu Predoi and Joellen Russell and Alistair Sellar and Federico Serva and Tobias Stacke and Ranjini Swaminathan and Verónica Torralba and Javier Vegas-Regidor and Jost von Hardenberg and Katja Weigel and Klaus Zimmermann},
  date         = {2020-07},
  journaltitle = {Geoscientific Model Development},
  title        = {Earth System Model Evaluation Tool ({ESMValTool}) v2.0 --- an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in {CMIP}},
  doi          = {10.5194/gmd-13-3383-2020},
  number       = {7},
  pages        = {3383--3438},
  volume       = {13},
  file         = {:Eyring2020.pdf:PDF},
  groups       = {ESMValTool},
  keywords     = {own},
  publisher    = {Copernicus {GmbH}},
}

@Article{Farquhar1980,
  author       = {G. D. Farquhar and S. von Caemmerer and J. A. Berry},
  date         = {1980-06},
  journaltitle = {Planta},
  title        = {A biochemical model of photosynthetic {CO}$_2$ assimilation in leaves of C$_3$ species},
  doi          = {10.1007/bf00386231},
  number       = {1},
  pages        = {78--90},
  volume       = {149},
  file         = {:Farquhar1980.pdf:PDF},
  groups       = {Carbon Cycle},
  publisher    = {Springer Science and Business Media {LLC}},
}

@Article{Fasullo2012,
  author       = {Fasullo, J. T. and Trenberth, K. E.},
  date         = {2012},
  journaltitle = {Science},
  title        = {A Less Cloudy Future: The Role of Subtropical Subsidence in Climate Sensitivity},
  doi          = {10.1126/science.1227465},
  number       = {6108},
  pages        = {792--794},
  volume       = {338},
  abstract     = {An observable constraint on climate sensitivity, based on variations in mid-tropospheric relative humidity (RH) and their impact on clouds, is proposed. We show that the tropics and subtropics are linked by teleconnections that induce seasonal RH variations that relate strongly to albedo (via clouds), and that this covariability is mimicked in a warming climate. A present-day analog for future trends is thus identified whereby the intensity of subtropical dry zones in models associated with the boreal monsoon is strongly linked to projected cloud trends, reflected solar radiation, and model sensitivity. Many models, particularly those with low climate sensitivity, fail to adequately resolve these teleconnections and hence are identifiably biased. Improving model fidelity in matching observed variations provides a viable path forward for better predicting future climate.},
  file         = {:Fasullo2012.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming, Clouds / Aerosols},
  keywords     = {temperature, feedbacks},
}

@Article{Flato2011,
  author       = {Gregory M. Flato},
  date         = {2011-11},
  journaltitle = {Wiley Interdisciplinary Reviews: Climate Change},
  title        = {Earth system models: an overview},
  doi          = {10.1002/wcc.148},
  number       = {6},
  pages        = {783--800},
  volume       = {2},
  file         = {:Flato2011.pdf:PDF},
  groups       = {ESM basics},
  publisher    = {Wiley},
}

@InBook{Flato2013,
  author       = {Flato, Gregory M. and Marotzke, Jochem and Abiodun, Babatunde and Braconnot, Pascale and Chou, S. Chan and Collins, William D. and Cox, Peter and Driouech, Fatima and Emori, Seita and Eyring, Veronika and Forest, Chris and Gleckler, Peter and Guilyardi, Eric and Jakob, Christian and Kattsov, Vladimir and Reason, Chris and Rummukainen, Markku},
  date         = {2013},
  title        = {Evaluation of Climate Models},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {741--866},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter09_FINAL.pdf},
  file         = {:Flato2013.pdf:PDF},
  groups       = {IPCC, ESM basics, ECS / TCR / Climate Sensitivity / Warming},
  journaltitle = {Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Article{Fleischer2019,
  author       = {Fleischer, K. and Rammig, A. and De Kauwe, M. G. and Walker, A. P. and Domingues, T. F. and Fuchslueger, L. and Garcia, S. and Goll, D. S. and Grandis, A. and Jiang, M. K. and Haverd, V. and Hofhansl, F. and Holm, J. A. and Kruijt, B. and Leung, F. and Medlyn, B. E. and Mercado, L. M. and Norby, R. J. and Pak, B. and von Randow, C. and Quesada, C. A. and Schaap, K. J. and Valverde-Barrantes, O. J. and Wang, Y. P. and Yang, X. J. and Zaehle, S. and Zhu, Q. and Lapola, D. M.},
  date         = {2019},
  journaltitle = {Nature Geoscience},
  title        = {Amazon forest response to {CO}$_2$ fertilization dependent on plant phosphorus acquisition},
  doi          = {10.1038/s41561-019-0404-9},
  number       = {9},
  pages        = {736--741},
  volume       = {12},
  abstract     = {Global terrestrial models currently predict that the Amazon rainforest will continue to act as a carbon sink in the future, primarily owing to the rising atmospheric carbon dioxide (CO 2 ) concentration. Soil phosphorus impoverishment in parts of the Amazon basin largely controls its functioning, but the role of phosphorus availability has not been considered in global model ensembles-for example, during the Fifth Climate Model Intercomparison Project. Here we simulate the planned free-air CO2 enrichment experiment AmazonFACE with an ensemble of 14 terrestrial ecosystem models. We show that phosphorus availability reduces the projected CO2-induced biomass carbon growth by about 50\% to 79 +/- 63 g C m(-2) yr(-1) over 15 years compared to estimates from carbon and carbon-nitrogen models. Our results suggest that the resilience of the region to climate change may be much less than previously assumed. Variation in the biomass carbon response among the phosphorus-enabled models is considerable, ranging from 5 to 140 g C m(-)2 yr(-1), owing to the contrasting plant phosphorus use and acquisition strategies considered among the models. The Amazon forest response thus depends on the interactions and relative contributions of the phosphorus acquisition and use strategies across individuals, and to what extent these processes can be upregulated under elevated CO2.},
  file         = {:Fleischer2019.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {basin-wide variations, model-data synthesis, carbon-dioxide, ecosystem model, elevated co2, productivity, nitrogen, nutrient, enrichment, air},
}

@Article{Forkel2016,
  author       = {Forkel, M. and Carvalhais, N. and Rodenbeck, C. and Keeling, R. and Heimann, M. and Thonicke, K. and Zaehle, S. and Reichstein, M.},
  date         = {2016},
  journaltitle = {Science},
  title        = {Enhanced seasonal {CO}$_2$ exchange caused by amplified plant productivity in northern ecosystems},
  doi          = {10.1126/science.aac4971},
  number       = {6274},
  pages        = {696--699},
  volume       = {351},
  abstract     = {Atmospheric monitoring of high northern latitudes (above 40 degrees N) has shown an enhanced seasonal cycle of carbon dioxide (CO2) since the 1960s, but the underlying mechanisms are not yet fully understood. The much stronger increase in high latitudes relative to low ones suggests that northern ecosystems are experiencing large changes in vegetation and carbon cycle dynamics. We found that the latitudinal gradient of the increasing CO2 amplitude is mainly driven by positive trends in photosynthetic carbon uptake caused by recent climate change and mediated by changing vegetation cover in northern ecosystems. Our results underscore the importance of climate-vegetation-carbon cycle feedbacks at high latitudes; moreover, they indicate that in recent decades, photosynthetic carbon uptake has reacted much more strongly to warming than have carbon release processes.},
  file         = {:Forkel2016.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {atmospheric co2, vegetation, agriculture, latitudes, cycle},
}

@Article{Forster2013,
  author       = {Piers M. Forster and Timothy Andrews and Peter Good and Jonathan M. Gregory and Lawrence S. Jackson and Mark Zelinka},
  date         = {2013-02},
  journaltitle = {Journal of Geophysical Research: Atmospheres},
  title        = {Evaluating adjusted forcing and model spread for historical and future scenarios in the {CMIP}5 generation of climate models},
  doi          = {10.1002/jgrd.50174},
  number       = {3},
  pages        = {1139--1150},
  volume       = {118},
  comment      = {ECS and TCR for CMIP5 models},
  file         = {:Forster2013.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {skimmed},
  publisher    = {American Geophysical Union ({AGU})},
  readstatus   = {skimmed},
}

@Article{Forster2020,
  author       = {Forster, P. M. and Maycock, A. C. and McKenna, C. M. and Smith, C. J.},
  date         = {2020},
  journaltitle = {Nature Climate Change},
  title        = {Latest climate models confirm need for urgent mitigation},
  doi          = {10.1038/s41558-019-0660-0},
  number       = {1},
  pages        = {7--10},
  volume       = {10},
  abstract     = {Many recently updated climate models show greater future warming than previously. Separate lines of evidence suggest that their warming rates may be unrealistically high, but the risk of such eventualities only emphasizes the need for rapid and deep reductions in emissions.},
  file         = {:Forster2020.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming},
}

@InProceedings{Freund1996,
  author    = {Freund, Yoav and Schapire, Robert E.},
  booktitle = {Proceedings of the Thirteenth International Conference on International Conference on Machine Learning},
  date      = {1996},
  title     = {Experiments with a New Boosting Algorithm},
  isbn      = {1558604197},
  location  = {Bari, Italy},
  pages     = {148--156},
  publisher = {Morgan Kaufmann Publishers Inc.},
  series    = {ICML'96},
  url       = {https://cseweb.ucsd.edu/~yfreund/papers/boostingexperiments.pdf},
  address   = {San Francisco, CA, USA},
  file      = {:Freund1996.pdf:PDF},
  groups    = {ML basics},
  numpages  = {9},
}

@Article{Friedlingstein2006,
  author       = {Friedlingstein, P. and Cox, P. and Betts, R. and Bopp, L. and Von Bloh, W. and Brovkin, V. and Cadule, P. and Doney, S. and Eby, M. and Fung, I. and Bala, G. and John, J. and Jones, C. and Joos, F. and Kato, T. and Kawamiya, M. and Knorr, W. and Lindsay, K. and Matthews, H. D. and Raddatz, T. and Rayner, P. and Reick, C. and Roeckner, E. and Schnitzler, K. G. and Schnur, R. and Strassmann, K. and Weaver, A. J. and Yoshikawa, C. and Zeng, N.},
  date         = {2006},
  journaltitle = {Journal of Climate},
  title        = {Climate-carbon cycle feedback analysis: Results from the ({CMIP})-M-4 model intercomparison},
  doi          = {10.1175/Jcli3800.1},
  number       = {14},
  pages        = {3337--3353},
  volume       = {19},
  abstract     = {Eleven coupled climate-carbon cycle models used a common protocol to study the coupling between climate change and the carbon cycle. The models were forced by historical emissions and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 anthropogenic emissions of CO2 for the 1850-2100 time period. For each model, two simulations were performed in order to isolate the impact of climate change on the land and ocean carbon cycle, and therefore the climate feedback on the atmospheric CO2 concentration growth rate. There was unanimous agreement among the models that future climate change will reduce the efficiency of the earth system to absorb the anthropogenic carbon perturbation. A larger fraction of anthropogenic CO2 will stay airborne if climate change is accounted for. By the end of the twenty-first century, this additional CO2 varied between 20 and 200 ppm for the two extreme models, the majority of the models lying between 50 and 100 ppm. The higher CO2 levels led to an additional climate warming ranging between 0.1 degrees and 1.5 degrees C.
All models simulated a negative sensitivity for both the land and the ocean carbon cycle to future climate. However, there was still a large uncertainty on the magnitude of these sensitivities. Eight models attributed most of the changes to the land, while three attributed it to the ocean. Also, a majority of the models located the reduction of land carbon uptake in the Tropics. However, the attribution of the land sensitivity to changes in net primary productivity versus changes in respiration is still subject to debate; no consensus emerged among the models.},
  file         = {:Friedlingstein2006.pdf:PDF},
  groups       = {Carbon Cycle, Feedbacks},
  keywords     = {tropical circulation model, amazonian forest dieback, space-time climate, atmospheric co2, soil respiration, ecosystem model, coupled climate, system model, stomatal conductance, vegetation dynamics, printed, read},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Friedlingstein2020,
  author       = {Pierre Friedlingstein and Michael O'Sullivan and Matthew W. Jones and Robbie M. Andrew and Judith Hauck and Are Olsen and Glen P. Peters and Wouter Peters and Julia Pongratz and Stephen Sitch and Corinne {Le Quéré} and Josep G. Canadell and Philippe Ciais and Robert B. Jackson and Simone Alin and Luiz E. O. C. Aragão and Almut Arneth and Vivek Arora and Nicholas R. Bates and Meike Becker and Alice Benoit-Cattin and Henry C. Bittig and Laurent Bopp and Selma Bultan and Naveen Chandra and Frédéric Chevallier and Louise P. Chini and Wiley Evans and Liesbeth Florentie and Piers M. Forster and Thomas Gasser and Marion Gehlen and Dennis Gilfillan and Thanos Gkritzalis and Luke Gregor and Nicolas Gruber and Ian Harris and Kerstin Hartung and Vanessa Haverd and Richard A. Houghton and Tatiana Ilyina and Atul K. Jain and Emilie Joetzjer and Koji Kadono and Etsushi Kato and Vassilis Kitidis and Jan Ivar Korsbakken and Peter Landschützer and Nathalie Lefèvre and Andrew Lenton and Sebastian Lienert and Zhu Liu and Danica Lombardozzi and Gregg Marland and Nicolas Metzl and David R. Munro and Julia E. M. S. Nabel and Shin-Ichiro Nakaoka and Yosuke Niwa and Kevin O'Brien and Tsuneo Ono and Paul I. Palmer and Denis Pierrot and Benjamin Poulter and Laure Resplandy and Eddy Robertson and Christian Rödenbeck and Jörg Schwinger and Roland Séférian and Ingunn Skjelvan and Adam J. P. Smith and Adrienne J. Sutton and Toste Tanhua and Pieter P. Tans and Hanqin Tian and Bronte Tilbrook and Guido van der Werf and Nicolas Vuichard and Anthony P. Walker and Rik Wanninkhof and Andrew J. Watson and David Willis and Andrew J. Wiltshire and Wenping Yuan and Xu Yue and Sönke Zaehle},
  date         = {2020-12},
  journaltitle = {Earth System Science Data},
  title        = {Global Carbon Budget 2020},
  doi          = {10.5194/essd-12-3269-2020},
  number       = {4},
  pages        = {3269--3340},
  volume       = {12},
  file         = {:Friedlingstein2020.pdf:PDF},
  groups       = {OBS, Carbon Cycle},
  publisher    = {Copernicus {GmbH}},
}

@Article{Friedman2001,
  author       = {Friedman, J. H.},
  date         = {2001},
  journaltitle = {Annals of Statistics},
  title        = {Greedy function approximation: A gradient boosting machine},
  doi          = {10.1214/aos/1013203451},
  number       = {5},
  pages        = {1189--1232},
  volume       = {29},
  abstract     = {Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent "boosting" paradigm is developed for additive expansions based on any fitting criterion. Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such "TreeBoost" models are presented. Gradient boosting of regression trees produces competitive, highly robust, interpretable procedures for both regression and classification, especially appropriate for ruining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire and Friedman, Hastie and Tibshirani are discussed.},
  comment      = {First GBRT paper},
  file         = {:Friedman2001.pdf:PDF},
  groups       = {ML basics},
  keywords     = {function estimation, boosting, decision trees, robust nonparametric regression, regression, prediction, skimmed},
  readstatus   = {skimmed},
}

@Article{Friedman2002,
  author       = {Friedman, J. H.},
  date         = {2002},
  journaltitle = {Computational Statistics \& Data Analysis},
  title        = {Stochastic gradient boosting},
  doi          = {10.1016/S0167-9473(01)00065-2},
  number       = {4},
  pages        = {367--378},
  volume       = {38},
  abstract     = {Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo"-residuals by least squares at each iteration. The pseudo-residuals are the gradient of the loss functional being minimized, with respect to the model values at each training data point evaluated at the current step. It is shown that both the approximation accuracy and execution speed of gradient boosting can be substantially improved by incorporating randomization into the procedure. Specifically, at each iteration a subsample of the training data is drawn at random (without replacement) from the full training data set. This randomly selected subsample is then used in place of the full sample to fit the base learner and compute the model update for the current iteration. This randomized approach also increases robustness against overcapacity of the base learner. (C) 2002 Elsevier Science B.V. All rights reserved.},
  file         = {:Friedman2002.pdf:PDF},
  groups       = {ML basics},
  keywords     = {read, printed},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Furtado2016,
  author       = {K. Furtado and P. R. Field and I. A. Boutle and C. J. Morcrette and J. M. Wilkinson},
  date         = {2016-01},
  journaltitle = {Journal of the Atmospheric Sciences},
  title        = {A Physically Based Subgrid Parameterization for the Production and Maintenance of Mixed-Phase Clouds in a General Circulation Model},
  doi          = {10.1175/jas-d-15-0021.1},
  number       = {1},
  pages        = {279--291},
  volume       = {73},
  file         = {:Furtado2016.pdf:PDF},
  groups       = {Clouds / Aerosols, Parameterizations},
  publisher    = {American Meteorological Society},
}

@Article{Geer2021,
  author       = {A. J. Geer},
  date         = {2021-02},
  journaltitle = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  title        = {Learning earth system models from observations: machine learning or data assimilation?},
  doi          = {10.1098/rsta.2020.0089},
  number       = {2194},
  pages        = {20200089},
  volume       = {379},
  file         = {:Geer2021.pdf:PDF},
  groups       = {ML in Climate Sciences},
  publisher    = {The Royal Society},
}

@Article{Gent2011,
  author       = {Gent, P. R. and Danabasoglu, G. and Donner, L. J. and Holland, M. M. and Hunke, E. C. and Jayne, S. R. and Lawrence, D. M. and Neale, R. B. and Rasch, P. J. and Vertenstein, M. and Worley, P. H. and Yang, Z. L. and Zhang, M. H.},
  date         = {2011},
  journaltitle = {Journal of Climate},
  title        = {The Community Climate System Model Version 4},
  doi          = {10.1175/2011jcli4083.1},
  number       = {19},
  pages        = {4973--4991},
  volume       = {24},
  abstract     = {The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1 degrees results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4 degrees-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Nino-Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden-Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4 degrees C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.},
  file         = {:Gent2011.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {general-circulation model, atmosphere model, mixed-layer, ccsm3, parameterization, 20th-century, temperature, variability, sensitivity, convection},
}

@Article{Gentine2018,
  author       = {P. Gentine and M. Pritchard and S. Rasp and G. Reinaudi and G. Yacalis},
  date         = {2018-06},
  journaltitle = {Geophysical Research Letters},
  title        = {Could Machine Learning Break the Convection Parameterization Deadlock?},
  doi          = {10.1029/2018gl078202},
  number       = {11},
  pages        = {5742--5751},
  volume       = {45},
  file         = {:Gentine2018.pdf:PDF},
  groups       = {Clouds / Aerosols, Parameterizations, ML in Climate Sciences},
  publisher    = {American Geophysical Union ({AGU})},
}

@Book{Gettelman2016,
  author     = {Gettelman, Andrew and Rood, Richard B.},
  date       = {2016},
  title      = {Demystifying Climate Models: A Users Guide to Earth System Models},
  publisher  = {Springer Nature},
  file       = {:Gettelman2016.pdf:PDF},
  groups     = {ESM basics},
  keywords   = {skimmed},
  readstatus = {skimmed},
}

@Article{Gettelman2019,
  author       = {Gettelman, A. and Hannay, C. and Bacmeister, J. T. and Neale, R. B. and Pendergrass, A. G. and Danabasoglu, G. and Lamarque, J. F. and Fasullo, J. T. and Bailey, D. A. and Lawrence, D. M. and Mills, M. J.},
  date         = {2019},
  journaltitle = {Geophysical Research Letters},
  title        = {High Climate Sensitivity in the Community Earth System Model Version 2 (CESM2)},
  doi          = {10.1029/2019gl083978},
  number       = {14},
  pages        = {8329--8337},
  volume       = {46},
  abstract     = {The Community Earth System Model Version 2 (CESM2) has an equilibrium climate sensitivity (ECS) of 5.3K. ECS is an emergent property of both climate feedbacks and aerosol forcing. The increase in ECS over the previous version (CESM1) is the result of cloud feedbacks. Interim versions of CESM2 had a land model that damped ECS. Part of the ECS change results from evolving the model configuration to reproduce the long-term trend of global and regional surface temperature over the twentieth century in response to climate forcings. Changes made to reduce sensitivity to aerosols also impacted cloud feedbacks, which significantly influence ECS. CESM2 simulations compare very well to observations of present climate. It is critical to understand whether the high ECS, outside the best estimate range of 1.5-4.5K, is plausible.},
  file         = {:Gettelman2019.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {climate sensitivity, climate models, water-vapor feedback, cloud feedbacks, ice-nucleation, reactive gases, parameterization, aerosols, impact, microphysics, simulations, emissions, skimmed},
  readstatus   = {skimmed},
}

@Article{Gettelman2019a,
  author       = {Gettelman, A. and Mills, M. J. and Kinnison, D. E. and Garcia, R. R. and Smith, A. K. and Marsh, D. R. and Tilmes, S. and Vitt, F. and Bardeen, C. G. and McInerny, J. and Liu, H. L. and Solomon, S. C. and Polvani, L. M. and Emmons, L. K. and Lamarque, J. F. and Richter, J. H. and Glanville, A. S. and Bacmeister, J. T. and Phillips, A. S. and Neale, R. B. and Simpson, I. R. and DuVivier, A. K. and Hodzic, A. and Randel, W. J.},
  date         = {2019},
  journaltitle = {Journal of Geophysical Research-Atmospheres},
  title        = {The Whole Atmosphere Community Climate Model Version 6 (WACCM6)},
  doi          = {10.1029/2019jd030943},
  number       = {23},
  pages        = {12380--12403},
  volume       = {124},
  abstract     = {The Whole Atmosphere Community Climate Model version 6 (WACCM6) is a major update of the whole atmosphere modeling capability in the Community Earth System Model (CESM), featuring enhanced physical, chemical and aerosol parameterizations. This work describes WACCM6 and some of the important features of the model. WACCM6 can reproduce many modes of variability and trends in the middle atmosphere, including the quasi-biennial oscillation, stratospheric sudden warmings, and the evolution of Southern Hemisphere springtime ozone depletion over the twentieth century. WACCM6 can also reproduce the climate and temperature trends of the 20th century throughout the atmospheric column. The representation of the climate has improved in WACCM6, relative to WACCM4. In addition, there are improvements in high-latitude climate variability at the surface and sea ice extent in WACCM6 over the lower top version of the model (CAM6) that comes from the extended vertical domain and expanded aerosol chemistry in WACCM6, highlighting the importance of the stratosphere and tropospheric chemistry for high-latitude climate variability.},
  file         = {:Gettelman2019a.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {greenhouse-gas concentrations, lidar observations, national-center, water-vapor, aerosol, simulations, parameterization, representation, convection, emissions},
}

@Misc{Gibbs2006,
  author    = {Gibbs, Holly},
  date      = {2006},
  title     = {Olson's Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: An Updated Database Using the GLC2000 Land Cover Product (NDP-017b, a 2006 update of the original 1985 and 2001 data file))},
  doi       = {10.3334/CDIAC/LUE.NDP017.2006},
  language  = {en},
  groups    = {OBS},
  keywords  = {54 ENVIRONMENTAL SCIENCES},
  publisher = {Environmental System Science Data Infrastructure for a Virtual Ecosystem; Carbon Dioxide Information Analysis Center (CDIAC); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)},
}

@Article{Giorgetta2013,
  author       = {Giorgetta, M. A. and Jungclaus, J. and Reick, C. H. and Legutke, S. and Bader, J. and Bottinger, M. and Brovkin, V. and Crueger, T. and Esch, M. and Fieg, K. and Glushak, K. and Gayler, V. and Haak, H. and Hollweg, H. D. and Ilyina, T. and Kinne, S. and Kornblueh, L. and Matei, D. and Mauritsen, T. and Mikolajewicz, U. and Mueller, W. and Notz, D. and Pithan, F. and Raddatz, T. and Rast, S. and Redler, R. and Roeckner, E. and Schmidt, H. and Schnur, R. and Segschneider, J. and Six, K. D. and Stockhause, M. and Timmreck, C. and Wegner, J. and Widmann, H. and Wieners, K. H. and Claussen, M. and Marotzke, J. and Stevens, B.},
  date         = {2013},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5},
  doi          = {10.1002/jame.20038},
  number       = {3},
  pages        = {572--597},
  volume       = {5},
  abstract     = {The new Max-Planck-Institute Earth System Model (MPI-ESM) is used in the Coupled Model Intercomparison Project phase 5 (CMIP5) in a series of climate change experiments for either idealized CO2-only forcing or forcings based on observations and the Representative Concentration Pathway (RCP) scenarios. The paper gives an overview of the model configurations, experiments related forcings, and initialization procedures and presents results for the simulated changes in climate and carbon cycle. It is found that the climate feedback depends on the global warming and possibly the forcing history. The global warming from climatological 1850 conditions to 2080-2100 ranges from 1.5 degrees C under the RCP2.6 scenario to 4.4 degrees C under the RCP8.5 scenario. Over this range, the patterns of temperature and precipitation change are nearly independent of the global warming. The model shows a tendency to reduce the ocean heat uptake efficiency toward a warmer climate, and hence acceleration in warming in the later years. The precipitation sensitivity can be as high as 2.5\% K-1 if the CO2 concentration is constant, or as small as 1.6\% K-1, if the CO2 concentration is increasing. The oceanic uptake of anthropogenic carbon increases over time in all scenarios, being smallest in the experiment forced by RCP2.6 and largest in that for RCP8.5. The land also serves as a net carbon sink in all scenarios, predominantly in boreal regions. The strong tropical carbon sources found in the RCP2.6 and RCP8.5 experiments are almost absent in the RCP4.5 experiment, which can be explained by reforestation in the RCP4.5 scenario.},
  file         = {:Giorgetta2013.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {climate, climate change, carbon cycle, cmip5, mpi-esm, madden-julian oscillation, land-use transitions, hydrological cycle, ocean-circulation, wood-harvest, variability, sensitivity, last, co2, dependence},
}

@Article{Gleckler2008,
  author       = {P. J. Gleckler and K. E. Taylor and C. Doutriaux},
  date         = {2008-03},
  journaltitle = {Journal of Geophysical Research},
  title        = {Performance metrics for climate models},
  doi          = {10.1029/2007jd008972},
  number       = {D6},
  pages        = {D06104},
  volume       = {113},
  file         = {:Gleckler2008.pdf:PDF},
  groups       = {ESM basics},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Golaz2019,
  author       = {Golaz, J. C. and Caldwell, P. M. and Van Roekel, L. P. and Petersen, M. R. and Tang, Q. and Wolfe, J. D. and Abeshu, G. and Anantharaj, V. and Asay-Davis, X. S. and Bader, D. C. and Baldwin, S. A. and Bisht, G. and Bogenschutz, P. A. and Branstetter, M. and Brunke, M. A. and Brus, S. R. and Burrows, S. M. and Cameron-Smith, P. J. and Donahue, A. S. and Deakin, M. and Easter, R. C. and Evans, K. J. and Feng, Y. and Flanner, M. and Foucar, J. G. and Fyke, J. G. and Griffin, B. M. and Hannay, C. and Harrop, B. E. and Hoffman, M. J. and Hunke, E. C. and Jacob, R. L. and Jacobsen, D. W. and Jeffery, N. and Jones, P. W. and Keen, N. D. and Klein, S. A. and Larson, V. E. and Leung, L. R. and Li, H. Y. and Lin, W. Y. and Lipscomb, W. H. and Ma, P. L. and Mahajan, S. and Maltrud, M. E. and Mametjanov, A. and McClean, J. L. and McCoy, R. B. and Neale, R. B. and Price, S. F. and Qian, Y. and Rasch, P. J. and Eyre, J. E. J. R. and Riley, W. J. and Ringler, T. D. and Roberts, A. F. and Roesler, E. L. and Salinger, A. G. and Shaheen, Z. and Shi, X. Y. and Singh, B. and Tang, J. Y. and Taylor, M. A. and Thornton, P. E. and Turner, A. K. and Veneziani, M. and Wan, H. and Wang, H. L. and Wang, S. L. and Williams, D. N. and Wolfram, P. J. and Worley, P. H. and Xie, S. C. and Yang, Y. and Yoon, J. H. and Zelinka, M. D. and Zender, C. S. and Zeng, X. B. and Zhang, C. Z. and Zhang, K. and Zhang, Y. and Zheng, X. and Zhou, T. and Zhu, Q.},
  date         = {2019},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution},
  doi          = {10.1029/2018ms001603},
  number       = {7},
  pages        = {2089--2129},
  volume       = {11},
  abstract     = {This work documents the first version of the U.S. Department of Energy (DOE) new Energy Exascale Earth System Model (E3SMv1). We focus on the standard resolution of the fully coupled physical model designed to address DOE mission-relevant water cycle questions. Its components include atmosphere and land (110-km grid spacing), ocean and sea ice (60 km in the midlatitudes and 30 km at the equator and poles), and river transport (55 km) models. This base configuration will also serve as a foundation for additional configurations exploring higher horizontal resolution as well as augmented capabilities in the form of biogeochemistry and cryosphere configurations. The performance of E3SMv1 is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima simulations consisting of a long preindustrial control, historical simulations (ensembles of fully coupled and prescribed SSTs) as well as idealized CO2 forcing simulations. The model performs well overall with biases typical of other CMIP-class models, although the simulated Atlantic Meridional Overturning Circulation is weaker than many CMIP-class models. While the E3SMv1 historical ensemble captures the bulk of the observed warming between preindustrial (1850) and present day, the trajectory of the warming diverges from observations in the second half of the twentieth century with a period of delayed warming followed by an excessive warming trend. Using a two-layer energy balance model, we attribute this divergence to the model's strong aerosol-related effective radiative forcing (ERFari+aci = -1.65 W/m(2)) and high equilibrium climate sensitivity (ECS = 5.3 K).},
  file         = {:Golaz2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {community atmosphere model, sea-ice, surface-temperature, climate sensitivity, el-nino, system model, melt ponds, data-sets, part i, aerosol},
}

@Article{Graven2013,
  author       = {Graven, H. D. and Keeling, R. F. and Piper, S. C. and Patra, P. K. and Stephens, B. B. and Wofsy, S. C. and Welp, L. R. and Sweeney, C. and Tans, P. P. and Kelley, J. J. and Daube, B. C. and Kort, E. A. and Santoni, G. W. and Bent, J. D.},
  date         = {2013},
  journaltitle = {Science},
  title        = {Enhanced Seasonal Exchange of {CO}$_2$ by Northern Ecosystems Since 1960},
  doi          = {10.1126/science.1239207},
  number       = {6150},
  pages        = {1085--1089},
  volume       = {341},
  abstract     = {Seasonal variations of atmospheric carbon dioxide (CO2) in the Northern Hemisphere have increased since the 1950s, but sparse observations have prevented a clear assessment of the patterns of long-term change and the underlying mechanisms. We compare recent aircraft-based observations of CO2 above the North Pacific and Arctic Oceans to earlier data from 1958 to 1961 and find that the seasonal amplitude at altitudes of 3 to 6 km increased by 50\% for 45 degrees to 90 degrees N but by less than 25\% for 10 degrees to 45 degrees N. An increase of 30 to 60\% in the seasonal exchange of CO2 by northern extratropical land ecosystems, focused on boreal forests, is implicated, substantially more than simulated by current land ecosystem models. The observations appear to signal large ecological changes in northern forests and a major shift in the global carbon cycle.},
  file         = {:Graven2013.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {atmospheric co2, amplitude increase, carbon-dioxide, plant-growth, mauna-loa, vegetation, climate, trends, alaska, balance},
}

@Article{Gray2014,
  author       = {Gray, J. M. and Frolking, S. and Kort, E. A. and Ray, D. K. and Kucharik, C. J. and Ramankutty, N. and Friedl, M. A.},
  date         = {2014},
  journaltitle = {Nature},
  title        = {Direct human influence on atmospheric {CO}$_2$ seasonality from increased cropland productivity},
  doi          = {10.1038/nature13957},
  number       = {7527},
  pages        = {398--401},
  volume       = {515},
  abstract     = {Ground- and aircraft-based measurements show that the seasonal amplitude of Northern Hemisphere atmospheric carbon dioxide (CO2) concentrations has increased by as much as 50 per cent over the past 50 years(1-3). This increase has been linked to changes in temperate, boreal and arctic ecosystem properties and processes such as enhanced photosynthesis, increased heterotrophic respiration, and expansion of woody vegetation(4-6). However, the precise causal mechanisms behind the observed changes in atmospheric CO2 seasonality remain unclear(2-4). Here we use production statistics and a carbon accounting model to show that increases in agricultural productivity, which have been largely overlooked in previous investigations, explain as much as a quarter of the observed changes in atmospheric CO2 seasonality. Specifically, Northern Hemisphere extratropical maize, wheat, rice, and soybean production grew by 240 per cent between 1961 and 2008, thereby increasing the amount of net carbon uptake by croplands during the Northern Hemisphere growing season by 0.33 petagrams. Maize alone accounts for two-thirds of this change, owing mostly to agricultural intensification within concentrated production zones in the midwestern United States and northern China. Maize, wheat, rice, and soybeans account for about 68 per cent of extratropical dry biomass production, so it is likely that the total impact of increased agricultural production exceeds the amount quantified here.},
  file         = {:Gray2014.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {net primary production, carbon sequestration, harvest index, northern ecosystems, use efficiency, grain-yield, mauna-loa, agriculture, climate, trends},
}

@Article{Gregory2004,
  author       = {Gregory, J. M. and Ingram, W. J. and Palmer, M. A. and Jones, G. S. and Stott, P. A. and Thorpe, R. B. and Lowe, J. A. and Johns, T. C. and Williams, K. D.},
  date         = {2004},
  journaltitle = {Geophysical Research Letters},
  title        = {A new method for diagnosing radiative forcing and climate sensitivity},
  doi          = {10.1029/2003gl018747},
  number       = {3},
  pages        = {L03205},
  volume       = {31},
  abstract     = {We describe a new method for evaluating the radiative forcing, the climate feedback parameter (W m(-2) K-1) and hence the effective climate sensitivity from any GCM experiment in which the climate is responding to a constant forcing. The method is simply to regress the top of atmosphere radiative flux against the global average surface air temperature change. This method does not require special integrations or off-line estimates, such as for stratospheric adjustment, to obtain the forcing, and eliminates the need for double radiation calculations and tropopause radiative fluxes. We show that for CO2 and solar forcing in a slab model and an AOGCM the method gives results consistent with those obtained by conventional methods. For a single integration it is less precise but since it does not require a steady state to be reached its precision could be improved by running an ensemble of short integrations.},
  comment      = {Definition of ECS using regression of net radiation over temperature change (Gregory regression)},
  file         = {:Gregory2004.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {models, printed, read},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Gregory2008,
  author       = {J. M. Gregory and P. M. Forster},
  date         = {2008-12},
  journaltitle = {Journal of Geophysical Research},
  title        = {Transient climate response estimated from radiative forcing and observed temperature change},
  doi          = {10.1029/2008jd010405},
  number       = {D23},
  pages        = {D23105},
  volume       = {113},
  comment      = {TCR definition},
  file         = {:Gregory2008.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {printed, skimmed},
  printed      = {printed},
  publisher    = {American Geophysical Union ({AGU})},
  readstatus   = {skimmed},
}

@Article{Gregory2008a,
  author       = {Jonathan M. Gregory and Mark J. Webb},
  date         = {2008-01},
  journaltitle = {Journal of Climate},
  title        = {Tropospheric Adjustment Induces a Cloud Component in {CO}$_2$ Forcing},
  doi          = {10.1175/2007jcli1834.1},
  number       = {1},
  pages        = {58--71},
  volume       = {21},
  file         = {:Gregory2008a.pdf:PDF},
  groups       = {Feedbacks, Clouds / Aerosols},
  publisher    = {American Meteorological Society},
}

@Article{Gregory2009,
  author       = {Gregory, J. M. and Jones, C. D. and Cadule, P. and Friedlingstein, P.},
  date         = {2009},
  journaltitle = {Journal of Climate},
  title        = {Quantifying Carbon Cycle Feedbacks},
  doi          = {10.1175/2009jcli2949.1},
  number       = {19},
  pages        = {5232--5250},
  volume       = {22},
  abstract     = {Perturbations to the carbon cycle could constitute large feedbacks on future changes in atmospheric CO2 concentration and climate. This paper demonstrates how carbon cycle feedback can be expressed in formally similar ways to climate feedback, and thus compares their magnitudes. The carbon cycle gives rise to two climate feedback terms: the concentration-carbon feedback, resulting from the uptake of carbon by land and ocean as a biogeochemical response to the atmospheric CO2 concentration, and the climate-carbon feedback, resulting from the effect of climate change on carbon fluxes. In the earth system models of the Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP), climate-carbon feedback on warming is positive and of a similar size to the cloud feedback. The concentration-carbon feedback is negative; it has generally received less attention in the literature, but in magnitude it is 4 times larger than the climate-carbon feedback and more uncertain. The concentration-carbon feedback is the dominant uncertainty in the allowable CO2 emissions that are consistent with a given CO2 concentration scenario. In modeling the climate response to a scenario of CO2 emissions, the net carbon cycle feedback is of comparable size and uncertainty to the noncarbon-climate response. To quantify simulated carbon cycle feedbacks satisfactorily, a radiatively coupled experiment is needed, in addition to the fully coupled and biogeochemically coupled experiments, which are referred to as coupled and uncoupled in C4MIP. The concentration-carbon and climate-carbon feedbacks do not combine linearly, and the concentration-carbon feedback is dependent on scenario and time.},
  comment      = {Defintions for carbon cycle feedbacks and TCRE in terms of thoses},
  file         = {:Gregory2009.pdf:PDF},
  groups       = {Carbon Cycle, Feedbacks, TCRE and Remaining Carbon Budgets},
  keywords     = {coupled climate model, co2, adjustment, ocean, emissions, spread, read, printed},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Grise2016,
  author       = {Kevin M. Grise and Brian Medeiros},
  date         = {2016-11},
  journaltitle = {Journal of Climate},
  title        = {Understanding the Varied Influence of Midlatitude Jet Position on Clouds and Cloud Radiative Effects in Observations and Global Climate Models},
  doi          = {10.1175/jcli-d-16-0295.1},
  number       = {24},
  pages        = {9005--9025},
  volume       = {29},
  comment      = {Potential missing cloud feedback},
  file         = {:Grise2016.pdf:PDF},
  groups       = {Feedbacks, Clouds / Aerosols},
  keywords     = {read, prio1},
  priority     = {prio1},
  publisher    = {American Meteorological Society},
  readstatus   = {read},
}

@Article{Guo2020,
  author       = {Yuyang Guo and Yongqiang Yu and Pengfei Lin and Hailong Liu and Bian He and Qing Bao and Shuwen Zhao and Xiaowei Wang},
  date         = {2020-05},
  journaltitle = {Advances in Atmospheric Sciences},
  title        = {Overview of the {CMIP}6 Historical Experiment Datasets with the Climate System Model {CAS} {FGOALS}-f3-L},
  doi          = {10.1007/s00376-020-2004-4},
  number       = {10},
  pages        = {1057--1066},
  volume       = {37},
  file         = {:Guo2020.pdf:PDF},
  groups       = {CMIP6 models},
  publisher    = {Springer Science and Business Media {LLC}},
}

@Article{Hajima2020,
  author       = {Hajima, T. and Watanabe, M. and Yamamoto, A. and Tatebe, H. and Noguchi, M. A. and Abe, M. and Ohgaito, R. and Ito, A. and Yamazaki, D. and Okajima, H. and Ito, A. and Takata, K. and Ogochi, K. and Watanabe, S. and Kawamiya, M.},
  date         = {2020},
  journaltitle = {Geoscientific Model Development},
  title        = {Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks},
  doi          = {10.5194/gmd-13-2197-2020},
  number       = {5},
  pages        = {2197--2244},
  volume       = {13},
  abstract     = {This article describes the new Earth system model (ESM), the Model for Interdisciplinary Research on Climate, Earth System version 2 for Long-term simulations (MIROC-ES2L), using a state-of-the-art climate model as the physical core. This model embeds a terrestrial biogeochemical component with explicit carbon-nitrogen interaction to account for soil nutrient control on plant growth and the land carbon sink. The model's ocean biogeochemical component is largely updated to simulate the biogeochemical cycles of carbon, nitrogen, phosphorus, iron, and oxygen such that oceanic primary productivity can be controlled by multiple nutrient limitations. The ocean nitrogen cycle is coupled with the land component via river discharge processes, and external inputs of iron from pyrogenic and lithogenic sources are considered. Comparison of a historical simulation with observation studies showed that the model could reproduce the transient global climate change and carbon cycle as well as the observed large-scale spatial patterns of the land carbon cycle and upper-ocean biogeochemistry. The model demonstrated historical human perturbation of the nitrogen cycle through land use and agriculture and simulated the resultant impact on the terrestrial carbon cycle. Sensitivity analyses under preindustrial conditions revealed that the simulated ocean biogeochemistry could be altered regionally (and substantially) by nutrient input from the atmosphere and rivers. Based on an idealized experiment in which CO2 was prescribed to increase at a rate of 1\% yr(-1), the transient climate response (TCR) is estimated to be 1.5 K, i.e., approximately 70\% of that from our previous ESM used in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The cumulative airborne fraction (AF) is also reduced by 15\% because of the intensified land carbon sink, which results in an airborne fraction close to the multimodel mean of the CMIP5 ESMs. The transient climate response to cumulative carbon emissions (TCRE) is 1.3KEgC(-1), i.e., slightly smaller than the average of the CMIP5 ESMs, which suggests that "optimistic" future climate projections will be made by the model. This model and the simulation results contribute to CMIP6. The MIROC-ES2L could further improve our understanding of climate-biogeochemical interaction mechanisms, projections of future environmental changes, and exploration of our future options regarding sustainable development by evolving the processes of climate, biogeochemistry, and human activities in a holistic and interactive manner.},
  file         = {:Hajima2020.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {marine primary production, organic-carbon storage, southern-ocean, dissolved iron, climate-change, global carbon, nitrogen-cycle, soil carbon, terrestrial biosphere, variability},
}

@Article{Hall2006,
  author       = {Hall, A. and Qu, X.},
  date         = {2006},
  journaltitle = {Geophysical Research Letters},
  title        = {Using the current seasonal cycle to constrain snow albedo feedback in future climate change},
  doi          = {10.1029/2005gl025127},
  number       = {3},
  pages        = {L03502},
  volume       = {33},
  abstract     = {Differences in simulations of climate feedbacks are sources of significant divergence in climate models' temperature response to anthropogenic forcing. Snow albedo feedback is particularly critical for climate change prediction in heavily-populated northern hemisphere land masses. Here we show its strength in current models exhibits a factor-of-three spread. These large intermodel variations in feedback strength in climate change are nearly perfectly correlated with comparably large intermodel variations in feedback strength in the context of the seasonal cycle. Moreover, the feedback strength in the real seasonal cycle can be measured and compared to simulated values. These mostly fall outside the range of the observed estimate, suggesting many models have an unrealistic snow albedo feedback in the seasonal cycle context. Because of the tight correlation between simulated feedback strength in the seasonal cycle and climate change, eliminating the model errors in the seasonal cycle will lead directly to a reduction in the spread of feedback strength in climate change. Though this comparison to observations may put the models in an unduly harsh light because of uncertainties in the observed estimate that are difficult to quantify, our results map out a clear strategy for targeted observation of the seasonal cycle to reduce divergence in simulations of climate sensitivity.},
  file         = {:Hall2006.pdf:PDF},
  groups       = {Emergent Constraints, Feedbacks},
  keywords     = {general-circulation models, ice, printed},
  printed      = {printed},
}

@Article{Hall2019,
  author       = {Alex Hall and Peter Cox and Chris Huntingford and Stephen Klein},
  date         = {2019-03},
  journaltitle = {Nature Climate Change},
  title        = {Progressing emergent constraints on future climate change},
  doi          = {10.1038/s41558-019-0436-6},
  number       = {4},
  pages        = {269--278},
  volume       = {9},
  file         = {:Hall2019.pdf:PDF},
  groups       = {Emergent Constraints},
  keywords     = {skimmed},
  publisher    = {Springer Science and Business Media {LLC}},
  readstatus   = {skimmed},
}

@Article{Hamilton2014,
  author       = {Douglas S. Hamilton and Lindsay A. Lee and Kirsty J. Pringle and Carly L. Reddington and Dominick V. Spracklen and Kenneth S. Carslaw},
  date         = {2014-12},
  journaltitle = {Proceedings of the National Academy of Sciences},
  title        = {Occurrence of pristine aerosol environments on a polluted planet},
  doi          = {10.1073/pnas.1415440111},
  number       = {52},
  pages        = {18466--18471},
  volume       = {111},
  file         = {:Hamilton2014.pdf:PDF},
  groups       = {Clouds / Aerosols},
  publisher    = {Proceedings of the National Academy of Sciences},
}

@Article{Hargreaves2012,
  author       = {Hargreaves, J. C. and Annan, J. D. and Yoshimori, M. and Abe-Ouchi, A.},
  date         = {2012},
  journaltitle = {Geophysical Research Letters},
  title        = {Can the Last Glacial Maximum constrain climate sensitivity?},
  doi          = {10.1029/2012gl053872},
  number       = {24},
  pages        = {L24702},
  volume       = {39},
  abstract     = {We investigate the relationship between the Last Glacial Maximum (LGM) and climate sensitivity across the PMIP2 multi-model ensemble of GCMs, and find a correlation between tropical temperature and climate sensitivity which is statistically significant and physically plausible. We use this relationship, together with the LGM temperature reconstruction of Annan and Hargreaves (2012), to generate estimates for the equilibrium climate sensitivity. We estimate the equilibrium climate sensitivity to be about 2.5 degrees C with a high probability of being under 4 degrees C, though these results are subject to several important caveats. The forthcoming PMIP3/CMIP5 models were not considered in this analysis, as very few LGM simulations are currently available from these models. We propose that these models will provide a useful validation of the correlation presented here. Citation: Hargreaves, J. C., J. D. Annan, M. Yoshimori, and A. Abe-Ouchi (2012), Can the Last Glacial Maximum constrain climate sensitivity?, Geophys. Res. Lett., 39, L24702, doi:10.1029/2012GL053872.},
  file         = {:Hargreaves2012.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {simulations, ensembles},
}

@Article{Hargreaves2016,
  author       = {Hargreaves, J. C. and Annan, J. D.},
  date         = {2016},
  journaltitle = {Climate of the Past},
  title        = {Could the Pliocene constrain the equilibrium climate sensitivity?},
  doi          = {10.5194/cp-12-1591-2016},
  number       = {8},
  pages        = {1591--1599},
  volume       = {12},
  abstract     = {The mid-Pliocene Warm Period (mPWP) is the most recent interval in which atmospheric carbon dioxide was substantially higher than in modern pre-industrial times. It is, therefore, a potentially valuable target for testing the ability of climate models to simulate climates warmer than the pre-industrial state. The recent Pliocene Model Intercomparison Project (PlioMIP) presented boundary conditions for the mPWP and a protocol for climate model experiments. Here we analyse results from the PlioMIP and, for the first time, discuss the potential for this interval to usefully constrain the equilibrium climate sensitivity. We observe a correlation in the ensemble between their tropical temperature anomalies at the mPWP and their equilibrium sensitivities. If the real world is assumed to also obey this relationship, then the reconstructed tropical temperature anomaly at the mPWP can in principle generate a constraint on the true sensitivity. Directly applying this methodology using available data yields a range for the equilibrium sensitivity of 1.9-3.7 degrees C, but there are considerable additional uncertainties surrounding the analysis which are not included in this estimate. We consider the extent to which these uncertainties may be better quantified and perhaps lessened in the next few years.},
  file         = {:Hargreaves2016.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {model intercomparison project, last glacial maximum, pliomip experimental-design, large-scale features, warm period, coupled model, simulations, future, cmip5, reconstruction},
}

@Article{Harris2014,
  author       = {Harris, I. and Jones, P. D. and Osborn, T. J. and Lister, D. H.},
  date         = {2014},
  journaltitle = {International Journal of Climatology},
  title        = {Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset},
  doi          = {10.1002/joc.3711},
  number       = {3},
  pages        = {623--642},
  volume       = {34},
  abstract     = {This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5 degrees latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet-day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well-known formulae. Time series for hemispheric averages and 20 large sub-continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series ( and ). (c) 2013 Royal Meteorological Society},
  file         = {:Harris2014.pdf:PDF},
  groups       = {OBS},
  keywords     = {gridded climate data, high resolution, temperature, precipitation, space-time climate, air-temperature, data set, variability, database, density, series},
}

@Article{Haustein2017,
  author       = {K. Haustein and M. R. Allen and P. M. Forster and F. E. L. Otto and D. M. Mitchell and H. D. Matthews and D. J. Frame},
  date         = {2017-11},
  journaltitle = {Scientific Reports},
  title        = {A real-time Global Warming Index},
  doi          = {10.1038/s41598-017-14828-5},
  number       = {1},
  pages        = {1--6},
  volume       = {7},
  file         = {:Haustein2017.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming, Detection and Attribution},
  publisher    = {Springer Science and Business Media {LLC}},
}

@Article{Haverd2020,
  author       = {Haverd, V. and Smith, B. and Canadell, J. G. and Cuntz, M. and Mikaloff-Fletcher, S. and Farquhar, G. and Woodgate, W. and Briggs, P. R. and Trudinger, C. M.},
  date         = {2020},
  journaltitle = {Global Change Biology},
  title        = {Higher than expected {CO}$_2$ fertilization inferred from leaf to global observations},
  doi          = {10.1111/gcb.14950},
  pages        = {2390--2402},
  abstract     = {Several lines of evidence point to an increase in the activity of the terrestrial biosphere over recent decades, impacting the global net land carbon sink (NLS) and its control on the growth of atmospheric carbon dioxide (c(a)). Global terrestrial gross primary production (GPP)-the rate of carbon fixation by photosynthesis-is estimated to have risen by (31 +/- 5)\% since 1900, but the relative contributions of different putative drivers to this increase are not well known. Here we identify the rising atmospheric CO2 concentration as the dominant driver. We reconcile leaf-level and global atmospheric constraints on trends in modeled biospheric activity to reveal a global CO2 fertilization effect on photosynthesis of 30\% since 1900, or 47\% for a doubling of c(a) above the pre-industrial level. Our historic value is nearly twice as high as current estimates (17 +/- 4)\% that do not use the full range of available constraints. Consequently, under a future low-emission scenario, we project a land carbon sink (174 PgC, 2006-2099) that is 57 PgC larger than if a lower CO2 fertilization effect comparable with current estimates is assumed. These findings suggest a larger beneficial role of the land carbon sink in modulating future excess anthropogenic CO2 consistent with the target of the Paris Agreement to stay below 2 degrees C warming, and underscore the importance of preserving terrestrial carbon sinks.},
  file         = {:Haverd2020.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {amplitude of seasonal cycle, carbonyl sulfide, co2 fertilization, coordination of photosynthesis, gross primary production, land carbon sink, land-use change, terrestrial carbon, photosynthetic capacity, nitrogen distribution, seasonal amplitude, earth system, cycle models, plant-growth, elevated co2, productivity},
}

@Article{Hawkins2009,
  author       = {Ed Hawkins and Rowan Sutton},
  date         = {2009-08},
  journaltitle = {Bulletin of the American Meteorological Society},
  title        = {The Potential to Narrow Uncertainty in Regional Climate Predictions},
  doi          = {10.1175/2009bams2607.1},
  number       = {8},
  pages        = {1095--1108},
  volume       = {90},
  file         = {:Hawkins2009.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  publisher    = {American Meteorological Society},
}

@Article{Hawkins2010,
  author       = {Ed Hawkins and Rowan Sutton},
  date         = {2010-04},
  journaltitle = {Climate Dynamics},
  title        = {The potential to narrow uncertainty in projections of regional precipitation change},
  doi          = {10.1007/s00382-010-0810-6},
  number       = {1-2},
  pages        = {407--418},
  volume       = {37},
  comment      = {Quantifying different sources of uncertainties in climate model projections},
  file         = {:Hawkins2010.pdf:PDF},
  groups       = {Precipitation, Climate Model Weighting / Reducing uncertainties},
  keywords     = {skimmed},
  publisher    = {Springer Science and Business Media {LLC}},
  readstatus   = {skimmed},
}

@Article{He2019,
  author       = {He, B. and Bao, Q. and Wang, X. C. and Zhou, L. J. and Wu, X. F. and Liu, Y. M. and Wu, G. X. and Chen, K. J. and He, S. C. and Hu, W. T. and Li, J. D. and Li, J. X. and Nian, G. K. and Wang, L. and Yang, J. and Zhang, M. H. and Zhang, X. Q.},
  date         = {2019},
  journaltitle = {Advances in Atmospheric Sciences},
  title        = {CAS FGOALS-f3-L Model Datasets for {CMIP}6 Historical Atmospheric Model Intercomparison Project Simulation},
  doi          = {10.1007/s00376-019-9027-8},
  number       = {8},
  pages        = {771--778},
  volume       = {36},
  abstract     = {The outputs of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic, Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project (CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total, there are three ensemble experiments over the period 1979-2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden-Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.},
  file         = {:He2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {cmip6, amip, fgoals-f3-l, mjo, tropical cyclone, extreme precipitation, global precipitation, general-circulation, parameterization, configuration, performance, resolution},
}

@Article{He2020,
  author       = {He, B. and Liu, Y. M. and Wu, G. X. and Bao, Q. and Zhou, T. J. and Wu, X. F. and Wang, L. and Li, J. D. and Wang, X. C. and Li, J. X. and Hu, W. T. and Zhang, X. Q. and Sheng, C. and Tang, Y. Q.},
  date         = {2020},
  journaltitle = {Advances in Atmospheric Sciences},
  title        = {CAS FGOALS-f3-L Model Datasets for {CMIP}6 GMMIP Tier-1 and Tier-3 Experiments},
  doi          = {10.1007/s00376-019-9085-y},
  number       = {1},
  pages        = {18--28},
  volume       = {37},
  abstract     = {The Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L) model datasets prepared for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Global Monsoons Model Intercomparison Project (GMMIP) Tier-1 and Tier-3 experiments are introduced in this paper, and the model descriptions, experimental design and model outputs are demonstrated. There are three simulations in Tier-1, with different initial states, and five simulations in Tier-3, with different topographies or surface thermal status. Specifically, Tier-3 contains four orographic perturbation experiments that remove the Tibetan-Iranian Plateau, East African and Arabian Peninsula highlands, Sierra Madre, and Andes, and one thermal perturbation experiment that removes the surface sensible heating over the Tibetan-Iranian Plateau and surrounding regions at altitudes above 500 m. These datasets will contribute to CMIP6's value as a benchmark to evaluate the importance of long-term and short-term trends of the sea surface temperature in monsoon circulations and precipitation, and to a better understanding of the orographic impact on the global monsoon system over highlands.},
  file         = {:He2020.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {global monsoon, cmip6, gmmip, tibetan plateau, orographic perturbation, asian summer monsoon, interannual variability, indian-ocean, general-circulation, mountain uplift, north-american, parameterization, climatology, precipitation},
}

@Article{Hirota2011,
  author       = {Nagio Hirota and Yukari N. Takayabu and Masahiro Watanabe and Masahide Kimoto},
  date         = {2011-09},
  journaltitle = {Journal of Climate},
  title        = {Precipitation Reproducibility over Tropical Oceans and Its Relationship to the Double {ITCZ} Problem in {CMIP}3 and {MIROC}5 Climate Models},
  doi          = {10.1175/2011jcli4156.1},
  number       = {18},
  pages        = {4859--4873},
  volume       = {24},
  file         = {:Hirota2011.pdf:PDF},
  groups       = {Precipitation, Clouds / Aerosols},
  publisher    = {American Meteorological Society},
}

@Book{Holton2004,
  author    = {Holton, James R.},
  date      = {2004},
  title     = {An Introduction to Dynamic Meteorology},
  edition   = {4},
  publisher = {Elsevier Academic Press},
  file      = {:Holton2004.pdf:PDF},
  groups    = {ESM basics},
}

@Article{Hoose2010,
  author       = {Corinna Hoose and Jón Egill Kristjánsson and Jen-Ping Chen and Anupam Hazra},
  date         = {2010-08},
  journaltitle = {Journal of the Atmospheric Sciences},
  title        = {A Classical-Theory-Based Parameterization of Heterogeneous Ice Nucleation by Mineral Dust, Soot, and Biological Particles in a Global Climate Model},
  doi          = {10.1175/2010jas3425.1},
  number       = {8},
  pages        = {2483--2503},
  volume       = {67},
  file         = {:Hoose2010.pdf:PDF},
  groups       = {Parameterizations, Clouds / Aerosols},
  publisher    = {American Meteorological Society},
}

@Article{Huang2014,
  author       = {Yi Huang and Maziar Bani Shahabadi},
  date         = {2014-12},
  journaltitle = {Journal of Geophysical Research: Atmospheres},
  title        = {Why logarithmic? A note on the dependence of radiative forcing on gas concentration},
  doi          = {10.1002/2014jd022466},
  number       = {24},
  pages        = {13683--13689},
  volume       = {119},
  file         = {:Huang2014.pdf:PDF},
  groups       = {Forcing},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Huntzinger2017,
  author       = {Huntzinger, D. N. and Michalak, A. M. and Schwalm, C. and Ciais, P. and King, A. W. and Fang, Y. and Schaefer, K. and Wei, Y. and Cook, R. B. and Fisher, J. B. and Hayes, D. and Huang, M. and Ito, A. and Jain, A. K. and Lei, H. and Lu, C. and Maignan, F. and Mao, J. and Parazoo, N. and Peng, S. and Poulter, B. and Ricciuto, D. and Shi, X. and Tian, H. and Wang, W. and Zeng, N. and Zhao, F.},
  date         = {2017},
  journaltitle = {Scientific Reports},
  title        = {Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions},
  doi          = {10.1038/s41598-017-03818-2},
  pages        = {1--8},
  volume       = {7},
  abstract     = {Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85\% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO2 which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.},
  file         = {:Huntzinger2017.pdf:PDF},
  groups       = {Carbon Cycle, Feedbacks},
  keywords     = {model intercomparison project, program multiscale synthesis, nitrogen interactions, land, co2, productivity, trends},
}

@Article{Hyder2018,
  author       = {Patrick Hyder and John M. Edwards and Richard P. Allan and Helene T. Hewitt and Thomas J. Bracegirdle and Jonathan M. Gregory and Richard A. Wood and Andrew J. S. Meijers and Jane Mulcahy and Paul Field and Kalli Furtado and Alejandro Bodas-Salcedo and Keith D. Williams and Dan Copsey and Simon A. Josey and Chunlei Liu and Chris D. Roberts and Claudio Sanchez and Jeff Ridley and Livia Thorpe and Steven C. Hardiman and Michael Mayer and David I. Berry and Stephen E. Belcher},
  date         = {2018-09},
  journaltitle = {Nature Communications},
  title        = {Critical Southern Ocean climate model biases traced to atmospheric model cloud errors},
  doi          = {10.1038/s41467-018-05634-2},
  number       = {1},
  pages        = {1--17},
  volume       = {9},
  file         = {:Hyder2018.pdf:PDF},
  groups       = {Clouds / Aerosols},
  publisher    = {Springer Science and Business Media {LLC}},
}

@InBook{IPCC2013,
  author       = {IPCC},
  date         = {2013},
  title        = {Summary for Policymakers},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {3--29},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_SPM_FINAL.pdf},
  file         = {:IPCC2013.pdf:PDF},
  groups       = {IPCC},
  journaltitle = {Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Book{IPCC2014,
  author    = {IPCC},
  date      = {2014},
  title     = {Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change},
  location  = {Geneva, Switzerland},
  publisher = {IPCC},
  url       = {https://www.ipcc.ch/site/assets/uploads/2018/02/SYR_AR5_FINAL_full.pdf},
  file      = {:IPCC2014.pdf:PDF},
  groups    = {IPCC},
}

@Article{Iversen2013,
  author       = {Iversen, T. and Bentsen, M. and Bethke, I. and Debernard, J. B. and Kirkevag, A. and Seland, O. and Drange, H. and Kristjansson, J. E. and Medhaug, I. and Sand, M. and Seierstad, I. A.},
  date         = {2013},
  journaltitle = {Geoscientific Model Development},
  title        = {The Norwegian Earth System Model, NorESM1-M - Part 2: Climate response and scenario projections},
  doi          = {10.5194/gmd-6-389-2013},
  number       = {2},
  pages        = {389--415},
  volume       = {6},
  abstract     = {) without (NorESM1-M) and with (NorESM1-ME) interactive carbon-cycling. Together with the accompanying paper by Bentsen et al. (2012), this paper documents that the core version NorESM1-M is a valuable global climate model for research and for providing complementary results to the evaluation of possible anthropogenic climate change. NorESM1-M is based on the model CCSM4 operated at NCAR, but the ocean model is replaced by a modified version of MICOM and the atmospheric model is extended with online calculations of aerosols, their direct effect and their indirect effect on warm clouds. Model validation is presented in the companion paper (Bentsen et al., 2012). NorESM1-M is estimated to have equilibrium climate sensitivity of ca. 2.9 K and a transient climate response of ca. 1.4 K. This sensitivity is in the lower range amongst the models contributing to CMIP5. Cloud feedbacks dampen the response, and a strong AMOC reduces the heat fraction available for increasing near-surface temperatures, for evaporation and for melting ice. The future projections based on RCP scenarios yield a global surface air temperature increase of almost one standard deviation lower than a 15-model average. Summer sea-ice is projected to decrease considerably by 2100 and disappear completely for RCP8.5. The AMOC is projected to decrease by 12 \%, 15-17 \%, and 32\% for the RCP2.6, 4.5, 6.0, and 8.5, respectively. Precipitation is projected to increase in the tropics, decrease in the subtropics and in southern parts of the northern extra-tropics during summer, and otherwise increase in most of the extra-tropics. Changes in the atmospheric water cycle indicate that precipitation events over continents will become more intense and dry spells more frequent. Extra-tropical storminess in the Northern Hemisphere is projected to shift northwards. There are indications of more frequent occurrence of spring and summer blocking in the Euro-Atlantic sector, while the amplitude of ENSO events weakens although they tend to appear more frequently. These indications are uncertain because of biases in the model's representation of present-day conditions. Positive phase PNA and negative phase NAO both appear less frequently under the RCP8.5 scenario, but also this result is considered uncertain. Single-forcing experiments indicate that aerosols and greenhouse gases produce similar geographical patterns of response for near-surface temperature and precipitation. These patterns tend to have opposite signs, although with important exceptions for precipitation at low latitudes. The asymmetric aerosol effects between the two hemispheres lead to a southward displacement of ITCZ. Both forcing agents, thus, tend to reduce Northern Hemispheric subtropical precipitation.},
  file         = {:Iversen2013.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {empirical orthogonal functions, community atmosphere model, geopotential height, circulation, sensitivity, ocean, simulations, transport, feedbacks, evolution},
}

@Misc{Jacobson2020,
  author    = {Jacobson, Andrew R. and Schuldt, Kenneth N. and Miller, John B. and Oda, Tomohiro and Tans, Pieter and Andrews, Arlyn and Mund, John and Ott, Lesley and Collatz, George J. and Aalto, Tuula and Afshar, Sara and Aikin, Ken and Aoki, Shuji and Apadula, Francesco and Baier, Bianca and Bergamaschi, Peter and Beyersdorf, Andreas and Biraud, Sebastien C. and Bollenbacher, Alane and Bowling, David and Brailsford, Gordon and Abshire, James Brice and Chen, Gao and Chen, Huilin and Chmura, Lukasz and Climadat, Sites and Colomb, Aurelie and Conil, Sébastien and Cox, Adam and Cristofanelli, Paolo and Cuevas, Emilio and Curcoll, Roger and Sloop, Christopher D. and Davis, Ken and Wekker, Stephan De and Delmotte, Marc and DiGangi, Joshua P. and Dlugokencky, Ed and Ehleringer, Jim and Elkins, James W. and Emmenegger, Lukas and Fischer, Marc L. and Forster, Grant and Frumau, Arnoud and Galkowski, Michal and Gatti, Luciana V. and Gloor, Emanuel and Griffis, Tim and Hammer, Samuel and Haszpra, László and Hatakka, Juha and Heliasz, Michal and Hensen, Arjan and Hermanssen, Ove and Hintsa, Eric and Holst, Jutta and Jaffe, Dan and Karion, Anna and Kawa, Stephan Randolph and Keeling, Ralph and Keronen, Petri and Kolari, Pasi and Kominkova, Katerina and Kort, Eric and Krummel, Paul and Kubistin, Dagmar and Labuschagne, Casper and Langenfelds, Ray and Laurent, Olivier and Laurila, Tuomas and Lauvaux, Thomas and Law, Bev and Lee, John and Lehner, Irene and Leuenberger, Markus and Levin, Ingeborg and Levula, Janne and Lin, John and Lindauer, Matthias and Loh, Zoe and Lopez, Morgan and Myhre, Cathrine Lund and Machida, Toshinobu and Mammarella, Ivan and Manca, Giovanni and Manning, Alistair and Manning, Andrew and Marek, Michal V. and Marklund, Per and Martin, Melissa Yang and Matsueda, Hidekazu and McKain, Kathryn and Meijer, Harro and Meinhardt, Frank and Miles, Natasha and Miller, Charles E. and Mölder, Meelis and Montzka, Stephen and Moore, Fred and Morgui, Josep-Anton and Morimoto, Shinji and Munger, Bill and Necki, Jaroslaw and Newman, Sally and Nichol, Sylvia and Niwa, Yosuke and O'Doherty, Simon and Ottosson-Löfvenius, Mikaell and Paplawsky, Bill and Peischl, Jeff and Peltola, Olli and Pichon, Jean-Marc and Piper, Steve and Plass-Dölmer, Christian and Ramonet, Michel and Reyes-Sanchez, Enrique and Richardson, Scott and Riris, Haris and Ryerson, Thomas and Saito, Kazuyuki and Sargent, Maryann and Sasakawa, Motoki and Sawa, Yousuke and Say, Daniel and Scheeren, Bert and Schmidt, Martina and Schmidt, Andres and Schumacher, Marcus and Shepson, Paul and Shook, Michael and Stanley, Kieran and Steinbacher, Martin and Stephens, Britton and Sweeney, Colm and Thoning, Kirk and Torn, Margaret and Turnbull, Jocelyn and Tørseth, Kjetil and Bulk, Pim Van Den and Laan-Luijkx, Ingrid T. Van Der and Dinther, Danielle Van and Vermeulen, Alex and Viner, Brian and Vitkova, Gabriela and Walker, Stephen and Weyrauch, Dietmar and Wofsy, Steve and Worthy, Doug and Young, Dickon and Zimnoch, Miroslaw},
  date      = {2020},
  title     = {CarbonTracker CT2019},
  doi       = {10.25925/39M3-6069},
  groups    = {OBS},
  publisher = {NOAA Earth System Research Laboratory, Global Monitoring Division},
}

@Article{Ji2014,
  author       = {Ji, D. and Wang, L. and Feng, J. and Wu, Q. and Cheng, H. and Zhang, Q. and Yang, J. and Dong, W. and Dai, Y. and Gong, D. and Zhang, R. H. and Wang, X. and Liu, J. and Moore, J. C. and Chen, D. and Zhou, M.},
  date         = {2014},
  journaltitle = {Geoscientific Model Development},
  title        = {Description and basic evaluation of Beijing Normal University Earth System Model (BNU-ESM) version 1},
  doi          = {10.5194/gmd-7-2039-2014},
  number       = {5},
  pages        = {2039--2064},
  volume       = {7},
  abstract     = {An earth system model has been developed at Beijing Normal University (Beijing Normal University Earth System Model, BNU-ESM); the model is based on several widely evaluated climate model components and is used to study mechanisms of ocean-atmosphere interactions, natural climate variability and carbon-climate feedbacks at inter-annual to interdecadal time scales. In this paper, the model structure and individual components are described briefly. Further, results for the CMIP5 (Coupled Model Intercomparison Project phase 5) pre-industrial control and historical simulations are presented to demonstrate the model's performance in terms of the mean model state and the internal variability. It is illustrated that BNU-ESM can simulate many observed features of the earth climate system, such as the climatological annual cycle of surface-air temperature and precipitation, annual cycle of tropical Pacific sea surface temperature (SST), the overall patterns and positions of cells in global ocean meridional overturning circulation. For example, the El Nino-Southern Oscillation (ENSO) simulated in BNU-ESM exhibits an irregular oscillation between 2 and 5 years with the seasonal phase locking feature of ENSO. Important biases with regard to observations are presented and discussed, including warm SST discrepancies in the major upwelling regions, an equatorward drift of midlatitude westerly wind bands, and tropical precipitation bias over the ocean that is related to the double Intertropical Convergence Zone (ITCZ).},
  file         = {:Ji2014.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {madden-julian oscillation, convective momentum transport, community atmosphere model, cmip5 multimodel ensemble, coupled equatorial waves, stochastic mixing model, global precipitation, climate models, pacific-ocean, el-nino},
}

@Article{JimenezdelaCuesta2019,
  author       = {Jimenez-de-la-Cuesta, D. and Mauritsen, T.},
  date         = {2019},
  journaltitle = {Nature Geoscience},
  title        = {Emergent constraints on Earth's transient and equilibrium response to doubled {CO}$_2$ from post-1970s global warming},
  doi          = {10.1038/s41561-019-0463-y},
  number       = {11},
  pages        = {902--905},
  volume       = {12},
  abstract     = {Future global warming is determined by both greenhouse gas emission pathways and Earth's transient and equilibrium climate response to doubled atmospheric CO2. Energy-balance inference from the instrumental record typically yields central estimates for the transient response of around 1.3 K and the equilibrium response of 1.5-2.0 K, which is at the lower end of those from contemporary climate models. Uncertainty arises primarily from poorly known aerosol-induced cooling since the early industrialization era and a temporary cooling induced by evolving sea surface temperature patterns. Here we present an emergent constraint on post-1970s warming, taking advantage of the weakly varying aerosol cooling during this period. We derive a relationship between the transient response and the post-1970s warming in Coupled Model Intercomparison Project Phase 5 (CMIP5) models. We thereby constrain, with the observations, the transient response to 1.67 K (1.17-2.16 K, 5-95th percentiles). This is a 20\% increase relative to energy-balance inference stemming from previously neglected upper-ocean energy storage. For the equilibrium climate sensitivity we obtain a best estimate of 2.83 K (1.72-4.12 K) contingent on the temporary pattern effects exhibited by climate models. If the real world's surface temperature pattern effects are substantially stronger, then the upper-bound equilibrium sensitivity may be higher than found here.},
  file         = {:JimenezdelaCuesta2019.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {energy budget constraints, climate sensitivity, spread, skimmed},
  readstatus   = {skimmed},
}

@Article{Jones2016,
  author       = {Chris D. Jones and Vivek Arora and Pierre Friedlingstein and Laurent Bopp and Victor Brovkin and John Dunne and Heather Graven and Forrest Hoffman and Tatiana Ilyina and Jasmin G. John and Martin Jung and Michio Kawamiya and Charlie Koven and Julia Pongratz and Thomas Raddatz and James T. Randerson and Sönke Zaehle},
  date         = {2016-08},
  journaltitle = {Geoscientific Model Development},
  title        = {{C4MIP} --- The Coupled Climate-Carbon Cycle Model Intercomparison Project: experimental protocol for {CMIP}6},
  doi          = {10.5194/gmd-9-2853-2016},
  number       = {8},
  pages        = {2853--2880},
  volume       = {9},
  file         = {:Jones2016.pdf:PDF},
  groups       = {CMIP and other MIPs},
  publisher    = {Copernicus {GmbH}},
}

@Article{Juckes2020,
  author       = {Martin Juckes and Karl E. Taylor and Paul J. Durack and Bryan Lawrence and Matthew S. Mizielinski and Alison Pamment and Jean-Yves Peterschmitt and Michel Rixen and Stéphane Sénési},
  date         = {2020-01},
  journaltitle = {Geoscientific Model Development},
  title        = {The {CMIP}6 Data Request ({DREQ}, version 01.00.31)},
  doi          = {10.5194/gmd-13-201-2020},
  number       = {1},
  pages        = {201--224},
  volume       = {13},
  file         = {:Juckes2020.pdf:PDF},
  groups       = {CMIP and other MIPs},
  publisher    = {Copernicus {GmbH}},
}

@Article{Jung2011,
  author       = {Jung, M. and Reichstein, M. and Margolis, H. A. and Cescatti, A. and Richardson, A. D. and Arain, M. A. and Arneth, A. and Bernhofer, C. and Bonal, D. and Chen, J. Q. and Gianelle, D. and Gobron, N. and Kiely, G. and Kutsch, W. and Lasslop, G. and Law, B. E. and Lindroth, A. and Merbold, L. and Montagnani, L. and Moors, E. J. and Papale, D. and Sottocornola, M. and Vaccari, F. and Williams, C.},
  date         = {2011},
  journaltitle = {Journal of Geophysical Research-Biogeosciences},
  title        = {Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations},
  doi          = {10.1029/2010jg001566},
  pages        = {G00J07},
  volume       = {116},
  abstract     = {We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10\%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.},
  file         = {:Jung2011.pdf:PDF},
  groups       = {OBS},
  keywords     = {net ecosystem exchange, energy-balance closure, co2 flux, primary productivity, vegetation model, climate, uncertainty, respiration, sensitivity, dynamics},
}

@Article{Karpechko2013,
  author       = {Karpechko, A. Y. and Maraun, D. and Eyring, V.},
  date         = {2013},
  journaltitle = {Journal of the Atmospheric Sciences},
  title        = {Improving Antarctic Total Ozone Projections by a Process-Oriented Multiple Diagnostic Ensemble Regression},
  doi          = {10.1175/Jas-D-13-071.1},
  number       = {12},
  pages        = {3959--3976},
  volume       = {70},
  abstract     = {Accurate projections of stratospheric ozone are required because ozone changes affect exposure to ultraviolet radiation and tropospheric climate. Unweighted multimodel ensemble-mean (uMMM) projections from chemistry-climate models (CCMs) are commonly used to project ozone in the twenty-first century, when ozone-depleting substances are expected to decline and greenhouse gases are expected to rise. Here, the authors address the question of whether Antarctic total column ozone projections in October given by the uMMM of CCM simulations can be improved by using a process-oriented multiple diagnostic ensemble regression (MDER) method. This method is based on the correlation between simulated future ozone and selected key processes relevant for stratospheric ozone under present-day conditions. The regression model is built using an algorithm that selects those process-oriented diagnostics that explain a significant fraction of the spread in the projected ozone among the CCMs. The regression model with observed diagnostics is then used to predict future ozone and associated uncertainty. The precision of the authors' method is tested in a pseudoreality; that is, the prediction is validated against an independent CCM projection used to replace unavailable future observations. The tests show that MDER has higher precision than uMMM, suggesting an improvement in the estimate of future Antarctic ozone. The authors' method projects that Antarctic total ozone will return to 1980 values at around 2055 with the 95\% prediction interval ranging from 2035 to 2080. This reduces the range of return dates across the ensemble of CCMs by about a decade and suggests that the earliest simulated return dates are unlikely.},
  comment      = {1st MDER paper},
  file         = {:Karpechko2013.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  keywords     = {chemistry-climate model, middle atmosphere model, technical note, simulations, stratosphere, transport, validation, surface, trends, impact, skimmed, printed},
  printed      = {printed},
  readstatus   = {skimmed},
}

@InBook{Kattenberg1996,
  author       = {Kattenberg, A. and Giorgi, F. and Grassl, H. and Meehl, G. A. and Mitchell, J. F. B. and Stouffer, R. J. and Tokioka, T. and Weaver, A. J. and Wigley, T. M. L.},
  date         = {1996},
  title        = {Climate models -- projections of future climate},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {285--358},
  publisher    = {Cambridge University Press},
  url          = {https://archive.ipcc.ch/ipccreports/sar/wg_I/ipcc_sar_wg_I_full_report.pdf},
  file         = {:Kattenberg1996.pdf:PDF},
  groups       = {IPCC, ESM basics, ECS / TCR / Climate Sensitivity / Warming},
  journaltitle = {Climate Change 1995, The Science of Climate Change: Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Article{Kay2016,
  author       = {Jennifer E. Kay and Line Bourdages and Nathaniel B. Miller and Ariel Morrison and Vineel Yettella and Helene Chepfer and Brian Eaton},
  date         = {2016-04},
  journaltitle = {Journal of Geophysical Research: Atmospheres},
  title        = {Evaluating and improving cloud phase in the Community Atmosphere Model version 5 using spaceborne lidar observations},
  doi          = {10.1002/2015jd024699},
  number       = {8},
  pages        = {4162--4176},
  volume       = {121},
  file         = {:Kay2016.pdf:PDF},
  groups       = {Clouds / Aerosols},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Keeling1976,
  author       = {Charles D. Keeling and Robert B. Bacastow and Arnold E. Bainbridge and Carl A. Ekdahl Jr. and Peter R. Guenther and Lee S. Waterman and John F. S. Chin},
  date         = {1976-01},
  journaltitle = {Tellus},
  title        = {Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii},
  doi          = {10.3402/tellusa.v28i6.11322},
  number       = {6},
  pages        = {538--551},
  volume       = {28},
  file         = {:Keeling1976.pdf:PDF},
  groups       = {OBS},
  publisher    = {Informa {UK} Limited},
}

@Article{Keeling1995,
  author       = {Keeling, C. D. and Whorf, T. P. and Wahlen, M. and Vanderplicht, J.},
  date         = {1995},
  journaltitle = {Nature},
  title        = {Interannual Extremes in the Rate of Rise of Atmospheric Carbon-Dioxide since 1980},
  doi          = {10.1038/375666a0},
  number       = {6533},
  pages        = {666--670},
  volume       = {375},
  abstract     = {OBSERVATIONS of atmospheric CO2 concentrations at Mauna Loa, Hawaii, and at the South Pole over the past four decades show an approximate proportionality between the rising atmospheric concentrations and industrial CO2 emissions(1). This proportionality, which is most apparent during the first 20 years of the records, was disturbed in the 1980s by a disproportionately high rate of rise of atmospheric CO2, followed after 1988 by a pronounced slowing down of the growth rate. To probe the causes of these changes, we examine here the changes expected from the variations in the rates of industrial CO2 emissions over this time(2), and also from influences of climate such as El Nino events. We use the C-13/C-12 ratio of atmospheric CO2 to distinguish the effects of interannual variations in biospheric and oceanic sources and sinks of carbon. We propose that the recent disproportionate rise and fan in CO2 growth rate were caused mainly by interannual variations in global air temperature (which altered both the terrestrial biospheric and the oceanic carbon sinks), and possibly also by precipitation. We suggest that the anomalous climate-induced rise in CO2 was partially masked by a slowing down in the growth rate of fossil-fuel combustion, and that the latter then exaggerated the subsequent climate-induced fall.},
  file         = {:Keeling1995.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {air-temperature variations, el-nino},
}

@Article{Keeling1996,
  author       = {Keeling, C. D. and Chin, J. F. S. and Whorf, T. P.},
  date         = {1996},
  journaltitle = {Nature},
  title        = {Increased activity of northern vegetation inferred from atmospheric {CO}$_2$ measurements},
  doi          = {10.1038/382146a0},
  number       = {6587},
  pages        = {146--149},
  volume       = {382},
  abstract     = {THROUGHOUT the Northern Hemisphere the concentration of atmospheric carbon dioxide rises in winter and declines in summer, mainly in response to the seasonal growth in land vegetation(1-4). In the far north the amplitude of the seasonal cycle, peak to trough, is between 15 and 20 parts per million by volume(5). The annual amplitude diminishes southwards to about 3 p.p.m. near the Equator, owing to the diminishing seasonality of plant activity towards the tropics. In spite of atmospheric mixing processes, enough spatial variability is retained in the seasonal cycle of CO2 to reveal considerable regional detail in seasonal plant activity(6). Here we report that the annual amplitude of the seasonal CO2 cycle has increased by 20\%, as measured in Hawaii, and by 40\% in the Arctic, since the early 1960s. These increases are accompanied by phase advances of about 7 days during the declining phase of the cycle, suggesting a lengthening of the growing season. In addition, the annual amplitudes show maxima which appear to reflect a sensitivity to global warming episodes that peaked in 1981 and 1990. We propose that the amplitude increases reflect increasing assimilation of CO2 by land plants in response to climate changes accompanying recent rapid increases in temperature.},
  file         = {:Keeling1996.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {carbon-dioxide, record, climate, ice},
}

@InBook{Keeling2005,
  author       = {Keeling, C. D. and Piper, S. C. and Bacastow, R. B. and Wahlen, M. and Whorf, T. P. and Heimann, M. and Meijer, H. A.},
  date         = {2005},
  title        = {Atmospheric {CO}$_2$ and $^{13}${CO}$_2$ exchange with the terrestrial biosphere and oceans from 1978 to 2000: Observations and carbon cycle implications},
  pages        = {83--113},
  groups       = {OBS},
  journaltitle = {A history of atmospheric {CO}$_2$ and its effects on plants, animals, and ecosystems},
}

@Article{Kiehl2007,
  author       = {Jeffrey T. Kiehl},
  date         = {2007-11},
  journaltitle = {Geophysical Research Letters},
  title        = {Twentieth century climate model response and climate sensitivity},
  doi          = {10.1029/2007gl031383},
  number       = {22},
  pages        = {L22710},
  volume       = {34},
  file         = {:Kiehl2007.pdf:PDF},
  groups       = {Clouds / Aerosols, ECS / TCR / Climate Sensitivity / Warming},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Knutti2013,
  author       = {Reto Knutti and David Masson and Andrew Gettelman},
  date         = {2013-03},
  journaltitle = {Geophysical Research Letters},
  title        = {Climate model genealogy: Generation {CMIP}5 and how we got there},
  doi          = {10.1002/grl.50256},
  number       = {6},
  pages        = {1194--1199},
  volume       = {40},
  file         = {:Knutti2013.pdf:PDF},
  groups       = {ESM basics},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Knutti2015,
  author       = {Reto Knutti and Maria A. A. Rugenstein},
  date         = {2015-11},
  journaltitle = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  title        = {Feedbacks, climate sensitivity and the limits of linear models},
  doi          = {10.1098/rsta.2015.0146},
  number       = {2054},
  pages        = {20150146},
  volume       = {373},
  file         = {:Knutti2015.pdf:PDF},
  groups       = {Feedbacks, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {skimmed},
  publisher    = {The Royal Society},
  readstatus   = {skimmed},
}

@Article{Knutti2017,
  author       = {Knutti, R. and Rugenstein, M. A. A. and Hegerl, G. C.},
  date         = {2017},
  journaltitle = {Nature Geoscience},
  title        = {Beyond equilibrium climate sensitivity},
  doi          = {10.1038/Ngeo3017},
  number       = {10},
  pages        = {727--736},
  volume       = {10},
  abstract     = {Equilibrium climate sensitivity characterizes the Earth's long-term global temperature response to increased atmospheric CO2 concentration. It has reached almost iconic status as the single number that describes how severe climate change will be. The consensus on the 'likely' range for climate sensitivity of 1.5 degrees C to 4.5 degrees C today is the same as given by Jule Charney in 1979, but now it is based on quantitative evidence from across the climate system and throughout climate history. The quest to constrain climate sensitivity has revealed important insights into the timescales of the climate system response, natural variability and limitations in observations and climate models, but also concerns about the simple concepts underlying climate sensitivity and radiative forcing, which opens avenues to better understand and constrain the climate response to forcing. Estimates of the transient climate response are better constrained by observed warming and are more relevant for predicting warming over the next decades. Newer metrics relating global warming directly to the total emitted CO2 show that in order to keep warming to within 2 degrees C, future CO2 emissions have to remain strongly limited, irrespective of climate sensitivity being at the high or low end.},
  file         = {:Knutti2017.pdf:PDF},
  groups       = {Reviews, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {of-atmosphere radiation, ocean heat uptake, energy budget constraints, cumulative co2 emissions, carbon-dioxide, observational constraints, surface-temperature, increasing co2, time scales, bayesian-estimation, printed, skimmed},
  printed      = {printed},
  readstatus   = {skimmed},
}

@Article{Knutti2017a,
  author       = {Knutti, R. and Sedlacek, J. and Sanderson, B. M. and Lorenz, R. and Fischer, E. M. and Eyring, V.},
  date         = {2017},
  journaltitle = {Geophysical Research Letters},
  title        = {A climate model projection weighting scheme accounting for performance and interdependence},
  doi          = {10.1002/2016gl072012},
  number       = {4},
  pages        = {1909--1918},
  volume       = {44},
  abstract     = {Uncertainties of climate projections are routinely assessed by considering simulations from different models. Observations are used to evaluate models, yet there is a debate about whether and how to explicitly weight model projections by agreement with observations. Here we present a straightforward weighting scheme that accounts both for the large differences in model performance and for model interdependencies, and we test reliability in a perfect model setup. We provide weighted multimodel projections of Arctic sea ice and temperature as a case study to demonstrate that, for some questions at least, it is meaningless to treat all models equally. The constrained ensemble shows reduced spread and a more rapid sea ice decline than the unweighted ensemble. We argue that the growing number of models with different characteristics and considerable interdependence finally justifies abandoning strict model democracy, and we provide guidance on when and how this can be achieved robustly.},
  file         = {:Knutti2017a.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  keywords     = {arctic sea-ice, emergent constraints, multimodel ensemble, variability, sensitivity, cmip5, robustness, carbon, cycle, co2, printed, skimmed},
  printed      = {printed},
  readstatus   = {skimmed},
}

@Article{Kuhlbrodt2018,
  author       = {Kuhlbrodt, T. and Jones, C. G. and Sellar, A. and Storkey, D. and Blockley, E. and Stringer, M. and Hill, R. and Graham, T. and Ridley, J. and Blaker, A. and Calvert, D. and Copsey, D. and Ellis, R. and Hewitt, H. and Hyder, P. and Ineson, S. and Mulcahy, J. and Siahaan, A. and Walton, J.},
  date         = {2018},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {The Low-Resolution Version of HadGEM3 GC3.1: Development and Evaluation for Global Climate},
  doi          = {10.1029/2018ms001370},
  number       = {11},
  pages        = {2865--2888},
  volume       = {10},
  abstract     = {A new climate model, HadGEM3 N96ORCA1, is presented that is part of the GC3.1 configuration of HadGEM3. N96ORCA1 has a horizontal resolution of similar to 135 km in the atmosphere and 1 degrees in the ocean and requires an order of magnitude less computing power than its medium-resolution counterpart, N216ORCA025, while retaining a high degree of performance traceability. Scientific performance is compared to both observations and the N216ORCA025 model. N96ORCA1 reproduces observed climate mean and variability almost as well as N216ORCA025. Patterns of biases are similar across the two models. In the northwest Atlantic, N96ORCA1 shows a cold surface bias of up to 6 K, typical of ocean models of this resolution. The strength of the Atlantic meridional overturning circulation (16 to 17 Sv) matches observations. In the Southern Ocean, a warm surface bias (up to 2 K) is smaller than in N216ORCA025 and linked to improved ocean circulation. Model El Nino/Southern Oscillation and Atlantic Multidecadal Variability are close to observations. Both the cold bias in the Northern Hemisphere (N96ORCA1) and the warm bias in the Southern Hemisphere (N216ORCA025) develop in the first few decades of the simulations. As in many comparable climate models, simulated interhemispheric gradients of top-of-atmosphere radiation are larger than observations suggest, with contributions from both hemispheres. HadGEM3 GC3.1 N96ORCA1 constitutes the physical core of the UK Earth System Model (UKESM1) and will be used extensively in the Coupled Model Intercomparison Project 6 (CMIP6), both as part of the UK Earth System Model and as a stand-alone coupled climate model.
Plain Language Summary In this article, a new version of the climate model currently used in the United Kingdom (HadGEM3) is presented and analyzed. The circulation of the atmosphere and the oceans is simulated on a relatively coarse spatial grid with a grid cell size of about 120 km. The advantage of using a coarse spatial grid is that less computing power (on a supercomputer) is needed compared to using a finer grid. This gives an opportunity to do many more simulations of the ways in which Earth's climate may evolve in the decades and centuries ahead. We have carefully compared a simulation of the climate around the year 2000 with climate observations from that time and with a simulation from the same model with a finer spatial grid. Our results show that our new, coarse-grid version is representing the current climate reasonably well, for instance, with regards to climate variability in the tropics and major ocean currents. However, there are clear differences between the two models. In the coarse-gridmodel, the ocean surface is too cold in the northwest Atlantic, while in the fine-grid version it is too warm in the Southern Ocean around Antarctica. We look into explanations for these inaccuracies.},
  file         = {:Kuhlbrodt2018.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {coupled climate model, earth system model, hadgem, model evaluation, cmip6, meridional overturning circulation, antarctic circumpolar current, coupled climate, sea-ice, southern-ocean, heat-flow, model, precipitation, simulations, temperature},
}

@Article{Kwiatkowski2017,
  author       = {Lester Kwiatkowski and Laurent Bopp and Olivier Aumont and Philippe Ciais and Peter M. Cox and Charlotte Laufkötter and Yue Li and Roland Séférian},
  date         = {2017-04},
  journaltitle = {Nature Climate Change},
  title        = {Emergent constraints on projections of declining primary production in the tropical oceans},
  doi          = {10.1038/nclimate3265},
  number       = {5},
  pages        = {355--358},
  volume       = {7},
  file         = {:Kwiatkowski2017.pdf:PDF},
  groups       = {Emergent Constraints, Carbon Cycle},
  keywords     = {read, printed},
  printed      = {printed},
  publisher    = {Springer Science and Business Media {LLC}},
  readstatus   = {read},
}

@Article{Lauer2018,
  author       = {Lauer, A. and Jones, C. and Eyring, V. and Evaldsson, M. and Stefan, H. A. and Makela, J. and Martin, G. and Roehrig, R. and Wang, S. Y.},
  date         = {2018},
  journaltitle = {Earth System Dynamics},
  title        = {Process-level improvements in {CMIP}5 models and their impact on tropical variability, the Southern Ocean, and monsoons},
  doi          = {10.5194/esd-9-33-2018},
  number       = {1},
  pages        = {33--67},
  volume       = {9},
  abstract     = {The performance of updated versions of the four earth system models (ESMs) CNRM, EC-Earth, HadGEM, and MPI-ESM is assessed in comparison to their predecessor versions used in Phase 5 of the Coupled Model Intercomparison Project. The Earth System Model Evaluation Tool (ESMValTool) is applied to evaluate selected climate phenomena in the models against observations. This is the first systematic application of the ESMValTool to assess and document the progress made during an extensive model development and improvement project. This study focuses on the South Asian monsoon (SAM) and the West African monsoon (WAM), the coupled equatorial climate, and Southern Ocean clouds and radiation, which are known to exhibit systematic biases in present-day ESMs.
The analysis shows that the tropical precipitation in three out of four models is clearly improved. Two of three updated coupled models show an improved representation of tropical sea surface temperatures with one coupled model not exhibiting a double Intertropical Convergence Zone (ITCZ). Simulated cloud amounts and cloudradiation interactions are improved over the Southern Ocean. Improvements are also seen in the simulation of the SAM and WAM, although systematic biases remain in regional details and the timing of monsoon rainfall. Analysis of simulations with EC-Earth at different horizontal resolutions from T159 up to T1279 shows that the synoptic-scale variability in precipitation over the SAM and WAM regions improves with higher model resolution. The results suggest that the reasonably good agreement of modeled and observed mean WAM and SAM rainfall in lower-resolution models may be a result of unrealistic intensity distributions.},
  file         = {:Lauer2018.pdf:PDF},
  groups       = {Clouds / Aerosols},
  keywords     = {african easterly waves, global precipitation, overturning circulation, atmosphere radiation, experimental-design, seasonal evolution, scale circulation, pacific-ocean, west-africa, heat uptake},
}

@Article{Lauer2020,
  author       = {Axel Lauer and Veronika Eyring and Omar Bellprat and Lisa Bock and Bettina K. Gier and Alasdair Hunter and Ruth Lorenz and Núria Pérez-Zanón and Mattia Righi and Manuel Schlund and Daniel Senftleben and Katja Weigel and Sabrina Zechlau},
  date         = {2020-09},
  journaltitle = {Geoscientific Model Development},
  title        = {Earth System Model Evaluation Tool ({ESMValTool}) v2.0 --- diagnostics for emergent constraints and future projections from Earth system models in {CMIP}},
  doi          = {10.5194/gmd-13-4205-2020},
  number       = {9},
  pages        = {4205--4228},
  volume       = {13},
  file         = {:Lauer2020.pdf:PDF},
  groups       = {ESMValTool},
  keywords     = {own},
  publisher    = {Copernicus {GmbH}},
}

@Article{Law2017,
  author       = {Law, R. M. and Ziehn, T. and Matear, R. J. and Lenton, A. and Chamberlain, M. A. and Stevens, L. E. and Wang, Y. P. and Srbinovsky, J. and Bi, D. H. and Yan, H. L. and Vohralik, P. F.},
  date         = {2017},
  journaltitle = {Geoscientific Model Development},
  title        = {The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) - Part 1: Model description and pre-industrial simulation},
  doi          = {10.5194/gmd-10-2567-2017},
  number       = {7},
  pages        = {2567--2590},
  volume       = {10},
  abstract     = {Earth system models (ESMs) that incorporate carbon-climate feedbacks represent the present state of the art in climate modelling. Here, we describe the Australian Community Climate and Earth System Simulator (ACCESS)-ESM1, which comprises atmosphere (UM7.3), land (CABLE), ocean (MOM4p1), and sea-ice (CICE4.1) components with OASIS-MCT coupling, to which ocean and land carbon modules have been added. The land carbon model (as part of CABLE) can optionally include both nitrogen and phosphorous limitation on the land carbon uptake. The ocean carbon model (WOMBAT, added to MOM) simulates the evolution of phosphate, oxygen, dissolved inorganic carbon, alkalinity and iron with one class of phytoplankton and zooplankton. We perform multi-centennial preindustrial simulations with a fixed atmospheric CO2 concentration and different land carbon model configurations (prescribed or prognostic leaf area index). We evaluate the equilibration of the carbon cycle and present the spatial and temporal variability in key carbon exchanges. Simulating leaf area index results in a slight warming of the atmosphere relative to the prescribed leaf area index case. Seasonal and interannual variations in land carbon exchange are sensitive to whether leaf area index is simulated, with interannual variations driven by variability in precipitation and temperature. We find that the response of the ocean carbon cycle shows reasonable agreement with observations. While our model overestimates surface phosphate values, the global primary productivity agrees well with observations. Our analysis highlights some deficiencies inherent in the carbon models and where the carbon simulation is negatively impacted by known biases in the underlying physical model and consequent limits on the applicability of this model version. We conclude the study with a brief discussion of key developments required to further improve the realism of our model simulation.},
  file         = {:Law2017.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {spin-up, cmip5, ocean, nitrogen, impact, implementation, performance, photosynthesis, exchange, feedback},
}

@Article{Lee2020,
  author       = {Lee, J. and Kim, J. and Sun, M. A. and Kim, B. H. and Moon, H. and Sung, H. M. and Kim, J. and Byun, Y. H.},
  date         = {2020},
  journaltitle = {Asia-Pacific Journal of Atmospheric Sciences},
  title        = {Evaluation of the Korea Meteorological Administration Advanced Community Earth-System model (K-ACE)},
  doi          = {10.1007/s13143-019-00144-7},
  number       = {3},
  pages        = {381--395},
  volume       = {56},
  abstract     = {Scientific community has been elaborating to better understand the observed climate and its variations, and to improve the capability for predicting future climate. Many modeling groups participating in the Coupled Model Inter-comparison Project (CMIP) have been working towards multi-model ensemble approach that have become a standard technique for projecting future climate and for assessing associated uncertainties to deal with intrinsic shortcomings of climate models. Within this context, the National Institute of Meteorological Sciences/Korea Meteorological Administration (NIMS/KMA) has developed the KMA Advanced Community Earth-system model (K-ACE) under KMA-Met Office collaboration for climate research. This paper provides general descriptions of the first generation K-ACE model including the coupling strategy, as well as preliminary evaluations of the model performance in mean climate fields. The first generation K-ACE model appears to capture the mean climatology and the inter-annual variability of the observed climate. Horizontal distributions and the variability of the surface and pressure-level variables agree well with observations with correlation coefficients of 0.88-0.99 and 0.69-0.99, respectively. Measured in terms of performance index between the observed and simulated fields, the K-ACE performance is comparable with those of 29 CMIP5 models. This study also identifies key weaknesses of the K-ACE in the present-day climate. Improving these deficiencies will be a topic of future studies. The NIMS/KMA will employ the K-ACE model to contribute to the CMIP6 experiment.},
  file         = {:Lee2020.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {cmip6, k-ace, earth-system model, coupled model, climatology, environment simulator jules, radiant energy system, thickness distribution, radiation budget, climate model, glomap-mode, ocean, representation, performance, configuration},
}

@Article{Lee2020a,
  author       = {Lee, W. L. and Wang, Y. C. and Shiu, C. J. and Tsai, I. C. and Tu, C. Y. and Lan, Y. Y. and Chen, J. P. and Pan, H. L. and Hsu, H. H.},
  date         = {2020},
  journaltitle = {Geoscientific Model Development},
  title        = {Taiwan Earth System Model Version 1: description and evaluation of mean state},
  doi          = {10.5194/gmd-13-3887-2020},
  number       = {9},
  pages        = {3887--3904},
  volume       = {13},
  abstract     = {The Taiwan Earth System Model (TaiESM) version 1 is developed based on Community Earth System Model version 1.2.2 of National Center for Atmospheric Research. Several innovative physical and chemical parameterizations, including trigger functions for deep convection, cloud macrophysics, aerosol, and three-dimensional radiation-topography interaction, as well as a one-dimensional mixed-layer model optional for the atmosphere component, are incorporated. The precipitation variability, such as diurnal cycle and propagation of convection systems, is improved in TaiESM. TaiESM demonstrates good model stability in the 500-year preindustrial simulation in terms of the net flux at the top of the model, surface temperatures, and sea ice concentration. In the historical simulation, although the warming before 1935 is weak, TaiESM captures the increasing trend of temperature after 1950 well. The current climatology of TaiESM during 1979-2005 is evaluated by observational and reanalysis datasets. Cloud amounts are too large in TaiESM, but their cloud forcing is only slightly weaker than observational data. The mean bias of the sea surface temperature is almost 0, whereas the surface air temperatures over land and sea ice regions exhibit cold biases. The overall performance of TaiESM is above average among models in Coupled Model Intercomparison Project phase 5, particularly in that the bias of precipitation is smallest. However, several common discrepancies shared by most models still exist, such as the double Intertropical Convergence Zone bias in precipitation and warm bias over the Southern Ocean.},
  file         = {:Lee2020a.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {3-d radiative-transfer, community atmosphere model, surface-temperature, climate simulations, sierra-nevada, long-term, impact, parameterization, convection, ocean},
}

@Article{Li2013,
  author       = {Li, L. J. and Lin, P. F. and Yu, Y. Q. and Wang, B. and Zhou, T. J. and Liu, L. and Liu, J. P. and Bao, Q. and Xu, S. M. and Huang, W. Y. and Xia, K. and Pu, Y. and Dong, L. and Shen, S. and Liu, Y. M. and Hu, N. and Liu, M. M. and Sun, W. Q. and Shi, X. J. and Zheng, W. P. and Wu, B. and Song, M. R. and Liu, H. L. and Zhang, X. H. and Wu, G. X. and Xue, W. and Huang, X. M. and Yang, G. W. and Song, Z. Y. and Qiao, F. L.},
  date         = {2013},
  journaltitle = {Advances in Atmospheric Sciences},
  title        = {The flexible global ocean-atmosphere-land system model, Grid-point Version 2: FGOALS-g2},
  doi          = {10.1007/s00376-012-2140-6},
  number       = {3},
  pages        = {543--560},
  volume       = {30},
  abstract     = {This study mainly introduces the development of the Flexible Global Ocean-Atmosphere-Land System Model: Grid-point Version 2 (FGOALS-g2) and the preliminary evaluations of its performances based on results from the pre-industrial control run and four members of historical runs according to the fifth phase of the Coupled Model Intercomparison Project (CMIP5) experiment design. The results suggest that many obvious improvements have been achieved by the FGOALS-g2 compared with the previous version,FGOALS-g1, including its climatological mean states, climate variability, and 20th century surface temperature evolution. For example,FGOALS-g2 better simulates the frequency of tropical land precipitation, East Asian Monsoon precipitation and its seasonal cycle, MJO and ENSO, which are closely related to the updated cumulus parameterization scheme, as well as the alleviation of uncertainties in some key parameters in shallow and deep convection schemes, cloud fraction, cloud macro/microphysical processes and the boundary layer scheme in its atmospheric model. The annual cycle of sea surface temperature along the equator in the Pacific is significantly improved in the new version. The sea ice salinity simulation is one of the unique characteristics of FGOALS-g2, although it is somehow inconsistent with empirical observations in the Antarctic.},
  file         = {:Li2013.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {fgoals-g2, climatological mean state, climate variability, 20th century climate, monsoon, sea-surface temperature, tropical pacific, seasonal cycle, part i, circulation, simulations, variability, sensitivity, oscillation, ice},
}

@Article{Li2020,
  author       = {Lijuan Li and Yongqiang Yu and Yanli Tang and Pengfei Lin and Jinbo Xie and Mirong Song and Li Dong and Tianjun Zhou and Li Liu and Lu Wang and Ye Pu and Xiaolong Chen and Lin Chen and Zhenghui Xie and Hongbo Liu and Lixia Zhang and Xin Huang and Tao Feng and Weipeng Zheng and Kun Xia and Hailong Liu and Jiping Liu and Yan Wang and Longhuan Wang and Binghao Jia and Feng Xie and Bin Wang and Shuwen Zhao and Zipeng Yu and Bowen Zhao and Jilin Wei},
  date         = {2020-09},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {The Flexible Global Ocean-Atmosphere-Land System Model Grid-Point Version 3 ({FGOALS}-g3): Description and Evaluation},
  doi          = {10.1029/2019ms002012},
  number       = {9},
  pages        = {e2019MS002012},
  volume       = {12},
  file         = {:Li2020.pdf:PDF},
  groups       = {CMIP6 models},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Lian2018,
  author       = {Lian, X. and Piao, S. L. and Huntingford, C. and Li, Y. and Zeng, Z. Z. and Wang, X. H. and Ciais, P. and McVicar, T. R. and Peng, S. S. and Ottle, C. and Yang, H. and Yang, Y. T. and Zhang, Y. Q. and Wang, T.},
  date         = {2018},
  journaltitle = {Nature Climate Change},
  title        = {Partitioning global land evapotranspiration using {CMIP}5 models constrained by observations},
  doi          = {10.1038/s41558-018-0207-9},
  number       = {7},
  pages        = {640--646},
  volume       = {8},
  abstract     = {The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface-atmosphere interactions. However, its magnitude remains highly uncertain at the global scale. Here we apply an emergent constraint approach that integrates CMIP5 Earth system models (ESMs) with 33 field T/ET measurements to re-estimate the global T/ET value. Our observational constraint strongly increases the original ESM estimates (0.41 +/- 0.11) and greatly alleviates intermodel discrepancy, which leads to a new global T/ET estimate of 0.62 +/- 0.06. For all the ESMs, the leaf area index is identified as the primary driver of spatial variations of T/ET, but to correct its bias generates a larger T/ET underestimation than the original ESM output. We present evidence that the ESM underestimation of T/ET is, instead, attributable to inaccurate representation of canopy light use, interception loss and root water uptake processes in the ESMs. These processes should be prioritized to reduce model uncertainties in the global hydrological cycle.},
  comment      = {Uses GBRT for derivation of feature importance},
  file         = {:Lian2018.pdf:PDF},
  groups       = {Emergent Constraints, Carbon Cycle},
  keywords     = {terrestrial water fluxes, plant rooting depth, climate impacts, transpiration, carbon, vegetation, forest, sensitivity, evaporation, radiation, printed, read},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Liang2020a,
  author       = {Yongxiao Liang and Nathan P. Gillett and Adam H. Monahan},
  date         = {2020-06},
  journaltitle = {Geophysical Research Letters},
  title        = {Climate Model Projections of 21st Century Global Warming Constrained Using the Observed Warming Trend},
  doi          = {10.1029/2019gl086757},
  number       = {12},
  pages        = {e2019GL086757},
  volume       = {47},
  file         = {:Liang2020a.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming, Climate Model Weighting / Reducing uncertainties},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Lipat2017,
  author       = {Lipat, B. R. and Tselioudis, G. and Grise, K. M. and Polvani, L. M.},
  date         = {2017},
  journaltitle = {Geophysical Research Letters},
  title        = {CMIP5 models' shortwave cloud radiative response and climate sensitivity linked to the climatological Hadley cell extent},
  doi          = {10.1002/2017gl073151},
  number       = {11},
  pages        = {5739--5748},
  volume       = {44},
  abstract     = {This study analyzes Coupled Model Intercomparison Project phase 5 (CMIP5) model output to examine the covariability of interannual Southern Hemisphere Hadley cell (HC) edge latitude shifts and shortwave cloud radiative effect (SWCRE). In control climate runs, during years when the HC edge is anomalously poleward, most models substantially reduce the shortwave radiation reflected by clouds in the lower midlatitude region (LML; approximate to 28 degrees S-approximate to 48 degrees S), although no such reduction is seen in observations. These biases in HC-SWCRE covariability are linked to biases in the climatological HC extent. Notably, models with excessively equatorward climatological HC extents have weaker climatological LML subsidence and exhibit larger increases in LML subsidence with poleward HC edge expansion. This behavior, based on control climate interannual variability, has important implications for the CO2-forced model response. In 4xCO(2)-forced runs, models with excessively equatorward climatological HC extents produce stronger SW cloud radiative warming in the LML region and tend to have larger climate sensitivity values than models with more realistic climatological HC extents.},
  file         = {:Lipat2017.pdf:PDF},
  groups       = {Emergent Constraints, Clouds / Aerosols},
  keywords     = {climate sensitivity, cloud feedback, hadley cell, sw cloud radiative effect, ecs, dynamics, eddy-driven jet, southern-hemisphere, poleward shift, spread, future, 21st-century, simulations},
}

@Article{Loeb2018,
  author       = {Loeb, N. G. and Doelling, D. R. and Wang, H. L. and Su, W. Y. and Nguyen, C. and Corbett, J. G. and Liang, L. S. and Mitrescu, C. and Rose, F. G. and Kato, S.},
  date         = {2018},
  journaltitle = {Journal of Climate},
  title        = {Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product},
  doi          = {10.1175/Jcli-D-17-0208.1},
  number       = {2},
  pages        = {895--918},
  volume       = {31},
  abstract     = {The Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA), Edition 4.0 (Ed4.0), data product is described. EBAF Ed4.0 is an update to EBAF Ed2.8, incorporating all of the Ed4.0 suite of CERES data product algorithm improvements and consistent input datasets throughout the record. A one-time adjustment to shortwave (SW) and longwave (LW) TOA fluxes is made to ensure that global mean net TOA flux for July 2005-June 2015 is consistent with the in situ value of 0.71 W m(-2). While global mean all-sky TOA flux differences between Ed4.0 and Ed2.8 are within 0.5 W m(-2), appreciable SW regional differences occur over marine stratocumulus and snow/sea ice regions. Marked regional differences in SW clear-sky TOA flux occur in polar regions and dust areas over ocean. Clear-sky LW TOA fluxes in EBAF Ed4.0 exceed Ed2.8 in regions of persistent high cloud cover. Owing to substantial differences in global mean clear-sky TOA fluxes, the net cloud radiative effect in EBAF Ed4.0 is -18 W m(-2) compared to -21 W m(-2) in EBAF Ed2.8. The overall uncertainty in 18 3 18 latitude-longitude regional monthly all-sky TOA flux is estimated to be 3 W m(-2) [one standard deviation (1 sigma)] for the Terra-only period and 2.5 W m(-2) for the Terra-Aqua period both for SW and LW fluxes. The SW clear-sky regional monthly flux uncertainty is estimated to be 6 W m(-2) for the Terra-only period and 5 W m(-2) for the Terra-Aqua period. The LW clear-sky regional monthly flux uncertainty is 5 W m(-2) for Terra only and 4.5 W m(-2) for Terra-Aqua.},
  file         = {:Loeb2018.pdf:PDF},
  groups       = {OBS},
  keywords     = {angular-distribution models, temporal interpolation, radiation budget, sea-ice, ocean, flux, methodology, instrument, imbalance, heat},
}

@Article{Lorenz2018,
  author       = {Ruth Lorenz and Nadja Herger and Jan Sedláček and Veronika Eyring and Erich M. Fischer and Reto Knutti},
  date         = {2018-05},
  journaltitle = {Journal of Geophysical Research: Atmospheres},
  title        = {Prospects and Caveats of Weighting Climate Models for Summer Maximum Temperature Projections Over North America},
  doi          = {10.1029/2017jd027992},
  number       = {9},
  pages        = {4509--4526},
  volume       = {123},
  file         = {:Lorenz2018.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Lucht2002,
  author       = {Lucht, W. and Prentice, I. C. and Myneni, R. B. and Sitch, S. and Friedlingstein, P. and Cramer, W. and Bousquet, P. and Buermann, W. and Smith, B.},
  date         = {2002},
  journaltitle = {Science},
  title        = {Climatic control of the high-latitude vegetation greening trend and Pinatubo effect},
  doi          = {10.1126/science.1071828},
  number       = {5573},
  pages        = {1687--1689},
  volume       = {296},
  abstract     = {A biogeochemical model of vegetation using observed climate data predicts the high northern latitude greening trend over the past two decades observed by satellites and a marked setback in this trend after the Mount Pinatubo, volcano eruption in 1991. The observed trend toward earlier spring budburst and increased maximum leaf area is produced by the model as a consequence of biogeochemical vegetation responses mainly to changes in temperature. The post-Pinatubo decline in vegetation in 1992-1993 is apparent as the effect of temporary cooling caused by the eruption. High-latitude CO2 uptake during these years is predicted as a consequence of the differential response of heterotrophic respiration and net primary production.},
  file         = {:Lucht2002.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {mount-pinatubo, land, index, co2},
}

@Article{Mace2009,
  author       = {Mace, G. G. and Zhang, Q. Q. and Vaughan, M. and Marchand, R. and Stephens, G. and Trepte, C. and Winker, D.},
  date         = {2009},
  journaltitle = {Journal of Geophysical Research-Atmospheres},
  title        = {A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data},
  doi          = {10.1029/2007jd009755},
  number       = {D8},
  pages        = {D00A26},
  volume       = {114},
  abstract     = {The occurrence statistics of hydrometeor layers covering the Earth's surface is described using the first year of millimeter radar data collected by Cloudsat merged with lidar data collected by CALIPSO (July 2006 to June 2007). These satellites are flown in a tight orbital configuration so that they probe nearly the same volumes of the atmosphere within 10-15 s of each other. This configuration combined with the capacity for millimeter radar to penetrate optically thick hydrometeor layers and the ability of the lidar to detect optically thin clouds has allowed us to characterize the vertical and horizontal structure of hydrometeor layers with unprecedented precision. We find that the global hydrometeor coverage averages 76\% and demonstrates a fairly smooth annual cycle with a range of 3\% peaking in October 2006 and reaching a minimum in March 2007. The geographic distribution of hydrometeor layers defined in terms of layer base, layer top, and layer thickness is described. The predominance of geometrically thin boundary layer clouds is illustrated as is the spatial distribution of upper tropospheric ice clouds in the tropics. The cooccurrence of multiple layers is shown to be a strong function of latitude and geography with cooccurring middle-level (3 km < layer base < 6 km) and high-level (base > 6 km) layers being predominant over the continents. Cloud layer overlap is also examined, and a bias due to an assumption of maximum fractional overlap in coarse resolution models is quantified and shown to be on the order of -5 to -7\% globally maximizing over the high- latitude continents of the Northern Hemisphere.},
  file         = {:Mace2009.pdf:PDF},
  groups       = {OBS},
  keywords     = {high-level clouds, vertical structure, radar, mission, isccp, climatology, lidar, hirs},
}

@Article{Maki2010,
  author       = {T. Maki and M. Ikegami and T. Fujita and T. Hirahara and K. Yamada and K. Mori and A. Takeuchi and Y. Tsutsumi and K. Suda and T. J. Conway},
  date         = {2010-01},
  journaltitle = {Tellus B: Chemical and Physical Meteorology},
  title        = {New technique to analyse global distributions of {CO}$_2$ concentrations and fluxes from non-processed observational data},
  doi          = {10.1111/j.1600-0889.2010.00488.x},
  number       = {5},
  pages        = {797--809},
  volume       = {62},
  file         = {:Maki2010.pdf:PDF},
  groups       = {OBS},
  publisher    = {Informa {UK} Limited},
}

@Article{Mansfield2020,
  author       = {L. A. Mansfield and P. J. Nowack and M. Kasoar and R. G. Everitt and W. J. Collins and A. Voulgarakis},
  date         = {2020-11},
  journaltitle = {npj Climate and Atmospheric Science},
  title        = {Predicting global patterns of long-term climate change from short-term simulations using machine learning},
  doi          = {10.1038/s41612-020-00148-5},
  number       = {1},
  pages        = {1--9},
  volume       = {3},
  file         = {:Mansfield2020.pdf:PDF},
  groups       = {ML in Climate Sciences},
  keywords     = {prio1},
  priority     = {prio1},
  publisher    = {Springer Science and Business Media {LLC}},
}

@Article{Mauritsen2019,
  author       = {Mauritsen, T. and Bader, J. and Becker, T. and Behrens, J. and Bittner, M. and Brokopf, R. and Brovkin, V. and Claussen, M. and Crueger, T. and Esch, M. and Fast, I. and Fiedler, S. and Flaeschner, D. and Gayler, V. and Giorgetta, M. and Goll, D. S. and Haak, H. and Hagemann, S. and Hedemann, C. and Hohenegger, C. and Ilyina, T. and Jahns, T. and Jimenez-de-la-Cuesta, D. and Jungclaus, J. and Kleinen, T. and Kloster, S. and Kracher, D. and Kinne, S. and Kleberg, D. and Lasslop, G. and Kornblueh, L. and Marotzke, J. and Matei, D. and Meraner, K. and Mikolajewicz, U. and Modali, K. and Mobis, B. and Muller, W. A. and Nabel, J. E. M. S. and Nam, C. C. W. and Notz, D. and Nyawira, S. S. and Paulsen, H. and Peters, K. and Pincus, R. and Pohlmann, H. and Pongratz, J. and Popp, M. and Raddatz, T. J. and Rast, S. and Redler, R. and Reick, C. H. and Rohrschneider, T. and Schemann, V. and Schmidt, H. and Schnur, R. and Schulzweida, U. and Six, K. D. and Stein, L. and Stemmler, I. and Stevens, B. and von Storch, J. S. and Tian, F. X. and Voigt, A. and Vrese, P. and Wieners, K. H. and Wilkenskjeld, S. and Winkler, A. and Roeckner, E.},
  date         = {2019},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing {CO}$_2$},
  doi          = {10.1029/2018ms001400},
  number       = {4},
  pages        = {998--1038},
  volume       = {11},
  abstract     = {A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI-ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low-level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO2 over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO2 forcing, which nonetheless can be represented by a simple two-layer model.},
  file         = {:Mauritsen2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {coupled climate model, model development, climate sensitivity, intercomparison project, general-circulation, convective parameterization, dinitrogen-fixation, radiative-transfer, sea-ice, carbon, cmip6, simulations},
}

@Article{McCoy2015,
  author       = {Daniel T. McCoy and Dennis L. Hartmann and Mark D. Zelinka and Paulo Ceppi and Daniel P. Grosvenor},
  date         = {2015-09},
  journaltitle = {Journal of Geophysical Research: Atmospheres},
  title        = {Mixed-phase cloud physics and Southern Ocean cloud feedback in climate models},
  doi          = {10.1002/2015jd023603},
  number       = {18},
  pages        = {9539--9554},
  volume       = {120},
  file         = {:McCoy2015.pdf:PDF},
  groups       = {Clouds / Aerosols, Feedbacks},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{McCoy2016,
  author       = {Daniel T. McCoy and Ivy Tan and Dennis L. Hartmann and Mark D. Zelinka and Trude Storelvmo},
  date         = {2016-05},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {On the relationships among cloud cover, mixed-phase partitioning, and planetary albedo in {GCMs}},
  doi          = {10.1002/2015ms000589},
  number       = {2},
  pages        = {650--668},
  volume       = {8},
  file         = {:McCoy2016.pdf:PDF},
  groups       = {Clouds / Aerosols},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Meehl2012,
  author       = {Meehl, G. A. and Washington, W. M. and Arblaster, J. M. and Hu, A. X. and Teng, H. Y. and Tebaldi, C. and Sanderson, B. N. and Lamarque, J. F. and Conley, A. and Strand, W. G. and White, J. B.},
  date         = {2012},
  journaltitle = {Journal of Climate},
  title        = {Climate System Response to External Forcings and Climate Change Projections in CCSM4},
  doi          = {10.1175/Jcli-D-11-00240.1},
  number       = {11},
  pages        = {3661--3683},
  volume       = {25},
  abstract     = {Results are presented from experiments performed with the Community Climate System Model, version 4 (CCSM4) for the Coupled Model Intercomparison Project phase 5 (CMIP5). These include multiple ensemble members of twentieth-century climate with anthropogenic and natural forcings as well as single-forcing runs, sensitivity experiments with sulfate aerosol forcing, twenty-first-century representative concentration pathway (RCP) mitigation scenarios, and extensions for those scenarios beyond 2100-2300. Equilibrium climate sensitivity of CCSM4 is 3.20 degrees C, and the transient climate response is 1.73 degrees C. Global surface temperatures averaged for the last 20 years of the twenty-first century compared to the 1986-2005 reference period for six-member ensembles from CCSM4 are +0.85 degrees, +1.64 degrees, +2.09 degrees, and +3.53 degrees C for RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The ocean meridional overturning circulation (MOC) in the Atlantic, which weakens during the twentieth century in the model, nearly recovers to early twentieth-century values in RCP2.6, partially recovers in RCP4.5 and RCP6, and does not recover by 2100 in RCP8.5. Heat wave intensity is projected to increase almost everywhere in CCSM4 in a future warmer climate, with the magnitude of the increase proportional to the forcing. Precipitation intensity is also projected to increase, with dry days increasing in most subtropical areas. For future climate, there is almost no summer sea ice left in the Arctic in the high RCP8.5 scenario by 2100, but in the low RCP2.6 scenario there is substantial sea ice remaining in summer at the end of the century.},
  file         = {:Meehl2012.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {sea-ice, model, circulation, aerosols, variability, simulation, irradiance, evolution, impacts, gases},
}

@Article{Meehl2020,
  author       = {Meehl, Gerald A. and Senior, Catherine A. and Eyring, Veronika and Flato, Gregory and Lamarque, Jean-Francois and Stouffer, Ronald J. and Taylor, Karl E. and Schlund, Manuel},
  date         = {2020},
  journaltitle = {Science Advances},
  title        = {Context for interpreting equilibrium climate sensitivity and transient climate response from the {CMIP}6 Earth system models},
  doi          = {10.1126/sciadv.aba1981},
  number       = {26},
  pages        = {eaba1981},
  volume       = {6},
  abstract     = {For the current generation of earth system models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), the range of equilibrium climate sensitivity (ECS, a hypothetical value of global warming at equilibrium for a doubling of CO2) is 1.8°C to 5.6°C, the largest of any generation of models dating to the 1990s. Meanwhile, the range of transient climate response (TCR, the surface temperature warming around the time of CO2 doubling in a 1\% per year CO2 increase simulation) for the CMIP6 models of 1.7°C (1.3°C to 3.0°C) is only slightly larger than for the CMIP3 and CMIP5 models. Here we review and synthesize the latest developments in ECS and TCR values in CMIP, compile possible reasons for the current values as supplied by the modeling groups, and highlight future directions. Cloud feedbacks and cloud-aerosol interactions are the most likely contributors to the high values and increased range of ECS in CMIP6.},
  file         = {:Meehl2020.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {own},
}

@Article{Merrifield2020,
  author       = {Anna Louise Merrifield and Lukas Brunner and Ruth Lorenz and Iselin Medhaug and Reto Knutti},
  date         = {2020-09},
  journaltitle = {Earth System Dynamics},
  title        = {An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles},
  doi          = {10.5194/esd-11-807-2020},
  number       = {3},
  pages        = {807--834},
  volume       = {11},
  file         = {:Merrifield2020.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  publisher    = {Copernicus {GmbH}},
}

@Article{Meyers1992,
  author       = {Michael P. Meyers and Paul J. DeMott and William R. Cotton},
  date         = {1992-07},
  journaltitle = {Journal of Applied Meteorology},
  title        = {New Primary Ice-Nucleation Parameterizations in an Explicit Cloud Model},
  doi          = {10.1175/1520-0450(1992)031<0708:npinpi>2.0.co;2},
  number       = {7},
  pages        = {708--721},
  volume       = {31},
  file         = {:Meyers1992.pdf:PDF},
  groups       = {Parameterizations, Clouds / Aerosols},
  publisher    = {American Meteorological Society},
}

@InBook{Mitchell1990,
  author       = {Mitchell, J. F. B. and Manabe, S. and Meleshko, V. and Tokioka, T.},
  date         = {1990},
  title        = {Equilibrium Climate Change and its Implications for the Future},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {131--164},
  url          = {https://archive.ipcc.ch/ipccreports/far/wg_I/ipcc_far_wg_I_chapter_05.pdf},
  file         = {:Mitchell1990.pdf:PDF},
  groups       = {IPCC, ECS / TCR / Climate Sensitivity / Warming},
  journaltitle = {Climate Change: The IPCC Scientific Assessment},
}

@Article{Morice2012,
  author       = {Morice, C. P. and Kennedy, J. J. and Rayner, N. A. and Jones, P. D.},
  date         = {2012},
  journaltitle = {Journal of Geophysical Research-Atmospheres},
  title        = {Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set},
  doi          = {10.1029/2011jd017187},
  number       = {D8},
  pages        = {D08101},
  volume       = {117},
  abstract     = {Recent developments in observational near-surface air temperature and sea-surface temperature analyses are combined to produce HadCRUT4, a new data set of global and regional temperature evolution from 1850 to the present. This includes the addition of newly digitized measurement data, both over land and sea, new sea-surface temperature bias adjustments and a more comprehensive error model for describing uncertainties in sea-surface temperature measurements. An ensemble approach has been adopted to better describe complex temporal and spatial interdependencies of measurement and bias uncertainties and to allow these correlated uncertainties to be taken into account in studies that are based upon HadCRUT4. Climate diagnostics computed from the gridded data set broadly agree with those of other global near-surface temperature analyses. Fitted linear trends in temperature anomalies are approximately 0.07 degrees C/decade from 1901 to 2010 and 0.17 degrees C/decade from 1979 to 2010 globally. Northern/southern hemispheric trends are 0.08/0.07 degrees C/decade over 1901 to 2010 and 0.24/0.10 degrees C/decade over 1979 to 2010. Linear trends in other prominent near-surface temperature analyses agree well with the range of trends computed from the HadCRUT4 ensemble members.},
  file         = {:Morice2012.pdf:PDF},
  groups       = {OBS},
  keywords     = {surface-temperature, in-situ, land, trends, reanalysis, icoads, update, sst},
}

@Article{Mueller2013,
  author       = {Mueller, B. and Hirschi, M. and Jimenez, C. and Ciais, P. and Dirmeyer, P. A. and Dolman, A. J. and Fisher, J. B. and Jung, M. and Ludwig, F. and Maignan, F. and Miralles, D. G. and McCabe, M. F. and Reichstein, M. and Sheffield, J. and Wang, K. and Wood, E. F. and Zhang, Y. and Seneviratne, S. I.},
  date         = {2013},
  journaltitle = {Hydrology and Earth System Sciences},
  title        = {Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis},
  doi          = {10.5194/hess-17-3707-2013},
  number       = {10},
  pages        = {3707--3720},
  volume       = {17},
  abstract     = {Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these individual data sets over the time periods 1989-1995 (7 yr) and 1989-2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the individual data sets, ET is derived from satellite and/ or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13mmyr(-2) in our merged product) followed by a significant decrease in this trend (-0.18mmyr(-2)), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493mmyr-1 (1.35mmd-1) for both the 1989-1995 and 1989-2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263mmyr(-1) (34 406 km3 yr-1) for a total land area of 130 922 000 km(2). Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial.},
  file         = {:Mueller2013.pdf:PDF},
  groups       = {OBS},
  keywords     = {reanalysis data, soil-moisture, global-scale, surface, climate, trends, model, 20th-century, variability, evaporation},
}

@Article{Muller2018,
  author       = {Muller, W. A. and Jungclaus, J. H. and Mauritsen, T. and Baehr, J. and Bittner, M. and Budich, R. and Bunzel, F. and Esch, M. and Ghosh, R. and Haak, H. and Ilyina, T. and Kleine, T. and Kornblueh, L. and Li, H. and Modali, K. and Notz, D. and Pohlmann, H. and Roeckner, E. and Stemmler, I. and Tian, F. and Marotzke, J.},
  date         = {2018},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {A Higher-resolution Version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR)},
  doi          = {10.1029/2017ms001217},
  number       = {7},
  pages        = {1383--1413},
  volume       = {10},
  abstract     = {The MPI-ESM1.2 is the latest version of the Max Planck Institute Earth System Model and is the baseline for the Coupled Model Intercomparison Project Phase 6 and current seasonal and decadal climate predictions. This paper evaluates a coupled higher-resolution version (MPI-ESM1.2-HR) in comparison with its lower-resolved version (MPI-ESM1.2-LR). We focus on basic oceanic and atmospheric mean states and selected modes of variability, the El Nino/Southern Oscillation and the North Atlantic Oscillation. The increase in atmospheric resolution in MPI-ESM1.2-HR reduces the biases of upper-level zonal wind and atmospheric jet stream position in the northern extratropics. This results in a decrease of the storm track bias over the northern North Atlantic, for both winter and summer season. The blocking frequency over the European region is improved in summer, and North Atlantic Oscillation and related storm track variations improve in winter. Stable Atlantic meridional overturning circulations are found with magnitudes of similar to 16Sv for MPI-ESM1.2-HR and similar to 20Sv for MPI-ESM1.2-LR at 26 degrees N. A strong sea surface temperature bias of similar to 5 degrees C along with a too zonal North Atlantic current is present in both versions. The sea surface temperature bias in the eastern tropical Atlantic is reduced by similar to 1 degrees C due to higher-resolved orography in MPI-ESM-HR, and the region of the cold-tongue bias is reduced in the tropical Pacific. MPI-ESM1.2-HR has a well-balanced radiation budget and its climate sensitivity is explicitly tuned to 3K. Although the obtained reductions in long-standing biases are modest, the improvements in atmospheric dynamics make this model well suited for prediction and impact studies.},
  file         = {:Muller2018.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {earth system modeling, climate variability, meridional overturning circulation, antarctic circumpolar current, global ocean, north-atlantic, atmospheric blocking, el-nino, simulated climate, enso asymmetry, transport, cmip5},
}

@InBook{Myhre2013,
  author       = {Gunnar Myhre and Drew Shindell and François-Marie Bréon and William J. Collins and Jan Fuglestvedt and Jianping Huang and Dorothy Koch and Jean-François Lamarque and David Lee and Blanca Mendoza and Teruyuki Nakajima and Alan Robock and Graeme Stephens and Toshihiko Takemura and Hua Zhang},
  date         = {2013},
  title        = {Anthropogenic and Natural Radiative Forcing},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {659--740},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter08_FINAL.pdf},
  file         = {:Myhre2013.pdf:PDF},
  groups       = {IPCC, Forcing},
  journaltitle = {Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Article{Myneni1997,
  author       = {Myneni, R. B. and Keeling, C. D. and Tucker, C. J. and Asrar, G. and Nemani, R. R.},
  date         = {1997},
  journaltitle = {Nature},
  title        = {Increased plant growth in the northern high latitudes from 1981 to 1991},
  doi          = {10.1038/386698a0},
  number       = {6626},
  pages        = {698--702},
  volume       = {386},
  abstract     = {Variations in the amplitude and timing of the seasonal cycle of atmospheric CO2 have shown an association with surface air temperature consistent with the hypothesis that warmer temperatures have promoted increases in plant growth during summer(1) and/or plant respiration during winter(2) in the northern high latitudes. Here we present evidence from satellite data that the photosynthetic activity of terrestrial vegetation increased from 1981 to 1991 in a manner that is suggestive of an increase in plant growth associated with a lengthening of the active growing season. The regions exhibiting the greatest increase lie between 45 degrees N and 70 degrees N, where marked warming has occurred in the spring time(3) due to an early disappearance of snow(4). The satellite data are concordant with an increase in the amplitude of the seasonal cycle of atmospheric carbon dioxide exceeding 20\% since the early 1970s, and an advance of up to seven days in the timing of the drawdown of CO2 in spring and early summer(1). Thus, both the satellite data and the CO2 record indicate that the global carbon cycle has responded to interannual fluctuations in surface air temperature which, although small at the global scale, are regionally highly significant.},
  file         = {:Myneni1997.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {vegetation index, avhrr data, rise},
}

@Article{Nijsse2020,
  author       = {Femke J. M. M. Nijsse and Peter M. Cox and Mark S. Williamson},
  date         = {2020-08},
  journaltitle = {Earth System Dynamics},
  title        = {Emergent constraints on transient climate response ({TCR}) and equilibrium climate sensitivity~({ECS}) from historical warming in {CMIP}5 and {CMIP}6 models},
  doi          = {10.5194/esd-11-737-2020},
  number       = {3},
  pages        = {737--750},
  volume       = {11},
  file         = {:Nijsse2020.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming, Emergent Constraints},
  publisher    = {Copernicus {GmbH}},
}

@Article{Nowack2020,
  author       = {Peer Nowack and Jakob Runge and Veronika Eyring and Joanna D. Haigh},
  date         = {2020-03},
  journaltitle = {Nature Communications},
  title        = {Causal networks for climate model evaluation and constrained projections},
  doi          = {10.1038/s41467-020-15195-y},
  number       = {1415},
  pages        = {1--11},
  volume       = {11},
  file         = {:Nowack2020.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties, Causality},
  publisher    = {Springer Science and Business Media {LLC}},
}

@Article{ONeill2013,
  author       = {Brian C. O'Neill and Elmar Kriegler and Keywan Riahi and Kristie L. Ebi and Stephane Hallegatte and Timothy R. Carter and Ritu Mathur and Detlef P. van Vuuren},
  date         = {2013-10},
  journaltitle = {Climatic Change},
  title        = {A new scenario framework for climate change research: the concept of shared socioeconomic pathways},
  doi          = {10.1007/s10584-013-0905-2},
  number       = {3},
  pages        = {387--400},
  volume       = {122},
  file         = {:ONeill2013.pdf:PDF},
  groups       = {CMIP and other MIPs},
  publisher    = {Springer Science and Business Media {LLC}},
}

@Article{ONeill2016,
  author       = {Brian C. O'Neill and Claudia Tebaldi and Detlef P. van Vuuren and Veronika Eyring and Pierre Friedlingstein and George Hurtt and Reto Knutti and Elmar Kriegler and Jean-Francois Lamarque and Jason Lowe and Gerald A. Meehl and Richard Moss and Keywan Riahi and Benjamin M. Sanderson},
  date         = {2016-09},
  journaltitle = {Geoscientific Model Development},
  title        = {The Scenario Model Intercomparison Project ({ScenarioMIP}) for {CMIP}6},
  doi          = {10.5194/gmd-9-3461-2016},
  number       = {9},
  pages        = {3461--3482},
  volume       = {9},
  file         = {:ONeill2016.pdf:PDF},
  groups       = {CMIP and other MIPs},
  publisher    = {Copernicus {GmbH}},
}

@Article{ONeill2017,
  author       = {Brian C. O'Neill and Elmar Kriegler and Kristie L. Ebi and Eric Kemp-Benedict and Keywan Riahi and Dale S. Rothman and Bas J. van Ruijven and Detlef P. van Vuuren and Joern Birkmann and Kasper Kok and Marc Levy and William Solecki},
  date         = {2017-01},
  journaltitle = {Global Environmental Change},
  title        = {The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century},
  doi          = {10.1016/j.gloenvcha.2015.01.004},
  pages        = {169--180},
  volume       = {42},
  file         = {:ONeill2017.pdf:PDF},
  groups       = {CMIP and other MIPs},
  publisher    = {Elsevier {BV}},
}

@Article{Park2019,
  author       = {Park, S. and Shin, J. and Kim, S. and Oh, E. and Kim, Y.},
  date         = {2019},
  journaltitle = {Journal of Climate},
  title        = {Global Climate Simulated by the Seoul National University Atmosphere Model Version 0 with a Unified Convection Scheme (SAM0-UNICON)},
  doi          = {10.1175/Jcli-D-18-0796.1},
  number       = {10},
  pages        = {2917--2949},
  volume       = {32},
  abstract     = {As a contribution to phase 6 of the Coupled Model Intercomparison Project (CMIP6), the global climate simulated by an atmospheric general circulation model (GCM), the Seoul National University Atmosphere Model version 0 with a Unified Convection Scheme (SAM0-UNICON), is compared with observation and climates simulated by the Community Atmosphere Model version 5 (CAM5) and Community Earth System Model version 1 (CESM1), on which SAM0-UNICON is based. Both SAM0-UNICON and CESM1 successfully reproduce observed global warming after 1970. The global mean climate simulated by SAM0-UNICON is roughly similar to that of CAM5/CESM1. However, SAM0-UNICON improves the simulations of the double intertropical convergence zone, shortwave cloud forcing, near-surface air temperature, aerosol optical depth, sea ice fraction, and sea surface temperature (SST), but is slightly poorer for the simulation of tropical relative humidity, Pacific surface wind stress, and ocean rainfall. Two important biases in the simulated mean climate in both models are a set of horseshoe-shaped biases of SST, sea level pressure, precipitation, and cloud radiative forcings in the central equatorial Pacific and a higher sea ice fraction in the Arctic periphery and Southern Hemispheric circumpolar regions. Both SAM0-UNICON and CESM1 simulate the observed El Nino-Southern Oscillation (ENSO) reasonably well. However, compared with CAM5/CESM1, SAM0-UNICON performs better in simulating the Madden-Julian oscillation (MJO), diurnal cycle of precipitation, and tropical cyclones. The aerosol indirect effect (AIE) simulated by SAM0-UNICON is similar to that from CAM5 but the magnitudes of the individual shortwave and longwave AIEs are substantially reduced.},
  file         = {:Park2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {climate models, cloud parameterizations, convective parameterization, cumulus clouds, integrated vertical overlap, storm tracks, part i, parameterization, cumulus, impact, precipitation, representation, circulation, transport},
}

@Article{Parker2009,
  author       = {Wendy S. Parker},
  date         = {2009-06},
  journaltitle = {Aristotelian Society Supplementary Volume},
  title        = {Confirmation and Adequacy-for-Purpose in Climate Modelling},
  doi          = {10.1111/j.1467-8349.2009.00180.x},
  number       = {1},
  pages        = {233--249},
  volume       = {83},
  file         = {:Parker2009.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  publisher    = {Oxford University Press ({OUP})},
}

@Article{Piao2018,
  author       = {Piao, S. L. and Liu, Z. and Wang, Y. L. and Ciais, P. and Yao, Y. T. and Peng, S. and Chevallier, F. and Friedlingstein, P. and Janssens, I. A. and Penuelas, J. and Sitch, S. and Wang, T.},
  date         = {2018},
  journaltitle = {Global Change Biology},
  title        = {On the causes of trends in the seasonal amplitude of atmospheric {CO}$_2$},
  doi          = {10.1111/gcb.13909},
  number       = {2},
  pages        = {608--616},
  volume       = {24},
  abstract     = {No consensus has yet been reached on the major factors driving the observed increase in the seasonal amplitude of atmospheric CO2 in the northern latitudes. In this study, we used atmospheric CO2 records from 26 northern hemisphere stations with a temporal coverage longer than 15 years, and an atmospheric transport model prescribed with net biome productivity (NBP) from an ensemble of nine terrestrial ecosystem models, to attribute change in the seasonal amplitude of atmospheric CO2. We found significant (p < .05) increases in seasonal peak-to-trough CO2 amplitude (AMP(P-T)) at nine stations, and in trough-to-peak amplitude (AMP(T-P)) at eight stations over the last three decades. Most of the stations that recorded increasing amplitudes are in Arctic and boreal regions (> 50 degrees N), consistent with previous observations that the amplitude increased faster at Barrow (Arctic) than at Mauna Loa (subtropics). The multi-model ensemble mean (MMEM) shows that the response of ecosystem carbon cycling to rising CO2 concentration (eCO(2)) and climate change are dominant drivers of the increase in AMP(P-T) and AMP(T-P) in the high latitudes. At the Barrow station, the observed increase of AMP(P-T) and AMP(T-P) over the last 33 years is explained by eCO(2) (39\% and 42\%) almost equally than by climate change (32\% and 35\%). The increased carbon losses during the months with a net carbon release in response to eCO(2) are associated with higher ecosystem respiration due to the increase in carbon storage caused by eCO(2) during carbon uptake period. Air-sea CO2 fluxes (10\% for AMP(P-T) and 11\% for AMP(T-P)) and the impacts of land-use change (marginally significant 3\% for AMP(P-T) and 4\% for AMP(T-P)) also contributed to the CO2 measured at Barrow, highlighting the role of these factors in regulating seasonal changes in the global carbon cycle.},
  file         = {:Piao2018.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {amplitude of atmospheric co2, attribution, climate change, co2 fertilization effect, detection, land-use change, model intercomparison project, program multiscale synthesis, mauna-loa, northern ecosystems, carbon-dioxide, land-use, cycle, temperature, vegetation, exchange},
}

@Article{PoChedley2018,
  author       = {Po-Chedley, S. and Proistosescu, C. and Armour, K. C. and Santer, B. D.},
  date         = {2018},
  journaltitle = {Nature},
  title        = {Climate constraint reflects forced signal},
  doi          = {10.1038/s41586-018-0640-y},
  number       = {7729},
  pages        = {E6-E9},
  volume       = {563},
  comment      = {Answer to Cox et al. (2018)},
  file         = {:PoChedley2018.pdf:PDF},
  groups       = {Emergent Constraints},
  keywords     = {emergent constraints, sensitivity},
}

@Article{Qu2013,
  author       = {Xin Qu and Alex Hall and Stephen A. Klein and Peter M. Caldwell},
  date         = {2013-09},
  journaltitle = {Climate Dynamics},
  title        = {On the spread of changes in marine low cloud cover in climate model simulations of the 21st century},
  doi          = {10.1007/s00382-013-1945-z},
  number       = {9-10},
  pages        = {2603--2626},
  volume       = {42},
  file         = {:Qu2013.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming, Clouds / Aerosols},
  keywords     = {printed, read},
  printed      = {printed},
  publisher    = {Springer Science and Business Media {LLC}},
  readstatus   = {read},
}

@Article{Rackow2018,
  author       = {Rackow, T. and Goessling, H. F. and Jung, T. and Sidorenko, D. and Semmler, T. and Barbi, D. and Handorf, D.},
  date         = {2018},
  journaltitle = {Climate Dynamics},
  title        = {Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability},
  doi          = {10.1007/s00382-016-3192-6},
  number       = {7-8},
  pages        = {2369--2394},
  volume       = {50},
  abstract     = {This study forms part II of two papers describing ECHAM6-FESOM, a newly established global climate model with a unique multi-resolution sea ice-ocean component. While part I deals with the model description and the mean climate state, here we examine the internal climate variability of the model under constant present-day (1990) conditions. We (1) assess the internal variations in the model in terms of objective variability performance indices, (2) analyze variations in global mean surface temperature and put them in context to variations in the observed record, with particular emphasis on the recent warming slowdown, (3) analyze and validate the most common atmospheric and oceanic variability patterns, (4) diagnose the potential predictability of various climate indices, and (5) put the multi-resolution approach to the test by comparing two setups that differ only in oceanic resolution in the equatorial belt, where one ocean mesh keeps the coarse similar to 1A degrees resolution applied in the adjacent open-ocean regions and the other mesh is gradually refined to similar to 0.25A degrees. Objective variability performance indices show that, in the considered setups, ECHAM6-FESOM performs overall favourably compared to five well-established climate models. Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability; periods of strong cooling in the model ('hiatus' analogs) are mainly associated with ENSO-related variability and to a lesser degree also to PDO shifts, with the AMO playing a minor role. Common atmospheric and oceanic variability patterns are simulated largely consistent with their real counterparts. Typical deficits also found in other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean and episodic periods of almost absent deep-water formation in the Labrador Sea, resulting in overestimated North Atlantic SST variability. Concerning the influence of locally (isotropically) increased resolution, the ENSO pattern and index statistics improve significantly with higher resolution around the equator, illustrating the potential of the novel unstructured-mesh method for global climate modeling.},
  file         = {:Rackow2018.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {multi-resolution, awi climate model, unstructured mesh, variability, hiatus analogs, el nino-southern oscillation, ocean model, natural variability, southern-ocean, circulation, hiatus, oscillation, temperature, pacific, predictability, formulation},
}

@InBook{Randall2007,
  author       = {Randall, David A. and Wood, Richard A. and Bony, Sandrine and Colman, Robert and Fichefet, Thierry and Fyfe, John and Kattsov, Vladimir and Pitman, Andrew and Shukla, Jagadish and Srinivasan, Jayaraman and Stouffer, Ronald J. and Sumi, Akimasa and Taylor, Karl E.},
  date         = {2007},
  title        = {Climate Models and Their Evaluation},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {589--662},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2018/02/ar4-wg1-chapter8-1.pdf},
  file         = {:Randall2007.pdf:PDF},
  groups       = {IPCC, ESM basics, ECS / TCR / Climate Sensitivity / Warming},
  journaltitle = {Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Article{Rasp2018,
  author       = {Rasp, S. and Pritchard, M. S. and Gentine, P.},
  date         = {2018},
  journaltitle = {Proceedings of the National Academy of Sciences of the United States of America},
  title        = {Deep learning to represent subgrid processes in climate models},
  doi          = {10.1073/pnas.1810286115},
  number       = {39},
  pages        = {9684--9689},
  volume       = {115},
  abstract     = {The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for shortterm simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.},
  file         = {:Rasp2018.pdf:PDF},
  groups       = {ML in Climate Sciences, Parameterizations},
  keywords     = {climate modeling, deep learning, subgrid parameterization, convection, resolution dependence, resolving model, clouds, parameterization, precipitation, circulation, sensitivity, atmosphere},
}

@Article{Rayner2003,
  author       = {Rayner, N. A. and Parker, D. E. and Horton, E. B. and Folland, C. K. and Alexander, L. V. and Rowell, D. P. and Kent, E. C. and Kaplan, A.},
  date         = {2003},
  journaltitle = {Journal of Geophysical Research-Atmospheres},
  title        = {Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century},
  doi          = {10.1029/2002jd002670},
  number       = {D14},
  pages        = {4407},
  volume       = {108},
  abstract     = {[1] We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1degrees latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5degrees latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reduced-space optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave-based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well, containing SST fields with more uniform variance through time and better month-to-month persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets.},
  file         = {:Rayner2003.pdf:PDF},
  groups       = {OBS},
  keywords     = {sea surface temperature, sea ice, night marine air temperature, climate data reconstruction, bias correction, climate change, antarctic circumpolar wave, tropical pacific, indian-ocean, time-series, data sets, sst data, climate, variability, record, model},
}

@Misc{Read2015,
  author    = {Read, W. and Livesey, N.},
  date      = {2015},
  title     = {MLS/Aura L2 Relative Humidity With Respect To Ice - Version 4},
  doi       = {10.5067/AURA/MLS/DATA2019},
  groups    = {OBS},
  publisher = {NASA Goddard Earth Sciences Data and Information Services Center},
}

@Article{Reichstein2019,
  author       = {Reichstein, M. and Camps-Valls, G. and Stevens, B. and Jung, M. and Denzler, J. and Carvalhais, N. and Prabhat},
  date         = {2019},
  journaltitle = {Nature},
  title        = {Deep learning and process understanding for data-driven Earth system science},
  doi          = {10.1038/s41586-019-0912-1},
  number       = {7743},
  pages        = {195--204},
  volume       = {566},
  abstract     = {Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.},
  file         = {:Reichstein2019.pdf:PDF},
  groups       = {ML in Climate Sciences, Parameterizations},
  keywords     = {neural-network approach, carbon-dioxide, random forests, convective parameterization, climate sensitivity, model, classification, resolution, framework, nitrogen, skimmed},
  readstatus   = {skimmed},
}

@Article{Renoult2020,
  author       = {Martin Renoult and James Douglas Annan and Julia Catherine Hargreaves and Navjit Sagoo and Clare Flynn and Marie-Luise Kapsch and Qiang Li and Gerrit Lohmann and Uwe Mikolajewicz and Rumi Ohgaito and Xiaoxu Shi and Qiong Zhang and Thorsten Mauritsen},
  date         = {2020-09},
  journaltitle = {Climate of the Past},
  title        = {A Bayesian framework for emergent constraints: case studies of climate sensitivity with {PMIP}},
  doi          = {10.5194/cp-16-1715-2020},
  number       = {5},
  pages        = {1715--1735},
  volume       = {16},
  file         = {:Renoult2020.pdf:PDF},
  groups       = {Emergent Constraints, Mathematical basics, ECS / TCR / Climate Sensitivity / Warming},
  publisher    = {Copernicus {GmbH}},
}

@Article{Riahi2011,
  author       = {Riahi, K. and Rao, S. and Krey, V. and Cho, C. H. and Chirkov, V. and Fischer, G. and Kindermann, G. and Nakicenovic, N. and Rafaj, P.},
  date         = {2011},
  journaltitle = {Climatic Change},
  title        = {RCP 8.5-A scenario of comparatively high greenhouse gas emissions},
  doi          = {10.1007/s10584-011-0149-y},
  number       = {1-2},
  pages        = {33--57},
  volume       = {109},
  abstract     = {This paper summarizes the main characteristics of the RCP8.5 scenario. The RCP8.5 combines assumptions about high population and relatively slow income growth with modest rates of technological change and energy intensity improvements, leading in the long term to high energy demand and GHG emissions in absence of climate change policies. Compared to the total set of Representative Concentration Pathways (RCPs), RCP8.5 thus corresponds to the pathway with the highest greenhouse gas emissions. Using the IIASA Integrated Assessment Framework and the MESSAGE model for the development of the RCP8.5, we focus in this paper on two important extensions compared to earlier scenarios: 1) the development of spatially explicit air pollution projections, and 2) enhancements in the land-use and land-cover change projections. In addition, we explore scenario variants that use RCP8.5 as a baseline, and assume different degrees of greenhouse gas mitigation policies to reduce radiative forcing. Based on our modeling framework, we find it technically possible to limit forcing from RCP8.5 to lower levels comparable to the other RCPs (2.6 to 6 W/m(2)). Our scenario analysis further indicates that climate policy-induced changes of global energy supply and demand may lead to significant co-benefits for other policy priorities, such as local air pollution.},
  file         = {:Riahi2011.pdf:PDF},
  groups       = {CMIP and other MIPs},
  keywords     = {climate-change impacts, agriculture, mitigation},
}

@Article{Riahi2017,
  author       = {Keywan Riahi and Detlef P. van Vuuren and Elmar Kriegler and Jae Edmonds and Brian C. O'Neill and Shinichiro Fujimori and Nico Bauer and Katherine Calvin and Rob Dellink and Oliver Fricko and Wolfgang Lutz and Alexander Popp and Jesus Crespo Cuaresma and Samir KC and Marian Leimbach and Leiwen Jiang and Tom Kram and Shilpa Rao and Johannes Emmerling and Kristie Ebi and Tomoko Hasegawa and Petr Havlik and Florian Humpenöder and Lara Aleluia Da Silva and Steve Smith and Elke Stehfest and Valentina Bosetti and Jiyong Eom and David Gernaat and Toshihiko Masui and Joeri Rogelj and Jessica Strefler and Laurent Drouet and Volker Krey and Gunnar Luderer and Mathijs Harmsen and Kiyoshi Takahashi and Lavinia Baumstark and Jonathan C. Doelman and Mikiko Kainuma and Zbigniew Klimont and Giacomo Marangoni and Hermann Lotze-Campen and Michael Obersteiner and Andrzej Tabeau and Massimo Tavoni},
  date         = {2017-01},
  journaltitle = {Global Environmental Change},
  title        = {The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview},
  doi          = {10.1016/j.gloenvcha.2016.05.009},
  pages        = {153--168},
  volume       = {42},
  file         = {:Riahi2017.pdf:PDF},
  groups       = {CMIP and other MIPs},
  publisher    = {Elsevier {BV}},
}

@InProceedings{Ribeiro2016,
  author    = {Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
  booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  date      = {2016},
  title     = {"Why Should I Trust You?": Explaining the Predictions of Any Classifier},
  doi       = {10.1145/2939672.2939778},
  isbn      = {9781450342322},
  location  = {San Francisco, California, USA},
  pages     = {1135--1144},
  publisher = {Association for Computing Machinery},
  series    = {KDD '16},
  abstract  = {Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one.In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally varound the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.},
  address   = {New York, NY, USA},
  file      = {:Ribeiro2016.pdf:PDF},
  groups    = {ML basics, Code},
  keywords  = {black box classifier, interpretable machine learning, interpretability, explaining machine learning},
  numpages  = {10},
}

@Article{Righi2020,
  author       = {Righi, M. and Andela, B. and Eyring, V. and Lauer, A. and Predoi, V. and Schlund, M. and Vegas-Regidor, J. and Bock, L. and Brotz, B. and de Mora, L. and Diblen, F. and Dreyer, L. and Drost, N. and Earnshaw, P. and Hassler, B. and Koldunov, N. and Little, B. and Tomas, S. L. and Zimmermann, K.},
  date         = {2020},
  journaltitle = {Geoscientific Model Development},
  title        = {Earth System Model Evaluation Tool ({ESMValTool}) v2.0 --- technical overview},
  doi          = {10.5194/gmd-13-1179-2020},
  number       = {3},
  pages        = {1179--1199},
  volume       = {13},
  abstract     = {This paper describes the second major release of the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). Compared to version 1.0, released in 2016, ESMValTool version 2.0 (v2.0) features a brand new design, with an improved interface and a revised preprocessor. It also features a significantly enhanced diagnostic part that is described in three companion papers. The new version of ESMValTool has been specifically developed to target the increased data volume of CMIP Phase 6 (CMIP6) and the related challenges posed by the analysis and the evaluation of output from multiple high-resolution or complex ESMs. The new version takes advantage of state-of-the-art computational libraries and methods to deploy an efficient and user-friendly data processing. Common operations on the input data (such as regridding or computation of multi-model statistics) are centralized in a highly optimized preprocessor, which allows applying a series of preprocessing functions before diagnostics scripts are applied for in-depth scientific analysis of the model output. Performance tests conducted on a set of standard diagnostics show that the new version is faster than its predecessor by about a factor of 3. The performance can be further improved, up to a factor of more than 30, when the newly introduced task-based parallelization options are used, which enable the efficient exploitation of much larger computing infrastructures. ESMValTool v2.0 also includes a revised and simplified installation procedure, the setting of user-configurable options based on modern language formats, and high code quality standards following the best practices for software development.},
  file         = {:Righi2020.pdf:PDF},
  groups       = {ESMValTool},
  keywords     = {products, modis, land, performance, vegetation, clouds, cmip5, ceres, own},
}

@Article{Rind2020,
  author       = {D. Rind and C. Orbe and J. Jonas and L. Nazarenko and T. Zhou and M. Kelley and A. Lacis and D. Shindell and G. Faluvegi and A. Romanou and G. Russell and N. Tausnev and M. Bauer and G. Schmidt},
  date         = {2020-05},
  journaltitle = {Journal of Geophysical Research: Atmospheres},
  title        = {{GISS} Model E2.2: A Climate Model Optimized for the Middle Atmosphere --- Model Structure, Climatology, Variability, and Climate Sensitivity},
  doi          = {10.1029/2019jd032204},
  number       = {10},
  pages        = {e2019JD032204},
  volume       = {125},
  file         = {:Rind2020.pdf:PDF},
  groups       = {CMIP6 models},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Roe2009,
  author       = {Gerard Roe},
  date         = {2009-05},
  journaltitle = {Annual Review of Earth and Planetary Sciences},
  title        = {Feedbacks, Timescales, and Seeing Red},
  doi          = {10.1146/annurev.earth.061008.134734},
  number       = {1},
  pages        = {93--115},
  volume       = {37},
  file         = {:Roe2009.pdf:PDF},
  groups       = {Feedbacks, Mathematical basics},
  keywords     = {skimmed},
  publisher    = {Annual Reviews},
  readstatus   = {skimmed},
}

@Article{Rogers2017,
  author       = {Rogers, A. and Medlyn, B. E. and Dukes, J. S. and Bonan, G. and von Caemmerer, S. and Dietze, M. C. and Kattge, J. and Leakey, A. D. B. and Mercado, L. M. and Niinemets, U. and Prentice, I. C. and Serbin, S. P. and Sitch, S. and Way, D. A. and Zaehle, S.},
  date         = {2017},
  journaltitle = {New Phytologist},
  title        = {A roadmap for improving the representation of photosynthesis in Earth system models},
  doi          = {10.1111/nph.14283},
  number       = {1},
  pages        = {22--42},
  volume       = {213},
  abstract     = {Accurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBMs by examining leaf and canopy level responses of photosynthetic CO2 assimilation (A) to key environmental variables: light, temperature, CO2 concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models.},
  file         = {:Rogers2017.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {carbon dioxide co2, light, soil water content, stomatal conductance, temperature, terrestrial biosphere models, vapor pressure deficit (vpd), environment simulator jules, plant functional types, mesophyll diffusion conductance, biochemically based model, carbon cycle feedback, water-use efficiency, elevated co2, temperature response, terrestrial biosphere},
}

@Article{Rong2018,
  author       = {Rong, X. Y. and Li, J. and Chen, H. M. and Xin, Y. F. and Su, J. Z. and Hua, L. J. and Zhou, T. J. and Qi, Y. J. and Zhang, Z. Q. and Zhang, G. and Li, J. D.},
  date         = {2018},
  journaltitle = {Journal of Meteorological Research},
  title        = {The CAMS Climate System Model and a Basic Evaluation of Its Climatology and Climate Variability Simulation},
  doi          = {10.1007/s13351-018-8058-x},
  number       = {6},
  pages        = {839--861},
  volume       = {32},
  abstract     = {A new coupled climate system model (CSM) has been developed at the Chinese Academy of Meteorological Sciences (CAMS) by employing several state-of-the-art component models. The coupled CAMS-CSM consists of the modified atmospheric model [ECmwf-HAMburg (ECHAM5)], ocean model [Modular Ocean Model (MOM4)], sea ice model [Sea Ice Simulator (SIS)], and land surface model [Common Land Model (CoLM)]. A detailed model description is presented and both the pre-industrial and historical simulations are preliminarily evaluated in this study. The model can reproduce the climatological mean states and seasonal cycles of the major climate system quantities, including the sea surface temperature, precipitation, sea ice extent, and the equatorial thermocline. The major climate variability modes are also reasonably captured by the CAMS-CSM, such as the Madden-Julian Oscillation (MJO), El Nino-Southern Oscillation (ENSO), East Asian Summer Monsoon (EASM), and Pacific Decadal Oscillation (PDO). The model shows a promising ability to simulate the EASM variability and the ENSO-EASM relationship. Some biases still exist, such as the false double-intertropical convergence zone (ITCZ) in the annual mean precipitation field, the overestimated ENSO amplitude, and the weakened Bjerknes feedback associated with ENSO; and thus the CAMS-CSM needs further improvements.},
  file         = {:Rong2018.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {cams-csm, climate system model, climate variability, model evaluation, asian summer monsoon, 40-50 day oscillation, western north pacific, interannual variability, el-nino, air-temperature, cmip5 models, intraseasonal oscillation, southern-oscillation, seasonal-variations},
}

@Article{Rossow1991,
  author       = {Rossow, W. B. and Schiffer, R. A.},
  date         = {1991},
  journaltitle = {Bulletin of the American Meteorological Society},
  title        = {Isccp Cloud Data Products},
  doi          = {10.1175/1520-0477(1991)072<0002:Icdp>2.0.Co;2},
  number       = {1},
  pages        = {2--20},
  volume       = {72},
  abstract     = {The operational data collection phase of the International Satellite Cloud Climatology Project (ISCCP) began in July 1983.  Since then, visible and infrared images from an international network of weather satellites have been routinely processed to produce a global cloud climatology.  This report outlines the key steps in the data processing, describes the main features of the data products, and indicates how to obtain these data.  We illustrate some early results of this analysis.},
  file         = {:Rossow1991.pdf:PDF},
  groups       = {OBS},
  keywords     = {climate-research, data set, satellite, cover, distributions, parameters, radiation},
}

@Article{Rotstayn2012,
  author       = {Rotstayn, L. D. and Jeffrey, S. J. and Collier, M. A. and Dravitzki, S. M. and Hirst, A. C. and Syktus, J. I. and Wong, K. K.},
  date         = {2012},
  journaltitle = {Atmospheric Chemistry and Physics},
  title        = {Aerosol- and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations},
  doi          = {10.5194/acp-12-6377-2012},
  number       = {14},
  pages        = {6377--6404},
  volume       = {12},
  abstract     = {We use a coupled atmosphere-ocean global climate model (CSIRO-Mk3.6) to investigate the drivers of trends in summer rainfall and circulation in the vicinity of northern Australia. As part of the Coupled Model Intercomparison Project Phase 5 (CMIP5), we perform a 10-member 21st century ensemble driven by Representative Concentration Pathway 4.5 (RCP4.5). To investigate the roles of different forcing agents, we also perform multiple 10-member ensembles of historical climate change, which are analysed for the period 1951-2010. The historical runs include ensembles driven by 'all forcings' (HIST), all forcings except anthropogenic aerosols (NO_AA) and forcing only from long-lived greenhouse gases (GHGAS). Anthropogenic aerosol-induced effects in a warming climate are calculated from the difference of HIST minus NO_AA.
CSIRO-Mk3.6 simulates a strong summer rainfall decrease over north-western Australia (NWA) in RCP4.5, whereas simulated trends in HIST are weakly positive (but insignificant) during 1951-2010. The weak rainfall trends in HIST are due to compensating effects of different forcing agents: there is a significant decrease in GHGAS, offset by an aerosol-induced increase.
Observations show a significant increase of summer rainfall over NWA during the last few decades. The large magnitude of the observed NWA rainfall trend is not captured by 440 unforced 60-yr trends calculated from a 500-yr pre-industrial control run, even though the model's decadal variability appears to be realistic. This suggests that the observed trend includes a forced component, despite the fact that the model does not simulate the magnitude of the observed rainfall increase in response to 'all forcings' (HIST).
We investigate the mechanism of simulated and observed NWA rainfall changes by exploring changes in circulation over the Indo-Pacific region. The key circulation feature associated with the rainfall increase in reanalyses is a lower-tropospheric cyclonic circulation trend off the coast of NWA, which enhances the monsoonal flow. The model shows an aerosol-induced cyclonic circulation trend off the coast of NWA in HIST minus NO_AA, whereas GHGAS shows an anticyclonic circulation trend. This explains why the aerosol-induced effect is an increase of rainfall over NWA, and the greenhouse gas-induced effect is of opposite sign.
Possible explanations for the cyclonic (anticyclonic) circulation trend in HIST minus NO_AA (GHGAS) involve changes in the Walker circulation or the local Hadley circulation. In either case, a plausible atmospheric mechanism is that the circulation anomaly is a Rossby wave response to convective heating anomalies south of the Equator. We also discuss the possible role of air-sea interactions, e.g. an increase (decrease) of sea-surface temperatures off the coast of NWA in HIST minus NO_AA (GHGAS). Further research is needed to better understand the mechanisms and the extent to which these are model-dependent.
In summary, our results suggest that anthropogenic aerosols may have 'masked' greenhouse gas-induced changes in rainfall over NWA and in circulation over the wider Indo-Pacific region. Due to the opposing effects of greenhouse gases and anthropogenic aerosols, future trends may be very different from trends observed over the last few decades.},
  file         = {:Rotstayn2012.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {sea-surface temperature, indian-ocean, australian monsoon, el-nino, anthropogenic aerosols, indonesian throughflow, tropical circulation, general-circulation, walker circulation, hydrological cycle},
}

@Article{Rugenstein2019,
  author       = {Rugenstein, M. A. A. and Bloch-Johnson, J. and Abe-Ouchi, A. and Andrews, T. and Beyerle, U. and Cao, L. and Chadha, T. and Danabasoglu, G. and Dufresne, J. L. and Duan, L. and Foujols, M. A. and Frolicher, T. and Geoffroy, O. and Gregory, J. and Knutti, R. and Li, C. and Marzocchi, A. and Mauritsen, T. and Menary, M. and Moyer, E. and Nazarenko, L. and Paynter, D. and Saint-Martin, D. and Schmidt, G. A. and Yamamoto, A. and Yang, S. T.},
  date         = {2019},
  journaltitle = {Bulletin of the American Meteorological Society},
  title        = {LongRunMIP: Motivation and Design for a Large Collection of Millennial-Length AOGCM Simulations},
  doi          = {10.1175/Bams-D-19-0068.1},
  number       = {12},
  pages        = {2551--2570},
  volume       = {100},
  abstract     = {We present a model intercomparison project, LongRunMIP, the first collection of millennial-length (1,000+ years) simulations of complex coupled climate models with a representation of ocean, atmosphere, sea ice, and land surface, and their interactions. Standard model simulations are generally only a few hundred years long. However, modeling the long-term equilibration in response to radiative forcing perturbation is important for understanding many climate phenomena, such as the evolution of ocean circulation, time- and temperature-dependent feedbacks, and the differentiation of forced signal and internal variability. The aim of LongRunMIP is to facilitate research into these questions by serving as an archive for simulations that capture as much of this equilibration as possible. The only requirement to participate in LongRunMIP is to contribute a simulation with elevated, constant CO2 forcing that lasts at least 1,000 years. LongRunMIP is an MIP of opportunity in that the simulations were mostly performed prior to the conception of the archive without an agreed-upon set of experiments. For most models, the archive contains a preindustrial control simulation and simulations with an idealized (typically abrupt) CO2 forcing. We collect 2D surface and top-of-atmosphere fields and 3D ocean temperature and salinity fields. Here, we document the collection of simulations and discuss initial results, including the evolution of surface and deep ocean temperature and cloud radiative effects. As of October 2019, the collection includes 50 simulations of 15 models by 10 modeling centers. The data of LongRunMIP are publicly available. We encourage submissions of more simulations in the future.},
  file         = {:Rugenstein2019.pdf:PDF},
  groups       = {CMIP and other MIPs},
  keywords     = {equilibrium climate sensitivity, model intercomparison project, ocean-atmosphere model, last glacial maximum, overturning circulation, coupled model, temperature variability, evolving patterns, carbon-dioxide, co2},
}

@Article{Rugenstein2020,
  author       = {Rugenstein, M. A. A. and Bloch-Johnson, J. and Gregory, J. and Andrews, T. and Mauritsen, T. and Li, C. and Frolicher, T. L. and Paynter, D. and Danabasoglu, G. and Yang, S. T. and Dufresne, J. L. and Cao, L. and Schmidt, G. A. and Abe-Ouchi, A. and Geoffroy, O. and Knutti, R.},
  date         = {2020},
  journaltitle = {Geophysical Research Letters},
  title        = {Equilibrium Climate Sensitivity Estimated by Equilibrating Climate Models},
  doi          = {10.1029/2019GL083898},
  number       = {4},
  pages        = {e2019GL083898},
  volume       = {47},
  abstract     = {The methods to quantify equilibrium climate sensitivity are still debated. We collect millennial-length simulations of coupled climate models and show that the global mean equilibrium warming is higher than those obtained using extrapolation methods from shorter simulations. Specifically, 27 simulations with 15 climate models forced with a range of CO2 concentrations show a median 17\% larger equilibrium warming than estimated from the first 150 years of the simulations. The spatial patterns of radiative feedbacks change continuously, in most regions reducing their tendency to stabilizing the climate. In the equatorial Pacific, however, feedbacks become more stabilizing with time. The global feedback evolution is initially dominated by the tropics, with eventual substantial contributions from the mid-latitudes. Time-dependent feedbacks underscore the need of a measure of climate sensitivity that accounts for the degree of equilibration, so that models, observations, and paleo proxies can be adequately compared and aggregated to estimate future warming.},
  file         = {:Rugenstein2020.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {surface-temperature-change, energy budget constraints, radiative feedbacks, evolving patterns, spatial-pattern, cloud feedback, coupled model, dependence, transient, circulation},
}

@Article{Runge2019,
  author       = {Runge, Jakob and Nowack, Peer and Kretschmer, Marlene and Flaxman, Seth and Sejdinovic, Dino},
  date         = {2019},
  journaltitle = {Science Advances},
  title        = {Detecting and quantifying causal associations in large nonlinear time series datasets},
  doi          = {10.1126/sciadv.aau4996},
  number       = {11},
  pages        = {eaau4996},
  volume       = {5},
  abstract     = {Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.},
  elocation-id = {eaau4996},
  file         = {:Runge2019.pdf:PDF},
  groups       = {Causality},
  publisher    = {American Association for the Advancement of Science},
}

@Article{Rypdal2018,
  author       = {Rypdal, M. and Fredriksen, H. B. and Rypdal, K. and Steene, R. J.},
  date         = {2018},
  journaltitle = {Nature},
  title        = {Emergent constraints on climate sensitivity},
  doi          = {10.1038/s41586-018-0639-4},
  number       = {7729},
  pages        = {E4-E5},
  volume       = {563},
  comment      = {Answer to Cox et al. (2018)},
  file         = {:Rypdal2018.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
}

@Article{Sanderson2015,
  author       = {Sanderson, B. M. and Knutti, R. and Caldwell, P.},
  date         = {2015},
  journaltitle = {Journal of Climate},
  title        = {A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble},
  doi          = {10.1175/Jcli-D-14-00362.1},
  number       = {13},
  pages        = {5171--5194},
  volume       = {28},
  abstract     = {The collection of Earth system models available in the archive of phase 5 of CMIP (CMIP5) represents, at least to some degree, a sample of uncertainty of future climate evolution. The presence of duplicated code as well as shared forcing and validation data in the multiple models in the archive raises at least three potential problems: biases in the mean and variance, the overestimation of sample size, and the potential for spurious correlations to emerge in the archive because of model replication. Analytical evidence is presented to demonstrate that the distribution of models in the CMIP5 archive is not consistent with a random sample, and a weighting scheme is proposed to reduce some aspects of model codependency in the ensemble. A method is proposed for selecting diverse and skillful subsets of models in the archive, which could be used for impact studies in cases where physically consistent joint projections of multiple variables (and their temporal and spatial characteristics) are required.},
  file         = {:Sanderson2015.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  keywords     = {climate-change, system model, predictions, uncertainty, cmip5},
}

@Article{Sanderson2015a,
  author       = {Sanderson, Benjamin M. and Knutti, Reto and Caldwell, Peter},
  date         = {2015-07},
  journaltitle = {Journal of Climate},
  title        = {{Addressing Interdependency in a Multimodel Ensemble by Interpolation of Model Properties}},
  doi          = {10.1175/JCLI-D-14-00361.1},
  issn         = {0894-8755},
  number       = {13},
  pages        = {5150--5170},
  volume       = {28},
  abstract     = {The diverse set of Earth system models used to conduct the CMIP5 ensemble can partly sample the uncertainties in future climate projections. However, combining those projections is complicated by the fact that models developed by different groups share ideas and code and therefore biases. The authors propose a method for combining model results into single or multivariate distributions that are more robust to the inclusion of models with a large degree of interdependency. This study uses a multivariate metric of present-day climatology to assess both model performance and similarity in two recent model intercomparisons, CMIP3 and CMIP5. Model characteristics can be interpolated and then resampled in a space defined by independent climate properties. A form of weighting can be applied by sampling more densely in the region of the space close to the projected observations, thus taking into account both model performance and interdependence. The choice of the sampling distribution’s parameters is a subjective decision that should reflect the researcher’s prior assumptions as to the acceptability of different model errors.},
  file         = {:Sanderson2015a.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  keywords     = {printed},
  printed      = {printed},
}

@Article{Sanderson2017,
  author       = {Sanderson, B. M. and Wehner, M. and Knutti, R.},
  date         = {2017},
  journaltitle = {Geoscientific Model Development},
  title        = {Skill and independence weighting for multi-model assessments},
  doi          = {10.5194/gmd-10-2379-2017},
  number       = {6},
  pages        = {2379--2395},
  volume       = {10},
  abstract     = {We present a weighting strategy for use with the CMIP5 multi-model archive in the fourth National Climate Assessment, which considers both skill in the climatological performance of models over North America as well as the inter-dependency of models arising from common parameterizations or tuning practices. The method exploits information relating to the climatological mean state of a number of projection-relevant variables as well as metrics representing long-term statistics of weather extremes. The weights, once computed can be used to simply compute weighted means and significance information from an ensemble containing multiple initial condition members from potentially co-dependent models of varying skill. Two parameters in the algorithm determine the degree to which model climatological skill and model uniqueness are rewarded; these parameters are explored and final values are defended for the assessment. The influence of model weighting on projected temperature and precipitation changes is found to be moderate, partly due to a compensating effect between model skill and uniqueness. However, more aggressive skill weighting and weighting by targeted metrics is found to have a more significant effect on inferred ensemble confidence in future patterns of change for a given projection.},
  file         = {:Sanderson2017.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  keywords     = {cmip5, ensemble, skimmed, printed},
  printed      = {printed},
  readstatus   = {skimmed},
}

@Article{Schlund2020,
  author       = {Schlund, Manuel and Eyring, Veronika and Camps-Valls, Gustau and Friedlingstein, Pierre and Gentine, Pierre and Reichstein, Markus},
  date         = {2020},
  journaltitle = {Journal of Geophysical Research: Biogeosciences},
  title        = {Constraining Uncertainty in Projected Gross Primary Production With Machine Learning},
  doi          = {10.1029/2019jg005619},
  number       = {11},
  pages        = {e2019JG005619},
  volume       = {125},
  abstract     = {The terrestrial biosphere is currently slowing down global warming by absorbing about 30\% of human emissions of carbon dioxide (CO2). The largest flux of the terrestrial carbon uptake is gross primary production (GPP) defined as the production of carbohydrates by photosynthesis. Elevated atmospheric CO2 concentration is expected to increase GPP ("CO2 fertilization effect"). However, Earth system models (ESMs) exhibit a large range in simulated GPP projections. In this study, we combine an existing emergent constraint on CO2 fertilization with a machine learning approach to constrain the spatial variations of multi-model GPP projections. In a first step, we use observed changes in the CO2 seasonal cycle at Cape Kumukahi to constrain the global mean GPP at the end of the 21st century (2091–2100) in Representative Concentration Pathway 8.5 simulations with ESMs participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to 171 ± 12 GtC yr-1, compared to the unconstrained model range of 156–247 GtC yr-1. In a second step, we use a machine learning model to constrain gridded future absolute GPP and gridded fractional GPP change in two independent approaches. For this, observational data is fed into the machine learning algorithm that has been trained on CMIP5 data to learn relationships between present-day physically relevant diagnostics and the target variable. In a leave-one-model-out cross-validation approach, the machine learning model shows superior performance to the CMIP5 ensemble mean. Our approach predicts an increased GPP change in northern high latitudes compared to regions closer to the equator.},
  file         = {:Schlund2020.pdf:PDF},
  groups       = {Carbon Cycle, Climate Model Weighting / Reducing uncertainties, ML in Climate Sciences},
  keywords     = {read, own},
  readstatus   = {read},
}

@Article{Schlund2020a,
  author       = {Manuel Schlund and Axel Lauer and Pierre Gentine and Steven C. Sherwood and Veronika Eyring},
  date         = {2020-12},
  journaltitle = {Earth System Dynamics},
  title        = {Emergent constraints on equilibrium climate sensitivity in {CMIP}5: do they hold for {CMIP}6?},
  doi          = {10.5194/esd-11-1233-2020},
  number       = {4},
  pages        = {1233--1258},
  volume       = {11},
  file         = {:Schlund2020a.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {read, own},
  publisher    = {Copernicus {GmbH}},
  readstatus   = {read},
}

@Article{Schmidt2006,
  author       = {Schmidt, G. A. and Ruedy, R. and Hansen, J. E. and Aleinov, I. and Bell, N. and Bauer, M. and Bauer, S. and Cairns, B. and Canuto, V. and Cheng, Y. and Del Genio, A. and Faluvegi, G. and Friend, A. D. and Hall, T. M. and Hu, Y. Y. and Kelley, M. and Kiang, N. Y. and Koch, D. and Lacis, A. A. and Lerner, J. and Lo, K. K. and Miller, R. L. and Nazarenko, L. and Oinas, V. and Perlwitz, J. and Perlwitz, J. and Rind, D. and Romanou, A. and Russell, G. L. and Sato, M. and Shindell, D. T. and Stone, P. H. and Sun, S. and Tausnev, N. and Thresher, D. and Yao, M. S.},
  date         = {2006},
  journaltitle = {Journal of Climate},
  title        = {Present-day atmospheric simulations using GISS ModelE: Comparison to in situ, satellite, and reanalysis data},
  doi          = {10.1175/Jcli3612.1},
  number       = {2},
  pages        = {153--192},
  volume       = {19},
  abstract     = {A full description of the ModelE version of the Goddard Institute for Space Studies (GISS) atmospheric general circulation model (GCM) and results are presented for present-day climate simulations (ca. 1979). This version is a complete rewrite of previous models incorporating numerous improvements in basic physics, the stratospheric circulation, and forcing fields. Notable changes include the following: the model top is now above the stratopause, the number of vertical layers has increased, a new cloud microphysical scheme is used, vegetation biophysics now incorporates a sensitivity to humidity, atmospheric turbulence is calculated over the whole column, and new land snow and lake schemes are introduced. The performance of the model using three configurations with different horizontal and vertical resolutions is compared to quality-controlled in situ data, remotely sensed and reanalysis products. Overall, significant improvements over previous models are seen, particularly in upper-atmosphere temperatures and winds, cloud heights, precipitation, and sea level pressure. Data-model comparisons continue, however, to highlight persistent problems in the marine stratocumulus regions.},
  file         = {:Schmidt2006.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {general-circulation model, surface air-temperature, global climate model, goddard-institute, sea-ice, cloud parameterization, longwave scattering, vertical transport, radiative forcings, refractive-index},
}

@Article{Seferian2019,
  author       = {Séférian, Roland and Nabat, Pierre and Michou, Martine and Saint-Martin, David and Voldoire, Aurore and Colin, Jeanne and Decharme, Bertrand and Delire, Christine and Berthet, Sarah and Chevallier, Matthieu and Sénési, Stephane and Franchisteguy, Laurent and Vial, Jessica and Mallet, Marc and Joetzjer, Emilie and Geoffroy, Olivier and Guérémy, Jean-François and Moine, Marie-Pierre and Msadek, Rym and Ribes, Aurélien and Rocher, Matthias and Roehrig, Romain and Salas-y-Mélia, David and Sanchez, Emilia and Terray, Laurent and Valcke, Sophie and Waldman, Robin and Aumont, Olivier and Bopp, Laurent and Deshayes, Julie and Éthé, Christian and Madec, Gurvan},
  date         = {2019},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System Processes in Present-Day and Future Climate},
  doi          = {10.1029/2019MS001791},
  number       = {12},
  pages        = {4182--4227},
  volume       = {11},
  abstract     = {This study introduces CNRM-ESM2-1, the Earth system (ES) model of second generation developed by CNRM-CERFACS for the sixth phase of the Coupled Model Intercomparison Project (CMIP6). CNRM-ESM2-1 offers a higher model complexity than the Atmosphere-Ocean General Circulation Model CNRM-CM6-1 by adding interactive ES components such as carbon cycle, aerosols, and atmospheric chemistry. As both models share the same code, physical parameterizations, and grid resolution, they offer a fully traceable framework to investigate how far the represented ES processes impact the model performance over present-day, response to external forcing and future climate projections. Using a large variety of CMIP6 experiments, we show that represented ES processes impact more prominently the model response to external forcing than the model performance over present-day. Both models display comparable performance at replicating modern observations although the mean climate of CNRM-ESM2-1 is slightly warmer than that of CNRM-CM6-1. This difference arises from land cover-aerosol interactions where the use of different soil vegetation distributions between both models impacts the rate of dust emissions. This interaction results in a smaller aerosol burden in CNRM-ESM2-1 than in CNRM-CM6-1, leading to a different surface radiative budget and climate. Greater differences are found when comparing the model response to external forcing and future climate projections. Represented ES processes damp future warming by up to 10\% in CNRM-ESM2-1 with respect to CNRM-CM6-1. The representation of land vegetation and the CO2-water-stomatal feedback between both models explain about 60\% of this difference. The remainder is driven by other ES feedbacks such as the natural aerosol feedback.},
  file         = {:Seferian2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {climate modeling, Earth system modeling, biogeochemical cycles, aerosols, CMIP6, future projections},
}

@Article{Seland2020,
  author       = {Øyvind Seland and Mats Bentsen and Dirk Olivié and Thomas Toniazzo and Ada Gjermundsen and Lise Seland Graff and Jens Boldingh Debernard and Alok Kumar Gupta and Yan-Chun He and Alf Kirkevåg and Jörg Schwinger and Jerry Tjiputra and Kjetil Schanke Aas and Ingo Bethke and Yuanchao Fan and Jan Griesfeller and Alf Grini and Chuncheng Guo and Mehmet Ilicak and Inger Helene Hafsahl Karset and Oskar Landgren and Johan Liakka and Kine Onsum Moseid and Aleksi Nummelin and Clemens Spensberger and Hui Tang and Zhongshi Zhang and Christoph Heinze and Trond Iversen and Michael Schulz},
  date         = {2020-12},
  journaltitle = {Geoscientific Model Development},
  title        = {Overview of the Norwegian Earth System Model ({NorESM}2) and key climate response of {CMIP}6 {DECK}, historical, and scenario simulations},
  doi          = {10.5194/gmd-13-6165-2020},
  number       = {12},
  pages        = {6165--6200},
  volume       = {13},
  file         = {:Seland2020.pdf:PDF},
  groups       = {CMIP6 models},
  publisher    = {Copernicus {GmbH}},
}

@Article{Sellar2019,
  author       = {Sellar, A. A. and Jones, C. G. and Mulcahy, J. P. and Tang, Y. M. and Yool, A. and Wiltshire, A. and O'Connor, F. M. and Stringer, M. and Hill, R. and Palmieri, J. and Woodward, S. and de Mora, L. and Kuhlbrodt, T. and Rumbold, S. T. and Kelley, D. I. and Ellis, R. and Johnson, C. E. and Walton, J. and Abraham, N. L. and Andrews, M. B. and Andrews, T. and Archibald, A. T. and Berthou, S. and Burke, E. and Blockley, E. and Carslaw, K. and Dalvi, M. and Edwards, J. and Folberth, G. A. and Gedney, N. and Griffiths, P. T. and Harper, A. B. and Hendry, M. A. and Hewitt, A. J. and Johnson, B. and Jones, A. and Jones, C. D. and Keeble, J. and Liddicoat, S. and Morgenstern, O. and Parker, R. J. and Predoi, V. and Robertson, E. and Siahaan, A. and Smith, R. S. and Swaminathan, R. and Woodhouse, M. T. and Zeng, G. and Zerroukat, M.},
  date         = {2019},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {UKESM1: Description and Evaluation of the UK Earth System Model},
  doi          = {10.1029/2019ms001739},
  number       = {12},
  pages        = {4513--4558},
  volume       = {11},
  abstract     = {We document the development of the first version of the U.K. Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2-ES, with enhancements to all component models and new feedback mechanisms. These include a new core physical model with a well-resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric-stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane, and nitrous oxide; two-moment, five-species, modal aerosol; and ocean biogeochemistry with two-way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land, and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall, the model performs well, with a stable pre-industrial state and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger-than-observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealized simulations show a high climate sensitivity relative to previous generations of models: Equilibrium climate sensitivity is 5.4 K, transient climate response ranges from 2.68 to 2.85 K, and transient climate response to cumulative emissions is 2.49 to 2.66 K TtC(-1).},
  file         = {:Sellar2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {environment simulator jules, stratospheric water-vapor, climate-composition model, intercomparison project, carbon-cycle, isoprene emissions, sea-ice, global database, air-quality, part 2},
}

@Article{Senftleben2020,
  author       = {Daniel Senftleben and Axel Lauer and Alexey Karpechko},
  date         = {2020-01},
  journaltitle = {Journal of Climate},
  title        = {Constraining Uncertainties in {CMIP}5 Projections of September Arctic Sea Ice Extent with Observations},
  doi          = {10.1175/jcli-d-19-0075.1},
  number       = {4},
  pages        = {1487--1503},
  volume       = {33},
  file         = {:Senftleben2020.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  keywords     = {skimmed},
  publisher    = {American Meteorological Society},
  readstatus   = {skimmed},
}

@Article{Sherwood2014,
  author       = {Sherwood, S. C. and Bony, S. and Dufresne, J. L.},
  date         = {2014},
  journaltitle = {Nature},
  title        = {Spread in model climate sensitivity traced to atmospheric convective mixing},
  doi          = {10.1038/nature12829},
  number       = {7481},
  pages        = {37--42},
  volume       = {505},
  abstract     = {Equilibrium climate sensitivity refers to the ultimate change in global mean temperature in response to a change in external forcing. Despite decades of research attempting to narrow uncertainties, equilibrium climate sensitivity estimates from climate models still span roughly 1.5 to 5 degrees Celsius for a doubling of atmospheric carbon dioxide concentration, precluding accurate projections of future climate. The spread arises largely from differences in the feedback from low clouds, for reasons not yet understood. Here we show that differences in the simulated strength of convective mixing between the lower and middle tropical troposphere explain about half of the variance in climate sensitivity estimated by 43 climate models. The apparent mechanism is that such mixing dehydrates the low-cloud layer at a rate that increases as the climate warms, and this rate of increase depends on the initial mixing strength, linking the mixing to cloud feedback. The mixing inferred from observations appears to be sufficiently strong to imply a climate sensitivity of more than 3 degrees for a doubling of carbon dioxide. This is significantly higher than the currently accepted lower bound of 1.5 degrees, thereby constraining model projections towards relatively severe future warming.},
  file         = {:Sherwood2014.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {general-circulation, hydrological cycle, relative-humidity, feedbacks, future, clouds, simulations, cmip5, skimmed},
  readstatus   = {skimmed},
}

@Article{Sherwood2020,
  author       = {S. C. Sherwood and M. J. Webb and J. D. Annan and K. C. Armour and P. M. Forster and J. C. Hargreaves and G. Hegerl and S. A. Klein and K. D. Marvel and E. J. Rohling and M. Watanabe and T. Andrews and P. Braconnot and C. S. Bretherton and G. L. Foster and Z. Hausfather and A. S. Heydt and R. Knutti and T. Mauritsen and J. R. Norris and C. Proistosescu and M. A. A. Rugenstein and G. A. Schmidt and K. B. Tokarska and M. D. Zelinka},
  date         = {2020-09},
  journaltitle = {Reviews of Geophysics},
  title        = {An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence},
  doi          = {10.1029/2019rg000678},
  number       = {4},
  pages        = {e2019RG000678},
  volume       = {58},
  file         = {:Sherwood2020.pdf:PDF},
  groups       = {Reviews, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {skimmed},
  publisher    = {American Geophysical Union ({AGU})},
  readstatus   = {skimmed},
}

@Article{Sidorenko2015,
  author       = {Sidorenko, D. and Rackow, T. and Jung, T. and Semmler, T. and Barbi, D. and Danilov, S. and Dethloff, K. and Dorn, W. and Fieg, K. and Goessling, H. and Handorf, D. and Harig, S. and Hiller, W. and Juricke, S. and Losch, M. and Schroter, J. and Sein, D. V. and Wang, Q.},
  date         = {2015},
  journaltitle = {Climate Dynamics},
  title        = {Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part I: model formulation and mean climate},
  doi          = {10.1007/s00382-014-2290-6},
  number       = {3-4},
  pages        = {757--780},
  volume       = {44},
  abstract     = {A new climate model has been developed that employs a multi-resolution dynamical core for the sea ice-ocean component. In principle, the multi-resolution approach allows one to use enhanced horizontal resolution in dynamically active regions while keeping a coarse-resolution setup otherwise. The coupled model consists of the atmospheric model ECHAM6 and the finite element sea ice-ocean model (FESOM). In this study only moderate refinement of the unstructured ocean grid is applied and the resolution varies from about 25 km in the northern North Atlantic and in the tropics to about 150 km in parts of the open ocean; the results serve as a benchmark upon which future versions that exploit the potential of variable resolution can be built. Details of the formulation of the model are given and its performance in simulating observed aspects of the mean climate is described. Overall, it is found that ECHAM6-FESOM realistically simulates many aspects of the observed climate. More specifically it is found that ECHAM6-FESOM performs at least as well as some of the most sophisticated climate models participating in the fifth phase of the Coupled Model Intercomparison Project. ECHAM6-FESOM shares substantial shortcomings with other climate models when it comes to simulating the North Atlantic circulation.},
  file         = {:Sidorenko2015.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {multi-resolution model, climate model, unstructured mesh, finite elements, climate model bias, north-atlantic, sea-ice, ocean circulation, interdecadal variability, ecmwf model, water, simulations, prediction, atmosphere, impact},
}

@Article{Simpkins2017,
  author       = {Graham Simpkins},
  date         = {2017-09},
  journaltitle = {Nature Climate Change},
  title        = {Progress in climate modelling},
  doi          = {10.1038/nclimate3398},
  number       = {10},
  pages        = {684--685},
  volume       = {7},
  file         = {:Simpkins2017.pdf:PDF},
  groups       = {ESM basics, CMIP and other MIPs},
  publisher    = {Springer Science and Business Media {LLC}},
}

@Article{Smith2003,
  author       = {Smith, T. M. and Reynolds, R. W.},
  date         = {2003},
  journaltitle = {Journal of Climate},
  title        = {Extended reconstruction of global sea surface temperatures based on COADS data (1854-1997)},
  doi          = {10.1175/1520-0442-16.10.1495},
  number       = {10},
  pages        = {1495--1510},
  volume       = {16},
  abstract     = {A monthly extended reconstruction of global SST (ERSST) is produced based on Comprehensive Ocean Atmosphere Data Set (COADS) release 2 observations from the 1854 - 1997 period. Improvements come from the use of updated COADS observations with new quality control procedures and from improved reconstruction methods. In addition error estimates are computed, which include uncertainty from both sampling and analysis errors. Using this method, little global variance can be reconstructed before the 1880s because data are too sparse to resolve enough modes for that period. Error estimates indicate that except in the North Atlantic ERSST is of limited value before 1880, when the uncertainty of the near-global average is almost as large as the signal. In most regions, the uncertainty decreases through most of the period and is smallest after 1950.
The large-scale variations of ERSST are broadly consistent with those associated with the Hadley Centre Global Sea Ice and Sea Surface Temperature ( HadISST) reconstruction produced by the Met Office. There are differences due to both the use of different historical bias corrections as well as different data and analysis procedures, but these differences do not change the overall character of the SST variations. Procedures used here produce a smoother analysis compared to HadISST. The smoother ERSST has the advantage of filtering out more noise at the possible cost of filtering out some real variations when sampling is sparse. A rotated EOF analysis of the ERSST anomalies shows that the dominant modes of variation include ENSO and modes associated with trends. Projection of the HadISST data onto the rotated eigenvectors produces time series similar to those for ERSST, indicating that the dominant modes of variation are consistent in both.},
  file         = {:Smith2003.pdf:PDF},
  groups       = {OBS},
  keywords     = {empirical orthogonal functions, ocean, variability, climate, model, simulations},
}

@Article{Soden2006,
  author       = {Brian J. Soden and Isaac M. Held},
  date         = {2006-07},
  journaltitle = {Journal of Climate},
  title        = {An Assessment of Climate Feedbacks in Coupled Ocean-Atmosphere Models},
  doi          = {10.1175/jcli3799.1},
  number       = {14},
  pages        = {3354--3360},
  volume       = {19},
  file         = {:Soden2006.pdf:PDF},
  groups       = {Feedbacks},
  publisher    = {American Meteorological Society},
}

@Article{Soden2008,
  author       = {Brian J. Soden and Isaac M. Held and Robert Colman and Karen M. Shell and Jeffrey T. Kiehl and Christine A. Shields},
  date         = {2008-07},
  journaltitle = {Journal of Climate},
  title        = {Quantifying Climate Feedbacks Using Radiative Kernels},
  doi          = {10.1175/2007jcli2110.1},
  number       = {14},
  pages        = {3504--3520},
  volume       = {21},
  file         = {:Soden2008.pdf:PDF},
  groups       = {Feedbacks},
  publisher    = {American Meteorological Society},
}

@InBook{Solomon2007,
  author       = {S. Solomon and D. Qin and M. Manning and R. B. Alley and T. Berntsen and N. L. Bindoff and Z. Chen and A. Chidthaisong and J. M. Gregory and G. C. Hegerl and M. Heimann and B. Hewitson and B. J. Hoskins and F. Joos and J. Jouzel and V. Kattsov and U. Lohmann and T. Matsuno and M. Molina and N. Nicholls and J. Overpeck and G. Raga and V. Ramaswamy and J. Ren and M. Rusticucci and R. Somerville and T. F. Stocker and P. Whetton and R. A. Wood and D. Wratt},
  date         = {2007},
  title        = {Technical Summary},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {19--91},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2018/02/ar4-wg1-ts-1.pdf},
  file         = {:Solomon2007.pdf:PDF},
  groups       = {IPCC},
  journaltitle = {Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@InBook{Stocker2013,
  author       = {T. F. Stocker and D. Qin and G.-K. Plattner and L. V. Alexander and S. K. Allen and N. L. Bindoff and F.-M. Bréon and J. A. Church and U. Cubasch and S. Emori and P. Forster and P. Friedlingstein and N. Gillett and J. M. Gregory and D. L. Hartmann and E. Jansen and B. Kirtman and R. Knutti and K. Krishna Kumar and P. Lemke and J. Marotzke and V. Masson-Delmotte and G. A. Meehl and I. I. Mokhov and S. Piao and V. Ramaswamy and D. Randall and M. Rhein and M. Rojas and C. Sabine and D. Shindell and L. D. Talley and D. G. Vaughan and S.-P. Xie},
  date         = {2013},
  title        = {Technical Summary},
  location     = {Cambridge, UK and New York, NY, USA},
  pages        = {33--115},
  publisher    = {Cambridge University Press},
  url          = {https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_TS_FINAL.pdf},
  file         = {:Stocker2013.pdf:PDF},
  groups       = {IPCC},
  journaltitle = {Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change},
}

@Article{Su2014,
  author       = {Su, H. and Jiang, J. H. and Zhai, C. X. and Shen, T. J. and Neelin, J. D. and Stephens, G. L. and Yung, Y. L.},
  date         = {2014},
  journaltitle = {Journal of Geophysical Research-Atmospheres},
  title        = {Weakening and strengthening structures in the Hadley Circulation change under global warming and implications for cloud response and climate sensitivity},
  doi          = {10.1002/2014jd021642},
  number       = {10},
  pages        = {5787--5805},
  volume       = {119},
  abstract     = {It has long been recognized that differences in climate model-simulated cloud feedbacks are a primary source of uncertainties for the model-predicted surface temperature change induced by increasing greenhouse gases such as CO2. Large-scale circulation broadly determines when and where clouds form and how they evolve. However, the linkage between large-scale circulation change and cloud radiative effect (CRE) change under global warming has not been thoroughly studied. By analyzing 15 climate models, we show that the change of the Hadley Circulation exhibits meridionally varying weakening and strengthening structures, physically consistent with the cloud changes in distinct cloud regimes. The regions that experience a weakening (strengthening) of the zonal-mean circulation account for 54\% (46\%) of the multimodel-mean top-of-atmosphere (TOA) CRE change integrated over 45 degrees S-40 degrees N. The simulated Hadley Circulation structure changes per degree of surface warming differ greatly between the models, and the intermodel spread in the Hadley Circulation change is well correlated with the intermodel spread in the TOA CRE change. This correlation underscores the close interactions between large-scale circulation and clouds and suggests that the uncertainties of cloud feedbacks and climate sensitivity reside in the intimate coupling between large-scale circulation and clouds. New model performance metrics proposed in this work, which emphasize how models reproduce satellite-observed spatial variations of zonal-mean cloud fraction and relative humidity associated with the Hadley Circulation, indicate that the models closer to the satellite observations tend to have equilibrium climate sensitivity higher than the multimodel mean.},
  file         = {:Su2014.pdf:PDF},
  groups       = {Emergent Constraints, Feedbacks, ECS / TCR / Climate Sensitivity / Warming, Clouds / Aerosols},
  keywords     = {interannual variability, feedbacks, future, cmip5, precipitation, mechanisms, pacific, monsoon, walker, spread, read},
  readstatus   = {read},
}

@Article{Swart2019,
  author       = {Swart, N. C. and Cole, J. N. S. and Kharin, V. V. and Lazare, M. and Scinocca, J. F. and Gillett, N. P. and Anstey, J. and Arora, V. and Christian, J. R. and Hanna, S. and Jiao, Y. J. and Lee, W. G. and Majaess, F. and Saenko, O. A. and Seiler, C. and Seinen, C. and Shao, A. and Sigmond, M. and Solheim, L. and von Salzen, K. and Yang, D. and Winter, B.},
  date         = {2019},
  journaltitle = {Geoscientific Model Development},
  title        = {The Canadian Earth System Model version 5 (CanESM5.0.3)},
  doi          = {10.5194/gmd-12-4823-2019},
  number       = {11},
  pages        = {4823--4873},
  volume       = {12},
  abstract     = {The Canadian Earth System Model version 5 (CanESM5) is a global model developed to simulate historical climate change and variability, to make centennial-scale projections of future climate, and to produce initialized seasonal and decadal predictions. This paper describes the model components and their coupling, as well as various aspects of model development, including tuning, optimization, and a reproducibility strategy. We also document the stability of the model using a long control simulation, quantify the model's ability to reproduce large-scale features of the historical climate, and evaluate the response of the model to external forcing. CanESM5 is comprised of three-dimensional atmosphere (T63 spectral resolution equivalent roughly to 2.8 degrees) and ocean (nominally 1 degrees) general circulation models, a sea-ice model, a land surface scheme, and explicit land and ocean carbon cycle models. The model features relatively coarse resolution and high throughput, which facilitates the production of large ensembles. CanESM5 has a notably higher equilibrium climate sensitivity (5.6 K) than its predecessor, CanESM2 (3.7 K), which we briefly discuss, along with simulated changes over the historical period. CanESM5 simulations contribute to the Coupled Model Intercomparison Project phase 6 (CMIP6) and will be employed for climate science and service applications in Canada.},
  file         = {:Swart2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {land-surface scheme, general-circulation model, climate-change simulation, sea-ice model, experimental-design, greenhouse-gas, coupled model, ocean model, data record, long-term},
}

@Article{Tan2016,
  author       = {I. Tan and T. Storelvmo and M. D. Zelinka},
  date         = {2016-04},
  journaltitle = {Science},
  title        = {Observational constraints on mixed-phase clouds imply higher climate sensitivity},
  doi          = {10.1126/science.aad5300},
  number       = {6282},
  pages        = {224--227},
  volume       = {352},
  file         = {:Tan2016.pdf:PDF},
  groups       = {Clouds / Aerosols, ECS / TCR / Climate Sensitivity / Warming},
  publisher    = {American Association for the Advancement of Science ({AAAS})},
}

@Article{Tatebe2019,
  author       = {Tatebe, H. and Ogura, T. and Nitta, T. and Komuro, Y. and Ogochi, K. and Takemura, T. and Sudo, K. and Sekiguchi, M. and Abe, M. and Saito, F. and Chikira, M. and Watanabe, S. and Mori, M. and Hirota, N. and Kawatani, Y. and Mochizuki, T. and Yoshimura, K. and Takata, K. and O'ishi, R. and Yamazaki, D. and Suzuki, T. and Kurogi, M. and Kataoka, T. and Watanabe, M. and Kimoto, M.},
  date         = {2019},
  journaltitle = {Geoscientific Model Development},
  title        = {Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6},
  doi          = {10.5194/gmd-12-2727-2019},
  number       = {7},
  pages        = {2727--2765},
  volume       = {12},
  abstract     = {The sixth version of the Model for Interdisciplinary Research on Climate (MIROC), called MIROC6, was cooperatively developed by a Japanese modeling community. In the present paper, simulated mean climate, internal climate variability, and climate sensitivity in MIROC6 are evaluated and briefly summarized in comparison with the previous version of our climate model (MIROC5) and observations. The results show that the overall reproducibility of mean climate and internal climate variability in MIROC6 is better than that in MIROC5. The tropical climate systems (e.g., summertime precipitation in the western Pacific and the eastward-propagating Madden-Julian oscillation) and the midlatitude atmospheric circulation (e.g., the westerlies, the polar night jet, and troposphere-stratosphere interactions) are significantly improved in MIROC6. These improvements can be attributed to the newly implemented parameterization for shallow convective processes and to the inclusion of the stratosphere. While there are significant differences in climates and variabilities between the two models, the effective climate sensitivity of 2.6K remains the same because the differences in radiative forcing and climate feedback tend to offset each other. With an aim towards contributing to the sixth phase of the Coupled Model Intercomparison Project, designated simulations tackling a wide range of climate science issues, as well as seasonal to decadal climate predictions and future climate projections, are currently ongoing using MIROC6.},
  file         = {:Tatebe2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {ice-thickness distribution, sea-surface temperature, of-atmosphere radiation, subgrid snow cover, gravity-wave drag, general-circulation, data assimilation, tropical pacific, high-resolution, summer monsoon},
}

@Article{Taylor2012,
  author       = {Taylor, K. E. and Stouffer, R. J. and Meehl, G. A.},
  date         = {2012},
  journaltitle = {Bulletin of the American Meteorological Society},
  title        = {An Overview of Cmip5 and the Experiment Design},
  doi          = {10.1175/Bams-D-11-00094.1},
  number       = {4},
  pages        = {485--498},
  volume       = {93},
  file         = {:Taylor2012.pdf:PDF},
  groups       = {CMIP and other MIPs},
  keywords     = {model intercomparison project, climate-change research, read},
  readstatus   = {read},
}

@Article{Tebaldi2007,
  author       = {Claudia Tebaldi and Reto Knutti},
  date         = {2007-06},
  journaltitle = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  title        = {The use of the multi-model ensemble in probabilistic climate projections},
  doi          = {10.1098/rsta.2007.2076},
  number       = {1857},
  pages        = {2053--2075},
  volume       = {365},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  publisher    = {The Royal Society},
}

@Article{Thomas2016,
  author       = {Thomas, R. T. and Prentice, L. C. and Graven, H. and Ciais, P. and Fisher, J. B. and Hayes, D. J. and Huang, M. Y. and Huntzinger, D. N. and Ito, A. and Jain, A. and Mao, J. F. and Michalak, A. M. and Peng, S. S. and Poulter, B. and Ricciuto, D. M. and Shi, X. Y. and Schwalm, C. and Tian, H. Q. and Zeng, N.},
  date         = {2016},
  journaltitle = {Geophysical Research Letters},
  title        = {Increased light-use efficiency in northern terrestrial ecosystems indicated by {CO}$_2$ and greening observations},
  doi          = {10.1002/2016gl070710},
  number       = {21},
  pages        = {11339--11349},
  volume       = {43},
  abstract     = {Observations show an increasing amplitude in the seasonal cycle of CO2 (ASC) north of 45 degrees N of 56 +/- 9.8\% over the last 50 years and an increase in vegetation greenness of 7.5-15\% in high northern latitudes since the 1980s. However, the causes of these changes remain uncertain. Historical simulations from terrestrial biosphere models in the Multiscale Synthesis and Terrestrial Model Intercomparison Project are compared to the ASC and greenness observations, using the TM3 atmospheric transport model to translate surface fluxes into CO2 concentrations. We find that the modeled change in ASC is too small but the mean greening trend is generally captured. Modeled increases in greenness are primarily driven by warming, whereas ASC changes are primarily driven by increasing CO2. We suggest that increases in ecosystem-scale light use efficiency (LUE) have contributed to the observed ASC increase but are underestimated by current models. We highlight potential mechanisms that could increase modeled LUE.},
  file         = {:Thomas2016.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {model intercomparison project, program multiscale synthesis, atmospheric carbon-dioxide, seasonal amplitude, land-use, vegetation, trends, climate, cycle, exchange},
}

@Article{Tian2015,
  author       = {Tian, B. J.},
  date         = {2015},
  journaltitle = {Geophysical Research Letters},
  title        = {Spread of model climate sensitivity linked to double-Intertropical Convergence Zone bias},
  doi          = {10.1002/2015gl064119},
  number       = {10},
  pages        = {4133--4141},
  volume       = {42},
  abstract     = {Despite decades of climate research and model development, two outstanding problems still plague the latest global climate models (GCMs): the double-Intertropical Convergence Zone (ITCZ) bias and the 2-5 degrees C spread of equilibrium climate sensitivity (ECS). Here we show that the double-ITCZ bias and ECS in 44 GCMs from Coupled Model Intercomparison Project Phases 3/5 are negatively correlated. The models with weak (strong) double-ITCZ biases have high (low)-ECS values of similar to 4.1(2.2)degrees C. This indicates that the double-ITCZ bias is a new emergent constraint for ECS based on which ECS might be in the higher end of its range (similar to 4.0 degrees C) and most models might have underestimated ECS. In addition, we argue that the double-ITCZ bias can physically affect both cloud and water vapor feedbacks (thus ECS) and is a more easily measured emergent constraint for ECS than previous ones. It can be used as a performance metric for evaluating and comparing different GCMs.},
  file         = {:Tian2015.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {global climate models (gcms), double-itcz bias, equilibrium climate sensitivity (ecs), coupled model intercomparison project (cmip5), emergent constraint, double-itcz problem, future, cmip5, temperature, pacific, skimmed},
  readstatus   = {skimmed},
}

@Article{Tibshirani1996,
  author       = {Tibshirani, R.},
  date         = {1996},
  journaltitle = {Journal of the Royal Statistical Society Series B-Methodological},
  title        = {Regression shrinkage and selection via the Lasso},
  doi          = {10.1111/j.2517-6161.1996.tb02080.x},
  number       = {1},
  pages        = {267--288},
  volume       = {58},
  abstract     = {We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.},
  file         = {:Tibshirani1996.pdf:PDF},
  groups       = {ML basics},
  keywords     = {quadratic programming, regression, shrinkage, subset selection},
}

@Article{Tokarska2020,
  author       = {Tokarska, K. B. and Stolpe, M. B. and Sippel, S. and Fischer, E. M. and Smith, C. J. and Lehner, F. and Knutti, R.},
  date         = {2020},
  journaltitle = {Science Advances},
  title        = {Past warming trend constrains future warming in {CMIP}6 models},
  doi          = {10.1126/sciadv.aaz9549},
  number       = {12},
  pages        = {eaaz9549},
  volume       = {6},
  abstract     = {Future global warming estimates have been similar across past assessments, but several climate models of the latest Sixth Coupled Model Intercomparison Project (CMIP6) simulate much stronger warming, apparently inconsistent with past assessments. Here, we show that projected future warming is correlated with the simulated warming trend during recent decades across CMIP5 and CMIP6 models, enabling us to constrain future warming based on consistency with the observed warming. These findings carry important policy- relevant implications: The observationally constrained CMIP6 median warming in high emissions and ambitious mitigation scenarios is over 16 and 14\% lower by 2050 compared to the raw CMIP6 median, respectively, and over 14 and 8\% lower by 2090, relative to 1995-2014. Observationally constrained CMIP6 warming is consistent with previous assessments based on CMIP5 models, and in an ambitious mitigation scenario, the likely range is consistent with reaching the Paris Agreement target.},
  file         = {:Tokarska2020.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming},
  keywords     = {sea-surface temperature, internal variability, emergent constraints, climate sensitivity, uncertainties, projections, generation, read, printed},
  printed      = {printed},
  readstatus   = {read},
}

@Article{UNFCCC2015,
  author = {UNFCCC},
  date   = {2015},
  title  = {Adoption of the Paris Agreement},
  pages  = {1--32},
  url    = {http://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf},
  file   = {:UNFCCC2015.pdf:PDF},
  groups = {Policy},
}

@Article{Vial2013,
  author       = {Vial, J. and Dufresne, J. L. and Bony, S.},
  date         = {2013},
  journaltitle = {Climate Dynamics},
  title        = {On the interpretation of inter-model spread in {CMIP}5 climate sensitivity estimates},
  doi          = {10.1007/s00382-013-1725-9},
  number       = {11-12},
  pages        = {3339--3362},
  volume       = {41},
  abstract     = {This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO2) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO2 radiative forcing. By considering tropospheric adjustments to CO2 as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO2 is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 \% weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 \% of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 x CO2), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.},
  file         = {:Vial2013.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming, Feedbacks},
  keywords     = {climate sensitivity, feedback, radiative forcing, fast adjustment, radiative kernel, cmip5 climate model simulations, climate change, inter-model spread, radiative kernel technique, tropospheric adjustment, feedback processes, cloud feedback, range, state},
}

@Article{Voldoire2013,
  author       = {Voldoire, A. and Sanchez-Gomez, E. and Melia, D. S. Y. and Decharme, B. and Cassou, C. and Senesi, S. and Valcke, S. and Beau, I. and Alias, A. and Chevallier, M. and Deque, M. and Deshayes, J. and Douville, H. and Fernandez, E. and Madec, G. and Maisonnave, E. and Moine, M. P. and Planton, S. and Saint-Martin, D. and Szopa, S. and Tyteca, S. and Alkama, R. and Belamari, S. and Braun, A. and Coquart, L. and Chauvin, F.},
  date         = {2013},
  journaltitle = {Climate Dynamics},
  title        = {The CNRM-CM5.1 global climate model: description and basic evaluation},
  doi          = {10.1007/s00382-011-1259-y},
  number       = {9-10},
  pages        = {2091--2121},
  volume       = {40},
  abstract     = {A new version of the general circulation model CNRM-CM has been developed jointly by CNRM-GAME (Centre National de Recherches M,t,orologiques-Groupe d',tudes de l'AtmosphSre M,t,orologique) and Cerfacs (Centre Europ,en de Recherche et de Formation Avanc,e) in order to contribute to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The purpose of the study is to describe its main features and to provide a preliminary assessment of its mean climatology. CNRM-CM5.1 includes the atmospheric model ARPEGE-Climat (v5.2), the ocean model NEMO (v3.2), the land surface scheme ISBA and the sea ice model GELATO (v5) coupled through the OASIS (v3) system. The main improvements since CMIP3 are the following. Horizontal resolution has been increased both in the atmosphere (from 2.8A degrees to 1.4A degrees) and in the ocean (from 2A degrees to 1A degrees). The dynamical core of the atmospheric component has been revised. A new radiation scheme has been introduced and the treatments of tropospheric and stratospheric aerosols have been improved. Particular care has been devoted to ensure mass/water conservation in the atmospheric component. The land surface scheme ISBA has been externalised from the atmospheric model through the SURFEX platform and includes new developments such as a parameterization of sub-grid hydrology, a new freezing scheme and a new bulk parameterisation for ocean surface fluxes. The ocean model is based on the state-of-the-art version of NEMO, which has greatly progressed since the OPA8.0 version used in the CMIP3 version of CNRM-CM. Finally, the coupling between the different components through OASIS has also received a particular attention to avoid energy loss and spurious drifts. These developments generally lead to a more realistic representation of the mean recent climate and to a reduction of drifts in a preindustrial integration. The large-scale dynamics is generally improved both in the atmosphere and in the ocean, and the bias in mean surface temperature is clearly reduced. However, some flaws remain such as significant precipitation and radiative biases in many regions, or a pronounced drift in three dimensional salinity.},
  file         = {:Voldoire2013.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {cmip5, gcm, global climate modelling, meridional overturning circulation, sea-ice, atlantic-ocean, part i, simple parameterization, florida current, surface scheme, boundary-layer, coupled model, annual cycle},
}

@Article{Voldoire2019,
  author       = {A. Voldoire and D. Saint-Martin and S. Sénési and B. Decharme and A. Alias and M. Chevallier and J. Colin and J.-F. Guérémy and M. Michou and M.-P. Moine and P. Nabat and R. Roehrig and D. Salas y Mélia and R. Séférian and S. Valcke and I. Beau and S. Belamari and S. Berthet and C. Cassou and J. Cattiaux and J. Deshayes and H. Douville and C. Ethé and L. Franchistéguy and O. Geoffroy and C. Lévy and G. Madec and Y. Meurdesoif and R. Msadek and A. Ribes and E. Sanchez-Gomez and L. Terray and R. Waldman},
  date         = {2019-07},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {Evaluation of {CMIP}6 {DECK} Experiments With {CNRM}-{CM}6-1},
  doi          = {10.1029/2019ms001683},
  number       = {7},
  pages        = {2177--2213},
  volume       = {11},
  file         = {:Voldoire2019.pdf:PDF},
  groups       = {CMIP6 models},
  publisher    = {American Geophysical Union ({AGU})},
}

@Article{Volodin2008,
  author       = {Volodin, E. M.},
  date         = {2008},
  journaltitle = {Izvestiya Atmospheric and Oceanic Physics},
  title        = {Relation between temperature sensitivity to doubled carbon dioxide and the distribution of clouds in current climate models},
  doi          = {10.1134/S0001433808030043},
  number       = {3},
  pages        = {288--299},
  volume       = {44},
  abstract     = {The paper considers a relation between equilibrium global warming at doubled carbon dioxide (climate sensitivity) and the distribution of clouds and relative humidity in 18 state-of-the-art climate models. There is a strong correlation among three indices: (1) model climate sensitivity, (2) mean cloud amount change due to global warming, and (3) the difference in cloud amount between the tropics and midlatitudes. In the simulation of the present-day current, models with high sensitivity produce smaller clouds amounts in the tropics and larger cloud amounts over midlatitude oceans than models with low sensitivity. The relative humidity in the tropics is smaller in models with high sensitivity than in models with low sensitivity. There is a similarity between vertical profiles of cloud amount and relative humidity under global warming and vertical profiles of the difference in these quantities averaged over the tropics and midlatitudes. Based on the correlations obtained and observations of cloud amount and relative humidity, an estimate is made of the sensitivity of a real climate system.},
  file         = {:Volodin2008.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming, Clouds / Aerosols},
  keywords     = {surface, isccp, skimmed},
  readstatus   = {skimmed},
}

@Article{Volodin2010,
  author       = {Volodin, E. M. and Dianskii, N. A. and Gusev, A. V.},
  date         = {2010},
  journaltitle = {Izvestiya Atmospheric and Oceanic Physics},
  title        = {Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations},
  doi          = {10.1134/S000143381004002x},
  number       = {4},
  pages        = {414--431},
  volume       = {46},
  abstract     = {The INMCM3.0 climate model has formed the basis for the development of a new climate-model version: the INMCM4.0. It differs from the previous version in that there is an increase in its spatial resolution and some changes in the formulation of coupled atmosphere-ocean general circulation models. A numerical experiment was conducted on the basis of this new version to simulate the present-day climate. The model data were compared with observational data and the INMCM3.0 model data. It is shown that the new model adequately reproduces the most significant features of the observed atmospheric and oceanic climate. This new model is ready to participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5), the results of which are to be used in preparing the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC).},
  file         = {:Volodin2010.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {model, atmosphere, ocean, climate, el nino, arctic oscillation, sea ice, sea-surface temperature, geopotential height, arctic-ocean, ice, centuries, cycle},
}

@Article{Volodin2017,
  author       = {Volodin, E. M. and Mortikov, E. V. and Kostrykin, S. V. and Galin, V. Y. and Lykosov, V. N. and Gritsun, A. S. and Diansky, N. A. and Gusev, A. V. and Yakovlev, N. G.},
  date         = {2017},
  journaltitle = {Izvestiya Atmospheric and Oceanic Physics},
  title        = {Simulation of modern climate with the new version of the INM RAS climate model},
  doi          = {10.1134/S0001433817020128},
  number       = {2},
  pages        = {142--155},
  volume       = {53},
  abstract     = {The INMCM5.0 numerical model of the Earth's climate system is presented, which is an evolution from the previous version, INMCM4.0. A higher vertical resolution for the stratosphere is applied in the atmospheric block. Also, we raised the upper boundary of the calculating area, added the aerosol block, modified parameterization of clouds and condensation, and increased the horizontal resolution in the ocean block. The program implementation of the model was also updated. We consider the simulation of the current climate using the new version of the model. Attention is focused on reducing systematic errors as compared to the previous version, reproducing phenomena that could not be simulated correctly in the previous version, and modeling the problems that remain unresolved.},
  file         = {:Volodin2017.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {model, climate, atmosphere, ocean, parameterization, circulation, prediction, centuries, water},
}

@Article{Volodin2017a,
  author       = {Volodin, E. M. and Mortikov, E. V. and Kostrykin, S. V. and Galin, V. Y. and Lykossov, V. N. and Gritsun, A. S. and Diansky, N. A. and Gusev, A. V. and Iakovlev, N. G.},
  date         = {2017},
  journaltitle = {Climate Dynamics},
  title        = {Simulation of the present-day climate with the climate model INMCM5},
  doi          = {10.1007/s00382-017-3539-7},
  number       = {11-12},
  pages        = {3715--3734},
  volume       = {49},
  abstract     = {In this paper we present the fifth generation of the INMCM climate model that is being developed at the Institute of Numerical Mathematics of the Russian Academy of Sciences (INMCM5). The most important changes with respect to the previous version (INMCM4) were made in the atmospheric component of the model. Its vertical resolution was increased to resolve the upper stratosphere and the lower mesosphere. A more sophisticated parameterization of condensation and cloudiness formation was introduced as well. An aerosol module was incorporated into the model. The upgraded oceanic component has a modified dynamical core optimized for better implementation on parallel computers and has two times higher resolution in both horizontal directions. Analysis of the present-day climatology of the INMCM5 (based on the data of historical run for 1979-2005) shows moderate improvements in reproduction of basic circulation characteristics with respect to the previous version. Biases in the near-surface temperature and precipitation are slightly reduced compared with INMCM4 as well as biases in oceanic temperature, salinity and sea surface height. The most notable improvement over INMCM4 is the capability of the new model to reproduce the equatorial stratospheric quasi-biannual oscillation and statistics of sudden stratospheric warmings.},
  file         = {:Volodin2017a.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {climate, model, atmosphere, ocean, parameterization, simulation, temperature, precipitation, bias, general-circulation model, sea-surface temperature, ocean circulation, atmospheric co2, cmip5, implementation, variability, cycle},
}

@Article{Walker2020,
  author       = {Anthony P. Walker and Martin G. De Kauwe and Ana Bastos and Soumaya Belmecheri and Katerina Georgiou and Ralph F. Keeling and Sean M. McMahon and Belinda E. Medlyn and David J. P. Moore and Richard J. Norby and Sönke Zaehle and Kristina J. Anderson-Teixeira and Giovanna Battipaglia and Roel J. W. Brienen and Kristine G. Cabugao and Maxime Cailleret and Elliott Campbell and Josep G. Canadell and Philippe Ciais and Matthew E. Craig and David S. Ellsworth and Graham D. Farquhar and Simone Fatichi and Joshua B. Fisher and David C. Frank and Heather Graven and Lianhong Gu and Vanessa Haverd and Kelly Heilman and Martin Heimann and Bruce A. Hungate and Colleen M. Iversen and Fortunat Joos and Mingkai Jiang and Trevor F. Keenan and Jürgen Knauer and Christian Körner and Victor O. Leshyk and Sebastian Leuzinger and Yao Liu and Natasha MacBean and Yadvinder Malhi and Tim R. McVicar and Josep Penuelas and Julia Pongratz and A. Shafer Powell and Terhi Riutta and Manon E. B. Sabot and Juergen Schleucher and Stephen Sitch and William K. Smith and Benjamin Sulman and Benton Taylor and César Terrer and Margaret S. Torn and Kathleen K. Treseder and Anna T. Trugman and Susan E. Trumbore and Phillip J. Mantgem and Steve L. Voelker and Mary E. Whelan and Pieter A. Zuidema},
  date         = {2020-10},
  journaltitle = {New Phytologist},
  title        = {Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric {CO}$_2$},
  doi          = {10.1111/nph.16866},
  number       = {5},
  pages        = {2413--2445},
  volume       = {229},
  file         = {:Walker2020.pdf:PDF},
  groups       = {Carbon Cycle},
  publisher    = {Wiley},
}

@Article{Wang2020,
  author       = {Wang, T. and Jiang, J. and Zhang, M. and Zhang, H. and He, J. and Hao, H. and Chi, X.},
  date         = {2020},
  journaltitle = {Earth and Space Science},
  title        = {Design and Research of CAS-CIG for Earth System Models},
  doi          = {10.1029/2019EA000965},
  number       = {7},
  pages        = {e2019EA000965},
  volume       = {7},
  abstract     = {The Chinese Academy of Sciences Coupling Interface Generator (CAS-CIG) is designed to address the complexities of the development and coupling of different component models in Earth System Models based on the Coupler 7 of the Community Earth System Model (CESM). Its application in the Chinese Academy of Sciences Earth System Model (CAS-ESM) is described. The CAS-CIG automatically generates the coupler code through a simple configuration file when a component model is accessed, enabling different component models to be easily ported to CPL7 to create simulation cases. Combined with the automatic generation of compile scripts, the precompilation and run directories are directly formed. The component model integration, model selection, experimental setup, and platform migration can be all accomplished in the CAS-CIG. Verification of the CAS-CIG is presented to show that the automatically generated codes can identically reproduce the simulation results of CAS-ESM. CAS-CIG presents a software tool for modeling centers to investigate the impact of component model selections on simulations of climate and weather.},
  file         = {:Wang2020.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {coupling interface generator</author_keyword>, automatic generation</author_keyword>, high performance experiments</author_keyword>, coupling software, community coupler, part i, climate, performance, improvements, sensitivity},
}

@Article{Watanabe2010,
  author       = {Watanabe, M. and Suzuki, T. and O'ishi, R. and Komuro, Y. and Watanabe, S. and Emori, S. and Takemura, T. and Chikira, M. and Ogura, T. and Sekiguchi, M. and Takata, K. and Yamazaki, D. and Yokohata, T. and Nozawa, T. and Hasumi, H. and Tatebe, H. and Kimoto, M.},
  date         = {2010},
  journaltitle = {Journal of Climate},
  title        = {Improved Climate Simulation by MIROC5. Mean States, Variability, and Climate Sensitivity},
  doi          = {10.1175/2010jcli3679.1},
  number       = {23},
  pages        = {6312--6335},
  volume       = {23},
  abstract     = {A new version of the atmosphere ocean general circulation model cooperatively produced by the Japanese research community known as the Model for Interdisciplinary Research on Climate (MIROC) has recently been developed A century long control experiment was performed using the new version (MIROC5) with the standard resolution of the T85 atmosphere and 1 degrees ocean models The climatological mean state and variability are then compared with observations and those in a previous version (MIROC3 2) with two different resolutions (medres hires) coarser and finer than the resolution of MIROC5
A few aspects of the mean fields in MIROC5 are similar to or slightly worse than MIROC3 2 but otherwise the climatological features are considerably better In particular improvements are found in precipitation zonal mean atmospheric fields equatorial ocean subsurface fields and the simulation of El Nino-Southern Oscillation The difference between MIROC5 and the previous model is larger than that between the two MIROC3 2 versions indicating a greater effect of updating parameterization schemes on the model climate than increasing the model resolution The mean cloud property obtained from the sophisticated prognostic schemes in MIROC5 shows good agreement with satellite measurements MIROC5 reveals an equilibrium climate sensitivity of 2 6 K which is lower than that in MIROC3 2 by 1 K This is probably due to the negative feedback of low clouds to the increasing concentration of CO2 which is opposite to that in MIROC3 2},
  file         = {:Watanabe2010.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {turbulence closure-model, general-circulation, thickness distribution, boundary-layer, cumulus parameterization, horizontal resolution, global distribution, temperature, surface, clouds},
}

@Article{Watanabe2011,
  author       = {Watanabe, S. and Hajima, T. and Sudo, K. and Nagashima, T. and Takemura, T. and Okajima, H. and Nozawa, T. and Kawase, H. and Abe, M. and Yokohata, T. and Ise, T. and Sato, H. and Kato, E. and Takata, K. and Emori, S. and Kawamiya, M.},
  date         = {2011},
  journaltitle = {Geoscientific Model Development},
  title        = {MIROC-ESM 2010: model description and basic results of {CMIP}5-20c3m experiments},
  doi          = {10.5194/gmd-4-845-2011},
  number       = {4},
  pages        = {845--872},
  volume       = {4},
  abstract     = {An earth system model (MIROC-ESM 2010) is fully described in terms of each model component and their interactions. Results for the CMIP5 (Coupled Model Inter-comparison Project phase 5) historical simulation are presented to demonstrate the model's performance from several perspectives: atmosphere, ocean, sea-ice, land-surface, ocean and terrestrial biogeochemistry, and atmospheric chemistry and aerosols. An atmospheric chemistry coupled version of MIROC-ESM (MIROC-ESM-CHEM 2010) reasonably reproduces transient variations in surface air temperatures for the period 1850-2005, as well as the present-day climatology for the zonal-mean zonal winds and temperatures from the surface to the mesosphere. The historical evolution and global distribution of column ozone and the amount of tropospheric aerosols are reasonably simulated in the model based on the Representative Concentration Pathways' (RCP) historical emissions of these precursors. The simulated distributions of the terrestrial and marine biogeochemistry parameters agree with recent observations, which is encouraging to use the model for future global change projections.},
  file         = {:Watanabe2011.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {general-circulation model, global vegetation model, carbon-cycle feedbacks, thermodynamic-equilibrium model, earth system model, gravity-wave drag, climate-change, tropospheric ozone, lower stratosphere, preindustrial times},
}

@WWW{WCRP2020,
  author = {WCRP},
  date   = {2020-11},
  title  = {WCRP Coupled Model Intercomparison Project ({CMIP})},
  url    = {https://www.wcrp-climate.org/wgcm-cmip},
  file   = {:WCRP2020.pdf:PDF},
  groups = {CMIP and other MIPs},
}

@Article{Webb2017,
  author       = {Webb, M. J. and Andrews, T. and Bodas-Salcedo, A. and Bony, S. and Bretherton, C. S. and Chadwick, R. and Chepfer, H. and Douville, H. and Good, P. and Kay, J. E. and Klein, S. A. and Marchand, R. and Medeiros, B. and Siebesma, A. P. and Skinner, C. B. and Stevens, B. and Tselioudis, G. and Tsushima, Y. and Watanabe, M.},
  date         = {2017},
  journaltitle = {Geoscientific Model Development},
  title        = {The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to {CMIP}6},
  doi          = {10.5194/gmd-10-359-2017},
  number       = {1},
  pages        = {359--384},
  volume       = {10},
  abstract     = {The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud-climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions "How does the Earth system respond to forcing?" and "What are the origins and consequences of systematic model biases?" and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.
A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloudradiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming?
CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions.
1. How well do clouds and other relevant variables simulated by models agree with observations?
2. What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?
3. Which models have the most credible representations of processes relevant to the simulation of clouds?
4. How do clouds and their changes interact with other elements of the climate system?},
  file         = {:Webb2017.pdf:PDF},
  groups       = {CMIP and other MIPs},
  keywords     = {general-circulation model, community atmosphere model, surface-temperature-change, climate sensitivity, tropical rainfall, southern-ocean, coupled model, satellite-observations, experimental-design, hydrological cycle},
}

@Article{Weigel2021,
  author       = {Katja Weigel and Lisa Bock and Bettina K. Gier and Axel Lauer and Mattia Righi and Manuel Schlund and Kemisola Adeniyi and Bouwe Andela and Enrico Arnone and Peter Berg and Louis-Philippe Caron and Irene Cionni and Susanna Corti and Niels Drost and Alasdair Hunter and Llorenç Lledó and Christian Wilhelm Mohr and Aytaç Paçal and Núria Pérez-Zanón and Valeriu Predoi and Marit Sandstad and Jana Sillmann and Andreas Sterl and Javier Vegas-Regidor and Jost von Hardenberg and Veronika Eyring},
  date         = {2021-06},
  journaltitle = {Geoscientific Model Development},
  title        = {Earth System Model Evaluation Tool ({ESMValTool}) v2.0 --- diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in {CMIP}},
  doi          = {10.5194/gmd-14-3159-2021},
  number       = {6},
  pages        = {3159--3184},
  volume       = {14},
  file         = {:Weigel2021.pdf:PDF},
  groups       = {ESMValTool},
  keywords     = {own},
  publisher    = {Copernicus {GmbH}},
}

@Article{Wenzel2014,
  author       = {Wenzel, S. and Cox, P. M. and Eyring, V. and Friedlingstein, P.},
  date         = {2014},
  journaltitle = {Journal of Geophysical Research-Biogeosciences},
  title        = {Emergent constraints on climate-carbon cycle feedbacks in the {CMIP}5 Earth system models},
  doi          = {10.1002/2013jg002591},
  number       = {5},
  pages        = {794--807},
  volume       = {119},
  abstract     = {An emergent linear relationship between the long-term sensitivity of tropical land carbon storage to climate warming (LT) and the short-term sensitivity of atmospheric carbon dioxide (CO2) to interannual temperature variability (IAV) has previously been identified by Cox et al. (2013) across an ensemble of Earth system models (ESMs) participating in the Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP). Here we examine whether such a constraint also holds for a new set of eight ESMs participating in Phase 5 of the Coupled Model Intercomparison Project. A wide spread in tropical land carbon storage is found for the quadrupling of atmospheric CO2, which is of the order of 252 +/- 112 GtC when carbon-climate feedbacks are enabled. Correspondingly, the spread in LT is wide (-49 +/- 40 GtC/K) and thus remains one of the key uncertainties in climate projections. A tight correlation is found between the long-term sensitivity of tropical land carbon and the short-term sensitivity of atmospheric CO2 (LT versus IAV), which enables the projections to be constrained with observations. The observed short-term sensitivity of CO2 (-4.4 +/- 0.9 GtC/yr/K) sharpens the range of LT to -44 +/- 14 GtC/K, which overlaps with the probability density function derived from the C4MIP models (-53 +/- 17 GtC/K) by Cox et al. (2013), even though the lines relating LT and IAV differ in the two cases. Emergent constraints of this type provide a means to focus ESM evaluation against observations on the metrics most relevant to projections of future climate change.},
  file         = {:Wenzel2014.pdf:PDF},
  groups       = {Carbon Cycle, Emergent Constraints},
  keywords     = {climate change, cmip5 earth system models, carbon cycle feedback constraints, emergent constraint, carbon losses, climate change projections, global vegetation model, land, sensitivity, biosphere, dioxide, future, productivity, simulations, reduction, extremes, read, printed},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Wenzel2016,
  author       = {Wenzel, S. and Cox, P. M. and Eyring, V. and Friedlingstein, P.},
  date         = {2016},
  journaltitle = {Nature},
  title        = {Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric {CO}$_2$},
  doi          = {10.1038/nature19772},
  number       = {7626},
  pages        = {499--501},
  volume       = {538},
  abstract     = {Uncertainties in the response of vegetation to rising atmospheric CO2 concentrations(1,2) contribute to the large spread in projections of future climate change(3,4). Climate-carbon cycle models generally agree that elevated atmospheric CO2 concentrations will enhance terrestrial gross primary productivity (GPP). However, the magnitude of this CO2 fertilization effect varies from a 20 per cent to a 60 per cent increase in GPP for a doubling of atmospheric CO2 concentrations in model studies(5-7). Here we demonstrate emergent constraints(8-11) on large-scale CO2 fertilization using observed changes in the amplitude of the atmospheric CO2 seasonal cycle that are thought to be the result of increasing terrestrial GPP(12-14). Our comparison of atmospheric CO2 measurements from Point Barrow in Alaska and Cape Kumukahi in Hawaii with historical simulations of the latest climate-carbon cycle models demonstrates that the increase in the amplitude of the CO2 seasonal cycle at both measurement sites is consistent with increasing annual mean GPP, driven in part by climate warming, but with differences in CO2 fertilization controlling the spread among the model trends. As a result, the relationship between the amplitude of the CO2 seasonal cycle and the magnitude of CO2 fertilization of GPP is almost linear across the entire ensemble of models. When combined with the observed trends in the seasonal CO2 amplitude, these relationships lead to consistent emergent constraints on the CO2 fertilization of GPP. Overall, we estimate a GPP increase of 37 +/- 9 per cent for high-latitude ecosystems and 32 +/- 9 per cent for extratropical ecosystems under a doubling of atmospheric CO2 concentrations on the basis of the Point Barrow and Cape Kumukahi records, respectively.},
  file         = {:Wenzel2016.pdf:PDF},
  groups       = {Emergent Constraints, Carbon Cycle},
  keywords     = {northern ecosystems, elevated co2, carbon, climate, cmip5, models, printed, read},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Wenzel2016a,
  author       = {Wenzel, S. and Eyring, V. and Gerber, E. P. and Karpechko, A. Y.},
  date         = {2016},
  journaltitle = {Journal of Climate},
  title        = {Constraining Future Summer Austral Jet Stream Positions in the {CMIP}5 Ensemble by Process-Oriented Multiple Diagnostic Regression},
  doi          = {10.1175/Jcli-D-15-0412.1},
  number       = {2},
  pages        = {673--687},
  volume       = {29},
  abstract     = {Stratospheric ozone recovery and increasing greenhouse gases are anticipated to have a large impact on the Southern Hemisphere extratropical circulation, shifting the jet stream and associated storm tracks. Models participating in phase 5 of the Coupled Model Intercomparison Project poorly simulate the austral jet, with a mean equatorward bias and 10 degrees latitude spread in their historical climatologies, and project a wide range of future trends in response to anthropogenic forcing in the representative concentration pathway (RCP) scenarios. Here, the question is addressed whether the unweighted multimodel mean (uMMM) austral jet projection of the RCP4.5 scenario can be improved by applying a process-oriented multiple diagnostic ensemble regression (MDER). MDER links future projections of the jet position to processes relevant to its simulation under present-day conditions. MDER is first targeted to constrain near-term (2015-34) projections of the austral jet position and selects the historical jet position as the most important of 20 diagnostics. The method essentially recognizes the equatorward bias in the past jet position and provides a bias correction of about 1.5 degrees latitude southward to future projections. When the target horizon is extended to midcentury (2040-59), the method also recognizes that lower-stratospheric temperature trends over Antarctica, a proxy for the intensity of ozone depletion, provide additional information that can be used to reduce uncertainty in the ensemble mean projection. MDER does not substantially alter the uMMM long-term position in jet position but reduces the uncertainty in the ensemble mean projection. This result suggests that accurate observational constraints on upper-tropospheric and lower-stratospheric temperature trends are needed to constrain projections of the austral jet position.},
  file         = {:Wenzel2016a.pdf:PDF},
  groups       = {Climate Model Weighting / Reducing uncertainties},
  keywords     = {circulation, dynamics, wind stress, mathematical and statistical techniques, bayesian methods, optimization, regression analysis, variability, climate variability, southern annular mode, global climate model, atmospheric circulation, stratospheric ozone, coupled model, system model, simulations, projections, validation, forcings, read},
  readstatus   = {read},
}

@Misc{Wieder2014,
  author    = {Wieder, W. R. and Boehnert, J. and Bonan, G. B. and Langseth, M.},
  date      = {2014},
  title     = {Regridded Harmonized World Soil Database v1.2},
  doi       = {10.3334/ORNLDAAC/1247},
  language  = {en},
  groups    = {OBS},
  publisher = {ORNL Distributed Active Archive Center},
}

@Article{Williams2018,
  author       = {Williams, K. D. and Copsey, D. and Blockley, E. W. and Bodas-Salcedo, A. and Calvert, D. and Comer, R. and Davis, P. and Graham, T. and Hewitt, H. T. and Hill, R. and Hyder, P. and Ineson, S. and Johns, T. C. and Keen, A. B. and Lee, R. W. and Megann, A. and Milton, S. F. and Rae, J. G. L. and Roberts, M. J. and Scaife, A. A. and Schiemann, R. and Storkey, D. and Thorpe, L. and Watterson, I. G. and Walters, D. N. and West, A. and Wood, R. A. and Woollings, T. and Xavier, P. K.},
  date         = {2018},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations},
  doi          = {10.1002/2017ms001115},
  number       = {2},
  pages        = {357--380},
  volume       = {10},
  abstract     = {The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Among other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific descriptions of these components are in companion papers) and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present-day forcing. The suitability of the configuration for predictability on shorter time scales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2-AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however, there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75\%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2-AO, many aspects of the present-day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extratropical variability and top-of-atmosphere and surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking.},
  file         = {:Williams2018.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {climate models, ocean temperature, salinity profiles, clouds, jules, precipitation, system, performance, transports, prediction},
}

@Article{Williams2020,
  author       = {K. D. Williams and A. J. Hewitt and A. Bodas-Salcedo},
  date         = {2020-04},
  journaltitle = {Journal of Advances in Modeling Earth Systems},
  title        = {Use of Short-Range Forecasts to Evaluate Fast Physics Processes Relevant for Climate Sensitivity},
  doi          = {10.1029/2019ms001986},
  number       = {4},
  pages        = {e2019MS001986},
  volume       = {12},
  file         = {:Williams2020.pdf:PDF},
  groups       = {ECS / TCR / Climate Sensitivity / Warming, Clouds / Aerosols},
  keywords     = {skimmed},
  publisher    = {American Geophysical Union ({AGU})},
  readstatus   = {skimmed},
}

@Article{Winkler2019,
  author       = {Winkler, A. J. and Myneni, R. B. and Alexandrov, G. A. and Brovkin, V.},
  date         = {2019},
  journaltitle = {Nature Communications},
  title        = {Earth system models underestimate carbon fixation by plants in the high latitudes},
  doi          = {10.1038/s41467-019-08633-z},
  pages        = {1--8},
  volume       = {10},
  abstract     = {Most Earth system models agree that land will continue to store carbon due to the physiological effects of rising CO2 concentration and climatic changes favoring plant growth in temperature-limited regions. But they largely disagree on the amount of carbon uptake. The historical CO2 increase has resulted in enhanced photosynthetic carbon fixation (Gross Primary Production, GPP), as can be evidenced from atmospheric CO2 concentration and satellite leaf area index measurements. Here, we use leaf area sensitivity to ambient CO2 from the past 36 years of satellite measurements to obtain an Emergent Constraint (EC) estimate of GPP enhancement in the northern high latitudes at two-times the pre-industrial CO2 concentration (3.4 +/- 0.2 Pg C yr(-1)). We derive three independent comparable estimates from CO2 measurements and atmospheric inversions. Our EC estimate is 60\% larger than the conventionally used multi-model average (44\% higher at the global scale). This suggests that most models largely underestimate photosynthetic carbon fixation and therefore likely overestimate future atmospheric CO2 abundance and ensuing climate change, though not proportionately.},
  file         = {:Winkler2019.pdf:PDF},
  groups       = {Emergent Constraints, Carbon Cycle},
  keywords     = {vegetation leaf-area, interannual variability, co2, climate, cycle, cmip5, projections, ecosystems, satellite, exchange, skimmed},
  readstatus   = {skimmed},
}

@Article{Winkler2019a,
  author       = {Winkler, A. J. and Myneni, R. B. and Brovkin, V.},
  date         = {2019},
  journaltitle = {Earth System Dynamics},
  title        = {Investigating the applicability of emergent constraints},
  doi          = {10.5194/esd-10-501-2019},
  number       = {3},
  pages        = {501--523},
  volume       = {10},
  abstract     = {Recent research on emergent constraints (ECs) has delivered promising results in narrowing down uncertainty in climate predictions. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in an entity of interest (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure first critically depends on an accurate estimation of the predictor from observations and models and second on a robust relationship between inter-model variations in the predictor-predictand space. Here, we investigate issues related to these two themes in a carbon cycle case study using observed vegetation greening sensitivity to CO2 forcing as a predictor of change in photosynthesis (gross primary productivity, GPP) for a doubling of preindustrial CO2 concentration. Greening sensitivity is defined as changes in the annual maximum of green leaf area index (LAI(max)) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the predictor of a large area (e.g., greening sensitivity in the northern high-latitude region) from pixel-level data. This requires an investigation into uncertainties in the observational data source and an evaluation of the spatial and temporal variability in the predictor in both the data and model simulations. Second, the predictor- predictand relationship across the model ensemble depends on a strong coupling between the two variables, i.e., simultaneous changes in GPP and LAI(max). This coupling depends in a complex manner on the magnitude (level), time rate of application (scenarios), and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can affect the EC estimate of the predictand (AGPP). Our results show that uncertainties in the EC method primarily originate from a lack of predictor comparability between observations and models, the observational data source, and temporal variability of the predictor. The disagreement between models on the mechanistic behavior of the system under intensifying forcing limits the EC applicability. The discussed limitations and sources of uncertainty in the EC method go beyond carbon cycle research and are generally applicable in Earth system sciences.},
  file         = {:Winkler2019a.pdf:PDF},
  groups       = {Emergent Constraints, Carbon Cycle},
  keywords     = {leaf-area index, climate-change, atmospheric co2, absorbed par, carbon, sensitivity, cmip5, earth, variability, projections},
}

@Article{Wu2014,
  author       = {Wu, T. W. and Song, L. C. and Li, W. P. and Wang, Z. Z. and Zhang, H. and Xin, X. G. and Zhang, Y. W. and Zhang, L. and Li, J. L. and Wu, F. H. and Liu, Y. M. and Zhang, F. and Shi, X. L. and Chu, M. and Zhang, J. and Fang, Y. J. and Wang, F. and Lu, Y. X. and Liu, X. W. and Wei, M. and Liu, Q. X. and Zhou, W. Y. and Dong, M. and Zhao, Q. G. and Ji, J. J. and Li, L. and Zhou, M. Y.},
  date         = {2014},
  journaltitle = {Journal of Meteorological Research},
  title        = {An Overview of BCC Climate System Model Development and Application for Climate Change Studies},
  doi          = {10.1007/s13351-014-3041-7},
  number       = {1},
  pages        = {34--56},
  volume       = {28},
  abstract     = {This paper reviews recent progress in the development of the Beijing Climate Center Climate System Model (BCC_CSM) and its four component models (atmosphere, land surface, ocean, and sea ice). Two recent versions are described: BCC_CSM1.1 with coarse resolution (approximately 2.8125 degrees x 2.8125 degrees) and BCC_CSM1.1(m) with moderate resolution (approximately 1.125 degrees x 1.125 degrees). Both versions are fully coupled climate-carbon cycle models that simulate the global terrestrial and oceanic carbon cycles and include dynamic vegetation. Both models well simulate the concentration and temporal evolution of atmospheric CO2 during the 20th century with anthropogenic CO2 emissions prescribed. Simulations using these two versions of the BCC_CSM model have been contributed to the Coupled Model Intercomparison Project phase five (CMIP5) in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). These simulations are available for use by both national and international communities for investigating global climate change and for future climate projections.
Simulations of the 20th century climate using BCC_CSM1.1 and BCC_CSM1.1(m) are presented and validated, with particular focus on the spatial pattern and seasonal evolution of precipitation and surface air temperature on global and continental scales. Simulations of climate during the last millennium and projections of climate change during the next century are also presented and discussed. Both BCC_CSM1.1 and BCC_CSM1.1(m) perform well when compared with other CMIP5 models. Preliminary analyses indicate that the higher resolution in BCC_CSM1.1(m) improves the simulation of mean climate relative to BCC_CSM1.1, particularly on regional scales.},
  file         = {:Wu2014.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {beijing climate center climate system model (bcc-csm), atmospheric general circulation model, land surface model, oceanic general circulation model, sea ice model, terrestrial ecosystem, air-temperature, china, parameterization, precipitation, projections, simulation, 20th-century, convection, globe},
}

@Article{Wu2019,
  author       = {Wu, T. W. and Lu, Y. X. and Fang, Y. J. and Xin, X. G. and Li, L. and Li, W. P. and Jie, W. H. and Zhang, J. and Liu, Y. M. and Zhang, L. and Zhang, F. and Zhang, Y. W. and Wu, F. H. and Li, J. L. and Chu, M. and Wang, Z. Z. and Shi, X. L. and Liu, X. W. and Wei, M. and Huang, A. N. and Zhang, Y. C. and Liu, X. H.},
  date         = {2019},
  journaltitle = {Geoscientific Model Development},
  title        = {The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from {CMIP}5 to {CMIP}6},
  doi          = {10.5194/gmd-12-1573-2019},
  number       = {4},
  pages        = {1573--1600},
  volume       = {12},
  abstract     = {The main advancements of the Beijing Climate Center (BCC) climate system model from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to phase 6 (CMIP6) are presented, in terms of physical parameterizations and model performance. BCC-CSM1.1 and BCC-CSM1.1m are the two models involved in CMIP5, whereas BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models configured for CMIP6. Historical simulations from 1851 to 2014 from BCC-CSM2-MR (CMIP6) and from 1851 to 2005 from BCC-CSM1.1m (CMIP5) are used for models assessment. The evaluation matrices include the following: (a) the energy budget at top-of-atmosphere; (b) surface air temperature, precipitation, and atmospheric circulation for the global and East Asia regions; (c) the sea surface temperature (SST) in the tropical Pacific; (d) sea-ice extent and thickness and Atlantic Meridional Overturning Circulation (AMOC); and (e) climate variations at different timescales, such as the global warming trend in the 20th century, the stratospheric quasi-biennial oscillation (QBO), the Madden-Julian Oscillation (MJO), and the diurnal cycle of precipitation. Compared with BCC-CSM1.1m, BCC-CSM2MR shows significant improvements in many aspects including the tropospheric air temperature and circulation at global and regional scales in East Asia and climate variability at different timescales, such as the QBO, the MJO, the diurnal cycle of precipitation, interannual variations of SST in the equatorial Pacific, and the long-term trend of surface air temperature.},
  file         = {:Wu2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {general-circulation, sea-ice, terrestrial ecosystem, turbulent exchange, optical-properties, ocean circulation, effective radius, scalar transfer, parameterization, aerosols},
}

@Article{Wyser2020,
  author       = {Wyser, K. and van Noije, T. and Yang, S. T. and von Hardenberg, J. and O'Donnell, D. and Doscher, R.},
  date         = {2020},
  journaltitle = {Geoscientific Model Development},
  title        = {On the increased climate sensitivity in the EC-Earth model from {CMIP}5 to {CMIP}6},
  doi          = {10.5194/gmd-13-3465-2020},
  number       = {8},
  pages        = {3465--3474},
  volume       = {13},
  abstract     = {Many modelling groups that contribute to CMIP6 (Coupled Model Intercomparison Project Phase 6) have found a larger equilibrium climate sensitivity (ECS) with their latest model versions compared with the values obtained with the earlier versions used in CMIP5. This is also the case for the EC-Earth model. Therefore, in this study, we investigate what developments since the CMIP5 era could have caused the increase in the ECS in this model. Apart from increases in the horizontal and vertical resolution, the EC-Earth model has also substantially changed the representation of aerosols; in particular, it has introduced a more sophisticated description of aerosol indirect effects. After testing the model with some of the recent updates switched off, we find that the ECS increase can be attributed to the more advanced treatment of aerosols, with the largest contribution coming from the effect of aerosols on cloud microphysics (cloud lifetime or second indirect effect). The increase in climate sensitivity is unrelated to model tuning, as all experiments were performed with the same tuning parameters and only the representation of the aerosol effects was changed. These results cannot be generalised to other models, as their CMIP5 and CMIP6 versions may differ with respect to aspects other than the aerosol-cloud interaction, but the results highlight the strong sensitivity of ECS to the details of the aerosol forcing.},
  file         = {:Wyser2020.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {parameterization, aerosols, emissions, evolution, gases},
}

@Article{Yukimoto2012,
  author       = {Yukimoto, S. and Adachi, Y. and Hosaka, M. and Sakami, T. and Yoshimura, H. and Hirabara, M. and Tanaka, T. Y. and Shindo, E. and Tsujino, H. and Deushi, M. and Mizuta, R. and Yabu, S. and Obata, A. and Nakano, H. and Koshiro, T. and Ose, T. and Kitoh, A.},
  date         = {2012},
  journaltitle = {Journal of the Meteorological Society of Japan},
  title        = {A New Global Climate Model of the Meteorological Research Institute: MRI-CGCM3-Model Description and Basic Performance},
  doi          = {10.2151/jmsj.2012-A02},
  pages        = {23--64},
  volume       = {90a},
  abstract     = {A new global climate model, MRI-CGCM3, has been developed at the Meteorological Research Institute (MRI). This model is an overall upgrade of MRI's former climate model MRI-CGCM2 series. MRI-CGCM3 is composed of atmosphere-land, aerosol, and ocean-ice models, and is a subset of the MRI's earth system model MRI-ESMI. Atmospheric component MRI-AGCM3 is interactively coupled with aerosol model to represent direct and indirect effects of aerosols with a new cloud microphysics scheme. Basic experiments for pre-industrial control, historical and climate sensitivity are performed with MRI-CGCM3. In the pre-industrial control experiment, the model exhibits very stable behavior without climatic drifts, at least in the radiation budget, the temperature near the surface and the major indices of ocean circulations. The sea surface temperature (SST) drift is sufficiently small, while there is a 1 W m(-2) heating imbalance at the surface. The model's climate sensitivity is estimated to be 2.11 K with Gregory's method. The transient climate response (TCR) to 1 \% yr(-1) increase of carbon dioxide (CO2) concentration is 1.6 K with doubling of CO2 concentration and 4.1 K with quadrupling of CO2 concentration. The simulated present-day mean climate in the historical experiment is evaluated by comparison with observations, including reanalysis. The model reproduces the overall mean climate, including seasonal variation in various aspects in the atmosphere and the oceans. Variability in the simulated climate is also evaluated and is found to be realistic, including El Nino and Southern Oscillation and the Arctic and Antarctic oscillations. However, some important issues are identified. The simulated SST indicates generally cold bias in the Northern Hemisphere (NH) and warm bias in the Southern Hemisphere (SH), and the simulated sea ice expands excessively in the North Atlantic in winter. A double ITCZ also appears in the tropical Pacific, particularly in the austral summer.},
  file         = {:Yukimoto2012.pdf:PDF},
  groups       = {CMIP5 models},
  keywords     = {sea-surface temperature, optical depth perturbations, turbulence closure-model, ocean circulation models, yamada level-3 model, boundary-layer, volcanic-eruptions, radiative properties, cloud microphysics, aerosol activation},
}

@Article{Yukimoto2019,
  author       = {Yukimoto, S. and Kawai, H. and Koshiro, T. and Oshima, N. and Yoshida, K. and Urakawa, S. and Tsujino, H. and Deushi, M. and Tanaka, T. and Hosaka, M. and Yabu, S. and Yoshimura, H. and Shindo, E. and Mizuta, R. and Obata, A. and Adachi, Y. and Ishii, M.},
  date         = {2019},
  journaltitle = {Journal of the Meteorological Society of Japan},
  title        = {The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component},
  doi          = {10.2151/jmsj.2019-051},
  number       = {5},
  pages        = {931--965},
  volume       = {97},
  abstract     = {The new Meteorological Research Institute Earth System Model version 2.0 (MRI-ESM2.0) has been developed based on previous models, MRI-CGCM3 and MRI-ESM1, which participated in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). These models underwent numerous improvements meant for highly accurate climate reproducibility. This paper describes model formulation updates and evaluates basic performance of its physical components. The new model has nominal horizontal resolutions of 100 km for atmosphere and ocean components, similar to the previous models. The atmospheric vertical resolution is 80 layers, which is enhanced from the 48 layers of its predecessor. Accumulation of various improvements concerning clouds, such as a new stratocumulus cloud scheme, led to remarkable reduction in errors in shortwave, longwave, and net radiation at the top of the atmosphere. The resulting errors are sufficiently small compared with those in the CMIP5 models. The improved radiation distribution brings the accurate meridional heat transport required for the ocean and contributes to a reduced surface air temperature (SAT) bias. MRI-ESM2.0 displays realistic reproduction of both mean climate and interannual variability. For instance, the stratospheric quasi-biennial oscillation can now be realistically expressed through the enhanced vertical resolution and introduction of non-orographic gravity wave drag parameterization. For the historical experiment, MRI-ESM2.0 reasonably reproduces global SAT change for recent decades; however, cooling in the 1950s through the 1960s and warming afterward are overestimated compared with observations. MRI-ESM2.0 has been improved in many aspects over the previous models, MRI-CGCM3 and MRI-ESM1, and is expected to demonstrate superior performance in many experiments planned for CMIP6.},
  file         = {:Yukimoto2019.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {earth system model, climate model, global climate, global climate model, boundary-layer, ocean circulation, parameterization scheme, experimental protocol, southern-ocean, cmip5 models, aerosol, atmosphere, representation},
}

@Article{Zelinka2020,
  author       = {Zelinka, M. D. and Myers, T. A. and Mccoy, D. T. and Po-Chedley, S. and Caldwell, P. M. and Ceppi, P. and Klein, S. A. and Taylor, K. E.},
  date         = {2020},
  journaltitle = {Geophysical Research Letters},
  title        = {Causes of Higher Climate Sensitivity in {CMIP}6 Models},
  doi          = {10.1029/2019GL085782},
  number       = {1},
  pages        = {e2019GL085782},
  volume       = {47},
  abstract     = {Equilibrium climate sensitivity, the global surface temperature response to CO2 doubling, has been persistently uncertain. Recent consensus places it likely within 1.5-4.5 K. Global climate models (GCMs), which attempt to represent all relevant physical processes, provide the most direct means of estimating climate sensitivity via CO2 quadrupling experiments. Here we show that the closely related effective climate sensitivity has increased substantially in Coupled Model Intercomparison Project phase 6 (CMIP6), with values spanning 1.8-5.6 K across 27 GCMs and exceeding 4.5 K in 10 of them. This (statistically insignificant) increase is primarily due to stronger positive cloud feedbacks from decreasing extratropical low cloud coverage and albedo. Both of these are tied to the physical representation of clouds which in CMIP6 models lead to weaker responses of extratropical low cloud cover and water content to unforced variations in surface temperature. Establishing the plausibility of these higher sensitivity models is imperative given their implied societal ramifications.},
  file         = {:Zelinka2020.pdf:PDF},
  groups       = {Feedbacks, ECS / TCR / Climate Sensitivity / Warming, Clouds / Aerosols},
  keywords     = {low-cloud cover, optical depth feedback, coupled model, phase, spread, constraints, mechanisms, dependence, surface, middle},
}

@Article{Zhai2015,
  author       = {Zhai, C. X. and Jiang, J. H. and Su, H.},
  date         = {2015},
  journaltitle = {Geophysical Research Letters},
  title        = {Long-term cloud change imprinted in seasonal cloud variation: More evidence of high climate sensitivity},
  doi          = {10.1002/2015gl065911},
  number       = {20},
  pages        = {8729--8737},
  volume       = {42},
  abstract     = {The large spread of model equilibrium climate sensitivity (ECS) is mainly caused by the differences in the simulated marine boundary layer cloud (MBLC) radiative feedback. We examine the variations of MBLC fraction in response to the changes of sea surface temperature (SST) at seasonal and centennial time scales for 27 climate models that participated in the Coupled Model Intercomparison Project phase 3 and phase 5. We find that the intermodel spread in the seasonal variation of MBLC fraction with SST is strongly correlated with the intermodel spread in the centennial MBLC fraction change per degree of SST warming and that both are well correlated with ECS. Seven models that are consistent with the observed seasonal variation of MBLC fraction with SST at a rate -1.28 +/- 0.56\%/K all have ECS higher than the multimodel mean of 3.3 K yielding an ensemble-mean ECS of 3.9 K and a standard deviation of 0.4 K.},
  file         = {:Zhai2015.pdf:PDF},
  groups       = {Emergent Constraints, ECS / TCR / Climate Sensitivity / Warming, Clouds / Aerosols},
  keywords     = {model simulations, spread, cmip5, atmosphere, read, printed},
  printed      = {printed},
  readstatus   = {read},
}

@Article{Zhang2020,
  author       = {Zhang, Y. and Parazoo, N. C. and Williams, A. P. and Zhou, S. and Gentine, P.},
  date         = {2020},
  journaltitle = {Proceedings of the National Academy of Sciences of the United States of America},
  title        = {Large and projected strengthening moisture limitation on end-of-season photosynthesis},
  doi          = {10.1073/pnas.1914436117},
  number       = {17},
  pages        = {9216--9222},
  volume       = {117},
  abstract     = {Terrestrial photosynthesis is regulated by plant phenology and environmental conditions, both of which experienced substantial changes in recent decades. Unlike early-season photosynthesis, which is mostly driven by temperature or wet-season onset, late-season photosynthesis can be limited by several factors and the underlying mechanisms are less understood. Here, we analyze the temperature and water limitations on the ending date of photosynthesis (EOP), using data from both remote-sensing and flux tower-based measurements. We find a contrasting spatial pattern of temperature and water limitations on EOP. The threshold separating these is determined by the balance between energy availability and soil water supply. This coordinated temperature and moisture regulation can be explained by "law of minimum," i.e., as temperature limitation diminishes, higher soil water is needed to support increased vegetation activity, especially during the late growing season. Models project future warming and drying, especially during late season, both of which should further expand the water-limited regions, causing large variations and potential decreases in photosynthesis.},
  file         = {:Zhang2020.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {end of photosynthesis, solar induced fluorescence (sif), gross primary production (gpp), climate change, water stress, contrasting response, soil-moisture, climate, vegetation, phenology, co2, temperature, sensitivity, water, fluorescence},
}

@Article{Zhao2014,
  author       = {Zhao, F. and Zeng, N.},
  date         = {2014},
  journaltitle = {Earth System Dynamics},
  title        = {Continued increase in atmospheric {CO}$_2$ seasonal amplitude in the 21st century projected by the {CMIP}5 Earth system models},
  doi          = {10.5194/esd-5-423-2014},
  number       = {2},
  pages        = {423--439},
  volume       = {5},
  abstract     = {In the Northern Hemisphere, atmospheric CO2 concentration declines in spring and summer, and rises in fall and winter. Ground-based and aircraft-based observation records indicate that the amplitude of this seasonal cycle has increased in the past. Will this trend continue in the future? In this paper, we analyzed simulations for historical (1850-2005) and future (RCP8.5, 2006-2100) periods produced by 10 Earth system models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Our results present a model consensus that the increase of CO2 seasonal amplitude continues throughout the 21st century. Multi-model ensemble relative amplitude of detrended global mean CO2 seasonal cycle increases by 62 +/- 19\% in 2081-2090, compared to 1961-1970. This amplitude increase corresponds to a 68 +/- 25\% increase in net biosphere production (NBP). The results show that the increase of NBP amplitude mainly comes from enhanced ecosystem uptake during Northern Hemisphere growing season under future CO2 and temperature conditions. Separate analyses on net primary production (NPP) and respiration reveal that enhanced ecosystem carbon uptake contributes about 75\% of the amplitude increase. Stimulated by higher CO2 concentration and high-latitude warming, enhanced NPP likely outcompetes increased respiration at higher temperature, resulting in a higher net uptake during the northern growing season. The zonal distribution and spatial pattern of NBP change suggest that regions north of 45 degrees N dominate the amplitude increase. Models that simulate a stronger carbon uptake also tend to show a larger increase of NBP seasonal amplitude, and the cross-model correlation is significant (R = 0.73, p < 0.05).},
  file         = {:Zhao2014.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {carbon-cycle, mauna-loa, climate, vegetation, biosphere, exchange, dioxide, variability, scenarios, component},
}

@Article{Zhao2016,
  author       = {Zhao, F. and Zeng, N. and Asrar, G. and Friedlingstein, P. and Ito, A. and Jain, A. and Kalnay, E. and Kato, E. and Koven, C. D. and Poulter, B. and Rafique, R. and Sitch, S. and Shu, S. J. and Stocker, B. and Viovy, N. and Wiltshire, A. and Zaehle, S.},
  date         = {2016},
  journaltitle = {Biogeosciences},
  title        = {Role of {CO}$_2$, climate and land use in regulating the seasonal amplitude increase of carbon fluxes in terrestrial ecosystems: a multimodel analysis},
  doi          = {10.5194/bg-13-5121-2016},
  number       = {17},
  pages        = {5121--5137},
  volume       = {13},
  abstract     = {We examined the net terrestrial carbon flux to the atmosphere (F-TA/simulated by nine models from the TRENDY dynamic global vegetation model project for its seasonal cycle and amplitude trend during 1961-2012. While some models exhibit similar phase and amplitude compared to atmospheric inversions, with spring drawdown and autumn rebound, others tend to rebound early in summer. The model ensemble mean underestimates the magnitude of the seasonal cycle by 40\% compared to atmospheric inversions. Global F-TA amplitude increase (19 +/- 8 \%) and its decadal variability from the model ensemble are generally consistent with constraints from surface atmosphere observations. However, models disagree on attribution of this long-term amplitude increase, with factorial experiments attributing 83 +/- 56 \%, 3 +/- 74 and 20 +/- 30\% to rising CO2, climate change and land use/cover change, respectively. Seven out of the nine models suggest that CO2 fertilization is the strongest control - with the notable exception of VEGAS, which attributes approximately equally to the three factors. Generally, all models display an enhanced seasonality over the boreal region in response to high-latitude warming, but a negative climate contribution from part of the Northern Hemisphere temperate region, and the net result is a divergence over climate change effect. Six of the nine models show that land use/cover change amplifies the seasonal cycle of global F-TA: some are due to forest regrowth, while others are caused by crop expansion or agricultural intensification, as revealed by their divergent spatial patterns. We also discovered a moderate cross-model correlation between F-TA amplitude increase and increase in land carbon sink (R-2 = 0.61). Our results suggest that models can show similar results in some benchmarks with different underlying mechanisms; therefore, the spatial traits of CO2 fertilization, climate change and land use/cover changes are crucial in determining the right mechanisms in seasonal carbon cycle change as well as mean sink change.},
  file         = {:Zhao2016.pdf:PDF},
  groups       = {Carbon Cycle},
  keywords     = {model intercomparison project, program multiscale synthesis, atmospheric co2, mauna-loa, cycle, trends, exchange, dioxide, plant, biosphere},
}

@Article{Zhu2013,
  author       = {Zhu, Z. C. and Bi, J. and Pan, Y. Z. and Ganguly, S. and Anav, A. and Xu, L. and Samanta, A. and Piao, S. L. and Nemani, R. R. and Myneni, R. B.},
  date         = {2013},
  journaltitle = {Remote Sensing},
  title        = {Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011},
  doi          = {10.3390/rs5020927},
  number       = {2},
  pages        = {927--948},
  volume       = {5},
  abstract     = {Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary layer. LAI and FPAR are also state variables in hydrological, ecological, biogeochemical and crop-yield models. The generation, evaluation and an example case study documenting the utility of 30-year long data sets of LAI and FPAR are described in this article. A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products for the overlapping period 2000-2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the El Nino-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website.},
  file         = {:Zhu2013.pdf:PDF},
  groups       = {OBS},
  keywords     = {lai, fpar, ndvi3g, modis, nasa nex, artificial neural networks, remote sensing of vegetation, net primary production, modis-lai product, remote-sensing data, satellite data, land-cover, biophysical parameters, multiscale analysis, biosphere model, spot-vegetation, north-america},
}

@Article{Ziehn2017,
  author       = {Ziehn, T. and Lenton, A. and Law, R. M. and Matear, R. J. and Chamberlain, M. A.},
  date         = {2017},
  journaltitle = {Geoscientific Model Development},
  title        = {The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) - Part 2: Historical simulations},
  doi          = {10.5194/gmd-10-2591-2017},
  number       = {7},
  pages        = {2591--2614},
  volume       = {10},
  abstract     = {Over the last decade many climate models have evolved into Earth system models (ESMs), which are able to simulate both physical and biogeochemical processes through the inclusion of additional components such as the carbon cycle. The Australian Community Climate and Earth System Simulator (ACCESS) has been recently extended to include land and ocean carbon cycle components in its ACCESS-ESM1 version. A detailed description of ACCESS-ESM1 components including results from pre-industrial simulations is provided in Part 1. Here, we focus on the evaluation of ACCESS-ESM1 over the historical period (1850-2005) in terms of its capability to reproduce climate and carbon-related variables. Comparisons are performed with observations, if available, but also with other ESMs to highlight common weaknesses. We find that climate variables controlling the exchange of carbon are well reproduced. However, the aerosol forcing in ACCESS-ESM1 is somewhat larger than in other models, which leads to an overly strong cooling response in the land from about 1960 onwards. The land carbon cycle is evaluated for two scenarios: running with a prescribed leaf area index (LAI) and running with a prognostic LAI. We overestimate the seasonal mean (1.7 vs. 1.4) and peak amplitude (2.0 vs. 1.8) of the prognostic LAI at the global scale, which is common amongst CMIP5 ESMs. However, the prognostic LAI is our preferred choice, because it allows for the vegetation feedback through the coupling between LAI and the leaf carbon pool. Our globally integrated land-atmosphere flux over the historical period is 98 PgC for prescribed LAI and 137 PgC for prognostic LAI, which is in line with estimates of land use emissions (ACCESS-ESM1 does not include land use change). The integrated ocean-atmosphere flux is 83 PgC, which is in agreement with a recent estimate of 82 PgC from the Global Carbon Project for the period 1959-2005. The seasonal cycle of simulated atmospheric CO2 is close to the observed seasonal cycle (up to 1 ppm difference for the station at Mace Head and up to 2 ppm for the station at Mauna Loa), but shows a larger amplitude (up to 6 ppm) in the high northern latitudes. Overall, ACCESS-ESM1 performs well over the historical period, making it a useful tool to explore the change in land and oceanic carbon uptake in the future.},
  file         = {:Ziehn2017.pdf:PDF},
  groups       = {CMIP6 models},
  keywords     = {southern-ocean, cmip5 simulations, model description, future climate, nitrogen, phosphorus, co2, variability, sensitivity, limitation},
}

@Comment{jabref-meta: databaseType:biblatex;}

@Comment{jabref-meta: saveOrderConfig:specified;citationkey;false;author;false;title;true;}