diff --git a/jose.00243/10.21105.jose.00243.crossref.xml b/jose.00243/10.21105.jose.00243.crossref.xml new file mode 100644 index 0000000..efe3030 --- /dev/null +++ b/jose.00243/10.21105.jose.00243.crossref.xml @@ -0,0 +1,182 @@ + + + + 20240819175050-ab82f5fa65c8c52e26eb0cc0b4254bec27bea117 + 20240819175050 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Education + JOSE + 2577-3569 + + 10.21105/jose + https://jose.theoj.org + + + + + 08 + 2024 + + + 7 + + 78 + + + + The University of Toronto Climate Downscaling Workflow: +Tools and Resources for Climate Change Impact Analysis + + + + Michael + Morris + https://orcid.org/0000-0002-5758-2182 + + + Paul J. + Kushner + https://orcid.org/0000-0002-6404-4518 + + + Karen L. + Smith + https://orcid.org/0000-0002-4652-6310 + + + + 08 + 19 + 2024 + + + 243 + + + 10.21105/jose.00243 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.12785645 + + + GitHub review issue + https://github.com/openjournals/jose-reviews/issues/243 + + + + 10.21105/jose.00243 + https://jose.theoj.org/papers/10.21105/jose.00243 + + + https://jose.theoj.org/papers/10.21105/jose.00243.pdf + + + + + + Downscaling Techniques for High-Resolution +Climate Projections: From Global Change to Local Impacts + Kotamarthi + 10.1017/9781108601269 + 978-1-108-47375-0 + 2021 + Kotamarthi, R., Hayhoe, K., Mearns, +L. O., Wuebbles, D., Jacobs, J., & Jurado, J. (2021). Downscaling +Techniques for High-Resolution Climate Projections: From Global Change +to Local Impacts. Cambridge University Press. +https://doi.org/10.1017/9781108601269 + + + A narrative approach to building +computational capacity for climate change impact assessment in +professional master’s students + Anderson + Journal of Open Source +Education + 46 + 4 + 10.21105/jose.00100 + 2577-3569 + 2021 + Anderson, C. I., & Smith, K. L. +(2021). A narrative approach to building computational capacity for +climate change impact assessment in professional master’s students. +Journal of Open Source Education, 4(46), 100. +https://doi.org/10.21105/jose.00100 + + + Statistical Downscaling and Bias Correction +for Climate Research + Maraun + 10.1017/9781107588783 + 978-1-107-06605-2 + 2018 + Maraun, D., & Widmann, M. (2018). +Statistical Downscaling and Bias Correction for Climate Research. +Cambridge University Press. +https://doi.org/10.1017/9781107588783 + + + Overview of the Coupled Model Intercomparison +Project Phase 6 (CMIP6) experimental design and +organization + Eyring + Geosci. Model Dev. + 5 + 9 + 10.5194/gmd-9-1937-2016 + 1991-9603 + 2016 + Eyring, V., Bony, S., Meehl, G. A., +Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). +Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) +experimental design and organization. Geosci. Model Dev., 9(5), +1937–1958. +https://doi.org/10.5194/gmd-9-1937-2016 + + + Xclim: Xarray-based climate data +analytics + Bourgault + Journal of Open Source +Software + 85 + 8 + 10.21105/joss.05415 + 2475-9066 + 2023 + Bourgault, P., Huard, D., Smith, T. +J., Logan, T., Aoun, A., Lavoie, J., Dupuis, E., Rondeau-Genesse, G., +Alegre, R., Barnes, C., Laperriere, A. B., Biner, S., Caron, D., +Ehbrecht, C., Fyke, J., Keel, T., Labonte, M.-P., Lierhammer, L., Low, +J.-F., … Whelan, C. (2023). Xclim: Xarray-based climate data analytics. +Journal of Open Source Software, 8(85), 5415. +https://doi.org/10.21105/joss.05415 + + + + + + diff --git a/jose.00243/10.21105.jose.00243.pdf b/jose.00243/10.21105.jose.00243.pdf new file mode 100644 index 0000000..2431961 Binary files /dev/null and b/jose.00243/10.21105.jose.00243.pdf differ diff --git a/jose.00243/paper.jats/10.21105.jose.00243.jats b/jose.00243/paper.jats/10.21105.jose.00243.jats new file mode 100644 index 0000000..6f36ad1 --- /dev/null +++ b/jose.00243/paper.jats/10.21105.jose.00243.jats @@ -0,0 +1,340 @@ + + +
+ + + + +Journal of Open Source Education +JOSE + +2577-3569 + +Open Journals + + + +243 +10.21105/jose.00243 + +The University of Toronto Climate Downscaling Workflow: +Tools and Resources for Climate Change Impact Analysis + + + +https://orcid.org/0000-0002-5758-2182 + +Morris +Michael + + + + +https://orcid.org/0000-0002-6404-4518 + +Kushner +Paul J. + + + + +https://orcid.org/0000-0002-4652-6310 + +Smith +Karen L. + + + + + +Department of Physics, University of Toronto + + + + +Department of Physical and Environmental Sciences, +University of Toronto Scarborough + + + +7 +78 +243 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + + + + + Summary +

The University of Toronto Climate Downscaling Workflow (UTCDW) is a + resource designed to teach users how to produce their own + statistically downscaled climate projections for the purpose of + climate change impact analysis. The target audience includes graduate + students and practitioners in engineering, the physical, mathematical, + and computational sciences, who are interested in how their subject of + study is sensitive to climate change. The main component of the UTCDW + is a + Jupyter + Book called the “UTCDW Guidebook”. The Guidebook introduces + users to basic climate science concepts and beginning-to-intermediate + concepts in the application of statistical climate downscaling. It + also works through the decisions that must be made when designing a + climate change impact study. Finally, it demonstrates how to download + climate data, do exploratory analysis and model validation, and + provides examples of end-to-end workflows for small climate + downscaling projects. The Guidebook was written for self-study by + members of the target audience, though it may be included as a part of + a course on climate change impact analysis for graduate students or + upper-year undergraduate students in the applied sciences. All of the + Guidebook source materials are accessible in the + UTCDW_Guidebook + GitHub repository.

+
+ + Statement of Need +

Important natural and human-designed systems are sensitive to + weather and climate conditions, and are therefore also sensitive to + the impacts of anthropogenic climate change. Experts in these fields + need to assess climate change risks, but unprocessed climate model + output such as those from the CMIP6 + (Eyring + et al., 2016) archive is usually not fit for this purpose. Raw + climate model outputs do not directly correspond to useful metrics for + calculating loads and hazards, and are often not available on the + required spatial or temporal scales. Downscaling + refers to a set of procedures that adjust and map climate data to + variables, times, and locations, relevant for discipline-specific + applications. It involves physical reasoning and statistical + bias-correction of climate model output using observations and + analysis requirements for specific study domains. Unfortunately, there + is no single accepted standard for downscaling, only a range of + methods that are centred on the practice of different climate-service + providers. This makes it hard for new researchers to get started and + perform their own analysis, especially when existing downscaled data + products do not suit their needs. The UTCDW documents state-of-the-art + methods for statistical bias correction and downscaling and show users + how to implement them.

+

Other educational materials on climate change impact assessment are + available, but they are either limited regarding the complexity of the + methods + (Anderson + & Smith, 2021), lack code examples to help new users + actually work with the data + (Kotamarthi + et al., 2021), or are too advanced for a first introduction to + downscaling + (Maraun + & Widmann, 2018). The UTCDW Guidebook fills this gap by + providing a basic introduction to concepts in climate science and + including worked examples and code for downscaling methods appropriate + for applications. This makes the UTCDW ideal for users who are new to + these concepts and wish to be able to start doing data analysis for + their impact study as quickly as possible.

+
+ + Guidebook Content +

The UTCDW Guidebook consists of six chapters. The first four + chapters introduce the reader the background knowledge required to + perform downscaling. Chapter 1 serves as an introduction, and guides + users in configuring their Python environment. Chapter 2 explains how + climate change projections are made and what their limitations are. + Chapter 3 demonstrates how to access observational, reanalysis, and + climate model data, and how to do exploratory analysis with + observational and raw climate model data. Chapter 4 contains + explanations of various methods of bias-correction and downscaling, + and examples of how to validate historical downscaled model output and + assess future projections. Python code for the downscaling methods is + provided, leveraging the utility of xclim + (Bourgault + et al., 2023) and the + Pangeo + software ecosystem.

+

The next two chapters focus on applying the content of Chapters + 1-4. Chapter 5 introduces the “Downscaling Workflow” part of the + UTCDW. This chapter unpacks the decisions that must be made when + designing a climate change impact study and breaks the analysis tasks + down into digestible steps. Chapter 6 contains examples of the + workflow. Each example demonstrates the process of stating a problem, + acquiring the necessary data, producing calibrated climate + projections, and quantifying uncertainty. The examples focus on + Canadian regions and observational data products, though we include + links to available observational and downscaled data products for + additional regions as well as global data. We welcome user submission + of additional worked examples to contribute to the gallery of examples + to be included on the website, or future versions of the UTCDW + Guidebook

+

The UTCDW also includes a + website + that contains a guided survey that helps a user design their climate + impact study and generates a flowchart that lays out the workflow. The + flowcharts are featured in the worked examples to explain the + procedure before conducting the analysis. We recommend that students + use them to explain their projects to their instructors and peers, or + even include them in publications produced using the Guidebook + methods.

+
+ + Teaching Experience +

The guidebook material has been used by students from three + cohorts: two undergraduate reading courses and two summer research + students. The progress of the students demonstrates the strength of + the Guidebook as a learning resource. They each started with little or + no background in climate science or Pangeo software, and by the end of + their terms they were able to independently conduct their own climate + change impact analysis projects. We also hosted a hackathon where 30 + participants used the Guidebook and workflow to tackle climate change + impact challenges related to irrigation water demand, snowfall, and + extreme heat. Most participants came to the event with little + experience working with climate data and left having successfully + implemented the downscaling workflow, further proving that Guidebooks + meets its purpose as a learning resource.

+
+ + Acknowledgements +

We acknowledge Anson Cheung, Peikun Guo, Claire Pan, Cassandra + Chanen, and Lilian Chan for testing the UTCDW Guidebook content during + its development. We acknowledge funding from the University of + Toronto’s + Centre + for Climate Science and Engineering, + Climate + Positive Energy Initiative, and + Data + Sciences Institute.

+
+ + + + + + + + KotamarthiRao + HayhoeKatharine + MearnsLinda O. + WuebblesDonald + JacobsJennifer + JuradoJennifer + + Downscaling Techniques for High-Resolution Climate Projections: From Global Change to Local Impacts + Cambridge University Press + Cambridge + 2021 + 978-1-108-47375-0 + https://www.cambridge.org/core/books/downscaling-techniques-for-highresolution-climate-projections/C261452F6DECC0372077B7533414CD95 + 10.1017/9781108601269 + + + + + + AndersonConor I. + SmithKaren L. + + A narrative approach to building computational capacity for climate change impact assessment in professional master’s students + Journal of Open Source Education + 202112 + 4 + 46 + 2577-3569 + https://jose.theoj.org/papers/10.21105/jose.00100 + 10.21105/jose.00100 + 100 + + + + + + + MaraunDouglas + WidmannMartin + + Statistical Downscaling and Bias Correction for Climate Research + Cambridge University Press + Cambridge + 2018 + 978-1-107-06605-2 + https://www.cambridge.org/core/books/statistical-downscaling-and-bias-correction-for-climate-research/4ED479BAA8309C7ECBE6136236E3960F + 10.1017/9781107588783 + + + + + + EyringVeronika + BonySandrine + MeehlGerald A. + SeniorCatherine A. + StevensBjorn + StoufferRonald J. + TaylorKarl E. + + Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization + Geosci. Model Dev. + 201605 + 9 + 5 + 1991-9603 + https://gmd.copernicus.org/articles/9/1937/2016/ + 10.5194/gmd-9-1937-2016 + 1937 + 1958 + + + + + + BourgaultPascal + HuardDavid + SmithTrevor James + LoganTravis + AounAbel + LavoieJuliette + DupuisEric + Rondeau-GenesseGabriel + AlegreRaquel + BarnesClair + LaperriereAlexis Beaupre + BinerSebastien + CaronDavid + EhbrechtCarsten + FykeJeremy + KeelTom + LabonteMarie-Pier + LierhammerLudwig + LowJwen-Fai + QuinnJamie + RoyPhilippe + SquireDougie + StephensAg + TanguyMaliko + WhelanChristopher + + Xclim: Xarray-based climate data analytics + Journal of Open Source Software + 202305 + 20231109 + 8 + 85 + 2475-9066 + https://joss.theoj.org/papers/10.21105/joss.05415 + 10.21105/joss.05415 + 5415 + + + + + +