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<?xml version="1.0" encoding="utf-8" ?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN"
"JATS-publishing1.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.2" article-type="other">
<front>
<journal-meta>
<journal-id></journal-id>
<journal-title-group>
<journal-title>Journal of Open Source Education</journal-title>
<abbrev-journal-title>JOSE</abbrev-journal-title>
</journal-title-group>
<issn publication-format="electronic">2577-3569</issn>
<publisher>
<publisher-name>Open Journals</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">243</article-id>
<article-id pub-id-type="doi">10.21105/jose.00243</article-id>
<title-group>
<article-title>The University of Toronto Climate Downscaling Workflow:
Tools and Resources for Climate Change Impact Analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-5758-2182</contrib-id>
<name>
<surname>Morris</surname>
<given-names>Michael</given-names>
</name>
<xref ref-type="aff" rid="aff-1"/>
</contrib>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-6404-4518</contrib-id>
<name>
<surname>Kushner</surname>
<given-names>Paul J.</given-names>
</name>
<xref ref-type="aff" rid="aff-1"/>
</contrib>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4652-6310</contrib-id>
<name>
<surname>Smith</surname>
<given-names>Karen L.</given-names>
</name>
<xref ref-type="aff" rid="aff-2"/>
</contrib>
<aff id="aff-1">
<institution-wrap>
<institution>Department of Physics, University of Toronto</institution>
</institution-wrap>
</aff>
<aff id="aff-2">
<institution-wrap>
<institution>Department of Physical and Environmental Sciences,
University of Toronto Scarborough</institution>
</institution-wrap>
</aff>
</contrib-group>
<volume>7</volume>
<issue>78</issue>
<fpage>243</fpage>
<permissions>
<copyright-statement>Authors of papers retain copyright and release the
work under a Creative Commons Attribution 4.0 International License (CC
BY 4.0)</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>The article authors</copyright-holder>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>Authors of papers retain copyright and release the work under
a Creative Commons Attribution 4.0 International License (CC BY
4.0)</license-p>
</license>
</permissions>
</article-meta>
</front>
<body>
<sec id="summary">
<title>Summary</title>
<p>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
<ext-link ext-link-type="uri" xlink:href="https://utcdw.physics.utoronto.ca/UTCDW_Guidebook/README.html">Jupyter
Book</ext-link> 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
<ext-link ext-link-type="uri" xlink:href="https://github.com/mikemorris12/UTCDW_Guidebook">UTCDW_Guidebook
GitHub repository</ext-link>.</p>
</sec>
<sec id="statement-of-need">
<title>Statement of Need</title>
<p>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
(<xref alt="Eyring et al., 2016" rid="ref-eyring2016" ref-type="bibr">Eyring
et al., 2016</xref>) 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. <italic>Downscaling</italic>
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.</p>
<p>Other educational materials on climate change impact assessment are
available, but they are either limited regarding the complexity of the
methods
(<xref alt="Anderson &amp; Smith, 2021" rid="ref-anderson2021" ref-type="bibr">Anderson
&amp; Smith, 2021</xref>), lack code examples to help new users
actually work with the data
(<xref alt="Kotamarthi et al., 2021" rid="ref-kotamarthi2021" ref-type="bibr">Kotamarthi
et al., 2021</xref>), or are too advanced for a first introduction to
downscaling
(<xref alt="Maraun &amp; Widmann, 2018" rid="ref-maraun2018" ref-type="bibr">Maraun
&amp; Widmann, 2018</xref>). 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.</p>
</sec>
<sec id="guidebook-content">
<title>Guidebook Content</title>
<p>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 <monospace>xclim</monospace>
(<xref alt="Bourgault et al., 2023" rid="ref-bourgault2023" ref-type="bibr">Bourgault
et al., 2023</xref>) and the
<ext-link ext-link-type="uri" xlink:href="https://pangeo.io/packages.html">Pangeo</ext-link>
software ecosystem.</p>
<p>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</p>
<p>The UTCDW also includes a
<ext-link ext-link-type="uri" xlink:href="https://utcdw.physics.utoronto.ca/">website</ext-link>
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.</p>
</sec>
<sec id="teaching-experience">
<title>Teaching Experience</title>
<p>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.</p>
</sec>
<sec id="acknowledgements">
<title>Acknowledgements</title>
<p>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
<ext-link ext-link-type="uri" xlink:href="https://uoftcse.ca/">Centre
for Climate Science and Engineering</ext-link>,
<ext-link ext-link-type="uri" xlink:href="https://cpe.utoronto.ca/">Climate
Positive Energy Initiative</ext-link>, and
<ext-link ext-link-type="uri" xlink:href="https://datasciences.utoronto.ca/">Data
Sciences Institute</ext-link>.</p>
</sec>
</body>
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