Climate Modeling and Analysis: Difference between revisions
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''This page is about the intersection of climate science and machine learning in the context of climate change adaptation. For an overview of climate science as a whole, please see the [https://en.wikipedia.org/wiki/Climatology Wikipedia page] on this topic.''[[File:Clim prediction.png|thumb|The main avenues through which machine learning can support climate science, as described in "Tackling Climate Change with Machine Learning." <ref name=":0">{{Cite journal|last=Rolnick|first=David|last2=Donti|first2=Priya L.|last3=Kaack|first3=Lynn H.|last4=Kochanski|first4=Kelly|last5=Lacoste|first5=Alexandre|last6=Sankaran|first6=Kris|last7=Ross|first7=Andrew Slavin|last8=Milojevic-Dupont|first8=Nikola|last9=Jaques|first9=Natasha|last10=Waldman-Brown|first10=Anna|last11=Luccioni|first11=Alexandra|date=2019-11-05|title=Tackling Climate Change with Machine Learning|url=
As described in "Tackling Climate Change with Machine Learning,"<ref name=":0" /><blockquote>The first global warming prediction was made in 1896, when Arrhenius estimated that burning fossil fuels could eventually release enough CO2 to warm the Earth by 5°C. The fundamental physics underlying those calculations has not changed, but our predictions have become far more detailed and precise. The predominant predictive tools are climate models, known as General Circulation Models (GCMs) or Earth System Models (ESMs). These models inform local and national government decisions<ref>{{Cite book|url=https://www.ipcc.ch/sr15/|title=Global warming of 1.5C. An IPCC special report on the impacts of global warming of 1.5C above
pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the
global response to the threat of climate change, sustainable development, and efforts to eradicate poverty|author=IPCC|coauthors=|date=October 2018.}}</ref><ref>{{Cite book|url=https://www.ipcc.ch/report/ar5/wg3/|author=IPCC|title=IPCC. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth
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Recent trends have created opportunities for ML to advance the state-of-the-art in climate prediction. First, new and cheaper satellites are creating petabytes of climate observation data<ref>{{Cite web|url=https://earth.esa.int/eogateway/}}</ref><ref>{{Cite web|url=earthdata.nasa.gov}}</ref>. Second, massive climate modeling projects are generating petabytes of simulated climate data<ref>{{Cite web|url=cmip.llnl.gov}}</ref>. Third, climate forecasts are computationally expensive<ref>{{Cite journal|title=Position paper on high performance computing needs in Earth system prediction|last=Carman|first=T|coauthors=T Clune, F Giraldo, M Govett, B Gross, A Kamrathe, T Lee, D McCarren, J Michalakes, S Sandgathe,
and T Whitcomb|url=https://repository.library.noaa.gov/view/noaa/14319|year=2017}}</ref> (some simulations have taken three weeks to run on NCAR supercomputers<ref>{{Cite journal|title=The Community Earth System Model (CESM) Large Ensemble project|year=2015|url=https://journals.ametsoc.org/bams/article/96/8/1333/69450}}</ref>), while ML methods are becoming increasingly fast to train and run, especially on next-generation computing hardware. As a result, climate scientists have recently begun to explore ML techniques, and are starting to team up with computer scientists to build new and exciting applications.</blockquote>
== Machine Learning Application Areas ==
=== Uniting data, ML, and climate science ===
* Data for climate models
* Accelerating climate models
* Working with climate models
=== Forecasting extreme events ===
* Storm tracking
* Local forecasts
== Background Readings ==
=== Textbooks ===
*'''Introduction to climate dynamics and climate modeling (2010)'''<ref>{{Cite book|title=Climate system dynamics and modeling|last=Goosse|first=Hugues|date=2015|publisher=Cambridge University Press|isbn=978-1-107-08389-9|location=New York, NY}}</ref>: A technical treatment of the climate system, energy balance, climate modeling, and climate perturbations. Available [http://www.climate.be/textbook/contents.html here].
=== Other ===
*'''An Introduction to Climate Modeling (2014)'''<ref>{{Cite web|url=https://www.youtube.com/watch?v=XGi2a0tNjOo&feature=youtu.be|title=5.1 Introduction to Climate Modeling - YouTube|website=www.youtube.com|access-date=2020-09-24}}</ref>: A video lesson from Climate Literacy's Youtube channel. Available [https://www.youtube.com/watch?v=XGi2a0tNjOo&feature=youtu.be here].
== Online Courses and Course Materials ==
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=== Journals and conferences ===
Climate science is a journal field. Noteworthy research appears in journals such as
* '''Bulletin of the American Meteorological Society''': A journal published by the AMS. Available [https://journals.ametsoc.org/bams here].
* '''Geophysical Research Letters''': The journal of the American Geophysical Union. Available [https://agupubs.onlinelibrary.wiley.com/journal/19448007 here].
* '''Proceedings of the National Academy of Sciences''': A wide-reaching journal often featuring climate science. Available [https://www.pnas.org/ here].
=== Societies and organizations ===
*'''American Geophysical Union''': An organization supporting work across the geophysical sciences. Website [https://www.agu.org/ here].
*'''Climate Informatics''': An organization dedicated to computing in climate science. Website [http://climateinformatics.org/
=== Past and upcoming events ===
*'''AGU Fall Meeting''': A yearly conference organized by the American Geophysical Union. Website [https://www.agu.org/fall-meeting
== Libraries and Tools ==
* Pangeo also maintains a list of packages useful for [https://github.com/pangeo-data/awesome-open-climate-science atmospheric, ocean, and climate science].
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