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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=http://arxiv.org/abs/1906.05433|journal=arXiv:1906.05433 [cs, stat]}}</ref>]]
As described in "Tackling Climate Change with Machine Learning,"<ref name=":0" /><blockquote>"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 name=":1">{{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=|first=|publisher=|year=2018|isbn=|location=|pages=}}</ref><ref name=":2">{{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 Assessment Report of the Intergovernmental Panel on Climate Change|date=2014|first=|publisher=|year=|isbn=|location=|pages=}}</ref><ref>{{Cite book|url=https://www.ipcc.ch/report/ar5/wg3/|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|author=IPCC|date=2014|first=|publisher=|year=|isbn=|location=|pages=}}</ref>), help people calculate their climate risks (see [[Policy, Markets, and Decision Science]] and [[Climate Change Adaptation]]) and allow us to estimate the potential impacts of [[Solar Geoengineering|solar geoengineering]] [and exploreexploring Earth System response to different emission future scenarios, and under differnet assumptions]. 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/|title=Earth Observation Gateway|date=|access-date=|website=|last=|first=|archive-url=|archive-date=|url-status=live}}</ref><ref>{{Cite web|url=https://earthdata.nasa.gov|title=NASA EarthData|date=|access-date=|website=|last=|first=|archive-url=|archive-date=|url-status=live}}</ref>. Second, massive climate modeling projects are generating petabytes of simulated climate data<ref>{{Cite web|url=https://pcmdi.llnl.gov/CMIP6/|title=Coupled Model Intercomparison Project (CMIP)|date=|access-date=|website=|last=|first=|archive-url=|archive-date=|url-status=live}}</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=|url=https://repository.library.noaa.gov/view/noaa/14319|year=2017|date=|journal=NOAA Institutional Repository|volume=|pages=|via=}}</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>
 
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/|title=Earth Observation Gateway|date=|access-date=|website=|last=|first=|archive-url=|archive-date=|url-status=live}}</ref><ref>{{Cite web|url=https://earthdata.nasa.gov|title=NASA EarthData|date=|access-date=|website=|last=|first=|archive-url=|archive-date=|url-status=live}}</ref>. Second, massive climate modeling projects are generating petabytes of simulated climate data<ref>{{Cite web|url=https://pcmdi.llnl.gov/CMIP6/|title=Coupled Model Intercomparison Project (CMIP)|date=|access-date=|website=|last=|first=|archive-url=|archive-date=|url-status=live}}</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=|url=https://repository.library.noaa.gov/view/noaa/14319|year=2017|date=|journal=NOAA Institutional Repository|volume=|pages=|via=}}</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 ===
 
*'''[[Remote Sensing|Collecting data for climate models]]''': Assimilation of diverse sources can improve climate models, and machine learning can transform raw sensor output into more relevant derived data. Relevant applications include sensor calibration and analyzing information in remote sensing data. Well-curated benchmark datasets have the potential to advance several geoscience problems.
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=== Other ===
 
*'''Intergovernmental Panel on Climate Change (IPCC)''' Assessment Reports (e.g., AR5)<ref name=":2" /> and the IPCC Special Report Global Warming of 1.5 ºC<ref name=":1" />
*'''Oxford Research Encyclopedia of Climate Science'''<ref>{{Cite web|url=https://oxfordre.com/climatescience/climatescience/|title=Oxford Research Encyclopedia of Climate Science|website=Oxford Research Encyclopedia of Climate Science|language=en|access-date=2020-11-19}}</ref>: A collection of articles on the climate systems, impacts of climate change, and the methods used in climate science.
 
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=== Major conferences ===
 
*'''AGUAmerican FallGeosciences MeetingUnion (AGU)''' Fall Meeting: A yearly conference organized by the AmericanAGU, Geophysicalusually Uniontakes place in December, location varies across different states of the USA. Website [https://www.agu.org/fall-meeting here].
*'''European Geosciences Union (EGU)''' General Assembly: A yearly conference organized by the EGU, usually takes place in April or Early May in Vienna, Austria. Website [https://www.egu.eu/meetings/general-assembly/ here].
*'''International Meeting on Statistical Climatology (IMSC)''': Meetings occurring approximately every three years, usually around June or July, in different locations worldwide. IMSC is organised by statisticians, climatologists and atmospheric scientists aiming to transfer knowledge among different communities (e.g., see the [http://www.meteo.fr/cic/meetings/2019/IMSC/ previous meeting])
*'''Climate Informatics''' '''(CI)''': annual workshop series, usually occurring in September in different locations worldwide (e.g., see the [http://climateinformatics.org/?q=node/2 previous meetings]).
 
=== Major journals ===
Applications of Machine Learning in different domains of climate science appear in different climate-related journals, such as:
Climate science is a journal field. Noteworthy research appears in journals such as
 
*'''Bulletin of the American Meteorological Society (BAMS)''': A journal published by the AMS. Available [https://journals.ametsoc.org/bams here].
*'''GeophysicalEarth System ResearchDynamics Letters(ESD)''': TheAn open-access journal of the AmericanEuropean Geophysical Union. Available [https://agupubswww.onlinelibraryearth-system-dynamics.wiley.com/journalnet/19448007 here].
*'''ProceedingsEnvironmental ofResearch theLetters National Academy of Sciences(ERL):''': AAn wideopen-reachingaccess journal oftenfrom featuringIOPscience climatepublishing sciencegroup. Available [https://wwwiopscience.pnasiop.org/journal/1748-9326 here].
*'''Geophysical Research Letters (GRL):''' The journal of the American Geophysical Union. Available [https://agupubs.onlinelibrary.wiley.com/journal/19448007 here].
*'''Journal of Climate:'''
*'''Proceedings of the National Academy of Sciences (PNAS):''' A wide-reaching journal often featuring climate science. Available [https://www.pnas.org/ here].
*'''Springer Nature''' journals: '''Nature, Nature Climate Change, Nature Geoscience''', '''Nature Communications''', '''Communications Earth and Environment''' (open access), '''npj Climate and Atmospheric Science''' (open access).
 
=== Major societies and organizations ===
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