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=http://arxiv.org/abs/1906.05433|journal=arXiv:1906.05433 [cs, stat]}}</ref>]] |
''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>]] |
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As described in "Tackling Climate Change with Machine Learning,"<ref name=":0" /><blockquote> |
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>{{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>{{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 explore Earth System response to different emission future scenarios, and under differnet assumptions]. |
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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/|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|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> |
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== Machine Learning Application Areas == |
== Machine Learning Application Areas == |