Climate Modeling and Analysis: Difference between revisions
→References
Krisrs1128 (talk | contribs) (→Data) |
Krisrs1128 (talk | contribs) |
||
Line 1:
''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 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=|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]].
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=
== Machine Learning Application Areas ==
Line 69 ⟶ 64:
The Earth and climate science community is also working to create benchmark datasets: https://is-geo.org/benchmarks/.
== References ==
<references />
| |||