Editing Climate Modeling and Analysis

''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=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>

== Machine Learning Application Areas ==

=== Uniting data, ML, and climate science ===

*'''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. Well-curated benchmark datasets have the potential to advance several geoscience problems.
*'''Accelerating climate models''': Physical constraints are key ingredients for cloud, aerosol, ice sheet, and sea level models. Traditional solutions to these physics-based models are computationally expensive, but machine learning components can help alleviate the most problematic bottlenecks.
*'''Working with climate models''': It is possible to streamline existing climate models, pruning them down to key relationships simplifying computation with ensembles.

=== Forecasting extreme events ===

*'''Storm tracking''': While climate models can forecast long-term changes in the climate system, separate systems are required to detect specific extreme weather phenomena, like cyclones, atmospheric rivers, and tornadoes.
*'''Local forecasts''': Machine learning can be used to refine what are otherwise coarse-grained forecasts. These high-resolution forecasts can guide improvements in system robustness and resilience.

== 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].
*'''Principles of Planetary Climate (2010)'''<ref>{{Cite book|url=https://www.worldcat.org/title/mediawiki/oclc/601113992|title=Principles of planetary climate|last=Pierrehumbert|first=Raymond T.|date=2010|publisher=Cambridge University Press|isbn=978-0-521-86556-2|location=Cambridge ; New York|oclc=601113992}}</ref>: An introduction to the physics of climate, with examples in python.

=== 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].
*'''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.

== Online Courses and Course Materials ==

== Community ==

=== Major conferences ===

*'''AGU Fall Meeting''': A yearly conference organized by the American Geophysical Union. Website [https://www.agu.org/fall-meeting here].

=== Major journals ===
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].

=== Major 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/ here].

== Libraries and Tools ==
'''Pangeo''': An open source python package for geoscience applications, available [https://pangeo.io/ here].

* Pangeo also maintains a list of packages useful for [https://github.com/pangeo-data/awesome-open-climate-science atmospheric, ocean, and climate science].

== Data ==
The largest climate prediction datasets are ensembles of many climate simulations. These include simulations with varied physics, architectures, or initial conditions, and they are used to explore the range of possible climate futures. The largest ensembles include:

*'''The Coupled Model Intercomparison Project (CMIP)''': A gateway to climate models in use and development, available [https://esgf-node.llnl.gov/projects/cmip5/ here]. CMIP is associated with the Earth System Grid Federation, which also provides data analysis tools and tutorials: https://esgf.llnl.gov/
*'''The CESM Large Ensemble''': Read about it in [https://journals.ametsoc.org/doi/full/10.1175/BAMS-D-13-00255.1 The Community Earth System Model (CESM) Large Ensemble Project]. Available [http://www.cesm.ucar.edu/experiments/cesm1.1/LE/ here].
*'''Google Cloud Weather and Climate Datasets''': Petabyte-scale weather and climate datasets from sources like NOAA’s [https://www.ncdc.noaa.gov/data-access/radar-data/nexrad NEXRAD] and NASA/USGS’s [https://landsat.gsfc.nasa.gov/ Landsat], made available for free as part of Google Cloud’s Public Datasets Program. Available [https://cloud.google.com/public-datasets/weather here].
*'''EARTHDATA''': NASA's gateway to earth science data. Data are available at multiple levels of processing. Available [https://earthdata.nasa.gov/ here].

''N.B.'' Climate model data is typically presented in [https://climatedataguide.ucar.edu/climate-data-tools-and-analysis/netcdf-overview netcdf4] format. These may be smoothly converted to csv files or [https://stackoverflow.com/questions/14035148/import-netcdf-file-to-pandas-dataframe pandas dataframes], but be aware that the data lies on irregular 3D spherical grids.

The Earth and climate science community is also working to create benchmark datasets: https://is-geo.org/benchmarks/.
== References ==
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[[category:Problem_domains]]