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Climate Modeling and Analysis: Difference between revisions
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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
Assessment Report of the Intergovernmental Panel on Climate Change|date=2014}}</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}}</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/}}</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.
== 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:▼
== Machine Learning Application Areas ==
* [https://esgf-node.llnl.gov/projects/cmip5/ The Coupled Model Intercomparison Project (CMIP)]▼
** CMIP is associated with the Earth System Grid Federation, which also provides data analysis tools and tutorials: https://esgf.llnl.gov/▼
* [http://www.cesm.ucar.edu/experiments/cesm1.1/LE/ 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]▼
* [https://cloud.google.com/public-datasets/weather/ 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.▼
*earthdata.nasa.gov▼
''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/.▼
[https://pangeo.io/ Pangeo] supports open source scientific python for geoscience applications.▼
* Pangeo also maintains a list of packages useful for [https://github.com/pangeo-data/awesome-open-climate-science atmospheric, ocean, and climate science].▼
▲== Recommended Readings ==
Some textbook length introductions to climate science include,
*
Other resources include,
*
== Online Courses and Course Materials ==
== Community ==
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=== Societies and organizations ===
*
*
=== Past and upcoming events ===
*
==
▲[https://pangeo.io/ Pangeo] supports open source scientific python for geoscience applications.
▲* 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:
▲* [https://esgf-node.llnl.gov/projects/cmip5/ The Coupled Model Intercomparison Project (CMIP)]
▲** CMIP is associated with the Earth System Grid Federation, which also provides data analysis tools and tutorials: https://esgf.llnl.gov/
▲* [http://www.cesm.ucar.edu/experiments/cesm1.1/LE/ 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]
▲* [https://cloud.google.com/public-datasets/weather/ 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.
▲*earthdata.nasa.gov
▲''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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