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
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|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 §10 and §8) and allow us to estimate the potential impacts of solar geoengineering.
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]].


Recent trends have created opportunities for ML to advance the state-of-the-art in climate prediction (Fig. 6). First, new and cheaper satellites are creating petabytes of climate observation data.32 Second, massive climate modeling projects are generating petabytes of simulated climate data.33 Third, climate forecasts are computationally expensive [473] (the simulations in [472] took three weeks to run on NCAR supercomputers), 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.
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 ==
== 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 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:
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* [https://cloud.google.com/public-datasets/weather/ Google Cloud Weather and Climate Datasets]
* [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.
** 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.
''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/
The Earth and climate science community is also working to create benchmark datasets: https://is-geo.org/benchmarks/.
<br />
== Methods and Software ==
== Methods and Software ==
[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 ==
== Recommended Readings ==
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* [https://www.youtube.com/watch?v=XGi2a0tNjOo&feature=youtu.be An Introduction to Climate Modeling], a video lesson from Climate Literacy's Youtube channel
* [https://www.youtube.com/watch?v=XGi2a0tNjOo&feature=youtu.be An Introduction to Climate Modeling], a video lesson from Climate Literacy's Youtube channel
*


<br />
== Community ==
== Community ==


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=== Societies and organizations ===
=== Societies and organizations ===

* [https://www.agu.org/ AGU]
* [http://climateinformatics.org/ Climate Informatics]


=== Past and upcoming events ===
=== Past and upcoming events ===

* [https://www.agu.org/fall-meeting AGU Fall Meeting 2020]


== Important considerations ==
== Important considerations ==

Revision as of 22:16, 12 August 2020

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[1][2][3]), 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.

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[4][5]. Second, massive climate modeling projects are generating petabytes of simulated climate data[6]. Third, climate forecasts are computationally expensive[7] (some simulations have taken three weeks to run on NCAR supercomputers[8]), 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:

N.B. Climate model data is typically presented in netcdf4 format. These may be smoothly converted to csv files or 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/.

Methods and Software

Pangeo supports open source scientific python for geoscience applications.

Recommended Readings

Some textbook length introductions to climate science include,

Other resources include,

Community

Journals and conferences

Climate science is a journal field. Noteworthy research appears in journals such as the Bulletin of the American Meteorological Society, Geophysical Research Letters and the Proceedings of the National Academy of Sciences.

Societies and organizations

Past and upcoming events

Important considerations

Next steps

References

  1. ↑ IPCC (October 2018.). 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. line feed character in |title= at position 94 (help); Check date values in: |date= (help)
  2. ↑ IPCC (2014). 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. line feed character in |title= at position 104 (help)
  3. ↑ IPCC (2014). 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. line feed character in |title= at position 97 (help)
  4. ↑ https://earth.esa.int/eogateway/. Missing or empty |title= (help)
  5. ↑ [earthdata.nasa.gov earthdata.nasa.gov] Check |url= value (help). Missing or empty |title= (help)
  6. ↑ [cmip.llnl.gov cmip.llnl.gov] Check |url= value (help). Missing or empty |title= (help)
  7. ↑ Carman, T (2017). "Position paper on high performance computing needs in Earth system prediction". Unknown parameter |coauthors= ignored (|author= suggested) (help); line feed character in |coauthors= at position 97 (help); Cite journal requires |journal= (help)
  8. ↑ "The Community Earth System Model (CESM) Large Ensemble project". 2015. Cite journal requires |journal= (help)
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