Climate Modeling and Analysis

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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 Wikipedia page on this topic.
The main avenues through which machine learning can support climate science, as described in [1].

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[2][3][4]), 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[5][6]. Second, massive climate modeling projects are generating petabytes of simulated climate data[7]. Third, climate forecasts are computationally expensive[8] (some simulations have taken three weeks to run on NCAR supercomputers[9]), 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.

Machine Learning Application Areas

Background Readings

Some textbook length introductions to climate science include,

Other resources include,

Online Courses and Course Materials

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

Libraries and Tools

Pangeo supports open source scientific python for geoscience 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/.

References

  1. "Tackling Climate Change with Machine Learning" (PDF). Cite journal requires |journal= (help)
  2. 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)
  3. 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)
  4. 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)
  5. https://earth.esa.int/eogateway/. Missing or empty |title= (help)
  6. [earthdata.nasa.gov earthdata.nasa.gov] Check |url= value (help). Missing or empty |title= (help)
  7. [cmip.llnl.gov cmip.llnl.gov] Check |url= value (help). Missing or empty |title= (help)
  8. 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)
  9. "The Community Earth System Model (CESM) Large Ensemble project". 2015. Cite journal requires |journal= (help)