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), 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. Second, massive climate modeling projects are generating petabytes of simulated climate data. Third, climate forecasts are computationally expensive (some simulations have taken 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.
Machine Learning Application Areas
Some textbook length introductions to climate science include,
Other resources include,
- An Introduction to Climate Modeling, a video lesson from Climate Literacy's Youtube channel
Online Courses and Course Materials
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.
- Pangeo also maintains a list of packages useful for atmospheric, ocean, and climate science.
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)
- 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 The Community Earth System Model (CESM) Large Ensemble Project
- Google Cloud Weather and Climate Datasets
The Earth and climate science community is also working to create benchmark datasets: https://is-geo.org/benchmarks/.
- "Tackling Climate Change with Machine Learning" (PDF). Cite journal requires
- 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
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- 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
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- 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
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- Carman, T (2017). "Position paper on high performance computing needs in Earth system prediction". Unknown parameter
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- "The Community Earth System Model (CESM) Large Ensemble project". 2015. Cite journal requires