Climate model evaluation
🌎 This article is a stub, and is currently under construction. You can help by adding to it!
This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.
Climate models can be extremely complex, and involve interactions and feedbacks among different components of the climate system. The resulting climate predictions are often made using the outputs of 20+ different climate models, which leads to a wide spread of future climate projections. However, since some components are shared among some climate models, the multi-model mean response is not truly independent. ML can help identify and leverage relationships between variables within climate models, which, together with the observed climate changes (i.e., observational constraint) could narrow down the spread in the future climate projections.
Background Readings[edit | edit source]
Conferences, Journals, and Professional Organizations[edit | edit source]
Libraries and Tools[edit | edit source]
Data[edit | edit source]
Future Directions[edit | edit source]
Relevant Groups and Organizations[edit | edit source]
References[edit | edit source]
- ↑ Knutti, Reto (2010-10-01). "The end of model democracy?". Climatic Change. 102 (3): 395–404. doi:10.1007/s10584-010-9800-2. ISSN 1573-1480.
- ↑ Nowack, Peer; Runge, Jakob; Eyring, Veronika; Haigh, Joanna D. (2020-03-16). "Causal networks for climate model evaluation and constrained projections". Nature Communications. 11 (1): 1415. doi:10.1038/s41467-020-15195-y. ISSN 2041-1723.
- ↑ Schlund, Manuel; Lauer, Axel; Gentine, Pierre; Sherwood, Steven C.; Eyring, Veronika (2020-12-21). "Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?". Earth System Dynamics. 11 (4): 1233–1258. doi:10.5194/esd-11-1233-2020. ISSN 2190-4979.
- ↑ Schlund, Manuel; Eyring, Veronika; Camps‐Valls, Gustau; Friedlingstein, Pierre; Gentine, Pierre; Reichstein, Markus (2020). "Constraining Uncertainty in Projected Gross Primary Production With Machine Learning". Journal of Geophysical Research: Biogeosciences. 125 (11): e2019JG005619. doi:10.1029/2019JG005619. ISSN 2169-8961.