Viewing forced climate patterns through an AI Lens
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Abstract Many problems in climate science require extracting forced signals from a background of internal climate variability. We demonstrate that artificial neural networks (ANNs) are a useful addition to the climate science “toolbox” for this purpose. Specifically, forced patterns are detected by an ANN trained on climate model simulations under historical and future climate scenarios. By identifying spatial patterns that serve as indicators of change in surface temperature and precipitation, the ANN can determine the approximate year from which the simulations came without first explicitly separating the forced signal from the noise of both internal climate variability and model uncertainty. Thus, the ANN indicator patterns are complex, nonlinear combinations of signal and noise and are identified from the 1960s onward in simulated and observed surface temperature maps. This approach suggests that viewing climate patterns through an artificial intelligence (AI) lens has the power to uncover new insights into climate variability and change.
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Concerning climate change in the urban context, some studies addressed mitigation concerning urban planning, mobility, and land use.
“Concerning climate change in the urban context, some studies addressed mitigation concerning urban planning, mobility, and land use (e.g., Ref. [211,212]).”
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- Authors
- Barnes, E. A., Hurrell, J. W., Ebert‐Uphoff, I., Anderson, C., & Anderson, D.
- Affiliations
- Colorado State University (US)
- Cooperative Institute for Research in the Atmosphere (US)
- Department of Computer Science Colorado State University Fort Collins CO USA
- Pattern Exploration LLC Fort Collins CO USA
- Corresponding author
- Barnes, Elizabeth A.
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2019-11-27
- Volume
- 46
- Issue
- 22
- Pages
- 13389-13398
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W2991419155
- Cited by
- 179
- References
- 41
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 13 · 2023: 45 · 2022: 41 · 2021: 34 · 2020: 14
- FWCI
- 11.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Geophysical Research Letters (publishedVersion, cc-by)
- NOAA Institutional Repository (submittedVersion, cc-by)
- Funders
- National Science Foundation (AGS‐1749261, AGS‐1445978)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Geoengineering
- Keywords
- natural climate variability, artificial neural networks, climate model simulation, surface temperature, precipitation, model uncertainty, climate change detection, climate change attribution
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