Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations
Open accessAbstract
Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high‐resolution simulations in an Earth system model (ESM) that systematically learns from both and quantifies uncertainties. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high‐resolution simulations, for example, of clouds and convection, through matching low‐order statistics between ESMs, observations, and high‐resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.
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The results provide strong evidence for the disputed hypothesis that learning to predict short-term weather is an effective way to tune parameterizations for climate.
“Our results provide strong evidence for the disputed hypothesis37–39 that learning to predict short-term weather is an effective way to tune parameterizations for climate.”
From Neural general circulation models for weather and climate (2024) · cites this paper as [38]
Cites
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Deep learning to represent subgrid processes in climate models
Rasp, S., Pritchard, M. S., & Gentine, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810286115 · Cited by 1,044 · see citing works
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“However, we will probably need alternative training strategies 38,39 to learn important processes for climate with subtle impacts on weather timescales, such as a cloud feedback.”
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Data‐Driven Equation Discovery of Ocean Mesoscale Closures
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“prediction performance remains far from challenging the prediction capabilities of physics modellingdriven procedures [16], they prove useful to improve physics models [19] or their parameter tuning [20, 21], to complementing them for analysis or pattern recognition [22], or to performing tasks not achievable with phy…”
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Gelbrecht, M., White, A., Bathiany, S., & Boers, N. (2023) · Geoscientific Model Development · doi:10.5194/gmd-16-3123-2023 · Cited by 31 · see citing works
- Authors
- Schneider, T., Lan, S., Stuart, A., & Teixeira, J.
- Affiliations
- California Institute of Technology (US)
- Jet Propulsion Laboratory (US)
- Corresponding author
- Schneider, Tapio
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2017-12-28
- Volume
- 44
- Issue
- 24
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W2750605337
- Cited by
- 391
- References
- 2
- Citations by year
- 2026: 35 · 2025: 45 · 2024: 44 · 2023: 44 · 2022: 57 · 2021: 82 · 2020: 37 · 2019: 35 · 2018: 11 · 2017: 1
- FWCI
- 13.29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Geophysical Research Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Office of Naval Research (N00014-17-1-2079)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
- Keywords
- Earth system models, data assimilation, machine learning, high-resolution simulation, cloud parameterization, convection parameterization, uncertainty quantification, climate change projections
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