Machine Learning Predictions of a Multiresolution Climate Model Ensemble
Open accessAbstract
Statistical models of high‐resolution climate models are useful for many purposes, including sensitivity and uncertainty analyses, but building them can be computationally prohibitive. We generated a unique multiresolution perturbed parameter ensemble of a global climate model. We use a novel application of a machine learning technique known as random forests to train a statistical model on the ensemble to make high‐resolution model predictions of two important quantities: global mean top‐of‐atmosphere energy flux and precipitation. The random forests leverage cheaper low‐resolution simulations, greatly reducing the number of high‐resolution simulations required to train the statistical model. We demonstrate that high‐resolution predictions of these quantities can be obtained by training on an ensemble that includes only a small number of high‐resolution simulations. We also find that global annually averaged precipitation is more sensitive to resolution changes than to any of the model parameters considered.
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Anderson and Lucas used random forests to make high-resolution predictions from a mix of high-and low-resolution models, which could reduce the costs of building multi-model ensembles.
“More recently, Anderson and Lucas used random forests to make high-resolution predictions from a mix of high-and low-resolution models, which could reduce the costs of building multi-model ensembles [23].”
From Tackling climate change with machine learning (2023) · cites this paper as [23]
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- Authors
- Anderson, G. J., & Lucas, D. D.
- Affiliations
- Lawrence Livermore National Laboratory (US)
- Corresponding author
- Anderson, Gemma J.
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2018-05-05
- Volume
- 45
- Issue
- 9
- Pages
- 4273-4280
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W2801390916
- Cited by
- 60
- References
- 31
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 7 · 2023: 17 · 2022: 10 · 2021: 6 · 2020: 4 · 2019: 4 · 2018: 4
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- Geophysical Research Letters (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- United States Department of Energy (DE-AC52-07NA27344)
- DOE's Office of Science Scientific Discovery
- 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
- perturbed parameter ensemble, random forest, statistical downscaling, global precipitation, model resolution sensitivity, uncertainty analysis, global climate models
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