Machine Learning Predictions of a Multiresolution Climate Model Ensemble
Abstract
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. Finally, we also find that global annually averaged precipitation is more sensitive to resolution changes than to any of the model parameters considered.
- Publication Date:
- Research Org.:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1488815
- Report Number(s):
- LLNL-JRNL-744178
Journal ID: ISSN 0094-8276; 899191
- Grant/Contract Number:
- AC52-07NA27344
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Geophysical Research Letters
- Additional Journal Information:
- Journal Volume: 45; Journal Issue: 9; Journal ID: ISSN 0094-8276
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Anderson, Gemma J., and Lucas, Donald D. Machine Learning Predictions of a Multiresolution Climate Model Ensemble. United States: N. p., 2018.
Web. doi:10.1029/2018GL077049.
Anderson, Gemma J., & Lucas, Donald D. Machine Learning Predictions of a Multiresolution Climate Model Ensemble. United States. https://doi.org/10.1029/2018GL077049
Anderson, Gemma J., and Lucas, Donald D. Thu .
"Machine Learning Predictions of a Multiresolution Climate Model Ensemble". United States. https://doi.org/10.1029/2018GL077049. https://www.osti.gov/servlets/purl/1488815.
@article{osti_1488815,
title = {Machine Learning Predictions of a Multiresolution Climate Model Ensemble},
author = {Anderson, Gemma J. and Lucas, Donald D.},
abstractNote = {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. Finally, we also find that global annually averaged precipitation is more sensitive to resolution changes than to any of the model parameters considered.},
doi = {10.1029/2018GL077049},
journal = {Geophysical Research Letters},
number = 9,
volume = 45,
place = {United States},
year = {Thu Apr 19 00:00:00 EDT 2018},
month = {Thu Apr 19 00:00:00 EDT 2018}
}
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Works referencing / citing this record:
Uncertainty Analysis of Simulations of the Turn‐of‐the‐Century Drought in the Western United States
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