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Title: Machine Learning Predictions of a Multiresolution Climate Model Ensemble

Journal Article · · Geophysical Research Letters
DOI:https://doi.org/10.1029/2018GL077049· OSTI ID:1488815
ORCiD logo [1]; ORCiD logo [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
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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.

Research Organization:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1488815
Report Number(s):
LLNL-JRNL-744178; 899191
Journal Information:
Geophysical Research Letters, Vol. 45, Issue 9; ISSN 0094-8276
Publisher:
American Geophysical UnionCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 25 works
Citation information provided by
Web of Science

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Cited By (5)

Uncertainty Analysis of Simulations of the Turn‐of‐the‐Century Drought in the Western United States journal December 2018
Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization journal January 2019
Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations journal October 2018
The Application of Machine Learning Techniques to Improve El Niño Prediction Skill journal October 2019
Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations text January 2018

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