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Title: 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.

Authors:
ORCiD logo [1]; ORCiD logo [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
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:
Journal Article: 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. doi:10.1029/2018GL077049.
Anderson, Gemma J., and Lucas, Donald D. Thu . "Machine Learning Predictions of a Multiresolution Climate Model Ensemble". United States. doi: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},
issn = {0094-8276},
number = 9,
volume = 45,
place = {United States},
year = {2018},
month = {4}
}

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Works referenced in this record:

Random Forests
journal, January 2001