Dynamically combining climate models to “supermodel” the tropical Pacific
Abstract
Abstract We construct an interactive ensemble of two different climate models to improve simulation of key aspects of tropical Pacific climate. Our so‐called supermodel is based on two atmospheric general circulation models (AGCMs) coupled to a single ocean GCM, which is driven by a weighted average of the air‐sea fluxes. Optimal weights are determined using a machine learning algorithm to minimize sea surface temperature errors over the tropical Pacific. This coupling strategy synchronizes atmospheric variability in the two AGCMs over the equatorial Pacific, where it improves the representation of ocean‐atmosphere interaction and the climate state. In particular, the common double Intertropical Convergence Zone error is suppressed, and the positive Bjerknes feedback improves substantially to match observations well, and the negative heat flux feedback is also much improved. This study supports the concept of supermodeling as a promising multimodel ensemble strategy to improve weather and climate predictions.
- Authors:
-
- Geophysical Institute University of Bergen and Bjerknes Centre for Climate Research Bergen Norway
- Royal Netherlands Meteorological Institute De Bilt Netherlands
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen Nijmegen Netherlands
- Geophysical Institute University of Bergen and Bjerknes Centre for Climate Research Bergen Norway, Department of Atmospheric and Oceanic Sciences University of Colorado Boulder Boulder Colorado USA
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1235948
- Alternate Identifier(s):
- OSTI ID: 1235949
- Grant/Contract Number:
- DE‐SC0005238
- Resource Type:
- Published Article
- Journal Name:
- Geophysical Research Letters
- Additional Journal Information:
- Journal Name: Geophysical Research Letters Journal Volume: 43 Journal Issue: 1; Journal ID: ISSN 0094-8276
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Shen, Mao‐Lin, Keenlyside, Noel, Selten, Frank, Wiegerinck, Wim, and Duane, Gregory S. Dynamically combining climate models to “supermodel” the tropical Pacific. United States: N. p., 2016.
Web. doi:10.1002/2015GL066562.
Shen, Mao‐Lin, Keenlyside, Noel, Selten, Frank, Wiegerinck, Wim, & Duane, Gregory S. Dynamically combining climate models to “supermodel” the tropical Pacific. United States. https://doi.org/10.1002/2015GL066562
Shen, Mao‐Lin, Keenlyside, Noel, Selten, Frank, Wiegerinck, Wim, and Duane, Gregory S. Sat .
"Dynamically combining climate models to “supermodel” the tropical Pacific". United States. https://doi.org/10.1002/2015GL066562.
@article{osti_1235948,
title = {Dynamically combining climate models to “supermodel” the tropical Pacific},
author = {Shen, Mao‐Lin and Keenlyside, Noel and Selten, Frank and Wiegerinck, Wim and Duane, Gregory S.},
abstractNote = {Abstract We construct an interactive ensemble of two different climate models to improve simulation of key aspects of tropical Pacific climate. Our so‐called supermodel is based on two atmospheric general circulation models (AGCMs) coupled to a single ocean GCM, which is driven by a weighted average of the air‐sea fluxes. Optimal weights are determined using a machine learning algorithm to minimize sea surface temperature errors over the tropical Pacific. This coupling strategy synchronizes atmospheric variability in the two AGCMs over the equatorial Pacific, where it improves the representation of ocean‐atmosphere interaction and the climate state. In particular, the common double Intertropical Convergence Zone error is suppressed, and the positive Bjerknes feedback improves substantially to match observations well, and the negative heat flux feedback is also much improved. This study supports the concept of supermodeling as a promising multimodel ensemble strategy to improve weather and climate predictions.},
doi = {10.1002/2015GL066562},
journal = {Geophysical Research Letters},
number = 1,
volume = 43,
place = {United States},
year = {Sat Jan 09 00:00:00 EST 2016},
month = {Sat Jan 09 00:00:00 EST 2016}
}
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