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dc.contributor.authorShen, Mao-Lin
dc.contributor.authorKeenlyside, Noel
dc.contributor.authorSelten, Frank
dc.contributor.authorWiegerinck, Wim
dc.contributor.authorDuane, Gregory
dc.PublishedGeophysical Research Letters 2016, 43(1):359-366eng
dc.description.abstractWe 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.en_US
dc.rightsAttribution CC BY-NC-NDeng
dc.titleDynamically combining climate models to "supermodel" the tropical Pacificen_US
dc.typePeer reviewed
dc.typeJournal article
dc.rights.holderCopyright 2015 The Author(s)en_US
dc.relation.projectEU: 648982,266722,323377,304243
dc.relation.projectNotur/NorStore: NN9385K
dc.relation.projectNotur/NorStore: NS9039K
dc.relation.projectNotur/NorStore: NN9039K
dc.relation.projectNotur/NorStore: NS9207K

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