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dc.contributor.authorCounillon, Francois Stephane
dc.contributor.authorKeenlyside, Noel Sebastian
dc.contributor.authorWang, Shuo
dc.contributor.authorDevilliers, Marion
dc.contributor.authorGupta, Alok Kumar
dc.contributor.authorKoseki, Shunya
dc.contributor.authorShen, Mao-Lin
dc.date.accessioned2023-06-29T09:45:50Z
dc.date.available2023-06-29T09:45:50Z
dc.date.created2023-04-13T17:45:33Z
dc.date.issued2023
dc.identifier.issn1942-2466
dc.identifier.urihttps://hdl.handle.net/11250/3074320
dc.description.abstractA supermodel connects different models interactively so that their systematic errors compensate and achieve a model with superior performance. It differs from the standard non-interactive multi-model ensembles (NI), which combines model outputs a-posteriori. Supermodels with Earth system models (ESMs) has not been developed because it is technically challenging to combine models with different state space. Here, we formulate the first supermodel framework for ESMs and use data assimilation to synchronise models. The ocean of three ESMs is synchronised every month by assimilating pseudo sea surface temperature (SST) observations generated by them on a common grid to handle discrepancies in grid and resolution. We compare the performance of two supermodel approaches to that of the NI. In the first (EW), the models are connected to the equal-weight multi-model mean, while in the second (SINGLE), they are connected to a single model. Both versions achieve synchronisation in the ocean and in the atmosphere, where the ocean drives the variability. The time variability of the supermodel multi-model mean SST is reduced compared to observations, most where synchronisation is not achieved and is lower-bounded by NI. The damping is larger in EW, for which variability in the individual models is also damped. Hence, under partial synchronisation, the unsynchronized variability gets damped in the multi-model average pseudo-observations, causing a deflation during the assimilation. The SST bias in individual models of EW is reduced compared to that of NI, and so is its multi-model mean in the synchronised regions. A trained supermodel remains to be tested.en_US
dc.language.isoengen_US
dc.publisherAmerican Geophysical Unionen_US
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.no*
dc.titleFramework for an Ocean-Connected Supermodel of the Earth Systemen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2023 the authorsen_US
dc.source.articlenumbere2022MS003310en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1029/2022MS003310
dc.identifier.cristin2140696
dc.source.journalJournal of Advances in Modeling Earth Systemsen_US
dc.identifier.citationJournal of Advances in Modeling Earth Systems. 2023, 15 (3), e2022MS003310.en_US
dc.source.volume15en_US
dc.source.issue3en_US


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Navngivelse-Ikkekommersiell 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse-Ikkekommersiell 4.0 Internasjonal