Abstract
A 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. We formulate
the first supermodel framework for Earth System Models (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 from them. Discrepancies in grid and
resolution are handled by constructing the synthetic pseudo-observations
on a common grid. We compare the performance of two supermodel
approaches to that of the NI for 1980—2006. 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 locations where the ocean drives the climate
variability. The time variability of the supermodel multi-model mean SST
is reduced compared to the observed variability; most where
synchronisation is not achieved and is bounded by NI. The damping is
larger in EW than in SINGLE because EW yields additional damping of the
variability in the individual models. Hence, under partial
synchronisation, the part of variability that is not synchronised 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. The performance of a trained supermodel
remains to be tested.