Framework for an Ocean‐Connected Supermodel of the Earth System

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. 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.

Impacts of Atmospheric Internal Variations on the Variability of Sea Surface Temperature Based on the Hydra‐SINTEX Model

“…The super‐model strategy facilitates the exchange of information among models during the simulation process, as opposed to combining the outcomes derived from the individual models afterward. This approach has been preliminarily demonstrated to significantly improve the simulation of ocean–atmosphere interactions and climate (e.g., Counillon et al., 2023; Shen et al., 2016). This justifies the need for ongoing dedication in the coming years.…”

Adaptive Covariance Hybridization for the Assimilation of SST Observations Within a Coupled Earth System Reanalysis

“…Therefore, the method presented in this work would benefit from further developments before it can be tested in a realistic system. Currently, several methods are being developed and tested within NorCPM to handle climate biases directly, namely: anomaly coupling (Counillon et al., 2021), multivariate parameter estimation (Singh et al., 2022), super‐resolution data assimilation (Barthélémy et al., 2022), and supermodeling (Counillon et al., 2023; Schevenhoven et al., 2023).…”

Symbiotic Ocean Modeling Using Physics‐Controlled Echo State Networks

“…(2022) and Counillon et al. (2023). Yet, here our broader aim is not a synchronized solution, but to be able to alternate between LR and HR models during time integration, with simultaneous transients only present during a training phase.…”

“…With the symbiotic approach we aim to improve the computational efficiency of HR models, while simultaneously enhancing the parameterizations of unresolved processes in LR models. Our approach shares similarities with the coupling techniques in Barthélémy et al (2022) and Counillon et al (2023). Yet, here our broader aim is not a synchronized solution, but to be able to alternate between LR and HR models during time integration, with MULDER ET AL.…”