Tag: devilliers

Supermodeling: improving predictions with an ensemble of interacting models

Schevenhoven , F., Keenlyside, N., Counillon, F., Carrassi, A., Chapman, W.E., Devilliers, M., Gupta, A., Koseki, S., Selten, F., Shen, M.L., Wang, S. 2023: Supermodeling: improving predictions with an ensemble of interacting models. BAMS. https://doi.org/10.1175/BAMS-D-22-0070.1

Summary: The modeling of weather and climate has been a success story. The skill of forecasts continues to improve and model biases continue to decrease. Combining the output of multiple models has further improved forecast skill and reduced biases. But are we exploiting the full capacity of state-of-the-art models in making forecasts and projections? Supermodeling is a recent step forward in the multimodel ensemble approach. Instead of combining model output after the simulations are completed, in a supermodel individual models exchange state information as they run, influencing each other’s behavior. By learning the optimal parameters that determine how models influence each other based on past observations, model errors are reduced at an early stage before they propagate into larger scales and affect other regions and variables. The models synchronize on a common solution that through learning remains closer to the observed evolution. Effectively a new dynamical system has been created, a supermodel, that optimally combines the strengths of the constituent models. The supermodel approach has the potential to rapidly improve current state-of-the-art weather forecasts and climate predictions. In this paper we introduce supermodeling, demonstrate its potential in examples of various complexity, and discuss learning strategies. We conclude with a discussion of remaining challenges for a successful application of supermodeling in the context of state-of-the-art models. The supermodeling approach is not limited to the modeling of weather and climate, but can be applied to improve the prediction capabilities of any complex system, for which a set of different models exists.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Framework for an Ocean-Connected Supermodel of the Earth System

Counillon, F., Keenlyside, N., Wang, S., Devilliers, M., Gupta, A., Koseki, S., Shen, M.-L. 2023: Framework for an Ocean-Connected Supermodel of the Earth System. JAMES. https://doi.org/10.1029/2022MS003310

Summary: Observed and future winter Arctic sea ice loss is strongest in the Barents Sea. However, the anthropogenic signal of the sea ice decline is superimposed by pronounced internal variability that represents a large source of uncertainty in future climate projections. A notable manifestation of internal variability is rapid ice change events (RICEs) that greatly exceed the anthropogenic trend. These RICEs are associated with large displacements of the sea ice edge which could potentially have both local and remote impacts on the climate system. In this study we present the first investigation of the frequency and drivers of RICEs in the future Barents Sea, using multi-member ensemble simulations from CMIP5 and CMIP6. A majority of RICEs are triggered by trends in ocean heat transport or surface heat fluxes. Ice loss events are associated with increasing trends in ocean heat transport and decreasing trends in surface heat loss. RICEs are a common feature of the future Barents Sea until the region becomes close to ice-free. As their evolution over time is closely tied to the average sea ice conditions, rapid ice changes in the Barents Sea may serve as a precursor for future changes in adjacent seas.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.