Beyond Inflation: Backscatter Parameterizations to Address the Variability Deficit in Global Ocean Data Assimilation

Abstract

Global ocean models at non-eddying resolutions currently used for subseasonal to seasonal to decadal (S2S2D) prediction suffer from a severe deficit in internal variability. In ensemble data assimilation (DA), this can lead to under-dispersed ensembles that require inflation schemes. However, inflation corrections do not persist into the forecast phase, causing ensemble spread to collapse at longer lead times. This study evaluates an alternative approach: addressing the variability deficit directly within the model physics using a “Backscatter Package” (BackPack) consisting of the stochastic Stanley, stochastic GM+E, and Leith+E backscatter parameterizations. Implemented within a global MOM6/CESM framework at nominal 2/3° resolution using the DART ensemble DA package, the BackPack’s impacts are compared against cutting-edge adaptive inflation. The results demonstrate that the BackPack substantially increases internal variability and ensemble spread, successfully lowering the amount of required inflation. While reductions in ensemble-mean state errors are modest, the BackPack significantly improves ensemble calibration, assessed using a novel spread–error calibration ratio metric. Although this study only addresses the data assimilation phase, we expect that physics-based BackPack schemes may provide a physically sustainable pathway to maintain spread during the subsequent forecast phase.

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