Impact of Enthalpy-Based Ensemble Filtering Sea Ice Data Assimilation on Decadal Predictions: Simulation with a Conceptual Pycnocline Prediction Model

Abstract

AbstractThe non-Gaussian probability distribution of sea ice concentration makes it difficult to directly assimilate sea ice observations into a climate model. Because of the strong impact of the atmospheric and oceanic forcing on the sea ice state, any direct assimilation adjustment on sea ice states is easily overridden by model physics. A new approach implements sea ice data assimilation in enthalpy space where a sea ice model represents a nonlinear function that transforms a positive-definite space into the sea ice concentration subspace. Results from observation–assimilation experiments using a conceptual pycnocline prediction model that characterizes the influences of sea ice on the decadal variability of the climate system show that the new scheme efficiently assimilates “sea ice observations” into the model: while improving sea ice variability itself, it consistently improves the estimates of all “climate” components. The resulted coupled initialization that is physically consistent among all coupled components significantly improves decadal-scale predictability of the coupled model.