Abstract
A data assimilation method capable of constraining the sea ice of an Earth system model in a dynamically consistent manner has the potential to enhance the accuracy of climate reconstructions and predictions. Finding such a method is challenging because the sea ice dynamics is highly non-linear, and sea ice variables are strongly non-Gaussian distributed and tightly coupled to the rest of the Earth system – particularly thermodynamically with the ocean. We investigate key practical implementations for assimilating sea ice concentration – the predominant source of observations in polar regions – with the Norwegian Climate Prediction Model that combines the Norwegian Earth System Model with the Ensemble Kalman Filter. The performances of the different configurations are investigated by conducting 10-year reanalyses in a perfect model framework. First, we find that with a flow-dependent assimilation method, strongly coupled ocean–sea ice assimilation outperforms weakly coupled (sea ice only) assimilation. An attempt to prescribe the covariance between the ocean temperature and the sea ice concentration performed poorly. Extending the ocean updates below the mixed layer is slightly beneficial for the Arctic hydrography. Second, we find that solving the analysis for the multicategory instead of the aggregated ice state variables greatly reduces the errors in the ice state. Updating the ice volumes induces a weak drift in the bias for the thick ice category that relates to the postprocessing of unphysical thicknesses. Preserving the ice thicknesses for each category during the assimilation mitigates the drift without degrading the performance. The robustness and reliability of the optimal setting is demonstrated for a 20-year reanalysis. The error of sea ice concentration reduces by 50% (65%), sea ice thickness by 25% (35%), sea surface temperature by 33% (23%) and sea surface salinity by 11% (25%) in the Arctic (Antarctic) compared to a reference run without assimilation.
Highlights
Sea ice is a key element of the climate system as it affects the radiative surface balance and strongly restricts exchanges of momentum, heat and moisture between the ocean and the atmosphere
In our assessment of the assimilation techniques we focussed on the treatment of ocean and multicategory sea ice state updates, leaving the atmosphere component unchanged
Accumulated sea ice concentration has been assimilated with the Ensemble Kalman Filter (EnKF) into the NorESM and the performance has been tested for a 10-year reanalysis
Summary
Sea ice is a key element of the climate system as it affects the radiative surface balance and strongly restricts exchanges of momentum, heat and moisture between the ocean and the atmosphere. Guemas et al (2016) envisioned that a system capable of initialising consistently the ocean and the sea ice components will lead to further improved model states over the weakly coupled approach when only the sea ice is updated by the assimilation. The EnKF approach has been extensively tested with atmospherically forced, coupled ocean and sea ice models for reanalyses and short-time forecasts up to 10 days (Lisæter et al, 2003; Mathiot et al, 2012; Sakov et al, 2012; Massonnet et al, 2015; Barth et al, 2015). We close the article with a section, where we discuss our findings and give an outlook for future developments
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