Abstract
Abstract. We investigate the initialisation of Northern Hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean thickness. Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates. Assimilation with these proportional mean-thickness analysis updates leads to good assimilation performance for sea-ice concentration and thickness, both in identical-twin experiments and when assimilating sea-ice observations. The simulation of other Arctic surface fields in the coupled model is, however, not significantly improved by the assimilation. To understand the physical aspects of assimilation errors, we construct a simple prognostic model of the sea-ice thermodynamics, and analyse its response to the assimilation. We find that an adjustment of mean ice thickness in the analysis update is essential to arrive at plausible state estimates. To understand the statistical aspects of assimilation errors, we study the model background error covariance between ice concentration and ice thickness. We find that the spatial structure of covariances is best represented by the proportional mean-thickness analysis updates. Both physical and statistical evidence supports the experimental finding that assimilation with proportional mean-thickness updates outperforms the other two methods considered. The method described here is very simple to implement, and gives results that are sufficiently good to be used for initialising sea ice in a global climate model for seasonal to decadal predictions.
Highlights
For skillful seasonal to decadal predictions, good initial conditions of atmosphere–ocean global climate models (AOGCMs) are of paramount importance
Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates
For seasonal to decadal predictions of sea ice, the total ice volume and the total area covered are arguably the most important parameters (Holland et al, 2010; BlanchardWrigglesworth et al, 2011b). They are closely related to local ice thickness and ice concentration: ice volume is proportional to the sum of the mean thickness for all grid cells, and ice extent is the area sum of all grid cells with ice concentration higher than 15 %
Summary
For skillful seasonal to decadal predictions, good initial conditions of atmosphere–ocean global climate models (AOGCMs) are of paramount importance. The initialisation of sea ice in an AOGCM with suitable data assimilation techniques is an important step towards more skillful seasonal to decadal predictions. The improvement in ice thickness is not straightforward, and Duliere and Fichefet (2007) emphasised that the assimilation can deteriorate the model performance if inappropriate assimilation techniques are chosen These findings from ice concentration assimilation in ice– ocean models forced by atmospheric surface conditions cannot be directly transferred to ice-concentration assimilation in AOGCMs, because in AOGCMs the atmospheric surface conditions are not necessarily consistent with the assimilated sea-ice state. They develop interactively from largescale dynamics and from local interaction with the sea-ice state This makes the impact of ice-concentration assimilation on ice thickness less obvious than in ice–ocean models and calls for dedicated studies on sea-ice data assimilation in an AOGCM.
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