Bivariate sea-ice assimilation for global ocean Analysis/Reanalysis

Abstract

Abstract. In the last decade, various satellite missions have been monitoring the status of cryoshopere and its evolution over time. Beside sea-ice concentration data, available since the 80s, sea-ice thickness retrievals are now ready to be used in operational prediction and reanalysis systems. Nevertheless, a straightforward ingestion of multiple sea-ice characteristics in a multivariate framework is prevented by the highly non-gaussian distribution of such variables together with the low accuracy of thickness observations. This study describes an extension of OceanVar, a 3Dvar system routinely employed in the production of global/regional operational/reanalysis products, designed to include sea-ice variables. Those variables are treated through an anamorphosis operator that transforms sea-ice anomalies into gaussian control variables, the benefit brought by such transformation is described. Several sensitivity experiments are carried out using a suite of diverse datasets. The assimilation of the sole Cryosat-2 provides a good spatial representation of thickness distribution but still overestimates the total volume that requires the inclusion of SMOS data to be properly constrained. The intermittent availability of thickness data along the year, leads to potential discontinuities in the integrated quantities that requires a dedicated tuning. The use of merged L4 product CS2SMOS produces similar skill score when validated against independent mooring data, compared to the ingestion of L3 CryoSat-2 and L3 SMOS data. The new sea-ice module is meant to simplify the future coupling with ocean variables.

Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard

“…Perturbation may be applied to the initial conditions or as a physics perturbation in the sea ice component by variation in empirical parameters in CICE. In addition, applying transform operators for SIC, e.g., through remapping of SIC to a Gaussian variable, can mitigate difficulties of the EnKF in assimilating SIC directly (e.g., Bishop, 2016;Chan et al, 2023;Cipollone et al, 2023).…”