a Novel Approach to Retrieve Arctic Sea Ice Thickness for Prediction and Analysis

In spite of October-November Arctic-sea-ice-volume loss exceeding 7000 km3 in the decade following ICESat launch, most global ocean reanalysis systems are not able to reproduce such a drastic decline. Knowledge of the sea ice properties and its thickness distribution is critical to our understanding of polar ocean processes and the role of the polar regions in the Earth's climate system. Existing large-scale sea ice thickness datasets are derived from freeboard observations made by different satellite altimeters (radar and lidar). These datasets are significantly different due to the remote sensing technique and spacecraft orbit, and they are limited in time. These differences increase the difficulty of using such data for sea ice initialization and assimilation, and increase the challenge for studying sea ice processes and interactions with the ocean and atmosphere. For the first time, we were able to reproduce the Arctic sea ice thickness field at 10 km resolution with success for fall, winter, and spring (April/May depending on melt conditions) from passive microwave data. Our results reveal the same patterns of thickness distribution in the Arctic basin and peripheral seas as CryoSat-2, and the majority of the retrievals are within 0.5 m of CryoSat-2. The range of CryoSat-2 ice thickness is correctly retrieved, including in the upper range (3-5 m). The amplitude is well reproduced too, as the distribution of differences is centered on 0 m (no bias). Some underestimations are visible between islands of the Canadian Archipelago, but due to the size of the field of view our confidence will always be lower in this region where there is land contamination. An initial comparison of the AMSR2 ice thickness with IceBridge airborne products in different sectors (Beaufort sea, central Arctic) demonstrates the quality of the retrievals. In this presentation, we will also quantify the prediction and nowcast gain obtained from assimilating these new retrievals. We carried-out the integration of 36 members of coupled NASA Goddard Earth Observing System Model, version 5 (GEOS-5) to enable the implementation of an Ensemble Kalman Smoother (EnKS) over the period September 2012 - January 2013. Assimilating our retrievals improves the nowcast of ice volume, the forecast and the retrospective forecast.