Thin Sea Ice Detection with CryoSat-2

Abstract

Arctic sea ice plays a vital role in the climate system, influencing atmospheric circulation, oceanic processes, and Arctic ecosystems. Thin sea ice is particularly important, as it governs seasonal transitions and contributes to feedback mechanisms such as Arctic amplification. Reliable estimates of sea ice thickness (SIT), especially within the thin ice regimes (less than 1 m), are essential for improving forecasting systems and reducing associated biases. This thesis investigates the performance of altimetry-based SIT retrievals from CryoSat-2 below 1 m, with a focus on the performance of the CryoSat-2 UiT product created by the Earth Observation Group at UiT. That incorporates a physical retracking algorithm known as LARM and a snow accumulation model called SnowModel-LG. The analysis includes an intercomparison with established CryoSat-2 SIT products from AWI, CPOM, and a Warren 1999 climatology configuration with LARM, using the Soil Moisture and Ocean Salinity (SMOS) AWI product as reference. The intercomparison highlights the influence of waveform retracking methods and snow climatology inputs, with the UiT product demonstrating the closest alignment with the SMOS AWI product. Validation against independent measurement from Operation IceBridge, the BGEP moorings, and the SMOSice campaign confirms that the UiT product provides reliable SIT estimates down to approximately 0.4-0.5 m. SMOS, in contrast, captures thin ice more consistently below 0.4 m but tends to saturate at thicknesses above approximately 0.5 m. These findings support the inclusion of CryoSat-2 thin-ice returns in data assimilation and the floe classification algorithm to improve model accuracy and enhance sea ice monitoring. The results also highlight the value of a complementary approach, where SMOS data is suited for early freeze-up periods and marginal ice zones, while CryoSat-2 products are preferred in thicker or mixed ice regimes. A tentative lower threshold of approximately 0.4 m is proposed for standalone use of CryoSat-2 data, guiding future data assimilation methods in sea ice forecasting.