Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method

Area of Science:

• * Earth Science
• * Cryosphere Science
• * Remote Sensing

Background:

• * Satellite altimetry is crucial for deriving large-scale sea ice thickness changes.
• * Accurate sea ice thickness retrieval depends on freeboard, snow depth, and various densities, which are challenging to measure concurrently.
• * Uncertainties in these parameters lead to inaccuracies in sea ice thickness estimations.

Purpose of the Study:

• * To introduce a novel least squares adjustment (LSA) method for estimating Arctic sea ice thickness using CryoSat-2 measurements.
• * To analyze the spatial and temporal variations of Arctic sea ice thickness from 2010 to 2019.
• * To compare the LSA method's results with existing CryoSat-2 sea ice thickness products and validate them with Operation IceBridge (OIB) data.

Main Methods:

• * Development of a sea ice freeboard to thickness model within a 5 km × 5 km grid.
• * Application of the least squares adjustment (LSA) method to calculate model coefficients and sea ice thickness.
• * Utilization of CryoSat-2 altimetry data from 2010–2019 and comparison with AWI, CPOM, and GSFC products, along with OIB data for validation.

Main Results:

• * The LSA method provides Arctic sea ice thickness estimates for 2010–2019.
• * Comparisons with AWI, CPOM, and GSFC products show overall differences of 0.025 ± 0.640 m, 0.143 ± 0.640 m, and -0.274 ± 0.628 m, respectively.
• * Validation with OIB data yields good agreement, with a difference of 0.065 ± 0.187 m, though discrepancies with products are noted in thin ice areas.

Conclusions:

• * The new LSA method offers a viable approach for estimating Arctic sea ice thickness from CryoSat-2 data.
• * The method demonstrates improved accuracy, particularly in challenging thin ice conditions.
• * The findings contribute to a better understanding of Arctic sea ice dynamics and thickness variations.