Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements

Abstract

Abstract. A method of simultaneously estimating snow depth and sea ice thickness using satellite-based freeboard measurements over the Arctic Ocean during winter was proposed. The ratio of snow depth to ice thickness (referred to as α) was defined and used in constraining the conversion from the freeboard to ice thickness in satellite altimetry. Then, α was empirically determined using the ratio of temperature difference of the snow layer to the difference of the ice layer, to allow the determination of α from satellite-derived snow surface temperature and snow–ice interface temperature. The proposed method was validated against NASA's Operation IceBridge measurements, and comparison results indicated that the algorithm adequately retrieves snow depth and ice thickness simultaneously: retrieved ice thickness was found to be better than the current satellite retrieval methods relying on the use of snow depth climatology as input, in terms of mean bias and RMSE. The application of the proposed method to CryoSat-2 ice freeboard measurements yields similar results. In conclusion, the developed α-based method has the capacity to derive ice thickness and snow depth, without relying on the snow depth information as input to the buoyancy equation for converting freeboard to ice thickness.

Winter Snow Depth on Arctic Sea Ice From Satellite Radiometer Measurements (2003–2020): Regional Patterns and Trends

“…This has been reported by other researchers (Kwok et al., 2020; Rostosky et al., 2018), as well. The overestimated mW99 h s will likely cause underestimation of H i when using lidar, and overestimation when using radar to estimate sea ice thickness (Shi et al., 2020). Such systematic bias due to mW99 can in turn cause a significant bias between H i s estimated from lidar and radar altimeters (Kim et al., 2020).…”

“…Since these relationships showed a time-invariant nature, an "α-prediction equation," which is an empirical relation between α, STT, SIIT, and ice bottom temperature, was obtained to estimate α from satellite-derived STT and SIIT. The α-prediction equation and coefficients can be found in Equation 15and Table 2 of Shi et al (2020). Ice bottom temperature is fixed at −1.5°C (Shi et al, 2020).…”

“…The α-prediction equation and coefficients can be found in Equation 15and Table 2 of Shi et al (2020). Ice bottom temperature is fixed at −1.5°C (Shi et al, 2020). Finally, monthly fields of α are obtained from monthly averaged STT and SIIT data.…”

“…In this study, we attempt to estimate Arctic basin-scale snow depth by combining the freeboard retrieval method of Lee et al (2021) and the snow depth retrieval method of Shi et al (2020) and applying this method to satellite data from the Advanced Microwave Scanning Radiometer (AMSR)-E and AMSR2. Estimated snow depths using this method are validated against OIB measurements.…”

A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval

“…A method of combining data from CryoSat-2 and ICESat-2 has been shown to alleviate some of these concerns (Kwok et al, 2020). Additional satellite-based methods include estimating the thickness of thin sea ice using low-frequency passive microwave satellite data (Tian-Kunze et al, 2014) and combining low-frequency passive microwave data with altimetry data in order to take advantage of their complementing data and spatial coverage (Ricker et al, 2017b;Zhou et al, 2018;Shi et al, 2020). Other strategies for retrieving sea ice thickness include a one-dimensional surface energy balance model driven by satellite products (Key et al, 2016) and a coupled ocean-sea ice model with assimilated observational data called the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS; Zhang and Rothrock, 2003).…”