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 without prior knowledge of snow depth. 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 evaluated against NASA's Operation IceBridge measurements, and results indicated that the algorithm adequately retrieves snow depth and ice thickness simultaneously; retrieved ice thickness was found to be better than the methods relying on the use of snow depth climatology as input in terms of mean bias. The application of the proposed method to CryoSat-2 radar 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 for the buoyancy equation or the radar penetration correction for converting freeboard to ice thickness.

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.

Emissivity retrieval for sea ice from passive microwave measurements has been an important problem in climate/environmental research because of its link to various snow/ice variables. However, so far, it has been a difficult task because of the influences of surface and snow/ice induced volume scatterings. Here we examine the influences of scatterings on the ice emissivity from 10.65, 18.7, 23.8, and 36.5 GHz Advanced Microwave Scanning Radiometer (AMSR)‐E brightness temperatures over the Arctic Ocean. In doing so, we use a two‐dimensional roughness parameterization, modified with surface facet orientations with an assumption that the facet emission follows the Fresnel relationship. Emitting layer temperature and refractive index retrieved from AMSR‐E 6.9 GHz brightness temperature measurements were used in this study and applied to other channels of interest. We demonstrated that the obtained roughness index has a strong linear relationship with the root‐mean‐square height measured by Atmospheric Terrain Mapper on the National Aeronautics and Space Administration (NASA) P‐3 aircraft. The obtained roughness index showed that surface scattering on the emissivity is generally insignificant except for some first‐year ice regions in particular at higher frequencies. This fact implies that Fresnel relations can be applicable for most of sea ice at the low‐frequency microwave spectrum. By contrast, volume scattering is found to be significant in emissivity retrieval in case of multiyear ice. Nonetheless, volume scattering influence over first‐year ice appears to be minor. We suggest that Fresnel‐type emissivity can be estimated once a correction factor is used for removing surface scattering and volume scattering contributions from the apparent emissivity.

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While an unambiguous climate change signature has been observed in Arctic sea ice coverage (Andersen et al., 2020;Stroeve et al., 2007), it has been difficult to quantify the changes in snow depth over the sea ice region (Webster et al., 2018). This holds in spite of snow accumulation being one of the most important geophysical parameters to understand Arctic climate, being related to albedo feedback, ice cover insulation, and associated heat transfer effects (Curry et al., 1995;Ledley, 1991;Webster et al., 2014). In addition to its importance in the polar climate system, snow depth influences the accuracy of ice thickness estimation based on satellite altimetry (Kern et al., 2015). Most knowledge regarding snow depth on Arctic sea ice is based on the climatological distribution provided by Warren et al. (1999) (W99), which was based on in situ data collected over multiyear sea ice (MYI) during the years of 1954-1991. Although W99 may not be considered as a representative Arctic snow depth distribution for the recent decades, it is still often used as a standard. For example, based on a report by Kurtz and Farrell (2011), many studies modified W99 by halving W99 snow depth over first-year ice (FYI), then using this scaled snow depth to estimate ice thickness from satellite altimeter measurements (Kwok & Cunningham, 2015;Ricker et al., 2014). A reliable data record of the snow depth is thus clearly needed by both climate and satellite communities (Webster et al., 2014).