Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards

Isobel R Lawrence,Andy L Ridout,Thomas W K Armitage,Michel C Tsamados,Julienne C Stroeve

doi:10.5194/tc-12-3551-2018

Abstract

Abstract. Snow depth on sea ice remains one of the largest uncertainties in sea ice thickness retrievals from satellite altimetry. Here we outline an approach for deriving snow depth that can be applied to any coincident freeboard measurements after calibration with independent observations of snow and ice freeboard. Freeboard estimates from CryoSat-2 (Ku band) and AltiKa (Ka band) are calibrated against data from NASA's Operation IceBridge (OIB) to align AltiKa with the snow surface and CryoSat-2 with the ice–snow interface. Snow depth is found as the difference between the two calibrated freeboards, with a correction added for the slower speed of light propagation through snow. We perform an initial evaluation of our derived snow depth product against OIB snow depth data by excluding successive years of OIB data from the analysis. We find a root-mean-square deviation of 7.7, 5.3, 5.9, and 6.7 cm between our snow thickness product and OIB data from the springs of 2013, 2014, 2015, and 2016 respectively. We further demonstrate the applicability of the method to ICESat and Envisat, offering promising potential for the application to CryoSat-2 and ICESat-2, which launched in September 2018.

Highlights

The addition of snow on sea ice, given its optical and thermal properties, generates several effects on the climate of the polar regions
It is fortunate that CS-2 pseudo-LRM (Low Resolution Mode) has a similar footprint to AltiKa (1.7 km diameter and 1.4 km diameter respectively), but how, for example, could the methodology be applied to CS-2 and ICESat-2 in order to retrieve contemporary snow depth estimates once AltiKa ceases functionality? we demonstrate our methodology applied to the AltiKa and CS-2 satellites, our intention is to outline an approach that can be applied more broadly
Along-track freeboard measurements for AltiKa and CS-2 are calibrated as a function of pulse peakiness (PP) according to the combined linear regression fits derived in the previous section and averaged onto a 1.5◦ longitude by 0.5◦ latitude monthly grids

Summary

Introduction

The addition of snow on sea ice, given its optical and thermal properties, generates several effects on the climate of the polar regions. Owing to its large air content, snow has a thermal conductivity 10 times less than that of ice (Maykut and Untersteiner, 1971). Snow has an optical albedo in the range of 0.7–0.85, compared to 0.6–0.65 for melting white ice (Grenfell and Maykut, 1977). At the onset of the melt season, short-wave solar radiation is reflected from the surface, limiting ice melt. These properties make snow on sea ice important in energy budget considerations, and the inclusion of accurate Arctic snow depth estimates would improve current weather and sea ice forecasting (Stroeve et al, 2018)

Objectives

Results

Conclusion