Abstract
Abstract. Although snow depth on sea ice is a key parameter for sea ice thickness (SIT) retrieval, there currently does not exist reliable estimations. In the Arctic, nearly all SIT products use a snow depth climatology (the modified Warren-99 climatology, W99m) constructed from in situ data obtained prior to the first significant impacts of climate change. In the Antarctic, the lack of information on snow depth remains a major obstacle in the development of reliable SIT products. In this study, we present the latest version of the altimetric snow depth (ASD) product computed over both hemispheres from the difference of the radar penetration into the snow pack between the Ka-band frequency SARAL/Altika and the Ku-band frequency CryoSat-2. The ASD solution is compared against a wide range of snow depth products including model data (Pan-Arctic Ice-Ocean Modelling and Assimilation System (PIOMAS) or its equivalent in the Antarctic the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), the MERCATOR model, and NASA's Eulerian Snow On Sea Ice Model (NESOSIM, only in the Arctic)), the Advanced Microwave Scanning Radiometer-2 (AMSR2) passive radiometer data, and the Dual-altimeter Snow Thickness (DuST) Ka–Ku product (only in the Arctic). The ASD product is further validated in the Arctic against the ice mass balance (IMB) buoys, the CryoSat Validation Experiment (CryoVEx) and Operation Ice Bridge's (OIB) airborne measurements. These comparisons demonstrate that ASD is a relevant snow depth solution, with spatiotemporal patterns consistent with those of the alternative Ka–Ku DuST product but with a mean bias of about 6.5 cm. We also demonstrate that ASD is consistent with the validation data: comparisons with OIB's airborne snow radar in the Arctic during the period of 2014–2018 show a correlation of 0.66 and a RMSE of about 6 cm. Furthermore, a first-guess monthly climatology has been constructed in the Arctic from the ASD product, which shows a good agreement with OIB during 2009–2012. This climatology is shown to provide a better solution than the W99m climatology when compared with validation data. Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013–2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction in the average SIT of about 30 cm.
Highlights
Since the launch of CryoSat-2 (CS-2) in 2010 (Wingham et al, 2006; Parrinello et al, 2018), sea ice thickness (SIT) observations are routinely derived from altimetric measurements
The statistical results which are presented in the Appendix (Table A1) show that the two Ka–Ku products (ASD and Dual-altimeter Snow Thickness (DuST)) are in good agreement in terms of spatial variability and the magnitude of their annual cycle considering the entire set of data over the 6 winter months
DuST and altimetric snow depth (ASD) spatial distributions are comparable, showcasing that the difference between the two Ka–Ku altimetric products is likely a bias resulting from the re-calibration of the DuST data with Operation Ice Bridge (OIB)
Summary
Since the launch of CryoSat-2 (CS-2) in 2010 (Wingham et al, 2006; Parrinello et al, 2018), sea ice thickness (SIT) observations are routinely derived from altimetric measurements. The principle is to measure the fraction of the sea ice above the sea level, called the sea ice freeboard, from differences between the heights in leads (cracks in the ice referring to the local sea level) and the heights of the ice floes (Laxon et al, 2003) By integrating such sea ice freeboard estimations in the hydrostatic equilibrium equation, several SIT products have been computed It acts as an insulator, slowing down sea ice melt in summer and slowing down sea ice growth in winter (e.g. Perovich et al, 2003; Sturm and Massom, 2016) Such processes of sea ice formation and melting govern sea ice physical and chemical properties that impact the biological processes in sea ice (Van Leeuwe et al, 2018). Snow cover modifies surface roughness that impacts the air–ice drag coefficient and transfer coefficients of latent and sensible heat fluxes (Andreas et al, 2005)
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