Abstract
Abstract. Snow over sea ice controls energy budgets and affects sea ice growth and melting and thus has essential effects on the climate. Passive microwave radiometers can be used for basin-scale snow depth estimation at a daily scale; however, previously published methods applied to the Antarctic clearly underestimated snow depth, limiting their further application. Here, we estimated snow depth using passive microwave radiometers and a newly constructed, robust method by incorporating lower frequencies, which have been available from AMSR-E and AMSR-2 since 2002. A regression analysis using 7 years of Operation IceBridge (OIB) airborne snow depth measurements showed that the gradient ratio (GR) calculated using brightness temperatures in vertically polarized 37 and 7 GHz, i.e. GR(37/7), was optimal for deriving Antarctic snow depth, with a correlation coefficient of −0.64. We hence derived new coefficients based on GR(37/7) to improve the current snow depth estimation from passive microwave radiometers. Comparing the new retrieval with in situ measurements from the Australian Antarctic Data Centre showed that this method outperformed the previously available method (i.e. linear regression model based on GR(37/19)), with a mean difference of 5.64 cm and an RMSD of 13.79 cm, compared to values of −14.47 and 19.49 cm, respectively. A comparison to shipborne observations from Antarctic Sea Ice Processes and Climate indicated that in thin-ice regions, the proposed method performed slightly better than the previous method (with RMSDs of 16.85 and 17.61 cm, respectively). We generated a complete snow depth product over Antarctic sea ice from 2002 to 2020 on a daily scale, and negative trends could be found in all sea sectors and seasons. This dataset (including both snow depth and snow depth uncertainty) can be downloaded from the National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences at http://data.tpdc.ac.cn/en/disallow/61ea8177-7177-4507-aeeb-0c7b653d6fc3/ (last access: 7 February 2022) (Shen and Ke, 2021, https://doi.org/10.11888/Snow.tpdc.271653).
Highlights
Snow is a basic element in the Antarctic sea ice system, and it changes the surface albedo of sea ice (Petrich et al, 2012), controls energy exchanges between the atmosphere and ocean (Kwok and Untersteiner, 2011), and affects sea ice growth and melting (Maykut and Untersteiner, 1971; Sturm et al, 2002)
To reduce the effect of uneven Operational IceBridge (OIB) measurement distributions within the passive microwave radiometer grid cells caused by their resolution difference, on the same day, one passive microwave radiometer grid cell (i.e. 25 km × 25 km) should contain at least 2500 OIB measurement points
The snow depth estimations derived from the two equations agreed well and had an root mean square deviation (RMSD) of 1.89 cm, and we corrected their original difference based on an empirical linear regression equation: SDGR(37/7)(cm) = SDGR(37/19)(cm) − 0.03
Summary
Snow is a basic element in the Antarctic sea ice system, and it changes the surface albedo of sea ice (Petrich et al, 2012), controls energy exchanges between the atmosphere and ocean (Kwok and Untersteiner, 2011), and affects sea ice growth and melting (Maykut and Untersteiner, 1971; Sturm et al, 2002). The theoretical basis of snow depth estimation from passive microwave radiometers is that the volume scattering of upper snow cover affects the radiation signal emitted from the underlying sea ice and reduces the observed brightness temperatures (Markus and Cavalieri, 1998). The AMSR-E/Aqua Daily L3 25 km Brightness Temperature Polar Grids (Version 3) product from the National Snow and Ice Center (NSIDC) were used, and pre-processing, bias correction and quality control were all applied (Cavalieri et al, 2014). Compared to AMSR-E, AMSR-2 has the same observation angle and frequency channels but has an additional frequency at 7.3 GHz. Here, the NSIDC AMSRE/AMSR-2 Unified L3 Daily 25 km Brightness Temperature Polar Grids (Version 1) product was used, and preprocessing, bias correction and quality control were applied (Markus et al, 2018). These brightness temperature observations from AMSR-E, AMSR-2 and SSMIS were used to obtain the full-time (2002–2020) sea ice concentrations by using the ARTIST Sea Ice (ASI) algorithm (Spreen et al, 2008)
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