Abstract

Snow over sea ice controls energy budgets and affects sea ice growth/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 Antarctica clearly underestimated snow depth, limiting their further application. Here, we estimated snow depth using 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 19 GHz, i.e., GR(37/7), was optimal for deriving Antarctic snow depth, with a correlation coefficient of −0.64. We hence derive 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, with a mean difference of 5.64 cm and an RMSD of 13.79 cm, compared to values of −14.47 cm 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 cm and 17.61 cm, respectively). Comparable performances during the growth and melting seasons suggest that the proposed method can still be used during the melting season. Gaussian error propagation found an average snow depth uncertainty of 3.81 cm, which accounted for 12 % of the estimated mean snow depth. 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 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/ (Shen and Ke, 2021, DOI: 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), 30 controls energy exchanges between the atmosphere and ocean (Kwok & Untersteiner, 2011) and affects sea ice growth andData melting (Maykut et al, 1971, Sturm et al, 2002)

  • An empirical linear regression equation was derived for snow depth estimation, and the regression coefficients were updated for successor microwave ratiometers (i.e., Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Comiso et al, 2003)

  • 55 this method can derive basin-scale snow depth, due to the snow penetration depth when 37 and 19 GHz frequencies are used and the strong influence liquid water in the snow layer has on the observed radiation from microwave ratiometers, this method is limited to dry snow less than 50 cm thick, which is clearly less than the snow cover over Antarctic sea ice (Kwok et al, 2014)

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Summary

Introduction

Snow is a basic element in the Antarctic sea ice system and it changes the surface albedo of sea ice (Petrich et al, 2012), 30 controls energy exchanges between the atmosphere and ocean (Kwok & Untersteiner, 2011) and affects sea ice growth and. 55 this method can derive basin-scale snow depth, due to the snow penetration depth when 37 and 19 GHz frequencies (i.e., higher frequencies) are used and the strong influence liquid water in the snow layer has on the observed radiation from microwave ratiometers, this method is limited to dry snow less than 50 cm thick, which is clearly less than the snow cover over Antarctic sea ice (Kwok et al, 2014) Given these influences, this method obviously underestimates thickness by a factor of 2.3 (Worby et al, 2008a) or between 2 and 4 (Kern et al, 2016). The SSMIS brightness temperature observations were calibrated based on the AMSR-E data, and we calibrated the AMSR-2 data to AMSR-E based on the correction parameters from Du et al (2014). 95 These brightness temperature observations were used to obtain sea ice concentrations by using the ARTIST Sea Ice (ASI) algorithm (Spreen et al, 2008)

Operational IceBridge airborne snow depth measurements
AADC in situ measurement data
ASPeCt shipborne observation data
The selection of optimal frequency channels
The derivation of new snow depth estimation equation
Accuracy evaluation
Comparison to AADC in situ measurements in growth season
Comparison to ASPeCt shipboard observations in melting season
Comparison to satellite laser altimeter-derived snow depth data in both spatial and temporal scales
The uncertainty from OIB data
The uncertainty from sea ice types
The uncertainty from applied spatial resolution
415 7 Data availability
Findings
Conclusions
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