DOI: 10.5281/zenodo.4556513
title: Simulations_gap_layers_HIGHTSI_FIPS_2012_Antarctic_Zhongshan_Station

description: We applied a 1‐D high‐resolution Thermodynamic Sea Ice and snow model HIGHTSI (Launiainen & Cheng, 1998). To simulate the evolution of snow and ice temperature profiles and mass balance, HIGHTSI solves nonlinear partial differential heat conduction equations, as well as melting and freezing processes. Solar radiation absorbed in the ice highly depends on the bulk extinction coefficient κ and the depth of ice surface scattering layer (SSL), i.e., a layer where a major part of the incoming solar radiation is either scattered or absorbed. The depth of SSL typically increases towards the end of the melt season, finally reaching some 0.1 m (Light et al., 2015). The solar radiation attenuates more rapidly in SSL than in the internal layer below. The parameterization of penetration of solar radiation within snow and ice allows HIGHTSI to quantitatively simulate sub-surface melting of snow and ice. HIGHTSI has been validated extensively and applied widely in both process studies and operational services (Cheng et al., 2008, 2013; Wang et al., 2015; Merkouriadi et al., 2017, 2019; Mäkynen et al., 2020; Zhao et al., 2020). Detailed model parameterizations are given in Supporting Information Table S2. We made a control model run on an MYI floe covering the entire melting season from late spring (1 November 2011) until autumn (31 March 2012). The initial snow depth and ice thickness were 0.17 m and 1.5 m, respectively, based on in situ observations. In early November, in-ice temperature revealed a linear profile (Lei et al., 2010) and therefore used as an initial condition for the control run. The meteorological parameters observed by an automatic weather station (AWS) at the Chinese Zhongshan Station were used as model forcing. The wind speed (Va), air temperature (Ta), and relative humidity (Rh) were observed at 10 m height with one-minute time interval. The total cloud fraction (CN) was observed visually four times daily. Total precipitation (Prec) was observed at the Russian Progress Station, 1 km southeast of Zhongshan Station. On the basis of previous studies in the Prydz Bay region, the oceanic heat flux (Fw) has an annual cycle with a maximum value in March and a minimum in September (Heil, 1996; Lei et al., 2010). Unfortunately, no summer observations are available due to unsafe ice conditions. We therefore assumed a simple linear increase of monthly mean oceanic heat flux from an observed 16 W/m2 in November to an observed 32 W/m2 in March (Zhao et al., 2019a). Figure S1 shows the time series of weather data and oceanic heat flux used for the control run. Snow depth and ice thickness were manually observed by members of the Russian Progress Station on a weekly basis between April and December 2011. The measurements were made by an ice gauge and a ruler. The accuracy of the measurements was 0.01 m. The measurements are compared with modeled values. In addition to the control run, several model sensitivity experiments were made. To study a regional pattern of gap layers, we run Fast Ice Prediction System (FIPS) for the domain of land-fast ice in Prydz Bay (Zhao et al., 2020). The AMSR2 ice concentration data were used to identify the aerial coverage of ice in the coastal region (Spreen et al., 2008). The ERA-Interim reanalysis products (ERA-I) from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used as weather forcing for HIGHTSI. When comparing four atmospheric reanalysis over the Antarctic sea ice, Jonassen et al. (2019) found that ERA-Interim had generally the best skill scores. The spatial and temporal resolutions of ERA-Interim are 0.125° and 6-hours, respectively. The AMSR2 data showed that the entire FIPS domain was covered by ice with a typical concentration of 80~100% . On the basis of climatological results of FIPS, the initial snow depth and ice thickness on 1 November varied spatially from 0.3 to 0.5 m and from 1.0 to 1.5 m, respectively.