Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions

We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice surface fraction (ISF) - SIC minus the per-grid-cell melt pond fraction (MPF) on sea ice - as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the National Oceanographic and Atmospheric Administration (NOAA) National Snow and Ice Data Center (NSIDC) SIC climate data record (CDR). Group III consists of Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) and National Aeronautics and Space Administration (NASA) Team (NT) algorithm products, and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find widespread positive and negative differences between PMW and MODIS SIC with magnitudes frequently reaching up to 20 %-25 % for groups I and III and up to 30 %-35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from group I products agrees with MODIS within 2 %-5 % accuracy during the entire melt period from May through September. Group II and IV products overestimate MODIS Arctic average SIC by 5 %-10 %. Out of group III, ASI is similar to group I products while NT SIC underestimates MODIS Arctic average SIC by 5 %-10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically overestimated by all products; NT provides the smallest overestimations (up to 25 %) and group II and IV products the largest overestimations (up to 45 %). The spatial distribution of the observed overestimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence of ice surface properties other than melt ponds, i.e. wet snow and coarse-grained snow/refrozen surface, on brightness temperatures and their ratios used as input to the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for (i) the observed differences between PMW SIC and MODIS ISF and for (ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high melt pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the unknown number of melt pond signatures likely included in the ice tie points plays an important role - particularly for groups I and II - and recommend conducting further research in this field

Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010-2018

In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations (SIC) derived from passive microwave (PM) products were compared with ship-based visual observations (OBS) collected during the Chinese National Arctic Research Expeditions (CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team (NT), Bootstrap (BT) and Climate Data Record (CDR) algorithms based on the SSMIS sensor, as well as the BT, enhanced NASA-Team (NT2) and ARTIST Sea Ice (ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients (CC), biases and root mean square deviations (RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89 (AMSR-E/AMSR-2 NT2) to 0.95 (SSMIS NT), biases range from -3.96% (SSMIS NT) to 12.05% (AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81% (SSMIS NT) to 20.15% (AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best (worst) agreement occurs between OBS SIC and SSMIS NT (AMSR-E/AMSR-2 NT2) SIC in the Arctic summer.Peer reviewe

Retrievals of Arctic sea ice melt pond depth and underlying ice thickness using optical data

Melt pond is a distinctive characteristic of the summer Arctic, which affects energy balance in the Arctic system. The Delta-Eddington model (BL) and Two-strEam rAdiative transfer model (TEA) are employed to retrieving pond depth Hp and underlying ice thickness Hi according to the ratio X of the melt-pond albedo in two bands. Results showed that when λ1 = 359 nm and λ2 = 605 nm, the Pearson’s correlation coefficient r between X and Hp is 0.99 for the BL model. The result of TEA model was similar to the BL model. The retrievals of Hp for the two models agreed well with field observations. For Hi, the highest r (0.99) was obtained when λ1 = 447 nm and λ2 = 470 nm for the BL model, λ1 = 447 nm and λ2 = 451 nm for the TEA model. Furthermore, the BL model was more suitable for the retrieval of thick ice (0 < Hi < 3.5 m, R2 = 0.632), while the TEA model is on the contrary (Hi < 1 m, R2 = 0.842). The present results provide a potential method for the remote sensing on melt pond and ice in the Arctic summer

2014 summer Arctic sea ice thickness and concentration from shipborne observations

A series of shipborne sea ice observations were performed during the Chinese National Arctic Research Expedition in the Pacific Arctic sector between 2 August 2014 and 1 September 2014. Undeformed sea ice thickness (SIT) as well as area fractions of open water, melt pond, and sea ice (Aw, Ap, and Ai) were monitored using downward-oriented and oblique-oriented cameras. The results show that SIT varied between 20 and 220 cm throughout the whole cruise, with the average and standard deviation equaling 104.9 and 29.1 cm, respectively. Mean Aw and Ai were 0.52 and 0.44 in the marginal ice zone, respectively, while mean Aw decreased to 0.23 and mean Ai increased to 0.73 in the pack ice zone. Limited variation between 0 and 0.32 in Ap was seen throughout the whole cruise. Shipborne sea ice concentration was then rectified and mapped across a large transect to validate estimates derived from the satellite sensors Special Sensor Microwave Imager/Sounder (SSMIS) (25 km) and AMSR2 (25 km). Overestimations were 9.5% and 9.9% for SSMIS and AMSR2 compared with measurements, respectively. The mean areal broadband surface albedo based on shipborne survey increased from 0.07 to 0.66 along the transect between 72°N and 81°N