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The microwave interferometric radiometer of the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission measures at a frequency of 1.4 GHz in the L-band. In contrast to other microwave satellites, low frequency measurements in L-band have a large penetration depth in sea ice and thus contain information on the ice thickness. Previous ice thickness retrievals have neglected a snow layer on top of the ice. Here, we implement a snow layer in our emission model and investigate how snow influences L-band brightness temperatures and whether it is possible to retrieve snow thickness over thick Arctic sea ice from SMOS data. We find that the brightness temperatures above snow-covered sea ice are higher than above bare sea ice and that horizontal polarisation is more affected by the snow layer than vertical polarisation. In accordance with our theoretical investigations, the root mean square deviation between simulated and observed horizontally polarised brightness temperatures decreases from 20.9K to 4.7 K, when we include the snow layer in the simulations. Although dry snow is almost transparent in L-band, we find brightness temperatures to increase with increasing snow thickness under cold Arctic conditions. The brightness temperatures’ dependence on snow thickness can be explained by the thermal insulation of snow and its dependence on the snow layer thickness. This temperature effect allows us to retrieve snow thickness over thick sea ice. For the best simulation scenario and snow thicknesses up to 35 cm, the average snow thickness retrieved from horizontally polarised SMOS brightness temperatures agrees within 0.1 cm with the average snow thickness measured during the IceBridge flight campaign in the Arctic in spring 2012. The corresponding root mean square deviation is 5.5 cm, and the coefficient of determination is r2 = 0.58 [ABSTRACT FROM AUTHOR]

The influence of spatial surface temperature changes over the Arctic Ocean on the 2-m air temperature variability is estimated using backward trajectories based on ERA-Interim and JRA25 wind fields. They are initiated at Alert, Barrow and at the Tara drifting station. Three different methods are used. The first one compares mean ice surface temperatures along the trajectories to the observed 2-m air temperatures at the stations. The second one correlates the observed temperatures to air temperatures obtained using a simple Lagrangian box model that only includes the effect of sensible heat fluxes. For the third method, mean sensible heat fluxes from the model are correlated with the difference of the air temperatures at the model starting point and the observed temperatures at the stations. The calculations are based on MODIS ice surface temperatures and four different sets of ice concentration derived from SSM/I (Special Sensor Microwave Imager) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS) data. Under nearly cloud-free conditions, up to 90% of the 2-m air temperature variance can be explained for Alert, and 70% for Barrow, using these methods. The differences are attributed to the different ice conditions, which are characterized by high ice concentration around Alert and lower ice concentration near Barrow. These results are robust for the different sets of reanalyses and ice concentration data. Trajectories based on 10-m wind fields from both reanalyses show large spatial differences in the Central Arctic, leading to differences in the correlations between modeled and observed 2-m air temperatures. They are most pronounced at Tara, where explained variances amount to 70% using JRA and 80% using ERA. The results also suggest that near-surface temperatures at a given site are influenced by the variability of surface temperatures in a domain of about 200 km radius around the site. [ABSTRACT FROM AUTHOR]

The article explores the melt ponds on sea ice in the Arctic Ocean which were driven from Moderate Resolution Image Spectroradiometer (MODIS) utilizing a spectral unmixing algorithm. It notes that the accuracy of the MODIS melt pond data set was carried with aerial images from the MELTEX campaign 2008 in the Beaufort Sea. It mentions that melt ponds on sea ice lessen the surface albedo and increase the decomposition of Arctic sea ice.

The article presents a study which offers an algorithm to identify leads that are wider than 3 kilometers (km) in the entire Arctic Ocean. It mentions that leads are the main sites of energy fluxes and brine releases which can be found at the air-ocean interface of sea-ice covered oceans. It notes that the said algorithm detects 50 percent of the lead area which was present in optical Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images.

The article presents a study on the potential of L-band radiometry in measuring sea-ice thickness in the Baltic Sea. It aims to examine the thermal microwave radiation emitted by a homogeneous closed ice cover at 1.4 GHz. It mentions the use of the incoherent model solution and the vertical model resolution.