Monitoring surface and atmospheric parameters - like water vapor - is challenging in the Arctic, despite the daily Arctic-wide coverage of spaceborne microwave radiometer data. This is mainly due to the difficulties in characterizing the sea ice surface emission: sea ice and snow microwave emission is high and highly variable. There are very few datasets combining relevant in situ measurements with co-located remote sensing data, which further complicates the development of accurate retrieval algorithms. Here, we present a multi-parameter retrieval based on the inversion of a forward model for both, atmosphere and surface, for non-melting conditions. The model consists of a layered microwave emission model of snow and ice. Since snow scattering and emission effects, as well as temperature gradients, are taken into account, a high variability in brightness temperatures can be simulated. For ocean regions and the atmosphere existing parameterized forward models are used. By using optimal estimation, the forward model can be inverted allowing for the simultaneous and consistent retrieval of nine variables: integrated water vapor, liquid water path, sea ice concentration, multi-year ice fraction, snow depth, snow-ice interface temperature and snow-air interface temperature as well as sea-surface temperature and wind speed (over open ocean). In addition, the method provides retrieval uncertainty estimates for each retrieved parameter. To evaluate the forward model as well as the retrieval, we use the extensive datasets acquired during the year-long Arctic expedition MOSAiC (2019-2020) as a reference.

The incidence angle dependence of synthetic aperture radar (SAR) backscatter from sea ice has been observed to differ between ice types. This is indicative of its complex dielectric and backscattering properties and cannot be analytically determined or easily modelled. Without assumptions this dependence can only be measured from SAR through multiple observations of the same ice. Due to the sea ice drift, doing so at high resolution is time inefficient and to some extent only feasible for land fast ice. Thus, the incidence angle dependence can currently not be easily related to ground measurements or be fully exploited for classification tasks. By reformulating the problem of incidence angle dependence to a domain transfer task and making use of known physical relations, we can train a Generative Adversarial Network (GAN) to predict the incidence angle dependence from a single Sentinel-1 satellite scene patch-wise using no only local data. The network’s predictions are validated for multiyear and first-year ice backscatter using coincident ICESat-2 altimeter measurements. The network suggests a diverse incidence angle dependence in the HV channel, that so far have been observed only scarsely. The results of this study are a novelty in sea ice remote sensing insofar that artificial intelligence manages to solve a task that would be nearly impossible for a human observer.

Comparing helicopter-borne surface temperature maps in winter and optical orthomosaics in summer from the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we find a strong geometric correlation between warm anomalies in winter and melt pond location the following summer. Warm anomalies are attributed to thinner snow and ice on level ice compared to the deformed ice in the surroundings or refrozen leads with only newly formed, thin ice. Warm surface temperature anomalies in January were 0.3 K to 2.5 K warmer on sea ice that later formed melt ponds. A one-dimensional steady-state thermodynamic model shows that the observed surface temperature differences are in line with the observed ice thickness and snow depth. We demonstrate the potential of seasonal prediction of summer melt pond location and coverage from winter surface temperature observations. A threshold-based classification achieves a correct classification for 41% of the melt ponds.

Melt ponds forming on Arctic sea ice in summer significantly reduce the surface albedo and impact the heat and mass balance of the sea ice. Their seasonal development features fast and local changes in fractions of surface types demonstrating the necessity of improving melt pond fraction (MPF) products. We present a renewed method to extract MPF from Sentinel-2 satellite imagery, which is evaluated by MPF products from higher resolution satellite and helicopter-borne imagery. The analysis of melt pond evolution during the MOSAiC campaign in summer 2020, shows a split of the Central Observatory (CO) into a level ice and a highly deformed part, the latter of which exhibits exceptional early melt pond formation compared to the vicinity. Average CO MPFs amount to 17 % before and 23 % after the major drainage. Arctic-wide analysis of MPF for years 2017-2021 shows a consistent seasonal cycle in all regions and years.