Snow on sea ice influences the Arctic energy and heat budgets and is therefore important for Arctic climate studies. Methods to derive snow depth based on satellite-borne microwave radiometer observations have existed since the 1990s. However, in the Arctic the most widely used algorithm can only be applied over first-year ice (FYI) and does not make use of the lower frequencies, which are available since 2002.Here we present three improvements to the current passive microwave snow depth retrieval: (a) We derive new coefficients based on a regression analysis using 5 years of Operation IceBridge airborne snow depth measurements. (b) We extend the algorithm to take advantage of the lower frequency channel at 7 GHz. (c) We consider an extension of the snow depth retrieval to multiyear ice (MYI) during spring. Our results show that the gradient ratio, GR(19/7) is most suited for deriving snow over both Arctic FYI (R =°À0.73) and MYI (R = À0.57). An evaluation of the new retrieval with Operation IceBridge snow depth measurements from March and April 2015 shows a good agreement over FYI (difference = À2.1 cm; 93% of the differences below 5 cm). Over MYI the difference is À4.0 cm and 56% of the differences are below 5 cm, that is, the root mean square distance (RMSD) is larger over MYI than over FYI. We demonstrate for the first time that spring snow depth measurements can be derived from passive microwave observations over both FYI and MYI.Plain Language Summary Snow on Arctic sea ice plays an important role in the Arctic climate system. It reflects the majority of the incoming solar radiation and isolates the sea ice from warm air in summer. However, the vast area and the extreme weather conditions make it difficult to monitor changes in snow depth during the Arctic winter. In this study, we develop a retrieval for snow depth on Arctic sea ice based on satellite observations. The advantages of satellite observations are that they provide daily coverage of the whole Arctic. We compare satellite observations in the microwave regime to airborne snow depth measurements, obtained in March and April from 2009 to 2015. We find a good agreement between changes in the signal observed by the satellite and changes in the measured snow depth when the ice is smooth. Over multiyear ice, ice that has survived at least one summer melt and that is often rough, the agreement is not as good. We demonstrate for the first time that spring snow depth estimations for the whole sea ice covered Arctic can be derived from satellite observations. While a clear signal can be found for changes in sea ice area, changes in snow depth on sea ice or snowfall are hard to quantify. In situ measurements and reanalysis data suggest a decline in summer snowfall (June to August) over the sea ice covered Arctic (Screen & Simmonds, 2012). Webster et al. (2014) analyzed spring ROSTOSKY ET AL. 7120

Abstract. We combine satellite data products to provide a first and general overview of the sea-ice conditions along the MOSAiC drift and a comparison with previous years. We find that the MOSAiC drift was around 25 % faster than the climatological mean drift, as a consequence of large-scale low-pressure anomalies prevailing around the Barents-Kara-Laptev Sea region between January and March. In winter (October–April), satellite observations show that the sea-ice in the vicinity of the Central Observatory (CO) was rather thin compared to the previous years along the same trajectory. Unlike ice thickness, satellite-derived sea-ice concentration, lead frequency, and snow thickness during winter month were close to the long-term mean with little variability. With the onset of spring and decreasing distance to Fram Strait, variability in ice concentration and lead activity increased. In addition, frequency and strength of deformation events (divergence and shear) were higher during summer than during winter. Overall, we find that sea-ice conditions observed close (~ 5 km) to the CO are representative for the wider (50 km and 100 km) surroundings. An exception is the ice thickness: Here we find that sea-ice near the CO (50 km radius) was 4 % thinner than sea-ice within a 100 km radius. Moreover, satellite acquisitions indicate that the formation of large melt ponds began earlier on the MOSAiC floe than on neighbouring floes.

Abstract. We combine satellite data products to provide a first and general overview of the physical sea ice conditions along the drift of the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition and a comparison with previous years (2005–2006 to 2018–2019). We find that the MOSAiC drift was around 20 % faster than the climatological mean drift, as a consequence of large-scale low-pressure anomalies prevailing around the Barents–Kara–Laptev sea region between January and March. In winter (October–April), satellite observations show that the sea ice in the vicinity of the Central Observatory (CO; 50 km radius) was rather thin compared to the previous years along the same trajectory. Unlike ice thickness, satellite-derived sea ice concentration, lead frequency and snow thickness during winter months were close to the long-term mean with little variability. With the onset of spring and decreasing distance to the Fram Strait, variability in ice concentration and lead activity increased. In addition, the frequency and strength of deformation events (divergence, convergence and shear) were higher during summer than during winter. Overall, we find that sea ice conditions observed within 5 km distance of the CO are representative for the wider (50 and 100 km) surroundings. An exception is the ice thickness; here we find that sea ice within 50 km radius of the CO was thinner than sea ice within a 100 km radius by a small but consistent factor (4 %) for successive monthly averages. Moreover, satellite acquisitions indicate that the formation of large melt ponds began earlier on the MOSAiC floe than on neighbouring floes.

Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the best-possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, which include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean–sea-ice model is used to assess the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application Facility, thin sea ice from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice from ESA's CryoSat-2 satellite, and a new snow-depth product derived from the National Space Agency's Advanced Microwave Scanning Radiometer (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a smaller but still positive short-term effect on snow depth and sea-ice concentration. In our study, the seasonal forecast showed that assimilating snow depth led to a less accurate long-term estimation of sea-ice extent compared to the other assimilation systems. The other three gave similar results. The improvements due to assimilation were found to last for at least 3–4 months, but possibly even longer.

Abstract. In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations.