Snow Depth on Arctic Sea Ice from Microwave Radiometers

Schnee auf Meereis gehört zu den wichtigsten Größen im arktischen Klimasystem. Trotzdem gibt es keine verlässliche Methode, die Schneedicke arktisweit zu bestimmen. In dieser Arbeit wurde ein neuer Algorithmus zur Bestimmung der Schneedicke auf arktischem Meereis aus Satellitenbeobachtungen entwickelt. Die Unsicherheiten des Algorithmus wurden mittels Monte-Carlo Modellierung abgeschätzt. Über saisonalem Eis liegt die abgeleitete Schneedicke im Mittel sehr nah an in-situ Beobachtungen, die Standartabweichung beträgt weniger als 5 cm und ist in der Größenordnung der ermittelten Unsicherheit. Über mehrjährigem Eis sind die Unsicherheiten der gewonnen Schneedicke deutlich größer und die Standartabweichung zu in-situ Messungen ist 8 cm. Es wurden zwei Schneedicke auf arktischem Meereis Datensätze veröffentlicht, welche genutzt werden können um Beispielsweise die Schneedicke in Klimamodellen zu evaluieren

AMSR-E winter snow depth on Arctic sea ice, Version 1.0 (NetCDF) (2002 to 2011)

The AMSR-E snow depth on Arctic sea ice product contains daily gridded snow depth data for the period from 2002 to 2011 (see also: AMSR-2 snow depth on Arctic sea ice product (2012 to 2018), doi:10.1594/PANGAEA.902747). The product is based on an empirical algorithm using passive microwave satellite observations from the AMSR-E (Advanced Microwave Scanning Radiometer for EOS) sensors on the NASA Aqua satellite, gridded on a polar stereographic grid (EPSG code 3411, Arctic) with 25 km grid resolution. Over seasonal ice, snow depth is available from November to April. Over Arctic multiyea ice (ice that has survived at least one summer melt) snow depth is available in March and April. Details about the algorithm are described in Rostosky et al. (2018). More details about the data product can be found in the product manual (https://seaice.uni-bremen.de/data/amsre/SnowDepth/

AMSR-2 winter snow depth on Arctic sea ice, Version 1.0 (NetCDF) (2012 to 2018)

The AMSR-2 snow depth on Arctic sea ice product contains daily gridded snow depth data for the period from 2012 to 2018 (see also: AMSR-E snow depth on Arctic sea ice product (2002 to 2011), doi:10.1594/PANGAEA.902748). The product is based on an empirical algorithm using passive microwave satellite observations from the AMSR-2 (Advanced Microwave Scanning Radiometer 2) sensors on the JAXA satellite GCOM-W1, gridded on a polar stereographic grid (EPSG code 3411, Arctic) with 25 km grid resolution. Over seasonal ice, the snow depth is available from November to April. Over Arctic multiyea ice (ice that has survived at least one summer melt) the snow depth is available in March and April. Details about the algorithm are described in Rostosky et al. (2018). More details about the data product can be found in the product manual (https://seaice.uni-bremen.de/data/amsr2/SnowDepth/

Brightness temperature raw data measured by UoM SBR radiometer at 19, 37 and 89 GHz from September 2019 to September 2020 during the MOSAiC expedition

This dataset contains ground-based radiometer observations from the University of Manitoba Surface Based Radiometer (UoM SBR) at 19, 37 and 89 GHz taken during Leg 1 to Leg 5 of the MOSAiC campaign (October 2019 - September 2020). Included are the raw observations and quick-look, uncalibrated quick-look brightness temperatures. In addition, images, automatically taken from a camera mounted on the instrument are provided. Details about the data format, usage and the instrument can be found in the file Data_manual.pdf

Brightness temperature measured by UoM SBR radiometer at 19, 37 and 89 GHz from September 2019 to September 2020 during the MOSAiC expedition

This dataset contains ground-based radiometer observations from the University of Manitoba Surface Based Radiometer (UoM SBR) at 19, 37 and 89 GHz taken during Leg 1 to Leg 5 of the MOSAiC campaign (October 2019 - September 2020). Included are I) calibrated brightness temperatures, II) quality controlled calibrated brightness temperatures, resampled to 1 minute temporal resolution. Details about the data format, usage and the instrument can be found in the file Data_manual.pdf

Brightness temperature measured by HUTRAD radiometer at 6.8 and 10.65 GHz from March to September 2020 during the MOSAiC expedition

This datasets contains ground-based on sea-ice floe observations from the Helsinki University of Technology RADiometer (HUTRAD) at microwave frequencies 6.8 GHz 10.65 GHz and 18.7 GHz taken during Leg 3 (April - May, 2020), Leg 4 (July 2020) and Leg 5 (August - September 2020) of the MOSAiC campaign. Two types of data are provided. Raw observations of individual measurement periods (hutrad_*.dat) for leg 3 to leg 5 and calibrated brightness temperatures (HUTRAD_*.txt) for leg 3 and leg 5. The raw observations of HUTRAD (counts) are calibrated to brightness temperature using a standard two-point calibration approach by assuming a linear relation between the measurements of the cold sky and measurements at ambient temperature using a microwave absorber. Details about the data format, usage and the instrument can be found in the file Data_manual.pdf

Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval

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

Sea ice concentration satellite retrievals influenced by surface changes due to warm air intrusions: A case study from the MOSAiC expedition

Warm air intrusions over Arctic sea ice can change the snow and ice surface conditions rapidly and can alter sea ice concentration (SIC) estimates derived from satellite-based microwave radiometry without altering the true SIC. Here we focus on two warm moist air intrusions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition that reached the research vessel Polarstern in mid-April 2020. After the events, SIC deviations between different satellite products, including climate data records, were observed to increase. Especially, an underestimation of SIC for algorithms based on polarization difference was found. To examine the causes of this underestimation, we used the extensive MOSAiC snow and ice measurements to model computationally the brightness temperatures of the surface on a local scale. We further investigated the brightness temperatures observed by ground-based radiometers at frequencies 6.9 GHz, 19 GHz, and 89 GHz. We show that the dop in the retrieved SIC of some satellite products can be attributed to large-scale surface glazing, that is, the formation of a thin ice crust at the top of the snowpack, caused by the warming events. Another mechanism affecting satellite products, which are mainly based on gradient ratios of brightness temperatures, is the interplay of the changed temperature gradient in the snow with snow metamorphism. From the two analyzed climate data record products, we found that one was less affected by the warming events. The low frequency channels at 6.9 GHz were less sensitive to these snow surface changes, which could be exploited in future to obtain more accurate retrievals of sea ice concentration. Strong warm air intrusions are expected to become more frequent in future and thus their influence on SIC algorithms will increase. In order to provide consistent SIC datasets, their sensitivity to warm air intrusions needs to be addressed

MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years

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

Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)

Arctic rain on snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack from rain and increased melt, there was a 4-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a 6-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89 GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of the CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows that the rain and refreeze were significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables.publishedVersio