Beschreibung: The aerosol climatology at the coastal Antarctic Neumayer Station (NM) was investigated based on continuous, 25-yr long observations of biogenic sulphur components (methanesulfonate and non-sea salt sulphate), sea salt and nitrate. Although significant long-term trends could only be detected for nitrate (-3.6 ± 2.5% per year between 1983 and 1993 and +4.0 ± 3.2% per year from 1993-2007), non-harmonic periodicities between 2 and 5 yr were typical for all species. Dedicated time series analyses revealed that relations to sea ice extent and various circulation indices are weak at best or not significant. In particular, no consistent link between sea ice extent and sea salt loadings was evident suggesting only a rather local relevance of the NM sea salt record. Nevertheless, a higher Southern Annular Mode index tended to entail a lower biogenic sulphur signal. In examining the spatial uniformity of the NM findings we contrasted them to respective 17 yr records from the coastal Dumont d'Urville Station. We found similar long-term trends for nitrate, indicating an Antarctic-wide but not identifiable atmospheric signal, although any significant impact of solar activity or pollution could be ruled out. No inter-site variability on the multiannual scale was evident for the other ionic compounds.

Beschreibung: Divergent sea ice motion breaks the ice and opens fractures and leads. Depending on the air temperature, those open-water areas can quickly refreeze. The open water or thin ice in leads play a crucial role in the heat and gas exchange between the ocean and the atmosphere, impacting atmospheric, ecological, and oceanic processes. Leads can be detected from space, using different types of instruments, e.g., thermal infrared, passive microwave, active microwave, or optical sensors. The retrieval methods have different sensitivities, especially concerning the minimum lead width and the maximum ice thickness, different spatial resolutions, and different limits. We presented a time series of lead fractions from different lead products (Oct 2019 - May 2020) along the drift of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Transpolar Drift. We compared 7 different lead products based on 1. accumulated divergence derived from SAR images, 2. divergence in linear kinematic features, 3. classified SAR data, 4. thermal infrared data from MODIS, 5. passive microwave data from AMSR-2, 6. radar altimetry from CryoSat-2 (lead fractions and total lead count), and 7. thermal infrared data from helicopter surveys. We extracted daily lead fractions in a circle with a radius of 50 km along the drift of MOSAiC. Data is available from 5 October 2019 to 15 May 2020 with shorter time series for some of the sensors. We found that the mean lead fractions varied by 1 magnitude across different lead products due to different physical lead and sea ice properties observed by the sensors and methodological factors such as spatial resolution. Thus, the choice of lead product should align with the specific application. Each file contains time and lead fraction for a circular area (radius 50 km) around the MOSAiC position of the particular time stamp. The thermal infrared data from helicopter surveys are available from doi:10.1594/PANGAEA.951569.

Beschreibung: The SMOS Level 3 Sea Ice Thickness product, in NetCDf format, provides daily estimations of SMOS-retrieved sea ice thickness (and its uncertainty) around the Antarctic continent during the austral winter (from 15. April to 15. October), from year 2010 to 2020. The sea ice thickness is retrieved from the v620 SMOS L1C product, up to a depth of approximately 0.5-1 m, depending on the ice temperature and salinity. Daily maps, projected on polar stereographic grid of 12.5 km, are generated by the Alfred-Wegener-Institut (AWI).

Beschreibung: The Soil Moisture and Ocean Salinity (SMOS) mission observes brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) with a daily coverage of the polar regions. L-band radiometry has been shown to provide information on the thickness of thin sea ice. Here, we apply a new emission model that has previously been used to investigate the impact of snow on thick Arctic sea ice. The model has not yet been used to retrieve ice thickness. In contrast to previous SMOS ice thickness retrievals, the new model allows us to include a snow layer in the brightness temperature simulations. Using ice thickness estimations from satellite thermal imagery, we simulate brightness temperatures during the ice growth season 2011 in the northern Baltic Sea. In both the simulations and the SMOS observations, brightness temperatures increase by more than 20 K, most likely due to an increase of ice thickness. Only if we include the snow in the model, the absolute values of the simulations and the observations agree well (mean deviations below 3.5 K). In a second comparison, we use high-resolution measurements of total ice thickness (sum of ice and snow thickness) from an electromagnetic (EM) sounding system to simulate brightness temperatures for 12 circular areas. While the SMOS observations and the simulations that use the EM modal ice thickness are highly correlated (r2=0.95), the simulated brightness temperatures are on average 12 K higher than observed by SMOS. This would correspond to an 8-cm overestimation of the modal ice thickness by the SMOS retrieval. In contrast, if the simulations take into account the shape of the EM ice thickness distributions (r2=0.87), the mean deviation between simulated and observed brightness temperatures is below 0.1 K.

Beschreibung: Following the launch of ESA's Soil Moisture and Ocean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).

Beschreibung: The impact of assimilating sea ice thickness data derived from ESA's Soil Moisture and Ocean Salinity (SMOS) satellite together with Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data of the National Snow and Ice Data Center (NSIDC) in a coupled sea ice-ocean model is examined. A period of 3 months from 1 November 2011 to 31 January 2012 is selected to assess the forecast skill of the assimilation system. The 24 h forecasts and longer forecasts are based on the Massachusetts Institute of Technology general circulation model (MITgcm), and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter. For comparison, the assimilation is repeated only with the SSMIS sea ice concentrations. By running two different assimilation experiments, and comparing with the unassimilated model, independent satellite-derived data, and in situ observation, it is shown that the SMOS ice thickness assimilation leads to improved thickness forecasts. With SMOS thickness data, the sea ice concentration forecasts also agree better with observations, although this improvement is smaller.

Beschreibung: Brightness temperatures at 1.4 GHz (L-band) measured by the Soil Moisture and Ocean Salinity (SMOS) Mission have been used to derive the thickness of sea ice. The retrieval method is applicable only for relatively thin ice and not during the melting period. Hitherto, the availability of ground truth sea ice thickness measurements for validation of SMOS sea ice products was mainly limited to relatively thick ice. The situation has improved with an extensive field campaign in the Barents Sea during an anomalous ice edge retreat and subsequent freeze-up event in March 2014. A sea ice forecast system for ship route optimisation has been developed and was tested during this field campaign with the ice-strengthened research vessel RV Lance. The ship cruise was complemented with coordinated measurements from a helicopter and the research aircraft Polar 5. Sea ice thickness was measured using an electromagnetic induction (EM) system from the bow of RV Lance and another EM-system towed below the helicopter. Polar 5 was equipped among others with the L-band radiometer EMIRAD-2. The experiment yielded a comprehensive data set allowing the evaluation of the operational forecast and route optimisation system as well as the SMOS-derived sea ice thickness product that has been used for the initialization of the forecasts. Two different SMOS sea ice thickness products reproduce the main spatial patterns of the ground truth measurements while the main difference being an underestimation of thick deformed ice. Ice thicknesses derived from the surface elevation measured by an airborne laser scanner and from simultaneous EMIRAD-2 brightness temperatures correlate well up to 1.5 m which is more than the previously anticipated maximal SMOS retrieval thickness.

Beschreibung: The retrieval of Arctic sea ice thickness is one of the major objectives of the European CryoSat-2 radar altimeter mission and the 7-year long period of operation has produced an unprecedented record of monthly sea ice thickness information. We present CryoSat-2 results that show changes and variability of Arctic sea ice for the period 2010 to 2018. CryoSat-2, however, was designed to observe thick perennial sea ice, while retrieving thin seasonal sea ice accurately is more challenging. We have therefore developed a method of completing and improving Arctic sea ice thickness information by merging complementary satellite retrievals. The European SMOS satellite can detect thin sea ice, whereas its companion CryoSat-2 lacks sensitivity. Using these satellite missions together overcomes several issues of single-mission retrievals and provides a more accurate and comprehensive view on the state of Arctic sea-ice thickness at higher temporal resolution. Nevertheless, stand-alone CryoSat-2 observations can be used as reference data for the exploitation of older radar altimetry data sets over sea ice. In order to observe trends in sea ice thickness, it is required to minimize inter-mission biases between subsequent satellite missions. A climate data record of sea ice thickness derived from satellite radar altimetry has been developed for both hemispheres, based on the 15-year (2002-2017) monthly retrievals from Envisat and CryoSat-2 and calibrated in the 2010-2012 overlap period.