Abstract. Following the launch of ESA's Soil Moisture andOcean 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).

[1] The Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission for the first time measures globally Earth's radiation at a frequency of 1.4 GHz (L-band). It had been hypothesized that L-band radiometry can be used to measure the sea ice thickness due to the large penetration depth in the sea ice medium. We demonstrate the potential of SMOS to derive the thickness of thin sea ice for the Arctic freeze-up period using a novel retrieval algorithm based on Level 1C brightness temperatures. The SMOS ice thickness product is compared with an ice growth model and independent sea ice thickness estimates from MODIS thermal infrared imagery. The ice thickness derived from SMOS is highly consistent with the temporal development of the growth simulation and agrees with the ice thickness from MODIS images with 10 cm standard deviation. The results confirm that SMOS can be used to retrieve sea ice thickness up to half a meter under ideal cold conditions with surface air temperatures below À10°C and highconcentration sea ice coverage. Citation: Kaleschke, L., X. Tian-Kunze, N. Maaß, M. Mäkynen, and M. Drusch (2012), Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period, Geophys. Res. Lett., 39, L05501,

Abstract. Sea ice thickness information is important for sea ice modelling and ship operations. Here a method to detect the thickness of sea ice up to 50 cm during the freeze-up season based on high incidence angle observations of the Soil Moisture and Ocean Salinity (SMOS) satellite working at 1.4 GHz is suggested. By comparison of thermodynamic ice growth data with SMOS brightness temperatures, a high correlation to intensity and an anticorrelation to the difference between vertically and horizontally polarised brightness temperatures at incidence angles between 40 and 50 • are found and used to develop an empirical retrieval algorithm sensitive to thin sea ice up to 50 cm thickness. The algorithm shows high correlation with ice thickness data from airborne measurements and reasonable ice thickness patterns for the Arctic freeze-up period.

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) present in the MODIS data. The accuracy is best for the 15-30 cm thickness range, $ $38%. The largest h i uncertainty comes from air temperature data. Our ice-thickness limits are more conservative than those in previous studies where numerical weather prediction model data were not used in the h i retrieval. Our study gives new detailed insight into the capability of T s -based h i retrieval in the Arctic marginal seas during freeze-up and wintertime, and should also benefit work where MODIS h i charts are used.