Thin sea ice in the Arctic: comparing L-band radiometry retrievals with an ocean reanalysis

Steffen Tietsche, Magdalena Alonso-Balmaseda, Patricia Rosnay, Hao Zuo, Xiangshan Tian-Kunze, Lars Kaleschke
2017 The Cryosphere Discussions  
L-band radiance measurements such as these from the SMOS satellite can be used to distinguish thin from thick ice under cold surface conditions. However, uncertainties can be large due to assumptions in the forward model that converts brightness temperatures into ice thickness, and due to uncertainties in ancillary fields which need to be independently modelled or observed. It is therefore advisable to perform a critical assessment with independent observational and model data, before using
more » ... a, before using these data for model validation or data assimilation. Here, we discuss version 3.1 of the University of Hamburg L3C SMOS sea-ice thickness data set (SMOS-SIT) from autumn 2010 to spring 2017, and compare it to the results of the global ocean-sea ice analysis ORAS5. It is concluded that SMOS-SIT provides valuable and unique information on thin sea ice during winter, both in terms of the seasonal evolution and interannual variability. Overall, there is a promising match between SMOS-SIT and ORAS5 early in the freezing season (October-December), while later in winter, sea ice is consistently modelled thicker than observed. This seems to be mostly due to deficiencies of the model to simulate polynyas and fracture zones. However, there are regions where biases in the observational data seem to play a role, as comparison to independent observational data suggests. Both the reanalysis and the observations are provided with uncertainty estimates. While the reanalysis uncertainty estimates for the thickness of thin sea ice are probably too small and do not include structural uncertainty of the simulation, these of SMOS-SIT are often large, and do not seem to adequately characterise the complex uncertainties of the retrieval model. Therefore, careful and manual assessment of the data when using it for model evaluation and data assimilation is advisable. Interannual variability and trends of the large-scale distribution of thin sea ice are in good agreement between SMOS-SIT and ORAS5. In summary, SMOS-SIT presents a unique source of information about thin sea ice in the winter-time Arctic, and its use in sea ice modelling, assimilation and forecasting application is nascent and promising.
doi:10.5194/tc-2017-247 fatcat:h3uhysrwcvg7hk5llvaoydyivq