Satellite sea ice observations for validating midterm climate models Marcus Huntemann1, Georg Heygster1, Thomas Krumpen2 1 Institute of Environmental Physics, University of Bremen, 2 Alfred Wegener Institute, Bremerhaven Monthly sea ice concentrations averages and time series of sea ice extent for model comparisons Abstract Ice cover: strongly influences the albedo of the Arctic surface linked to atmospheric circulation important for radiative budget Sea ice is an essential climate component. Due to its radiative properties, the sea ice has a high influence on the energy budget of the whole Earth. Thin ice is distinct from first-year and multi-year ice by albedo, heat transfer and deformation properties. Sea ice concentration (SIC) is observed since several decades with mircowave radiometers on satellites like SMMR, SSMI, AMSR-E, AMSR2 covering the Arctic daily. Due to the high penetration depth into the sea ice at lower microwave frequencies like 1.4 GHz, the SMOS satellite is suited for investigation of thicknesses of thin sea ice (SIT). Observations of sea ice concentration and sea ice thickness are needed for validating climate models. Jun 2007 Sep 2007 Nov 2007 Validation of sea ice thickness from SMOS AWI EM-bird attached to helicopter AWI EM-bird measurements in the Laptev Sea in April 2012 SMOS (background) EM bird (small spots) Sea ice concentration definition and retrieval Fraction of ice covered sea surface per satellite pixel several algorithms were developed since late 70's using different frequencies of the microwave spectrum, e.g. ASI (ARTIST sea ice algorithm) using 89GHz, highes resolution NASA Team (NT) algorithm using 19 and 37 NT ASI Empirical sea ice thickness retrieval from SMOS using 40° - 50° incidence angle Averaging of sea ice thickness from SMOS Monthly averages produced for model comparison: daily averages → filter of radio frequency interference → monthly averages → land and distance to coast mask → climatological mask → final product Oct 2011 Nov 2011 Dec 2011 1 2 3 4 5 6 7 8 9 10 =TBv-TBh Fitting functions Conclusions 30 years of monthly sea ice concentrations and 3 years of SMOS ice thicknesses produced for model comparisons. Retrieval of sea ice thickness from SMOS intensity and polarization difference observations up to about 50cm possible SMOS sea ice thickness algorithm shows good agreement with EM-Bird sea ice thicknesses Refinement for incidence angle dependence for sea ice thickness retrieval from SMOS ongoing. Ice thickness x [cm] Ice thickness x [cm] Q References Ice Thickness [cm] Ice thickness for every I, Q (intensity, polarisation difference) pair can be assigned SIT < 20cm: SIT variations have high influence on I 20cm< SIT < 50 cm: SIT variations have more influence on Q [1] Huntemann, M., Heygster, G., Kaleschke, L., Krumpen, T., Mäkynen, M., and Drusch, M.: Empirical sea ice thickness retrieval during the freeze up period from SMOS high incident angle observations, The Cryosphere Discuss., 7, 4379-4405, doi:10.5194/tcd-7-4379-2013, 2013. [2] Haas, C. et al. (2009). Helicopter-borne measurements of sea ice thickness, using a small and lightweight, digital EM system. Journal of Applied Geophysics, 67(3), 234–241. doi:10.1016/j.jappgeo.2008.05.005 [3] Spreen, G., L. Kaleschke, and G.Heygster(2008), Sea ice remote sensing using AMSR-E 89 GHz channels J. Geophys. Res.,vol. 113, C02S03, doi:10.1029/2005JC003384. Acknowledgements Support by the following projects is gratefully acknowledged: ESA project SMOS-Ice (ESA 4000101476-10-NL-CT) German BMBF project MIKLIP (01LP1159B) EU project SIDARUS (262927) 8.11.13