1. 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, , doi: /tcd , 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– doi: /j.jappgeo
[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: /2005JC
Acknowledgements
Support by the following projects is gratefully acknowledged:
ESA project SMOS-Ice (ESA NL-CT)
German BMBF project MIKLIP (01LP1159B)
EU project SIDARUS (262927)