Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models

Abstract. Sea ice is a crucial component for short-, medium- and long-term numerical weather predictions. Most importantly, changes of sea ice coverage and areas covered by thin sea ice have a large impact on heat fluxes between the ocean and the atmosphere. L-band brightness temperatures from ESA's Earth Explorer SMOS (Soil Moisture and Ocean Salinity) have been proven to be a valuable tool to derive thin sea ice thickness. These retrieved estimates were already successfully assimilated in forecasting models to constrain the ice analysis, leading to more accurate initial conditions and subsequently more accurate forecasts. However, the brightness temperature measurements can potentially be assimilated directly in forecasting systems, reducing the data latency and providing a more consistent first guess. As a first step towards such a data assimilation system we studied the forward operator that translates geophysical parameters provided by a model into brightness temperatures. We use two different radiative transfer models to generate top of atmosphere brightness temperatures based on ORAP5 model output for the 2012/2013 winter season. The simulations are then compared against actual SMOS measurements. The results indicate that both models are able to capture the general variability of measured brightness temperatures over sea ice. The simulated brightness temperatures are dominated by sea ice coverage and thickness changes are most pronounced in the marginal ice zone where new sea ice is formed. There we observe the largest differences of more than 20 K over sea ice between simulated and observed brightness temperatures. We conclude that the assimilation of SMOS brightness temperatures yields high potential for forecasting models to correct for uncertainties in thin sea ice areas and suggest that information on sea ice fractional coverage from higher-frequency brightness temperatures should be used simultaneously.

[1]  Y. Kerr,et al.  Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations , 2012 .

[2]  Robert Ricker,et al.  Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation , 2014 .

[3]  Ron Kwok,et al.  ICESat over Arctic sea ice: Estimation of snow depth and ice thickness , 2008 .

[4]  A. Semtner A MODEL FOR THE THERMODYNAMIC GROWTH OF SEA ICE IN NUMERICAL INVESTIGATIONS OF CLIMATE , 1975 .

[5]  Pedro Elosegui,et al.  New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator , 2017 .

[6]  Calvin T. Swift,et al.  Low‐frequency passive‐microwave observations of sea ice in the Weddell Sea , 1993 .

[7]  Yann Kerr,et al.  Measuring Ocean Salinity with ESA’s SMOS Mission – Advancing the Science , 2002 .

[8]  Yann Kerr,et al.  Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission , 2001, IEEE Trans. Geosci. Remote. Sens..

[9]  C. Swift,et al.  Microwave remote sensing , 1980, IEEE Antennas and Propagation Society Newsletter.

[10]  D. Rothrock,et al.  Thin ice thickness from satellite thermal imagery , 1996 .

[11]  Malcolm Davidson,et al.  CryoSat‐2 estimates of Arctic sea ice thickness and volume , 2013 .

[12]  M. Maqueda,et al.  An elastic-viscous-plastic sea ice model formulated on Arakawa B and C grids , 2009 .

[13]  Ron Lindsay,et al.  Comparison of thin ice thickness distributions derived from RADARSAT Geophysical Processor System and advanced very high resolution radiometer data sets , 2003 .

[14]  Jiping Xie,et al.  Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system , 2016, The Cryosphere.

[15]  Matthias Drusch,et al.  Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data , 2013 .

[16]  Lars Kaleschke,et al.  A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data , 2017 .

[17]  L. Kaleschke,et al.  Snow thickness retrieval from L-band brightness temperatures: a model comparison , 2015, Annals of Glaciology.

[18]  Rasmus T. Tonboe,et al.  A sea-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice , 2009 .

[19]  Peter Toose,et al.  Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: A Synthetic Analysis , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  M. Balmaseda,et al.  The new eddy-permitting ORAP5 ocean reanalysis: description, evaluation and uncertainties in climate signals , 2017, Climate Dynamics.

[21]  J. Ridley,et al.  Sea ice concentration and motion assimilation in a sea ice−ocean model , 2008 .

[22]  Niels Skou,et al.  SMOS sea ice product: operational application and validation in the Barents Sea marginal ice zone , 2016 .

[23]  K. Isaksen,et al.  Changes in Winter Warming Events in the Nordic Arctic Region , 2015 .

[24]  R. Lindsay,et al.  Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations , 2014 .

[25]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[26]  Wilford F. Weeks,et al.  Equations for Determining the Gas and Brine Volumes in Sea-Ice Samples , 1982, Journal of Glaciology.

[27]  Jeffrey R. Key,et al.  Arctic sea ice in transformation: A review of recent observed changes and impacts on biology and human activity , 2014 .

[28]  Peter R. Oke,et al.  TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic , 2012 .

[29]  G. Maykut,et al.  Some results from a time‐dependent thermodynamic model of sea ice , 1971 .

[30]  Norbert Untersteiner,et al.  The Geophysics of Sea Ice , 1986, NATO ASI Series.

[31]  Andrew Shepherd,et al.  Near-real-time Arctic sea ice thickness and volume from CryoSat-2 , 2016 .

[32]  Terhikki Manninen,et al.  The brine and gas content of sea ice with attention to low salinities and high temperatures , 1988 .

[33]  W. J. Burke,et al.  Comparison of 2.8‐ and 21‐cm microwave radiometer observations over soils with emission model calculations , 1979 .

[34]  N. Untersteiner Calculations of temperature regime and heat budget of sea ice in the central Arctic , 1964 .

[35]  Nadine Gobron,et al.  Observation and integrated Earth-system science: A roadmap for 2016–2025 , 2016 .

[36]  Gary A. Maykut,et al.  The Surface Heat and Mass Balance , 1986 .

[37]  Matthias Drusch,et al.  SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification , 2013 .

[38]  C. Swift,et al.  An improved model for the dielectric constant of sea water at microwave frequencies , 1977, IEEE Journal of Oceanic Engineering.

[39]  Lars Nerger,et al.  Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter , 2014 .

[40]  Vassilios Makios,et al.  The complex‐dielectric constant of sea ice at frequencies in the range 0.1–40 GHz , 1978 .

[41]  Qinghua Yang,et al.  Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System , 2017 .

[42]  Steffen Tietsche,et al.  Will Arctic sea ice thickness initialization improve seasonal forecast skill? , 2014 .

[43]  Yann Kerr,et al.  Sea Surface Salinity Observations from Space with the SMOS Satellite: A New Means to Monitor the Marine Branch of the Water Cycle , 2014, Surveys in Geophysics.

[44]  A. Sihvola,et al.  The complex dielectric constant of snow at microwave frequencies , 1984 .

[45]  Matthias Drusch,et al.  Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze‐up period , 2012 .

[46]  Jacqueline Boutin,et al.  Issues concerning the sea emissivity modeling at L band for retrieving surface salinity , 2003 .

[47]  M. H. Savoie,et al.  A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring , 2013 .

[48]  Richard K. Moore,et al.  Microwave Remote Sensing, Active and Passive , 1982 .

[49]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[50]  J. Haarpaintner,et al.  SSM/I Sea Ice Remote Sensing for Mesoscale Ocean-Atmosphere Interaction Analysis , 2001 .

[51]  D. A. Rothrock,et al.  Modeling Global Sea Ice with a Thickness and Enthalpy Distribution Model in Generalized Curvilinear Coordinates , 2003 .

[52]  L. Kaleschke,et al.  Sea ice remote sensing using AMSR‐E 89‐GHz channels , 2008 .

[53]  Kristian Mogensen,et al.  Arctic sea ice in the global eddy-permitting ocean reanalysis ORAP5 , 2017, Climate Dynamics.