Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter

The impact of assimilating sea ice thickness data derived from ESA's Soil Moisture and Ocean Salinity (SMOS) satellite together with Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data of the National Snow and Ice Data Center (NSIDC) in a coupled sea ice-ocean model is examined. A period of 3 months from 1 November 2011 to 31 January 2012 is selected to assess the forecast skill of the assimilation system. The 24 h forecasts and longer forecasts are based on the Massachusetts Institute of Technology general circulation model (MITgcm), and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter. For comparison, the assimilation is repeated only with the SSMIS sea ice concentrations. By running two different assimilation experiments, and comparing with the unassimilated model, independent satellite-derived data, and in situ observation, it is shown that the SMOS ice thickness assimilation leads to improved thickness forecasts. With SMOS thickness data, the sea ice concentration forecasts also agree better with observations, although this improvement is smaller.

[1]  Donald J. Cavalieri,et al.  Intersensor Calibration Between F13 SSMI and F17 SSMIS for Global Sea Ice Data Records , 2012, IEEE Geoscience and Remote Sensing Letters.

[2]  William D. Hibler,et al.  On an efficient numerical method for modeling sea ice dynamics , 1997 .

[3]  Averaged Pulse-Shape Measurements of Radar Sea Return at near-Vertical Incidence , 1971 .

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

[5]  D. Menemenlis,et al.  On the formulation of sea-ice models. Part 1: Effects of different solver implementations and parameterizations , 2010 .

[6]  Ron Kwok,et al.  Arctic Ocean Sea Ice Thickness and Kinematics: Satellite Retrievals and Modeling , 2010 .

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

[8]  Ron Kwok,et al.  Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008 , 2009 .

[9]  Geir Evensen,et al.  Assimilating synthetic CryoSat sea ice thickness in a coupled ice-ocean model , 2007 .

[10]  Jens Schröter,et al.  On domain localization in ensemble based Kalman Flter algorithms , 2008 .

[11]  Jens Schröter,et al.  Assimilating NOAA SST data into BSH operational circulation model for the North and Baltic Seas: Part 2. Sensitivity of the forecast's skill to the prior model error statistics , 2014 .

[12]  T. Lee,et al.  ECCO2: High Resolution Global Ocean and Sea Ice Data Synthesis , 2008 .

[13]  Jens Schröter,et al.  Assimilation of sea ice motion in a finite‐element sea ice model , 2009 .

[14]  G. Kivman,et al.  An entropy approach to tuning weights and smoothing in the generalized inversion , 2001 .

[15]  Humfrey Melling,et al.  Measurements of the Underside Topography of Sea Ice by Moored Subsea Sonar , 1995 .

[16]  Walter H. F. Smith,et al.  Global Sea Floor Topography from Satellite Altimetry and Ship Depth Soundings , 1997 .

[17]  J. Kay,et al.  The Arctic’s rapidly shrinking sea ice cover: a research synthesis , 2012, Climatic Change.

[18]  Ron Kwok,et al.  Source and Pathway of the Western Arctic Upper Halocline in a Data-Constrained Coupled Ocean and Sea Ice Model , 2012 .

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

[20]  Lars Nerger,et al.  Software for ensemble-based data assimilation systems - Implementation strategies and scalability , 2013, Comput. Geosci..

[21]  Yann Kerr,et al.  ESA's Soil Moisture and Ocean Salinity Mission: Mission Performance and Operations , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Matthias Drusch,et al.  Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations , 2013 .

[23]  Geir Evensen,et al.  Assimilation of ice concentration in a coupled ice–ocean model, using the Ensemble Kalman filter , 2003 .

[24]  J. Marotzke,et al.  Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects , 2012 .

[25]  Keran Claffey,et al.  Ice mass-balance buoys: a tool for measuring and attributing changes in the thickness of the Arctic sea-ice cover , 2006, Annals of Glaciology.

[26]  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 .

[27]  Jens Schröter,et al.  Using sea-level data to constrain a finite-element primitive-equation ocean model with a local SEIK filter , 2006 .

[28]  Jens Schröter,et al.  Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data , 2012 .

[29]  M. Losch,et al.  Assimilating summer sea-ice concentration into a coupled ice–ocean model using a LSEIK filter , 2015, Annals of Glaciology.

[30]  Ron Lindsay,et al.  Assimilation of Ice Concentration in an Ice–Ocean Model , 2006 .

[31]  L. Perelman,et al.  A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers , 1997 .

[32]  R. Kwok,et al.  Arctic ice-ocean simulation with optimized model parameters: Approach and assessment , 2011 .

[33]  S. Cohn,et al.  Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .

[34]  D. Pham.  Stochastic Methods for Sequential Data Assimilation in Strongly Nonlinear Systems , 2001 .

[35]  S. Kobayashi,et al.  The JRA-25 Reanalysis , 2007 .

[36]  Ron Kwok,et al.  Uncertainty in modeled Arctic sea ice volume , 2011 .

[37]  H. Eicken.  Ocean science: Arctic sea ice needs better forecasts , 2013, Nature.