Error analysis and assimilation of remotely sensed ice motion within an Arctic sea ice model

New sea ice motion fields available from remotely sensed data are potentially useful for assessing and improving models of the polar ice pack. Here we investigate the error characteristics of the observed ice motions relative to drifting buoys and a dynamic-thermodynamic ice model. A data assimilation approach is then used to assess the potential of the motion data for reducing model biases, as well as the potential of the model to serve as an interpolation tool to generate improved ice motion data sets. Special Sensor Microwave/Imager (SSM/I) derived and model simulated ice motions for the years 1988 through 1993 are compared with ice displacement observations from drifting buoys. Variability and biases are summarized for seasonal and regional means. SSM/I motions are assimilated into the model using an optimal interpolation method that accounts for the modeled and SSM/I motion error variances and the number and distribution of the SSM/I motions. Modeled and SSM/I-derived motions are found to have comparable mean errors, with some notable regional and seasonal differences. Assimilation substantially reduces the error standard deviation and improves the correlation of the simulated motions relative to the buoy observations, but some biases remain. In the model framework used here, assimilation of the SSM/I data substantially alters average ice thickness in some regions of the Arctic and affects ice mass outflow through the Fram Strait but has a small effect on mean ice concentration. The assimilation yields an increase in the spatial and temporal variability in ice deformation. The observations are particularly suited for improving the simulation of specific synoptic events, where substantial differences can occur between simulated and observed ice transport.

[1]  Ron Kwok,et al.  Sea ice motion from satellite passive microwave imagery assessed with ERS SAR and buoy motions , 1998 .

[2]  Peter Lemke,et al.  Simulation of sea ice transport through Fram Strait: Natural variability and sensitivity to forcing , 1998 .

[3]  J. Maslanik,et al.  On the role of sea-ice transport in modifying Arctic responses to global climate change , 1997 .

[4]  William J. Emery,et al.  Automated extraction of pack ice motion from advanced very high resolution radiometer imagery , 1986 .

[5]  Lakshmi Kantha,et al.  Barotropic tides in the global oceans from a nonlinear tidal model assimilating altimetric tides: 1. Model description and results , 1995 .

[6]  W. Hibler.  A Dynamic Thermodynamic Sea Ice Model , 1979 .

[7]  Tom Hirose,et al.  Estimation of Large-Scale Sea-Ice Motion from SSM/I 85.5 GHz Imagery , 1997 .

[8]  Ross M. McConnell,et al.  An ice-motion tracking system at the Alaska SAR facility , 1990 .

[9]  D. R. Thomas,et al.  Blending sequential scanning multichannel microwave radiometer and buoy data into a sea ice model , 1989 .

[10]  D. Rothrock,et al.  Assimilating satellite concentration data into an Arctic sea ice mass balance model, 1979–1985 , 1996 .

[11]  Donald J. Cavalieri,et al.  On sea ice drift from the wavelet analysis of the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) data , 1998 .

[12]  D. Ullman,et al.  Model parameter estimation from data assimilation modeling: Temporal and spatial variability of the bottom drag coefficient , 1998 .

[13]  M. Ikeda,et al.  Variational assimilation of Geosat altimeter data into a two-layer quasi-geostrophic model over the Newfoundland , 1998 .

[14]  John E. Walsh,et al.  Multiyear sea ice in the Arctic: Model‐ and satellite‐derived , 1990 .

[15]  Ron Kwok,et al.  Determination of the age distribution of sea ice from lagrangian observations of ice motion , 1995 .

[16]  D. R. Thomas,et al.  The Arctic Ocean ice balance - A Kalman smoother estimate , 1993 .

[17]  J. A. Catipovic,et al.  Performance of sequential decoding of convolutional codes over fully fading ocean acoustic channels , 1990 .

[18]  S. Häkkinen.  An Arctic source for the great salinity anomaly: A simulation of the Arctic ice‐ocean system for 1955–1975 , 1993 .

[19]  William J. Emery,et al.  Fram Strait satellite image‐derived ice motions , 1991 .

[20]  J. Curry,et al.  An intermediate one‐dimensional thermodynamic sea ice model for investigating ice‐atmosphere interactions , 1993 .

[21]  William J. Emery,et al.  Satellite‐derived maps of Arctic and Antarctic sea ice motion: 1988 to 1994 , 1997 .

[22]  J. Charney,et al.  Use of Incomplete Historical Data to Infer the Present State of the Atmosphere , 1969 .

[23]  W. Emery,et al.  New satellite derived sea ice motion tracks Arctic contamination , 1997 .

[24]  Nina Nordlund,et al.  Monitoring ice thickness in Fram Strait , 1998 .

[25]  Ronald D. McPherson,et al.  Progress, Problems, and Prospects in Meteorological Data Assimilation , 1975 .

[26]  Robert N. Miller,et al.  Ocean data assimilation using optimal interpolation with a quasi-geostrophic model , 1991 .

[27]  Eli Tziperman,et al.  The Oceanographic Data Assimilation Problem: Overview, Motivation and Purposes , 1996 .

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

[29]  Antony K. Liu,et al.  Sea‐ice motion derived from satellite agrees with buoy observations , 1998 .

[30]  K. Bryan,et al.  A Diagnostic Ice–Ocean Model , 1987 .

[31]  M. Ghil,et al.  Data assimilation in meteorology and oceanography , 1991 .