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

Abstract

Abstract. 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 dynamicthermodynamic 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.

Validating satellite derived and modelled sea-ice drift in the Laptev Sea with in situ measurements from the winter of 2007/2008

“…To achieve this, data gathered by manned stations, drifting buoys and lately satellite sensors have been used extensively in pan-Arctic sea-ice model validations (Lemke et al 1997;Kreyscher et al 2000;Martin & Gerdes 2007) and data assimilations (Meier et al 2000;Rollenhagen et al 2009). For remote coastal regions such as the Laptev Sea Shelf, there are very few ice drift field observations available.…”

On the assimilation of ice velocity and concentration data into large-scale sea ice models

“…The study of Meier et al (2000) was the first attempt to assimilate observed ice motion data into a large-scale model of the Arctic sea ice cover in order to maximize the model accuracy. These authors employed an optimal interpolation scheme to assimilate ice velocity data derived from passive microwave imagery.…”

Sea‐ice deformation rates from satellite measurements and in a model

“…It is possible to test the mean motion of ice calculated from models by comparing it to the trajectories of buoys and drifting ice stations (e.g., Thomas, 1999; Zhang et al, 2003;Meier et al, 2000). But it has not been possible to test ice deformation computed in the models adequately for lack of an appropriate comparison dataset.…”

“…In sea-ice models, the ice velocity is established through a balance of forces that depends on the winds and currents (forcing), the model state (mean thickness), the model physics (drag and constitutive laws), as well as the model resolution. Accurate modelling of the ice velocity and deformation rate is essential if ice is to be properly represented in climate models.It is possible to test the mean motion of ice calculated from models by comparing it to the trajectories of buoys and drifting ice stations (e.g., Thomas, 1999; Zhang et al, 2003;Meier et al, 2000). But it has not been possible to test ice deformation computed in the models adequately for lack of an appropriate comparison dataset.…”