Abstract
Abstract. A multi-category numerical sea ice model CICE was used along with data assimilation to derive sea ice parameters in the region of Baffin Bay and Labrador Sea. The assimilation of ice concentration was performed using the data derived from the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2). The model uses a mixed-layer slab ocean parameterization to compute the sea surface temperature (SST) and thereby to compute the freezing and melting potential of ice. The data from Advanced Very High Resolution Radiometer (AVHRR-only optimum interpolation analysis) were used to assimilate SST. The modelled ice parameters including concentration, ice thickness, freeboard and keel depth were compared with parameters estimated from remote-sensing data. The ice thickness estimated from the model was compared with the measurements derived from Soil Moisture Ocean Salinity – Microwave Imaging Radiometer using Aperture Synthesis (SMOS–MIRAS). The model freeboard estimates were compared with the freeboard measurements derived from CryoSat2. The ice concentration, thickness and freeboard estimates from the model assimilated with both ice concentration and SST were found to be within the uncertainty in the observation except during March. The model-estimated draft was compared with the measurements from an upward-looking sonar (ULS) deployed in the Labrador Sea (near Makkovik Bank). The difference between modelled draft and ULS measurements estimated from the model was found to be within 10 cm. The keel depth measurements from the ULS instruments were compared to the estimates from the model to retrieve a relationship between the ridge height and keel depth.
Highlights
Regional sea ice forecasting is important for climate studies, operational activities including navigation, exploration of offshore mineral resources and ecological applications; e.g. the North Water Polynya in Baffin Bay provides a warm environment for marine animals (Stirling, 1980).Sea ice is a heterogeneous media, making it practically difficult for remote sensing instruments to measure the ice thickness, freeboard and ridge parameters (Carsey, 1992)
Three model results are discussed here: M0, the nonassimilated model; M1, the model assimilated with ice concentration from Advanced Microwave Scanning Radiometer (AMSR-E)/AMSR2; and M2, the model assimilated with ice concentration from AMSR-E/AMSR2 and sea surface temperature (SST) from AVHRR-only OISST
The thin ice category thicknesses are overestimated from October to the end of November but the values are within the uncertainty limits of SMOS from December to March
Summary
Regional sea ice forecasting is important for climate studies, operational activities including navigation, exploration of offshore mineral resources and ecological applications; e.g. the North Water Polynya in Baffin Bay provides a warm environment for marine animals (Stirling, 1980). The climate forecast researchers and operational ice modelling communities depend on numerical modelling techniques implementing the physical process of atmosphere and ocean on large-scale computational platforms along with data assimilation methods to retrieve the information on sea ice parameters. The CIS used the model developed by Sayed and Carrieres (1999); Sayed et al (2002) to study the ice thickness distribution in the Gulf of St Lawrence (Kubat et al, 2010) These modelling studies were improved by the data assimilation methods The simulated sea ice parameters are validated with the observations in the region of the Baffin Bay and the Labrador Sea. This work uses a high-resolution model configuration which was previously described in the work of Prasad et al (2015). The work suggests a methodology to extract the level ice draft and keel depth information from upward-looking sonar (ULS) measurements, which was used to describe the relationship between ridge and keel
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