Observed Ice Surface Temperatures and the Potential Integration into a Coupled Ocean and Sea Ice Model

This presentation discusses the feasibility and the effect of assimilating remotely sensed ice surface temperatures (IST) in a coupled ocean and sea-ice model. To evaluate the result of the assimilation, the sea-ice extent, the sea-ice thickness and the temperature profiles of snow and sea-ice is evaluated.

Validation of the near surface thermodynamic parameters derived by an atmospheric reanalysis that covers the Arctic region is generally constrained by lack of observational data. This may lead to a relatively large bias and uncertainties that directly influence the thermodynamics of an atmospherically forced coupled ocean and sea-ice models. This study establishes a methodology for assimilating remotely sensed observations of IST, with high spatial resolution, into a coupled ocean and sea-ice model. The method corrects the 2 meter air temperature based on the difference between the modeled and the observed IST. Thus the correction includes biases in the surface forcing and the ability of the model to convert incoming parameters at the surface to a net heat flux.

The evaluation is based on four experimental model runs with and without corrections of the 2m atmospheric temperature and with and without assimilation sea ice concentration. The experiments are executed using the coupled ocean and sea ice system of the Danish Meteorological Institute, which runs in a regional configuration that covers the Arctic and North Atlantic oceans forced with ERA-Interim.

In order to use a new type of observation for assimilation, it is important to understand strength and weaknesses of the observation. The quality of the IST products is based on an inter-comparison of the IST measured by buoys, the remotely sensed IST and the modeled IST. Ground based measurements are typically considered to be the ground truth. Whether the buoys that drift with the sea ice can be used for this purpose will also be discussed. The model and the buoys provide a temperature profile of the snow and the sea ice, thus a comparison of the temperature profiles are feasible as well.

The study demonstrates the potential benefits of using IST as a dynamical bias correction in coupled ocean and sea-ice models; however this study also illustrates the challenges of estimating the ground truth based on buoys that measures IST as the quality varies from buoy to buoy.