Data assimilation of satellite snow thickness products in the Arctic Ocean

The low abundance of available observations means that the initial conditions used for Arctic forecasts are significantly less accurate than for lower latitudes, such that the assimilation of newly acquired data has the potential to significantly increase the accuracy of Arctic forecasts. While the repercussions of the changing Arctic climate on the sea ice extent and thickness are well documented, changes in the snow cover remain harder to monitor with limited in-situ and airborne observations. This project will investigate the representation of the snow-sea ice relationship in selected CMIP6 models, before exploring and assimilating new snow thickness products into a hierarchy of increasingly complex sea ice models, including the Met Office’s Ocean and Sea-ice Forecasting Systems.

Then, the impact of a more complex representation of snow structure will be investigated by coupling the CICE sea-ice model with CROCUS, a complex snow model. A preliminary coupled model code will be validated with existing data and revised accordingly, after which the resulting modelling system will be coupled with SMRT, a radiative transfer model. This will allow us to investigate the impact of snow thickness assimilation and more complex representation of snow structure on weather forecasts and sea-ice variables.

Grant reference
Natural Environment Research Council
Total awarded
£0 GBP
Start date
27 Sep 2020
4 years 2 days
End date
29 Sep 2024