Improving the Met Office's Forecast Ocean Assimilation Model (FOAM) with the assimilation of satellite-derived sea-ice thickness data from CryoSat-2 and SMOS in the Arctic

• Published in: Quarterly Journal of the Royal Meteorological Society
• Main Authors: Davi Mignac, Matt Martin, Emma Fiedler, Ed Blockley, Nicolas Fournier
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Zenodo 2022
• Subjects: Data assimilation; sea ice thickness; Arctic prediction; Arctic; ice pack; Sea ice; ice covered areas
• Online Access: https://doi.org/10.1002/qj.4252

Derived from two complementary satellites, CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS), sea ice thickness (SIT) data are assimilated into the Met Office’s global ocean–sea ice forecasting system, FOAM, using a 3D-Var assimilation scheme, NEMOVAR. CryoSat-2 along-track SITs, which are converted from freeboard measurements using the model snow depth, and a daily, gridded SMOS SIT product are used in the assimilation to constrain the Arctic sea ice thickness. When using only CryoSat-2 assimilation, SIT forecast fields within the ice pack are greatly improved with respect to independent airborne measurements. However, the positive impacts of CryoSat-2 assimilation in thick ice regions are counteracted by an SIT overestimation in areas of thin ice, due to biased freeboard measurements there. Adding the SMOS assimilation results in much thinner SITs in those regions, which performs better than the control when compared to SIT objective analyses and mooring measurements in the Beaufort and Barents Seas. Furthermore, SMOS assimilation enhances the short-term predictive skill of the marginal sea-ice concentration relative to the control. This is translated into a consistent retreat of the sea-ice covered areas in the 5-day forecasts duringMarch 2017, which is in better agreement with independent ice edge products. This work successfully demonstrates improvements in FOAMsea ice when SIT observations fromboth CryoSat-2 and SMOS are assimilated, representing an important step towards the operational implementation of SIT assimilation within Met Office forecasting systems.