Taking into account atmospheric uncertainty improves sequential assimilation of SMOS sea ice thickness data in an ice-ocean model

• Published in: Journal of Atmospheric and Oceanic Technology
• Main Authors: Yang, Qinghua, Losch, Martin, Losa, Svetlana N., Jung, Thomas, Nerger, Lars
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2016
• Subjects: Arctic Ocean; Sea ice
• Online Access: https://epic.awi.de/id/eprint/42206/; https://epic.awi.de/id/eprint/42206/1/Yang_etal_JAOT33_397_2016.pdf; https://hdl.handle.net/10013/epic.48939; https://hdl.handle.net/10013/epic.48939.d001

The sensitivity of assimilating sea ice thickness data to uncertainty in atmospheric forcing fields is examined using ensemble-based data assimilation experiments with the Massachusetts Institute of Technology General Circulation Model (MITgcm) in the Arctic Ocean during November 2011–January 2012 and the Met Office (UKMO) ensemble atmospheric forecasts. The assimilation system is based on a local singular evolutive interpolated Kalman (LSEIK) filter. It combines sea ice thickness data derived from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) satellite and Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data with the numerical model. The effect of representing atmospheric uncertainty implicit in the ensemble forcing is assessed by three different assimilation experiments. The first two experiments use a single deterministic forcing dataset and a different forgetting factor to inflate the ensemble spread. The third experiment uses 23 members of the UKMO atmospheric ensemble prediction system. It avoids additional ensemble inflation and is hence easier to implement. As expected, the model-data misfits are substantially reduced in all three experiments, but with the ensemble forcing the errors in the forecasts of sea ice concentration and thickness are smaller compared to the experiments with deterministic forcing. This is most likely because the ensemble forcing results in a more plausible spread of the model state ensemble, which represents model uncertainty and produces a better forecast.