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Assessing Sea Ice and SST Data Assimilation in MPI-ESM

This study evaluates assimilation quality of ESA-CCI sea ice concentration and sea surface temperature data into MPI-ESM through experimental runs assessing inconsistencies and impact on climate forecasts.

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Assessing Sea Ice and SST Data Assimilation in MPI-ESM

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  1. Polar ECV cross assessment:Assimilation of ice concentrations and sea surface temperatures into MPI-ESM Felix Bunzel and Dirk Notz Max Planck Institute for Meteorology Hamburg, Germany

  2. Aim and experimental setup Aim of this study: Assessing the quality and consistency of ESA-CCI sea ice concentration (SIC) and sea surface temperature (SST) data Experimental setup: We performed combined SIC/SST assimilation runs with the Max Planck Institute Earth System Model (MPI-ESM) for all combinations of the following data products: • Sea ice concentration: • ESA-CCI • NSIDC/Bootstrap • Sea surface temperature: • ESA-CCI • ERAinterim

  3. Sea surface temperature differences:CCI minus ERAinterim March September 1.8 -1.8 -1.8 1.8 • Difference between SST data products largest in northern high latitudes.

  4. Sea ice concentration differences:CCI minus NSIDC • Difference between SIC data products largest in the Arctic close to the ice edge, and in a few specific regions.

  5. Inconsistencies between SIC and SST data products • Inconsistencies between SIC and SST data products in a few specific regions, e.g. the Davis Strait and Baltic Sea.

  6. Inconsistencies between SIC and SST data products • No substantial inconsistencies in the Antarctic.

  7. Arctic sea ice area • When assimilating SIC and SST data from ESA-CCI a certain amount of sea ice is lost in the assimilation process.

  8. Arctic sea ice concentrations:Data minus assimilation • Spurious ice concentrations related to weather effects are lost when the ESA-CCI SIC product is assimilated into the model.

  9. Antarctic sea ice area • Behaviour of assimilation runs relatively similar in the Antarctic.

  10. Antarctic sea ice concentrations: Data minus assimilation • Differences between datasets and assimilation runs almost cancel each other out in the Antarctic.

  11. Conclusions • Differences and inconsistencies between SIC and SST datasets more prominent in the Arctic compared to the Antarctic. • Quality of ESA-CCI SIC and SST products appears in general to be comparable to other data products. • Differences between the ESA-CCI SIC product and other datasets originate from a different setup of the applied algorithm, e.g. weather filter. • Differences between available SIC products large enough to yield a significant impact on seasonal surface temperature predictions in northern high latitudes. • Seasonal climate forecasts significantly affected by observational uncertainty of Arctic sea-ice concentration • Bunzel, F., D. Notz, J. Baehr, W.A. Müller, and K. Fröhlich, Geophys. Res. Lett., 2016.

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