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Volume 12, issue 8 | Copyright
The Cryosphere, 12, 2569-2594, 2018
https://doi.org/10.5194/tc-12-2569-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 13 Aug 2018

Research article | 13 Aug 2018

Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance

Thomas Kaminski1, Frank Kauker2,5, Leif Toudal Pedersen3,6, Michael Voßbeck1, Helmuth Haak4, Laura Niederdrenk4, Stefan Hendricks5, Robert Ricker5, Michael Karcher2,5, Hajo Eicken7, and Ola Gråbak8 Thomas Kaminski et al.
  • 1The Inversion Lab, Hamburg, Germany
  • 2Ocean Atmosphere Systems, Hamburg, Germany
  • 3eolab.dk, Copenhagen, Denmark
  • 4Max Planck Institute for Meteorology, Hamburg, Germany
  • 5Alfred Wegener Institute, Bremerhaven, Germany
  • 6DTU-Space, Technical University of Denmark, Lyngby, Denmark
  • 7University of Alaska Fairbanks, Fairbanks, USA
  • 8European Space Agency ESRIN, Frascati, Rome, Italy

Abstract. Assimilation of remote-sensing products of sea ice thickness (SIT) into sea ice–ocean models has been shown to improve the quality of sea ice forecasts. Key open questions are whether assimilation of lower-level data products such as radar freeboard (RFB) can further improve model performance and what performance gains can be achieved through joint assimilation of these data products in combination with a snow depth product. The Arctic Mission Benefit Analysis system was developed to address this type of question. Using the quantitative network design (QND) approach, the system can evaluate, in a mathematically rigorous fashion, the observational constraints imposed by individual and groups of data products. We demonstrate the approach by presenting assessments of the observation impact (added value) of different Earth observation (EO) products in terms of the uncertainty reduction in a 4-week forecast of sea ice volume (SIV) and snow volume (SNV) for three regions along the Northern Sea Route in May 2015 using a coupled model of the sea ice–ocean system, specifically the Max Planck Institute Ocean Model. We assess seven satellite products: three real products and four hypothetical products. The real products are monthly SIT, sea ice freeboard (SIFB), and RFB, all derived from CryoSat-2 by the Alfred Wegener Institute. These are complemented by two hypothetical monthly laser freeboard (LFB) products with low and high accuracy, as well as two hypothetical monthly snow depth products with low and high accuracy.

On the basis of the per-pixel uncertainty ranges provided with the CryoSat-2 SIT, SIFB, and RFB products, the SIT and RFB achieve a much better performance for SIV than the SIFB product. For SNV, the performance of SIT is only low, the performance of SIFB is higher and the performance of RFB is yet higher. A hypothetical LFB product with low accuracy (20cm uncertainty) falls between SIFB and RFB in performance for both SIV and SNV. A reduction in the uncertainty of the LFB product to 2cm yields a significant increase in performance.

Combining either of the SIT or freeboard products with a hypothetical snow depth product achieves a significant performance increase. The uncertainty in the snow product matters: a higher-accuracy product achieves an extra performance gain. Providing spatial and temporal uncertainty correlations with the EO products would be beneficial not only for QND assessments, but also for assimilation of the products.

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We present mathematically rigorous assessments of the observation impact (added value) of remote-sensing products and in terms of the uncertainty reduction in a 4-week forecast of sea ice volume and snow volume for three regions along the Northern Sea Route by a coupled model of the sea-ice–ocean system. We quantify the difference in impact between rawer (freeboard) and higher-level (sea ice thickness) products, and the impact of adding a snow depth product.
We present mathematically rigorous assessments of the observation impact (added value) of...
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