Evaluation of simulated sea-ice concentrations from sea-ice/ ocean models using satellite data and polynya classification methods


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Polona.Itkin [ at ] awi.de

Abstract

Sea-ice concentrations in the Laptev Sea simulated by the coupled North Atlantic—Arctic Ocean—Sea-Ice Model and Finite Element Sea-Ice Ocean Model are evaluated using sea-ice concentrations from Advanced Microwave Scanning Radiometer—Earth Observing System satellite data and a polynya classification method for winter 2007/08. While developed to simulate largescale sea-ice conditions, both models are analysed here in terms of polynya simulation. The main modification of both models in this study is the implementation of a landfast-ice mask. Simulated sea-ice fields from different model runs are compared with emphasis placed on the impact of this prescribed landfast-ice mask. We demonstrate that sea-ice models are not able to simulate flaw polynyas realistically when used without fast-ice description. Our investigations indicate that without landfast ice and with coarse horizontal resolution the models overestimate the fraction of open water in the polynya. This is not because a realistic polynya appears but due to a larger-scale reduction of ice concentrations and smoothed ice-concentration fields. After implementation of a landfast-ice mask, the polynya location is realistically simulated but the total open-water area is still overestimated in most cases. The study shows that the fast-ice parameterization is essential for model improvements. However, further improvements are necessary in order to progress from the simulation of large-scale features in the Arctic towards a more detailed simulation of smaller-scaled features (here polynyas) in an Arctic shelf sea.



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Peer-reviewed
Publication Status
Published
Eprint ID
34888
DOI 10.3402/polar.v30i0.7124

Cite as
Adams, S. , Willmes, S. , Heinemann, G. , Rozman, P. , Timmermann, R. and Schröder, D. (2011): Evaluation of simulated sea-ice concentrations from sea-ice/ ocean models using satellite data and polynya classification methods , Polar Research, 30 (7124) . doi: 10.3402/polar.v30i0.7124


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