A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval
Abstract As changes to Earth's polar climate accelerate, the need for robust, long–term sea ice thickness observation datasets for monitoring those changes and for verification of global climate models is clear. By linking an algorithm for retrieving snow–ice interface temperature from passive microwave satellite data to a thermodynamic sea ice energy balance relation known as Stefan's law, we have developed a retrieval method for estimating thermodynamic sea ice thickness growth from space: Stefan's Law Integrated Conducted Energy (SLICE). With an initial condition at the beginning of the sea ice growth season, the method can model basin-wide absolute sea ice thickness by combining the one-dimensional SLICE retrieval with an ice motion dataset. The advantages of the SLICE retrieval method include daily basin-wide coverage, lack of atmospheric reanalysis product input requirement, and a potential for use beginning in 1987. Validation of the retrieval against measurements from 10 ice mass balance buoys shows a mean correlation of 0.89 and a mean bias of 0.06 m over the course of an entire sea ice growth season. Despite its simplifications and assumptions relative to models like the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS), basin-wide SLICE performs nearly as well as PIOMAS when compared against CryoSat-2 and Operation IceBridge using a linear correlation between collocated points.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval ; volume:16 ; number:10 ; year:2022 ; pages:4403-4421 ; extent:19
The Cryosphere ; 16, Heft 10 (2022), 4403-4421 (gesamt 19)
- Creator
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Anheuser, James
Liu, Yinghui
Key, Jeffrey R.
- DOI
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10.5194/tc-16-4403-2022
- URN
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urn:nbn:de:101:1-2022102705281715016245
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:33 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Anheuser, James
- Liu, Yinghui
- Key, Jeffrey R.
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