Sensitivity of two melt pond schemes to the uncertainties in atmospheric reanalyses for global climate models
| Main Authors: | Sterlin, Jean, Fichefet, Thierry, Massonnet, François, Lecomte, Olivier, Vancoppenolle, Martin |
|---|---|
| Format: | Proceeding poster Journal |
| Bahasa: | eng |
| Terbitan: |
, 2019
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4545439 |
| ctrlnum |
4545439 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Sterlin, Jean</creator><creator>Fichefet, Thierry</creator><creator>Massonnet, François</creator><creator>Lecomte, Olivier</creator><creator>Vancoppenolle, Martin</creator><date>2019-08-14</date><description>Melt ponds appear during the melt season in the Arctic, when the surface melt water collects in the depressions of the ice field. The albedo of the ponds is lower than the surrounding ice and snow areas, and for this reason the ponds are hot-spots for the ice-albedo feedback. There is two main approaches to represent the melt ponds in Global Climate Models. The first approach is empiric and relies on observations to determine the available water capacity of the ponds from the sea ice state. Then, a fraction of the surface melt water accumulates is the ponds. The second makes use of the Ice Thickness Distribution to infer the surface topography of the sea ice and distribute the melt water among the ice categories. Although the role of melt ponds has been extensively studied, less is known on the response of the ponds to climate change. Insights can be gained from using different reanalyses of the atmospheric surface state to force the ocean and ice components. Because of a lack of observations in remote areas, reanalyses still suffer from biases notably in the polar regions. The choice of a reanalysis has a strong influence on the representation of the sea ice state of the Antarctic. We expect similar deviations in the Northern Hemisphere. To evaluate the effect of the melt pond schemes on the sea ice when subject to uncertainties in the atmospheric state, we have run the empiric and topographic schemes forced with JRA-55, DFS 5.2, and NCEP/NCAR atmospheric reanalyses. From the simulations, We expect to see the degree of difference between the pond schemes and the influence of the forcing onto their climatic response. We will be able to assess the importance of the melt ponds for the climate and check the consistency of the parameterizations. This will allow us to formulate a recommendation on the use of melt ponds in climate models.</description><identifier>https://zenodo.org/record/4545439</identifier><identifier>10.5281/zenodo.4545439</identifier><identifier>oai:zenodo.org:4545439</identifier><language>eng</language><relation>info:eu-repo/grantAgreement/EC/H2020/727862/</relation><relation>doi:10.5281/zenodo.4545438</relation><relation>url:https://zenodo.org/communities/applicate</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><subject>Melt ponds</subject><subject>Atmospheric reanalysis and forcing</subject><subject>Sea Ice Model</subject><subject>Ocean General Circulation Model</subject><subject>Arctic climate</subject><subject>Albedo</subject><title>Sensitivity of two melt pond schemes to the uncertainties in atmospheric reanalyses for global climate models</title><type>Journal:Proceeding</type><type>Other:poster</type><recordID>4545439</recordID></dc>
|
| language |
eng |
| format |
Journal:Proceeding Journal Other:poster Other Journal:Journal |
| author |
Sterlin, Jean Fichefet, Thierry Massonnet, François Lecomte, Olivier Vancoppenolle, Martin |
| title |
Sensitivity of two melt pond schemes to the uncertainties in atmospheric reanalyses for global climate models |
| publishDate |
2019 |
| topic |
Melt ponds Atmospheric reanalysis and forcing Sea Ice Model Ocean General Circulation Model Arctic climate Albedo |
| url |
https://zenodo.org/record/4545439 |
| contents |
Melt ponds appear during the melt season in the Arctic, when the surface melt water collects in the depressions of the ice field. The albedo of the ponds is lower than the surrounding ice and snow areas, and for this reason the ponds are hot-spots for the ice-albedo feedback. There is two main approaches to represent the melt ponds in Global Climate Models. The first approach is empiric and relies on observations to determine the available water capacity of the ponds from the sea ice state. Then, a fraction of the surface melt water accumulates is the ponds. The second makes use of the Ice Thickness Distribution to infer the surface topography of the sea ice and distribute the melt water among the ice categories. Although the role of melt ponds has been extensively studied, less is known on the response of the ponds to climate change. Insights can be gained from using different reanalyses of the atmospheric surface state to force the ocean and ice components. Because of a lack of observations in remote areas, reanalyses still suffer from biases notably in the polar regions. The choice of a reanalysis has a strong influence on the representation of the sea ice state of the Antarctic. We expect similar deviations in the Northern Hemisphere. To evaluate the effect of the melt pond schemes on the sea ice when subject to uncertainties in the atmospheric state, we have run the empiric and topographic schemes forced with JRA-55, DFS 5.2, and NCEP/NCAR atmospheric reanalyses. From the simulations, We expect to see the degree of difference between the pond schemes and the influence of the forcing onto their climatic response. We will be able to assess the importance of the melt ponds for the climate and check the consistency of the parameterizations. This will allow us to formulate a recommendation on the use of melt ponds in climate models. |
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IOS16997.4545439 |
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ZAIN Publications |
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7213 |
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Cognizance Journal of Multidisciplinary Studies |
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Cognizance Journal of Multidisciplinary Studies |
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Multidisciplinary |
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Stockholm |
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INTERNASIONAL |
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1 |
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2022-06-06T06:04:36Z |
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2022-06-06T06:04:36Z |
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17.60987 |