Suitability of CICE Sea Ice Model for Seasonal Prediction and Positive Impact of CryoSat-2 Ice Thickness Initialization
The Los Alamos sea ice model (CICE) is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction, where CICE is driven by atmospheric forcings from the NCEP Climate Forecast System Reanalysis (CFSR) and a built-in mixed layer ocean model in CICE. The initi...
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ftcopernicus:oai:publications.copernicus.org:tcd113413 2023-10-01T03:52:06+02:00 Suitability of CICE Sea Ice Model for Seasonal Prediction and Positive Impact of CryoSat-2 Ice Thickness Initialization Sun, Shan Solomon, Amy 2023-08-28 application/pdf https://doi.org/10.5194/tc-2023-116 https://tc.copernicus.org/preprints/tc-2023-116/ eng eng doi:10.5194/tc-2023-116 https://tc.copernicus.org/preprints/tc-2023-116/ eISSN: 1994-0424 Text 2023 ftcopernicus https://doi.org/10.5194/tc-2023-116 2023-09-04T16:24:19Z The Los Alamos sea ice model (CICE) is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction, where CICE is driven by atmospheric forcings from the NCEP Climate Forecast System Reanalysis (CFSR) and a built-in mixed layer ocean model in CICE. The initial conditions for the sea ice and mixed layer ocean are also from CFSR in the control experiments. The simulated sea ice extent agrees well with observations during the warm season at all lead times up to 12 months, in both the Arctic and Antarctic. This suggests that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the model’s Arctic sea ice thickness forecast has a positive bias that originates from the initial conditions. This bias often persists for more than a season, which limits the model’s seasonal forecast skill. To address this limitation, additional CS2_IC experiments were conducted, where the Arctic ice thickness was initialized using CryoSat-2 satellite observations while keeping all other initial fields the same as in the control experiments. This reduced the positive bias in the ice thickness in the initial conditions, leading to improvements in both the simulated ice edge and thickness at the seasonal time scale. This study highlights that the suitability of CICE for seasonal prediction depends on various factors, including initial conditions such as sea ice thickness, oceanic and atmospheric conditions in addition to sea ice coverage. By reducing the bias in the initial ice thickness, CICE has the potential to improve its seasonal forecast skill and provide more accurate predictions of sea ice extent and thickness. Text Antarc* Antarctic Arctic Sea ice Copernicus Publications: E-Journals Arctic Antarctic |
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Copernicus Publications: E-Journals |
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The Los Alamos sea ice model (CICE) is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction, where CICE is driven by atmospheric forcings from the NCEP Climate Forecast System Reanalysis (CFSR) and a built-in mixed layer ocean model in CICE. The initial conditions for the sea ice and mixed layer ocean are also from CFSR in the control experiments. The simulated sea ice extent agrees well with observations during the warm season at all lead times up to 12 months, in both the Arctic and Antarctic. This suggests that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the model’s Arctic sea ice thickness forecast has a positive bias that originates from the initial conditions. This bias often persists for more than a season, which limits the model’s seasonal forecast skill. To address this limitation, additional CS2_IC experiments were conducted, where the Arctic ice thickness was initialized using CryoSat-2 satellite observations while keeping all other initial fields the same as in the control experiments. This reduced the positive bias in the ice thickness in the initial conditions, leading to improvements in both the simulated ice edge and thickness at the seasonal time scale. This study highlights that the suitability of CICE for seasonal prediction depends on various factors, including initial conditions such as sea ice thickness, oceanic and atmospheric conditions in addition to sea ice coverage. By reducing the bias in the initial ice thickness, CICE has the potential to improve its seasonal forecast skill and provide more accurate predictions of sea ice extent and thickness. |
format |
Text |
author |
Sun, Shan Solomon, Amy |
spellingShingle |
Sun, Shan Solomon, Amy Suitability of CICE Sea Ice Model for Seasonal Prediction and Positive Impact of CryoSat-2 Ice Thickness Initialization |
author_facet |
Sun, Shan Solomon, Amy |
author_sort |
Sun, Shan |
title |
Suitability of CICE Sea Ice Model for Seasonal Prediction and Positive Impact of CryoSat-2 Ice Thickness Initialization |
title_short |
Suitability of CICE Sea Ice Model for Seasonal Prediction and Positive Impact of CryoSat-2 Ice Thickness Initialization |
title_full |
Suitability of CICE Sea Ice Model for Seasonal Prediction and Positive Impact of CryoSat-2 Ice Thickness Initialization |
title_fullStr |
Suitability of CICE Sea Ice Model for Seasonal Prediction and Positive Impact of CryoSat-2 Ice Thickness Initialization |
title_full_unstemmed |
Suitability of CICE Sea Ice Model for Seasonal Prediction and Positive Impact of CryoSat-2 Ice Thickness Initialization |
title_sort |
suitability of cice sea ice model for seasonal prediction and positive impact of cryosat-2 ice thickness initialization |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-2023-116 https://tc.copernicus.org/preprints/tc-2023-116/ |
geographic |
Arctic Antarctic |
geographic_facet |
Arctic Antarctic |
genre |
Antarc* Antarctic Arctic Sea ice |
genre_facet |
Antarc* Antarctic Arctic Sea ice |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2023-116 https://tc.copernicus.org/preprints/tc-2023-116/ |
op_doi |
https://doi.org/10.5194/tc-2023-116 |
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1778517679122939904 |