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...
| Main Authors: | , |
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| Format: | Text |
| Language: | English |
| Published: |
2023
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| Online Access: | https://doi.org/10.5194/tc-2023-116 https://tc.copernicus.org/preprints/tc-2023-116/ |
| Summary: | 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. |
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