the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Suitability of CICE Sea Ice Model for Seasonal Prediction and Positive Impact of CryoSat-2 Ice Thickness Initialization
Abstract. 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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RC1: 'Comment on tc-2023-116', Anonymous Referee #1, 06 Oct 2023
The Los Alamos sea ice model (CICE) is tested in standalone mode in the study and the performance on seasonal sea ice prediction is examined. The paper is well written and organized with clear logics that readers can readily follow. The results present now is informative. However, one more aspect can be considered to further improve the manuscript. My comments are generally listed below:
- I would recommend add “Summary” or “Conclusion” in the text. Currently, the paper is rather a technique report for the authors themselves rather than new findings that the whole community can learn from, since the effect of CS2 SIT on sea ice prediction has been studied widely and comprehensively for years including CICE model (literatures can be found easily not limited to what the authors currently provided). Therefore, a paragraph with “Summary” or “Conclusion” is necessary for distinct new findings specifically in this study.
- Regarding “2.3 Initial Conditions”, the process to map CS2 SIT onto native model grid is not clear, for example, how to redistribute the mean thickness data to each category? what do the authors mean about the vicinity data to fill the North Pole, to what extent? which CS2 SIT record do you use, the daily or weekly? how the interpolation does? Bilinearly or conserved remapping?
- Captions for Figure1 is not correct.
- L119, we normally cite Zhang’s paper instead of Schweiger et al., 2011 as Zhang is the main developer.
- L128-L131: please rephrase the text to discuss the results quantitively.
- L134: Normally the seasonality of sea ice in the Antarctic cannot be like that, it’s not about the initialization but rather a fundamental problem in the model!
- Figure 2a,b: I didn’t get it why Nov has such distinct small bias over lead month >2? Same in Figure3 but in April.
- L187: With respect to tendencies arising from the thermodynamics and dynamics, for readers don’t use CICE will never know what that means! A description on which terms the two terms account for is necessary.
Citation: https://doi.org/10.5194/tc-2023-116-RC1 -
AC1: 'Reply on RC1', Shan Sun, 05 Jan 2024
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2023-116/tc-2023-116-AC1-supplement.pdf
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RC2: 'Comment on tc-2023-116', Anonymous Referee #2, 30 Nov 2023
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AC2: 'Reply on RC2', Shan Sun, 05 Jan 2024
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2023-116/tc-2023-116-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Shan Sun, 05 Jan 2024
Status: closed
-
RC1: 'Comment on tc-2023-116', Anonymous Referee #1, 06 Oct 2023
The Los Alamos sea ice model (CICE) is tested in standalone mode in the study and the performance on seasonal sea ice prediction is examined. The paper is well written and organized with clear logics that readers can readily follow. The results present now is informative. However, one more aspect can be considered to further improve the manuscript. My comments are generally listed below:
- I would recommend add “Summary” or “Conclusion” in the text. Currently, the paper is rather a technique report for the authors themselves rather than new findings that the whole community can learn from, since the effect of CS2 SIT on sea ice prediction has been studied widely and comprehensively for years including CICE model (literatures can be found easily not limited to what the authors currently provided). Therefore, a paragraph with “Summary” or “Conclusion” is necessary for distinct new findings specifically in this study.
- Regarding “2.3 Initial Conditions”, the process to map CS2 SIT onto native model grid is not clear, for example, how to redistribute the mean thickness data to each category? what do the authors mean about the vicinity data to fill the North Pole, to what extent? which CS2 SIT record do you use, the daily or weekly? how the interpolation does? Bilinearly or conserved remapping?
- Captions for Figure1 is not correct.
- L119, we normally cite Zhang’s paper instead of Schweiger et al., 2011 as Zhang is the main developer.
- L128-L131: please rephrase the text to discuss the results quantitively.
- L134: Normally the seasonality of sea ice in the Antarctic cannot be like that, it’s not about the initialization but rather a fundamental problem in the model!
- Figure 2a,b: I didn’t get it why Nov has such distinct small bias over lead month >2? Same in Figure3 but in April.
- L187: With respect to tendencies arising from the thermodynamics and dynamics, for readers don’t use CICE will never know what that means! A description on which terms the two terms account for is necessary.
Citation: https://doi.org/10.5194/tc-2023-116-RC1 -
AC1: 'Reply on RC1', Shan Sun, 05 Jan 2024
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2023-116/tc-2023-116-AC1-supplement.pdf
-
RC2: 'Comment on tc-2023-116', Anonymous Referee #2, 30 Nov 2023
-
AC2: 'Reply on RC2', Shan Sun, 05 Jan 2024
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2023-116/tc-2023-116-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Shan Sun, 05 Jan 2024
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