the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Apr 2025
An Assessment of Antarctic Sea-ice Thickness in CMIP6 Simulations with Comparison to the Satellite-based Observations and Reanalyses
Abstract. Sea-ice thickness, though critical to our understanding of sea-ice variability, remains relatively understudied compared to surface sea-ice parameters in the Southern Ocean. To remedy this, we examine spatio-temporal variations in sea-ice thickness by analyzing historical simulations from 39 coupled climate models in CMIP6, comparing them with three sea-ice products, including satellite-derived observations and reanalyses. Furthermore, we compare seasonal trends in simulated sea ice thickness with trends in sea ice area. Our results indicate that CMIP6 models can replicate the mean seasonal cycle and spatial patterns of sea-ice thickness. During its maximum in February, these models align well with satellite-based observation products. However, during the annual minima, CMIP6 models show significant agreement with the reanalysis products. Certain models exhibit unrealistic historical mean states compared to the sea-ice products resulting in significant inter-model spread. CMIP6 models can simulate sea-ice area more accurately than the sea-ice thickness. They also simulate a positive relationship between the two parameters in September such that models with greater area tend to exhibit thicker ice. In contrast, there is a negative relationship in February when greater area is associated with lower thickness since only the thicker ice survives the summer melt. Moreover, our study highlights significant positive trends in sea-ice thickness observed during the cooler seasons, which are nearly absent in the warmer seasons where positive trends are predominantly observed in sea-ice area. The spatial distribution of SIT biases is closely linked to uncertainties in modeling the ice edge and the dynamic processes, emphasizing the need for better model representation of both. This study, therefore, highlights the need for improved representation of Antarctic sea-ice processes in models for accurate projections of thickness and related volume changes.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Supplement
(1781 KB) - BibTeX
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2744', Anonymous Referee #1, 29 Apr 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2024-2744/egusphere-2024-2744-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2024-2744-RC1 - AC1: 'Reply on RC1', Shreya Trivedi, 24 Jun 2025
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RC2: 'Comment on egusphere-2024-2744', Anonymous Referee #2, 05 May 2025
This study provides a systematic evaluation of Antarctic sea ice simulations in CMIP6 models by integrating satellite observations and reanalysis data. The paper is well-structured, employs rigorous methodology, and presents comprehensive data analysis. It contributes meaningfully to the field of Antarctic sea ice modeling. Below are my detailed review comments:
- The manuscript mentions GIOMAS and GECCO3 reanalysis products but does not explicitly clarify whether these products assimilate observational data (e.g., sea ice concentration, satellite retrievals).
- Lines 211–213 mention that the IPSL and EC-Earth3 models produce anomalously thick ice, but there is no explanation of why these models behave differently (e.g., ocean/ice dynamics, resolution, parameterizations). I suggest adding a paragraph comparing key models, such as IPSL vs. CESM2, to highlight how differences in physical schemes (e.g., ridging, thermodynamics) lead to divergent sea ice thickness simulations.
- Although the paper discusses biases in CMIP6 models when simulating Antarctic sea ice, the analysis of the underlying causes of these biases is somewhat brief. I recommend a more detailed discussion of the sources of model discrepancies, particularly with respect to how different models perform in different regions and the specific impacts these factors have on sea ice simulations.
- The right column of Figure 2 (SIA anomalies) excludes GECCO3 and GIOMAS, unlike the left and middle columns (SIT/SIV). I suggest either adding the SIA data from GECCO3 and GIOMAS or providing justification for their exclusion (e.g., NSIDC is the primary SIA reference).
- The analysis stops at 2014, while Antarctic sea ice has shown a significant decline since 2015 (Raphael & Handcock, 2022). It would be helpful to briefly discuss whether CMIP6 models (e.g., SSP scenarios) capture this reversal, even if reanalysis data does not include post-2014 information.
- The Taylor diagrams in Figure 3 use "satellite products" as the reference, but it is unclear whether Envisat-CryoSat-2 is averaged or used separately. I recommend explicitly stating which dataset is used as the reference for each variable and justifying any averaging.
- Section 3.4 relies on GIOMAS as the primary reference due to satellite biases in September. However, GIOMAS shows large errors in February (Figure 1c, early maxima). I suggest acknowledging the limitations of GIOMAS and cross-validating key results with GECCO3 and satellite data where possible.
- Some of the legends are not clear enough. It is recommended to directly mark them in the figures or explain them in detail in the captions.
- Although the paper proposes directions for model improvements (e.g., improving ice-edge simulation accuracy, enhancing dynamic process representations), these suggestions are rather general and lack specific implementation details.
Citation: https://doi.org/10.5194/egusphere-2024-2744-RC2 - AC2: 'Reply on RC2', Shreya Trivedi, 24 Jun 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2744', Anonymous Referee #1, 29 Apr 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2024-2744/egusphere-2024-2744-RC1-supplement.pdf
- AC1: 'Reply on RC1', Shreya Trivedi, 24 Jun 2025
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RC2: 'Comment on egusphere-2024-2744', Anonymous Referee #2, 05 May 2025
This study provides a systematic evaluation of Antarctic sea ice simulations in CMIP6 models by integrating satellite observations and reanalysis data. The paper is well-structured, employs rigorous methodology, and presents comprehensive data analysis. It contributes meaningfully to the field of Antarctic sea ice modeling. Below are my detailed review comments:
- The manuscript mentions GIOMAS and GECCO3 reanalysis products but does not explicitly clarify whether these products assimilate observational data (e.g., sea ice concentration, satellite retrievals).
- Lines 211–213 mention that the IPSL and EC-Earth3 models produce anomalously thick ice, but there is no explanation of why these models behave differently (e.g., ocean/ice dynamics, resolution, parameterizations). I suggest adding a paragraph comparing key models, such as IPSL vs. CESM2, to highlight how differences in physical schemes (e.g., ridging, thermodynamics) lead to divergent sea ice thickness simulations.
- Although the paper discusses biases in CMIP6 models when simulating Antarctic sea ice, the analysis of the underlying causes of these biases is somewhat brief. I recommend a more detailed discussion of the sources of model discrepancies, particularly with respect to how different models perform in different regions and the specific impacts these factors have on sea ice simulations.
- The right column of Figure 2 (SIA anomalies) excludes GECCO3 and GIOMAS, unlike the left and middle columns (SIT/SIV). I suggest either adding the SIA data from GECCO3 and GIOMAS or providing justification for their exclusion (e.g., NSIDC is the primary SIA reference).
- The analysis stops at 2014, while Antarctic sea ice has shown a significant decline since 2015 (Raphael & Handcock, 2022). It would be helpful to briefly discuss whether CMIP6 models (e.g., SSP scenarios) capture this reversal, even if reanalysis data does not include post-2014 information.
- The Taylor diagrams in Figure 3 use "satellite products" as the reference, but it is unclear whether Envisat-CryoSat-2 is averaged or used separately. I recommend explicitly stating which dataset is used as the reference for each variable and justifying any averaging.
- Section 3.4 relies on GIOMAS as the primary reference due to satellite biases in September. However, GIOMAS shows large errors in February (Figure 1c, early maxima). I suggest acknowledging the limitations of GIOMAS and cross-validating key results with GECCO3 and satellite data where possible.
- Some of the legends are not clear enough. It is recommended to directly mark them in the figures or explain them in detail in the captions.
- Although the paper proposes directions for model improvements (e.g., improving ice-edge simulation accuracy, enhancing dynamic process representations), these suggestions are rather general and lack specific implementation details.
Citation: https://doi.org/10.5194/egusphere-2024-2744-RC2 - AC2: 'Reply on RC2', Shreya Trivedi, 24 Jun 2025
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William R. Hobbs
Marilyn Raphael
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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(2385 KB) - Metadata XML
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Supplement
(1781 KB) - BibTeX
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