Key Points: • CMIP6 models can capture the timing of annual cycle (particularly in February) and spatial patterns of SIT resembling the observations. • Compared to sea-ice area, CMIP6 models exhibit larger negative biases in thickness/volume, with a higher degree of variation among models. • Seasonal variations in sea-ice show positive (negative) relationships between sea ice area and thickness during September (February). Abstract This study assesses less-explored Southern Ocean sea-ice parameters, namely Sea-ice Thickness and Volume, through a comprehensive comparison of 26 CMIP6 models with reanalyses and satellite observations. Findings indicate that models replicate the mean seasonal cycle and spatial patterns of sea-ice thickness, particularly during its maxima in February. However, some models simulate implausible historical mean states compared to satellite observations, leading to large inter-model spread. September sea-ice thickness is consistently biased low across the models. Our results show a positive relationship between modeled mean sea-ice area and thickness in September (i.e., models with more area tend to have thicker ice); in February this relationship becomes negative. While CMIP6 models demonstrate proficiency in simulating Area, thickness accuracy remains a challenge. This study, therefore, highlights the need for improved representation of Antarctic sea-ice processes in models for accurate projections of thickness and volume changes. Plain Language Summary In this study, we investigated sea-ice thickness and volume in the Southern Ocean using data from 26 different climate models and observation datasets. Our findings show that the models generally capture the seasonal cycle and spatial patterns of sea-ice thickness well, with the highest average thickness occurring in February. We also found that the models tend to perform better in simulating sea-ice area compared to thickness. Furthermore, simulated sea-ice area and thickness tend to behave differently during different seasons-positively (negatively) covarying in September (February). The models that performed well in simulating sea-ice area faced challenges in accurately representing thickness and volume. This raises the question regarding the overall performance of such models or, more definitively, whether it's reliable to evaluate model accuracy or performance based solely on sea-ice area. Nevertheless, sea-ice thickness simulations in CMIP6 can offer a basis for initial analyses of absolute sea-ice changes in the Southern Ocean, despite the need for more reliable observational thickness.

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. While CMIP6 models perform well in simulating sea-ice area and its relationship with thickness, accuracy in the latter remains a challenge. 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.

This study assesses less-explored Southern Ocean sea-ice parameters, namely Sea-ice Thickness and Volume, through a comprehensive comparison of 26 CMIP6 models with reanalyses and satellite observations. Findings indicate that models replicate the mean seasonal cycle and spatial patterns of sea-ice thickness, particularly during its maxima in February. However, some models simulate implausible historical mean states compared to satellite observations, leading to large inter-model spread. September sea-ice thickness is consistently biased low across the models. Our results show a positive relationship between modeled mean sea-ice area and thickness in September (i.e., models with more area tend to have thicker ice); in February this relationship becomes negative. While CMIP6 models demonstrate proficiency in simulating Area, thickness accuracy remains a challenge. This study, therefore, highlights the need for improved representation of Antarctic sea-ice processes in models for accurate projections of thickness and volume changes.