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.

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.

Antarctic sea ice is a critical component of the climate system and a vital habitat for Southern Ocean ecosystems. Understanding the underlying physical processes and improving Antarctic sea ice predictability is of broad interest. Using model data, we investigate sea ice and upper ocean predictability at interannual timescales in the Weddell Sea region. We find that oceanic predictability is largely confined to the Winter Water layer and responds to seasonal modifications of the water column, mainly driven by sea ice processes. Predictability depends not only on the depth of the Winter Water layer, but also on how strongly stratified its base is. Predictability is lost when warm Circumpolar Deep Water with no sea ice-related memory entrains into the mixed layer. We show the strong dependence of sea ice predictability on the local upper ocean vertical structure, which suggests that both are likely to change in a warming climate.