Analysis of ecologically relevant sea ice and ocean variables for the Southern Ocean using a high-resolution model to inform ecosystem studies

Abstract

Southern Ocean organisms are uniquely adapted to the extreme environmental conditions that characterise this region, making them especially vulnerable to climate change. Alterations to the physical environment have already been linked to alterations in the structure and functioning of entire ecosystems, and ecological disruptions are expected to continue to occur. Although our understanding of the physical processes driving ecological change in the Southern Ocean has improved in recent years, significant knowledge gaps remain largely as a result of insufficient observational data being available. High resolution ocean models are an important tool that can help us overcome data scarcity. However, models generally contain biases that may affect their ability to accurately represent environmental conditions in the region of interest. Thus, their outputs must be evaluated to understand if and how model outputs can be used to answer questions about ecological impacts. Here, we examined the suitability of ACCESS-OM2-01, a high-resolution coupled ocean sea ice model, for ecological applications. Our results highlight the heterogeneous nature of the mean state of the environmental variables examined and their trends across the Southern Ocean. Our assessment shows that the ACCESS-OM2-01 model simulated well the observed seasonal cycle and broad baseline climatological conditions of the mixed layer depth and sea ice variables for the Southern Ocean over the past 50 years. However, the model performance varies across regions and at seasonal time scales: the model simulated much deeper winter mixed layer depth in the Weddell Sea and within the Antarctic Circumpolar Current, and the climatological sea-ice concentration for summer was lower in the model compared to available observations. Finally, we provide detailed Python-based scripts with all the code and steps for this analysis, which can be used as reference material to reapply elsewhere and evaluate the suitability of other model outputs for ecological applications.