Representation of Southern Ocean properties across Coupled Model Intercomparison Project generations: CMIP3 to CMIP6 Representation of Southern Ocean properties across Coupled Model Intercomparison Project generations: CMIP3 to CMIP6
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Representation of Southern Ocean properties across Coupled Model Intercomparison Project generations: CMIP3 to CMIP6 Representation of Southern Ocean properties across Coupled Model Intercomparison Project generations: CMIP3 to CMIP6

Published Web Location

https://doi.org/10.1175/JCLI-D-19-0970.1
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Abstract

Abstract: The air–sea exchange of heat and carbon in the Southern Ocean (SO) plays an important role in mediating the climate state. The dominant role the SO plays in storing anthropogenic heat and carbon is a direct consequence of the unique and complex ocean circulation that exists there. Previous generations of climate models have struggled to accurately represent key SO properties and processes that influence the large-scale ocean circulation. This has resulted in low confidence ascribed to twenty-first-century projections of the state of the SO from previous generations of models. This analysis provides a detailed assessment of the ability of models contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to represent important observationally based SO properties. Additionally, a comprehensive overview of CMIP6 performance relative to CMIP3 and CMIP5 is presented. CMIP6 models show improved performance in the surface wind stress forcing, simulating stronger and less equatorward-biased wind fields, translating into an improved representation of the Ekman upwelling over the Drake Passage latitudes. An increased number of models simulate an Antarctic Circumpolar Current (ACC) transport within observational uncertainty relative to previous generations; however, several models exhibit extremely weak transports. Generally, the upper SO remains biased warm and fresh relative to observations, and Antarctic sea ice extent remains poorly represented. While generational improvement is found in many metrics, persistent systematic biases are highlighted that should be a priority during model development. These biases need to be considered when interpreting projected trends or biogeochemical properties in this region.

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