Evaluation of the thermohaline profiles simulated by CMIP6 models based on an unsupervised clustering method

Earth System Models have deficiencies and biases that raise large uncertainties in the simulated ocean temperature and salinity. Methods of assessing vertical thermohaline structures traditionally rely on computing the mean profile in somewhat ad hoc regions. However, the global ocean has complex spatial variability, complicating regional classification. Here, to assess the global thermohaline profiles simulated by CMIP6 models, we apply an unsupervised clustering method to classify the Argo-observed profiles and compare them with the historical simulations of 8 CMIP6 models. The results show that in each season the Argo-observed thermohaline profiles in the global ocean can be divided into around 60 classes, which have distinct geographical distributions. CESM2 (MRI-ESM2-0) performs best in simulating the main climatological spatial patterns and vertical structures of temperature (salinity) in 0–1,000 m, with an overall bias of 1.19 °C (0.31). Temperature biases in 8-CMIP6 multi-model mean highlight several hotspots: (1) the tropical Atlantic Ocean and the Gulf of Mexico; (2) the Kuroshio Extension; (3) south of the equator in the western Pacific Ocean; and (4) the Agulhas retroflection region. Vertical structures of temperature and salinity biases vary in these four regions. Composite analysis of vertical bias patterns and dynamic characteristics suggests that: region 1 may reflect remote advective influences; regions 2 and 4 may be associated with the inaccuracy of mode water simulations; region 3 appears attributable to wind stress and mixed layer depth biases. The proposed evaluation framework will provide important insights for optimizing global ocean simulations in future modeling efforts.