Subsurface temperature biases in coupled models can seriously impair their capability in generating skillful seasonal forecasts. The National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), coupled model, which is used for seasonal forecast in several countries including India, displays warm (cold) subsurface (surface) temperature bias in the tropical Indian Ocean (TIO), with deeper than observed mixed layer and thermocline. In the model, the maximum warm bias is reported between 150- and 200-m depth. Detailed analysis reveals that the enhanced vertical mixing by strong vertical shear of horizontal currents is primarily responsible for TIO subsurface warming. Weak upper-ocean stability corroborated by surface cold and subsurface warm bias further strengthens the subsurface warm bias in the model. Excess inflow of warm subsurface water from Indonesian Throughflow to the TIO region is partially contributing to the warm bias mainly over the southern TIO region. Over the north Indian Ocean, Ekman convergence and downwelling due to wind stress bias deepen the thermocline, which do favor subsurface warming. Further, upper-ocean meridional and zonal cells are deeper in CFSv2 compared to the Ocean Reanalysis System data manifesting the deeper mixing. This study outlines the need for accurate representation of vertical structure in horizontal currents and associated vertical gradients to simulate subsurface temperatures for skillful seasonal forecasts.

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