Sea levels of different atmosphere–ocean general circulation models (AOGCMs) respond to climate change forcing in different ways, representing a crucial uncertainty in climate change research. We isolate the role of the ocean dynamics in setting the spatial pattern of dynamic sea-level (ζ) change by forcing several AOGCMs with prescribed identical heat, momentum (wind) and freshwater flux perturbations. This method produces a ζ projection spread comparable in magnitude to the spread that results from greenhouse gas forcing, indicating that the differences in ocean model formulation are the cause, rather than diversity in surface flux change. The heat flux change drives most of the global pattern of ζ change, while the momentum and water flux changes cause locally confined features. North Atlantic heat uptake causes large temperature and salinity driven density changes, altering local ocean transport and ζ. The spread between AOGCMs here is caused largely by differences in their regional transport adjustment, which redistributes heat that was already in the ocean prior to perturbation. The geographic details of the ζ change in the North Atlantic are diverse across models, but the underlying dynamic change is similar. In contrast, the heat absorbed by the Southern Ocean does not strongly alter the vertically coherent circulation. The Arctic ζ change is dissimilar across models, owing to differences in passive heat uptake and circulation change. Only the Arctic is strongly affected by nonlinear interactions between the three air-sea flux changes, and these are model specific.

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The present study aims to examine the role of air–sea interactions and upper ocean processes in determining the tropical Indian Ocean (TIO) seasonal sea surface temperature (SST) bias in Climate Forecast System version 1 (CFSv1) and version 2 (CFSv2) free runs. CFSv1 displayed dipole like east–west SST bias over the equatorial Indian Ocean from boreal summer to winter and is consistent with errors (bias) in surface winds and upper ocean advection. Large zonal gradients in sea level pressure (SLP) bias and the associated surface wind biases are primarily responsible for the upper ocean current bias. However, over the southern Indian Ocean and parts of Arabian Sea, strong bias in heat flux and mixed layer depth (MLD) have mainly contributed for the SST biases in CFSv1. Equatorial current system is better represented in CFSv2 compared to CFSv1. Improvement in the representation of land‐surface processes appears to be contributing towards improving atmospheric circulation and SLP gradients in CFSv2, which may be responsible for the improved ocean circulation. Importantly, east–west dipole like SST bias prevalent in CFSv1 is absent in CFSv2. However, there is a prominent systematic basin‐wide TIO cold SST bias in CFSv2. Large biases in surface heat flux (net negative bias) and MLD (deeper) are mainly responsible for SST biases in CFSv2. Negative net heat flux bias in CFSv2 is primarily due to specific humidity bias‐induced excess latent heat flux (LHF). Deepening of MLD is mainly due to strong convective mixing, a resultant of anomalous LHF release, which in turn leads to negative SST bias. Models comparison reveals that although representation of SST in CFSv2 is better than in CFSv1, it is essential to improve further the equatorial ocean dynamics and off‐equatorial thermodynamics in the form of moist processes and radiative parameterization in order to reduce SST bias in CFSv2.

Many climate models have problems in simulating the sea surface temperature (SST) in the tropical Indian Ocean (TIO). The Coupled Model Inter-comparison Project Phase 5 (CMIP5) models, in general, underestimate SST over the entire TIO region. This study examines the SST evolution during spring to summer transition months (May and June) over the Arabian Sea (AS) region in the historical simulations of 13 CMIP5 models and the Climate Forecasting System coupled models CFSv1 and CFSv2. The annual cycle of SST shows that the summer monsoon cooling is not adequately captured by many models. Based on the state of June SST tendency, models have been divided in to three groups, the first group (G1) consists of models having stronger than observed cooling, second group (G2) considers models having closer to observed cooling and the third group (G3) includes models having lesser than observed cooling. Mixed layer heat budget analysis revealed that atmospheric flux is mainly responsible for unrealistic SST warming in most of the G3 models during June. The vertical mixing and horizontal advection contribute considerably to the SST cooling in summer (June) especially for G1 and G2 models. On the other hand, spring warming in all the models is consistently forced by the surface heat flux. It is also found that the monsoon low-level jet (LLJ) is not accurately represented in most of the models. The misrepresentation of LLJ causes bias in the oceanic processes leading to unrealistic SST evolution in many models. One way of LLJ affecting the oceanic processes is by modulating mixed layer depth (MLD). It is observed in general that the models with deeper MLD display strong SST cooling. The model deficiency in representing AS SST is speculated to be a major limiting factor in capturing the monsoon rainfall in the current coupled models. The proper simulation of AS SST is therefore very crucial for the accurate representation of Indian summer monsoon precipitation.