Impact of Regional SST Anomalies on the Indian Monsoon Response to Global Warming in the CNRM Climate Model.

While transient climate change experiments with coupled atmosphere–ocean general circulation models undoubtedly represent the most comprehensive tool for studying the climate response to increasing concentrations of greenhouse gases (GHGs), less computationally expensive time-slice experiments with atmospheric GCMs are still useful to test the robustness of the projected climate change. In the present study, three sets of time-slice experiments with prescribed sea surface temperature (SST) are compared to a reference climate scenario obtained with the Centre National de Recherches Météorologiques Coupled Climate Model (CCM). The main objective is to assess the sensitivity of the monsoon response to the magnitude or pattern of SST anomalies in two regions where such anomalies are highly model dependent, namely, the circumpolar Southern Ocean and the tropical Pacific Ocean. On the one hand, it is shown that the regional climate anomalies predicted by the CCM can be reproduced at least qualitatively by a pair of time-slice experiments in which the present-day SST biases of the CCM are removed. On the other hand, the results indicate that the Indian monsoon response to increasing amounts of GHG is sensitive to regional uncertainties in the prescribed SST warming. Increasing the sea surface warming in the southern high latitudes to compensate for the weak sea ice feedback simulated by the CCM around the Antarctic has a significant influence on the regional climate change simulated over India, through a perturbation of the regional Hadley circulation. Prescribing zonal mean rather than El Niño–like SST anomalies in the tropical Pacific has an even stronger impact on the monsoon response, through a modification of the Walker circulation. These results suggest that both deficiencies in simulating present-day climate (even at high latitudes) and uncertainties in the SST patterns caused by enhanced GHG concentrations (especially in the tropical Pacific) are major obstacles for predicting climate change at the regional scale. [ABSTRACT FROM AUTHOR]