Abstract
AbstractUsing carefully designed coupled model experiments, we have demonstrated that the prediction skill of the all India summer monsoon rainfall (AISMR) in Climate Forecast System version 2 (CFSv2) model basically comes from the El‐Niňo Southern Oscillation‐Monsoon teleconnection. On the other hand, contrary to observations, the Indian Ocean coupled dynamics do not have a crucial role in controlling the prediction skill of the AISMR in CFSv2. We show that the inadequate representation of the Indian Ocean coupled dynamics in CFSv2 is responsible for this dichotomy. Hence, the improvement of the Indian Ocean coupled dynamics is essential for further improvement of the AISMR prediction skill in CFSv2.
Highlights
And accurate seasonal prediction of Indian summer monsoon (ISM) during June through September (JJAS) is very important for proper planning and the socioeconomic well-being of India as majority of people in this region depend on rain-fed agriculture for their life and existence
The Climate Forecast System version 2 (CFSv2) model has a dry bias over Indian land region, while the misrepresentation of Indian Ocean dynamics leads to improper ocean–atmosphere interactions and overestimated oceanic rainfall coexisting with cold sea surface temperature (SST) biases
Better simulation of ocean–atmosphere interactions and reduced dry bias over Indian land along with simulation of warmer SST in northern Indian Ocean in the Indian Ocean slab (ISLAB) run confirms that the dry bias over the Indian landmass is primarily due to cold SST simulated in the tropical Indian Ocean
Summary
And accurate seasonal prediction of Indian summer monsoon (ISM) during June through September (JJAS) is very important for proper planning and the socioeconomic well-being of India as majority of people in this region depend on rain-fed agriculture for their life and existence. Gadgil and Srinivasan (2011) have studied the simulation of the all India summer monsoon rainfall (AISMR) in five atmospheric general circulation models (AGCMs) and they have shown that the poor prediction skill in many AGCMs arise due to excessive teleconnection with El-Nino Southern Oscillation (ENSO). Tropical Ocean Global Atmosphere (TOGA), Global Ocean Global Atmosphere, Seasonal Prediction of Indian Monsoon (SPIM) and similar experiments, mainly focus on the response of the atmosphere to the sea surface temperature (SST) forcing (Lau and Nath, 2000; Gadgil and Srinivasan, 2011) based on two-tier modeling strategies where the atmosphere and ocean are treated separately by using the AGCM, which is forced by either observed boundary condition or output from the ocean general circulation models.
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