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| Abstract Submission No. | ABS-2022-11-0013 |
| Title of Abstract | Simulation of interannual relationship between the Atlantic zonal mode and Indian summer monsoon in CFSv2 |
| Authors | Vijay Pottapinjara*, Mathew Koll Roxy, M. S. Girishkumar, Karumuri Ashok, Sudheer Joseph, M. Ravichandran, R. Murtugudde |
| Organisation | INCOIS, Hyderabad |
| Address | INCOIS, Pragathi Nagar Hyderabad, Telangana, India Pincode: 500090 Mobile: 8179717709 E-mail: vijay.p@incois.gov.in |
| Country | India |
| Presentation | Oral |
| Abstract | Recent studies have shown that the Atlantic zonal mode (AZM) can significantly influence the Indian summer monsoon (ISM). In an earlier study, we proposed that AZM influence propagates in tropospheric temperature as Kelvin wave-like fea- tures to the east to reach the Indian Ocean and influences the monsoon by modulating the mid-tropospheric land-sea thermal gradient and thereby the seasonal mean flow. The changes thus induced in the mean flow were shown to affect the monsoon depressions in the Bay of Bengal and rainfall over India. In the present study, we use the Coupled Forecast System version 2, which is utilized for seasonal prediction of ISM in India, to examine how well the model simulates this AZM-monsoon link. In the sensitivity experiment, a warm AZM SST anomaly is added over the tropical Atlantic in the boreal summer and the ISM response is studied. We find that the model simulates the important aspects of the AZM-monsoon link. The model also simulates a known dynamics-based mechanism wherein a warm AZM SST anomaly produces a Matsuno-Gill type response, which in turn induces a sinking motion over India causing a reduction in rainfall. However, some finer details of these mechanisms are not simulated due to mean state biases in the tropical Atlantic in the model, a problem common to many coupled models. Our study highlights the need for the improvement of mean state of model in the tropical Atlantic to better capture the AZM-ISM relationship which will ultimately improve the monsoon forecasts issued using this model. |