Dynamical relationship between Indian summer monsoon rainfall and South Asian High in seasonal coupled models

Author:

Sripa G1,Ramu Dandi A.2,Saikrishna T.S.1,Osuri Krishna Kishore1ORCID,Rao A.S.3

Affiliation:

1. National Institute of Technology Rourkela

2. University of Hyderabad

3. IITM: Indian Institute of Tropical Meteorology

Abstract

Abstract The present study aims to understand the dynamical linkage between Indian summer monsoon rainfall (ISMR) and South Asian High (SAH) in four seasonal models (CANCM4, NEMO, CANSIP, and CFSv2) initialized with May conditions for 1982–2016. Observational analyses indicate that the northwest-southeast (INW−SE) index of SAH is strongly correlated (~ 0.62) with ISMR whereas the east-west (IEW) index is negatively correlated (~-0.57). All the models reasonably capture the relation between ISMR and the INW−SE/IEW indices with a slightly varying correlation. The positively regressed rainfall anomalies are (90%) significant during INW−SE years and attributed to the strong cold sea surface temperature anomalies over the equatorial eastern Pacific (i.e., La Niña) and positive vorticity associated with the strong cyclonic circulation over monsoon region. Similarly, significant negative rainfall anomalies are identified during IEW years, strongly associated with El Niño patterns in the eastern Pacific and negative vorticity anomalies over monsoon region. The CANCM4, NEMO, and CANSIP models show strong positive (negative) regressed rainfall anomalies over India in INW−SE (IEW) years, unlike the CFSv2 and observations and mainly due to strong linkage with El Niño and the Indian Ocean Dipole in the models. Overall, the high resolution CFSv2 performs better than the other models for mean rainfall and teleconnections between ISMR and SAH. Of the three models, the CANCM4 performs better in capturing dynamic circulations such as vorticity, velocity potential, stream function etc. The study highlights the seasonal model’s ability to capture the linkage of ISMR and SAH indices and helps understand rainfall variability.

Publisher

Research Square Platform LLC

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