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Simulation of synoptic features during summer monsoon onset over GWB, India, with CFSv2 coupled model: skill and bias assessment
Published in Springer-Verlag Wien
Volume: 136
Issue: 43528
Pages: 1311 - 1323

The aim of this study is to examine the skill of climate forecast system version 2 (CFSv2) in simulating the synoptic features of Bay of Bengal (BOB) branch of summer monsoon (SM) during the onset over Gangetic West Bengal (GWB), India. Precise prediction of the onset time and the synoptic features associated with the onset is a major challenge in SM study. Better understanding of the synoptic and intra-seasonal variability during the propagation along with the mean simulation of monsoon features is crucial, especially for the operational models. The earlier studies focused mainly on the mean simulation of SM during June–September (JJAS) period. However, the main objective of the present study is to improve the understanding of CFSv2 model biases in simulating the synoptic features during the propagation of BOB branch of SM system till the onset over GWB. The skill of the coupled model is estimated for the years 2011 to 2015 with tropospheric temperature (TT), sea surface temperature (SST), mean sea level pressure (MSLP), winds at 850 and 500 hPa pressure levels, and rainfall rate (RR). The result shows that the observed characteristics are simulated, reasonably well, by CFSv2 model with quite high reliability unlike other coupled models. The CFSv2 has been able to simulate the position/variation during the onset; however, the model has not been able to estimate the intensity in some occasions. The gradients of pressure and SST have been slightly overestimated by the model. The model has not been able to simulate the winds at 850 and 500 hPa pressure levels in some occasions. The CFSv2 model in simulating the features during propagation of BOB branch of SM system shows disparity from observation in some occasions during 2011 to 2015. The result also reveals that the model biases remain unaltered during El Niño episode of 2011. © 2018, Springer-Verlag GmbH Austria, part of Springer Nature.

About the journal
JournalData powered by TypesetTheoretical and Applied Climatology
PublisherData powered by TypesetSpringer-Verlag Wien