Robustness of BSISO and air-sea interactions in the CMIP (Phase-6) models over the North Indian Ocean

Abstract

Historical runs of 30 Coupled Model Intercomparison Project Phase-6 (CMIP6) General circulation models (GCMs) are evaluated for the representation of Boreal Summer Intraseasonal Oscillations (BSISO). Several statistical metrics were developed to evaluate the characteristic features of BSISO, such as propagation, phase speed, and exchange of air-sea fluxes at the air-sea interface over the major regions of the Indian Summer Monsoon (ISM). The mean state of the monsoon precipitation in the CMIP6 models is evaluated by using seasonal mean bias, pattern correlation, and root mean square error. The majority of CMIP6 models underestimate the precipitation over central India and overestimate the precipitation over the eastern equatorial region. Multi-model mean (MME) of the models shows good agreement of precipitation pattern with the observations. In the observations, the precipitation anomalies propagate northward from the equatorial latitudes to the northern latitudes over the ISM region (60°E-100°E longitudes). However, the initiation of northward propagating convection shows a significant variation with time in the CMIP6 models. Most of the models well simulated the BSISO propagation over the Bay of Bengal (BoB) and the Indian subcontinent. The majority of the models underestimate the phase speed of BSISO over the Arabian Sea (AS), and easterlies from the western north Pacific, which led to the failure of models in representing the northwest-southeast tilt of convection. Surface turbulent fluxes and zonal winds lag the deep convection over the North Indian Ocean on intraseasonal timescales. However, misrepresentation of air-sea fluxes in the CMIP6 models leads to the significant biases of intraseasonal variances. This study examines the simulation characteristic features of BSISO by CMIP6 models and is mainly attributes them to the atmospheric internal dynamics and air-sea interactions. The present study further suggests that improving atmospheric-oceanic feedback mechanisms, specific humidity, and low-level winds in the CMIP6 models is necessary to accurately predict the ISM intraseasonal variability.