This study explores the variability of tropical cyclone (TC) intensification rates (IRs) in the postmonsoon Bay of Bengal (BoB) for the satellite period of 1980–2015. It is found that both number of rapid intensification (RI) events and magnitude of IRs show a robust increase, with a northeastward shift of intensification events. Analyses show that the temporal variability of sea surface temperature dominated the IR variability during 1980–1997. However, the thick barrier layer in the northern BoB was considerably responsible for IR variability during 1998–2015, which significantly contributed to the IR increase. Due to more intensification events occurring over the northeastern region in two recent decades, the thick barrier layer with strong salinity stratification in the northern BoB limits TC-induced sea surface cooling and in turn favors TC intensification. This study has an important implication that air–sea coupled climate model need to realistically simulate upper ocean salinity variability on projecting TC intensity change over the BoB.

Monsoonal climate dominates the Northern Indian Ocean (NIO; Schott & McCreary, 2001). The salinity distribution of NIO is controlled by local evaporation, precipitation, runoff, and dynamical processes in the ocean. The two major basins that characterize the NIO are the Arabian Sea (AS) in the west with high salinity surface water and the Bay of Bengal (BoB) with the significantly low salinity surface water in the east. In the AS, during the winter monsoon, the cold and dry northeast monsoon winds, combined with Ekman pumping, cause subduction of high-salinity surface waters in the interior northern AS. This generates the widespread AS Water salinity maximum just underneath the mixed layer (ML) (Schott & Fischer, 2000; Schott & McCreary, 2001). Some of the high salinity water will overflow from the Red Sea and the Persian Gulf (Tomczak & Godfrey, 2003). In the BoB, a large quantity of fresh water from river runoff and precipitation cause strong stratification in the upper ocean. The current along India's east coast, known as the East India Coastal Current, is seasonally reversed. Its poleward phase is most developed in March and April, while the equatorial phase prevails as the southwest monsoon recedes (Shetye et al., 1996). These characteristics will lead to great seasonal changes in the upper water mass of NIO. Therefore, it is considerably difficult to estimate the thermohaline structure in the upper NIO. Satellite sea surface observations such as sea surface height (SSH), temperature (SST), and salinity (SSS) are now routinely available on a global scale. These well-sampled data have provided us an unprecedented opportunity to understand ocean circulations and mesoscale processes (e.g.,

Dynamical models used in climate prediction often have systematic errors that can deteriorate predictions. In this study, we work in a twin experiment framework with a reduced-order coupled ocean-atmosphere model and aim to demonstrate the benefit of machine learning for climate prediction. Machine learning is applied to learn the model error and thus build a data-driven model to emulate the dynamical model error. Then we build a hybrid model by combining the data-driven and dynamical models. The prediction skill of the hybrid model is compared to that of the standalone dynamical model. We applied this approach to the ocean-atmosphere coupled model. The results show that the hybrid model outperforms the dynamical model alone for both atmospheric and oceanic variables. Also, we build two other hybrid models only correcting either atmospheric errors or oceanic errors. It was found that correcting both atmospheric and oceanic errors leads to the best performance.

No abstract