The Indian Ocean dipole (IOD) is a remarkable interannual variability in the tropical Indian Ocean. The improved prediction of IOD is of a great value because of its large socioeconomic impacts. Previous studies reported that both El Niño-Southern Oscillation (ENSO) and South China Sea summer monsoon (SM) play a dominant role in the western and eastern pole of the IOD, respectively. They can be used as predictors of the IOD at 3 month lead beyond self-persistence. Here, we develop an empirical model of multi-factors in which the western pole is predicted by ENSO and persistence and the eastern pole is predicted by SM and persistence. This new empirical model outperforms largely the average level of the dynamical models from the North American multi-model ensemble (NMME) project in predicting the peak IOD in boreal autumn, with a correlation coefficient of ∼0.86 and a root mean square error of ∼0.24 °C. Furthermore, the hit rate of positive culminated IOD in this new empirical model is equivalent to that in current NMME models (above 65%), much higher than that for negative culminated IOD. This improvement of skill using the empirical model suggests a perspective for better understanding and predicting the IOD.

The open-water deep convection induced by brine rejection constitutes a primary process for deep water formation in high latitudes, playing a critical role in the global thermohaline circulation. Proper parameterization of subgrid-scale convective salt plumes arising from brine rejection is crucial for improving climate model simulations of ocean convection. Traditional physically driven parameterization schemes require numerous physical parameters. In this study, we aim to explore an objective, few-data-driven approach by using deep learning for subgrid scale salt plume parameterization. The Backpropagation Neural Network (BPNN) is trained using the output results of the Large Eddy Simulation (LES) model, which is well-suited for simulating the highly turbulent salt plumes. Results show that using the BPNN driven solely by salinity and latitude effectively captures essential information about the mixing coefficient induced by salt plumes. The application of deep learning provides a new perspective for proposing the subgrid scale parameterization of salt plumes.

In this study, based on the data-driven parameterization proposed in previous studies, we implemented a datadriven vertical turbulence parameterization scheme (backpropagation neural network, BPNN) into a regional ocean model and compared the simulation results with those obtained using the traditional physics-driven scheme (K -profile parameterization, KPP). The Kuroshio-Oyashio Confluence Region (KOCR) with rich ocean dynamics was selected as the study region. Comparisons of modeled temperature, salinity, and velocity outputs show that the two parameterization schemes produce similar spatial and temporal distribution characteristics. For the sea temperature, the maximum difference appeared at the subsurface layer (3.6 degrees C) while the maximum mean difference appeared at the surface layer (0.1 degrees C). Overall, sea temperature in the surface layer simulated by the model adopting the BPNN parameterization scheme was warmer. For the salinity, the maximum difference (0.28 psu) and maximum mean difference (1 x 10-3 psu) appeared in the subsurface layer. In the surface layer, when the model adopting BPNN parameterization scheme overestimated sea temperature compared to the KPP scheme, the result was usually accompanied by overestimated salinity. In the bottom layer, when the model adopting the BPNN parameterization scheme simulated temperature overestimation, the result corresponded to underestimated salinity. For the velocity, the maximum difference (0.31 m/s) and maximum mean difference (-3 x 10-3 m/s) between the two parameterization schemes appeared in the surface layer, and the high values of velocity difference generally occurred in sea areas with energetic dynamic processes. The KPP scheme and the model adopting BPNN parameterization produced similar mixed layer depth (MLD) spatial distribution and ocean fronts spatial distribution, but the latter parameterization scheme simulated an overall shallower mixed layer. For the ocean fronts, the differences between the two parameterization schemes (O-10-5) is smaller than the intensity of the fronts (O-10-4). This study presents a new attempt to apply the data-driven parameterization scheme in ocean numerical models.

The large-scale anomalous anticyclone in the western North Pacific (WNP) has been extensively studied, but the large-scale anomalous cyclone has not received much attention in the past years. In this study, we use observational data to find that the occurrence numbers of the anomalous cyclone and anticyclone in the WNP have been roughly the same from 1979 to 2020. Our analyses indicate that the WNP anomalous cyclone is an interannual circulation anomaly in the WNP, which can persist from boreal autumn to the subsequent spring during a La Niña year and from spring to summer during a developing El Niño year. To confirm the roles of the central equatorial Pacific, tropical Indian Ocean, and central WNP sea surface temperatures, we perform a suite of model experiments using an atmospheric general circulation model. The model experiments demonstrate that central equatorial Pacific warming contributes to the WNP anomalous cyclone during a developing El Niño year. Cooling in the central equatorial Pacific or the tropical Indian Ocean alone cannot induce the WNP anomalous cyclone, but the combination of central equatorial Pacific cooling, tropical Indian Ocean cooling, and central WNP warming can jointly induce the WNP anomalous cyclone during a La Niña year. Similar to the WNP anomalous anticyclone, the WNP anomalous cyclone and its climatic impacts deserve attention.

Based on satellite data after 1979, we find that the tropical cyclone (TC) variations in the Western North Pacific (WNP) can be divided into three-periods: a high-frequency period from 1979 to 1997 (P1), a low-frequency period from 1998 to 2010 (P2), and a high-frequency period from 2011 to 2020 (P3). Previous studies have focused on WNP TC activity during P1 and P2. Here we use observational data to study the WNP TC variation and its possible mechanisms during P3. Compared with P2, more TCs during P3 are due to the large-scale atmospheric favorable conditions of vertical velocity, relative vorticity and relative humidity. Warm sea surface temperature (SST) anomalies are found during P3 and migrate from east to west, which is also favorable for TC genesis. The correlation between the WNP TC frequency and SST shows a significant positive correlation around the equator and a significant negative correlation around 36°N, which is similar to the warm phase of the Pacific Decadal Oscillation (PDO). The correlation coefficient between the PDO and TC frequency is 0.71, above the 95% confidence level. The results indicate that the increase of the WNP TC frequency during 2011–2020 is associated with the PDO and warm SST anomalies.