This study examines the possible impacts of coupling processes on simulations of the Indian Ocean Dipole (IOD). Emphasis is placed on the atmospheric model resolution and physics. Five experiments were conducted for this purpose, including one control run of the ocean‐only model, four coupled experiments using two different versions of the Community Atmosphere Model (CAM4 and CAM5) and two different resolutions. The results show that the control run could effectively simulate various features of the IOD. The coupled experiments run at the higher resolution yielded more realistic IOD period and intensity than their counterparts at the low resolution. The coupled experiments using CAM5 generally showed a better simulation skill in the tropical Indian SST climatology and phase‐locking than those using CAM4, but the wind anomalies were stronger and the IOD period were longer in the former experiments than in the latter. In all coupled experiments, the IOD intensity was much stronger than the observed intensity, which is attributable to wind‐thermocline depth feedback and thermocline depth‐subsurface temperature feedback. The CAM5 physics seems beneficial for the simulation of summer rainfall over the eastern equatorial Indian Ocean and the CAM4 physics tends to produce less biases over the western equatorial Indian Ocean, whereas the higher resolution tends to generate unrealistically strong meridional winds. The IOD‐ENSO relationship was captured reasonably well in coupled experiments, with improvements in CAM5 relative to CAM4. However, the teleconnection of the IOD‐Indian summer monsoon and ENSO‐Indian summer monsoon was not realistically simulated in all experiments.

The Pacific Decadal Oscillation (PDO), the dominant pattern of sea surface temperature anomalies in the North Pacific basin, is an important low-frequency climate phenomenon. Leveraging data spanning from 1871 to 2010, we employed machine learning models to predict the PDO based on variations in several climatic indices: the Niño3.4, North Pacific index (NPI), sea surface height (SSH), and thermocline depth over the Kuroshio–Oyashio Extension (KOE) region (SSH_KOE and Ther_KOE), as well as the Arctic Oscillation (AO) and Atlantic Multi-decadal Oscillation (AMO). A comparative analysis of the temporal and spatial performance of six machine learning models was conducted, revealing that the Gated Recurrent Unit model demonstrated superior predictive capabilities compared to its counterparts, through the temporal and spatial analysis. To better understand the inner workings of the machine learning models, SHapley Additive exPlanations (SHAP) was adopted to present the drivers behind the model’s predictions and dynamics for modeling the PDO. Our findings indicated that the Niño3.4, North Pacific index, and SSH_KOE were the three most pivotal features in predicting the PDO. Furthermore, our analysis also revealed that the Niño3.4, AMO, and Ther_KOE indices were positively associated with the PDO, whereas the NPI, SSH_KOE, and AO indices exhibited negative correlations.

In this paper, we employ reanalysis data to systematically investigate the development of the Indian Ocean dipole (IOD), thereby distinguishing the SST–wind causal relationship during IOD development. The results indicate that the variations in sea surface temperature anomalies (SSTA) are particularly important during IOD development. SSTAs over the eastern Indian Ocean (EIO) lead to variations in Sumatran coastal winds and equatorial zonal winds, whereas SSTAs over the western Indian Ocean (WIO) lag behind these variations. On this basis, the Community Earth System Model (CESM) is adopted to examine the influences of different atmospheric physical processes and model resolutions on the simulation of the IOD evolution. For this purpose, four sets of sensitivity experiments are carried out involving two versions of the Community Atmospheric Model (CAM4 or CAM5) and two atmospheric model resolutions (0.9° × 1.25° or 1.9° × 2.5°). The CAM5 simulation experiments better capture the detailed characteristics of IOD development, especially the wind–SST causal relationship, than the CAM4 experiments. Moreover, increasing the resolution of the atmospheric model can effectively reduce the simulation bias, thus benefiting the simulation of the SST–wind relationship.

Three typhoons (Rammasun, Kalmaegi, and Sarika) travelled through the deployed stations in the northern South China Sea from 2014–2016. During the passage of typhoons, strong winds and vigorous currents resulted in horizontal displacement of buoy over 2000 m, vertical displacement of ropes on buoys as much as 200 m. The rectification can correct the warm anomaly to cool anomaly of temperature. These movements lead to biases of raw data, with temperature bias as much as 4°C, salinity as much as 0.05 psu, velocity bias as much as 0.4 m/s. The crosscheck of current velocity from different instruments shows that the bias of overlapping velocity and correlation coefficient after depth rectification obviously enhances. The observation shows that temperature cools 1.5 °C, and 0.1 psu saltier in maximum, the near-inertial current increases to 0.4 m/s in the upper layer. The inertial kinetic energy propagates downward with the upward phase, and the maximum depth can reach over 2000 m.