Indian Ocean Dipole (IOD) is a large-scale physical ocean phenomenon in the Indian Ocean that plays an important role in predicting the El Nino Southern Oscillation in the tropical Pacific. Predicting the occurrence of IOD is of great significance to the study of climate change and other marine phenomena. Generally, the IOD index is calculated to judge whether the IOD occurs. In this paper, a convolutional LSTM (convLSTM) neural network is used to build the deep learning model to predict the sea surface temperature in the next seven months and calculate the IOD index. Through the analysis of marine atmospheric data with complex temporal and spatial relationships, the wind field signal knowledge of the physical ocean is introduced to predict IOD phenomenon by combining the prior knowledge of the physical ocean and deep learning. The experimental results show that the average correlation of IOD index time series to the true IOD index time series is 82.87% from 2015 to 2018, seven months ahead for IOD prediction. IOD manifests as sea surface temperature (SST) anomaly changes, and this thesis verifies that the wind field signal information has a positive impact on the prediction of IOD changes. Moreover, the convLSTM can predict this anomaly better. The IOD index line graph can generally fit the real IOD index variation trend, which has a profound impact on the study of the IOD phenomenon.

Oceanic eddies populated in the western boundary current regions in the midlatitude have been found to exert significant influence on atmospheric boundary layer, storm tracks, and large‐scale atmospheric circulation. However, mechanisms governing how mesoscale sea surface temperature (SST) anomalies associated with oceanic eddies affect extratropical cyclogenesis remains unclear. Here, we investigate the influence of Kuroshio oceanic eddies on cyclogenesis in the North Pacific in high resolution climate model simulations using a cyclone tracking approach. Based on cyclone tracking and composite analyses, we find that presence of mesoscale SST anomalies almost doubles water vapor supply, leading to significant increase of diabatic heating release and eddy potential energy to eddy kinetic energy conversion and thus supporting stronger storm growth rate and intensified cyclones. This finding implies that moisture process is the key linking mesoscale oceanic eddies in the western boundary current regions with storm tracks in the midlatitude.

Impacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obtain accurate large-scale and long-term SST predictions. In this study, we used the reanalysis data sets provided by the National Centers for Environmental Prediction based on the Internet of Things technology and temporal convolutional network (TCN) to predict the monthly SSTs of the Indian Ocean from 2014 to 2018. The results yielded two points: Firstly, the TCN model can accurately predict long-term SSTs. In this paper, we used the Pearson correlation coefficient (hereafter this will be abbreviated as “correlation”) to measure TCN model performance. The correlation coefficient between the predicted and true values was 88.23%. Secondly, compared with the CFSv2 model of the American National Oceanic and Atmospheric Administration (NOAA), the TCN model had a longer prediction time and produced better results. In short, TCN can accurately predict the long-term SST and provide a basis for studying large oceanic physical phenomena.

The influence of greenhouse warming on mesoscale air-sea interactions, crucial for modulating ocean circulation and climate variability, remains largely unexplored due to the limited resolution of current climate models. Additionally, there is a lack of theoretical frameworks for assessing changes in mesoscale coupling due to warming. Here, we address these gaps by analyzing eddy-resolving high-resolution climate simulations and observations, focusing on the mesoscale thermal interaction dominated by mesoscale sea surface temperature (SST) and latent heat flux (LHF) coupling in winter. Our findings reveal a consistent increase in mesoscale SST-LHF coupling in the major western boundary current regions under warming, characterized by a heightened nonlinearity between warm and cold eddies and a more pronounced enhancement in the northern hemisphere. To understand the dynamics, we develop a theoretical framework that links mesoscale thermal coupling changes to large-scale factors, which indicates that the projected changes are collectively determined by historical background wind, SST, and the rate of SST warming. Among these factors, the large-scale SST and its warming rate are the primary drivers of hemispheric asymmetry in mesoscale coupling intensification. This study introduces a simplified approach for assessing the projected mesoscale thermal coupling changes in a warming world.

As the haemolymph supplier of the tachypleus amebocyte lysate industry in China, Chinese horseshoe crab (Tachypleus tridentatus) inevitably undergoes air‐exposure stress during transportation and haemolymph collection. In this study, the effects of air‐exposure stress and subsequent recovery process on the activities of phosphatases, antioxidant enzymes and haemocyanin concentration in Chinese horseshoe crab were investigated to understand its ability to resist air‐exposure stress. Results showed no significant effects on the total antioxidant capacity (T‐AOC), superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA) and alkaline phosphatase (AKP) between the control and experimental groups during the air‐exposure stress period (0–4 hr), except acid phosphatase (ACP). During the subsequent recovery period in water, significant differences in plasma T‐AOC levels and SOD, CAT, AKP and ACP activities were observed between both groups. No significant effects of CAT activity, SOD activity, MDA content and T‐AOC level were noted between the two groups after 12 hr of recovery. However, the AKP and ACP activities of the experimental group were significantly lower than those of the control group. Results revealed that air exposure may affect the activities of most non‐specific immune enzymes in the plasma of Chinese horseshoe crab, showing a ‘delayed” interference effect. Nevertheless, tested antioxidant enzymes have a strong recovery ability and can generally recover to the control group level after 12 hr. Haemocyanin concentration was not affected by air‐exposure stress during the whole experiment. Hereby, Chinese horseshoe crab can strongly resist air‐exposure stress.

Two typical classes of cyclones originating from the tropics (ToCs) and extratropics (EoCs) with distinct preconditions, and passing the Kuroshio Extension (KE) region in cold season in the North Pacific are detected and tracked based on high‐resolution atmospheric simulations with and without mesoscale sea surface temperature (SST) forcing. A comparison between ToC‐to‐mesoscale SST and EoC‐to‐mesoscale SST responses indicates that the different cyclone development and the environmental water vapor restoration in the KE are primarily attributed to different ToC and EOC's preconditions. The higher initial intensity of ToCs leads to stronger atmospheric response to mesoscale SSTs and extended cyclone growth whilst the colder and drier precondition of EoCs induces stronger water vapor restoration in the KE after EoCs passing‐by. It is further demonstrated that the water vapor restoration is dominated by moisture convergence. The study provides new insights into the physical understanding of mesoscale SSTs' influence on cyclogenesis in the midlatitudes.