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The generation and propagation of internal tides in the Andaman Sea are investigated using a three-dimensional high-resolution numerical model. Three categories of experiments, including driving the model with four main semidiurnal tides (M2, S2, N2, and K2), four main diurnal tides (K1, O1, P1, and Q1), and eight main tides (M2, S2, N2, K2, K1, O1, P1, and Q1), are designed to examine the effects of barotropic tides. The results show that the semidiurnal internal tides are dominant in the Andaman Sea, and the inclusion of diurnal barotropic tides negligibly modulates this result. That is partly due to the strength of the diurnal barotropic tides is generally one order smaller than that of the semidiurnal barotropic tides in this region. The sensitivity experiments put this on a firmer footing. In terms of the internal tidal energy, the experiments driven by the diurnal barotropic tides are three orders and one order smaller than those driven by the semidiurnal barotropic tides, respectively, during the spring and neap tides. In addition, the experiments result in total barotropic-to-baroclinic energy conversion rates over the Andaman Sea 29.15 GW (driven by the eight tides), 29.24 GW (driven by the four semidiurnal tides), and 0.05 GW (driven by the fourdiurnal tides) in the spring tidal period and 3.08 GW, 2.56 GW, and 0.31 GW in the neap tidal period, respectively. Four potential generation regions of internal tides are found, three of which are in the Andaman and Nicobar Islands and one in the northeastern Andaman Sea.

Aluminum and manganese are both key parameters in the GEOTRACES program. Data on dissolved aluminum (dAl) and dissolved manganese (dMn) relative to their geochemical behavior remain limited in the northeastern Indian Ocean (IO; including the Bay of Bengal (BoB) and equatorial Indian Ocean (Eq. IO)). Seawater samples collected in the BoB and Eq. IO during the spring inter-monsoon period (7 March to 9 April) of 2017 were analyzed to investigate the behavior and main processes controlling the distributions of dAl and dMn in the northeastern IO. The average concentrations of dAl and dMn in the mixed layer of the BoB were 16.6 and 6.7 nM, respectively. A modified 1-D box-model equation was utilized to estimate the contributions of different sources to dAl and dMn in the mixed layer. Al released from the desorption of and/or dissolution of the lithogenic sediments discharged by the Ganga-Brahmaputra (G-B) river system predominantly controlled the dAl distributions in the mixed layer of the BoB, while the desorption from the lithogenic sediments only contributed approximately 13%-21% dMn. Additional dMn input from the advection of Andaman Sea water and photo-reduction-dissolution of particulate Mn(IV) contributed more than 60% dMn in the mixed layer of the BoB. dAl and dMn in the surface mixed layer of the Eq. IO were mainly affected by the mixing of dAl- and dMn-enriched BoB surface water and low-dAl, low-dMn southern Arabian Sea surface water. Considering water mass properties and dAl concentrations, the distributions of dAl in the intermediate water (750-1,500 m) of northeastern IO were controlled by the mixing of Red Sea Intermediate Water, Indonesian Intermediate Water, and intermediate water of the BoB. Different from dAl, the apparent oxygen utilization relationship with dMn concentrations indicated that the regeneration of lithogenic particles under hypoxic conditions played a more important role than the remineralization of settling organic particles in co

The southeastern Indian Ocean (SEIO) exhibits prominent decadal variability in sea surface salinity (SSS), showing salinity decreases during 1995-2000 and 2005-2011 and increases during 2000-2005 and after 2011. These salinity changes are linked to the Indo-Pacific climate and have impacts on the regional marine environment. Yet, the underlying mechanism has not been firmly established. In this study, decadal SSS variability of the SEIO is successfully simulated by a high-resolution regional ocean model, and the mechanism is explored through a series of sensitivity experiments. The results suggest that freshwater transport of the Indonesian throughflow (ITF) and local precipitation are two major drivers for the SSS decadal variability. They mutually cause most of the variability, with a generally larger contribution of precipitation. Other processes, such as evaporation and advection driven by local winds, play a minor role. Further analysis shows that the decadal precipitation in the SEIO is mainly associated with the decadal variability of Ningaloo Niño. Ocean dynamic processes significantly modify the relationship between SSS and precipitation, greatly shortening their lag time. The changes in both volume transport and salinity of the ITF water can cause large salinity changes in the SEIO region. Although local wind forcing gives rise to considerable changes in evaporation rate and ocean current advection, its overall contribution to decadal SSS variability is small compared to local precipitation and the ITF.

To better develop and protect the pelagic fishery in the northwest Indian Ocean, China's fishing enterprises have been producing pelagic fisheries in the said area for a long time. Based on the fishing log data of light falling gear in the northwest Indian Ocean from 2016 to 2020, this study analyzed the impact of different time scales on the catch rate and fishing ground center of gravity of light falling gear fishing grounds. We also explored the relationship between different time scales and catch per unit effort (CPUE) by using the fishing ground center of gravity, the Random Forest model (RF), and the generalized additive model (GAM). The results were shown as follows: (1) From 2016 to 2020, 76,576 t were captured, and 16,496 nets were operated; (2) The gravity center of fishing ground in the Northwest Indian Ocean moved to the northeast as a whole, and the monthly fishing ground gravity center changed first to the Southern and then to the northern; (3) RF model (R² = 0.709, RMSE = 0.2034, and prediction accuracy is 55.8%), which is better than the GAM model (R² = 0.632, RMSE = 0.2242, and prediction accuracy is 37.3%). In the RF model, the importance of time variables on CPUE was in the order of week, year, operation time, and lunar phase; in the GAM model, it was week, year, lunar phase, and operation time. On the whole, the importance of the long time scale (year, week) is greater than that of the short time scale (lunar phase and operation time). (4) The RF model and GAM model show that the most critical environmental variables were SST, DO, SSS, and Chla, and the least important were SSH, Δ50, and CV50. SST, Chla, and DO significantly impact pelagic fishing and CPUE and are critical reference indexes for predicting the Northwest Indian Ocean light falling gear fishing ground. (5) The 95% confidence interval showed that the suitable interval of time, space, and environmental variables in the RF model was much smaller than in the GAM model.