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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.

Saudi Arabia blockades Yemeni ports as expats try to flee.

Saudi Arabia blockades Yemeni ports as expats try to flee.

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.

The Lombok Strait (LS), Ombai Strait (OS), and Timor Passage (TP) are three major outlets of the Indonesian Throughflow to the Indian Ocean. Here, sources and pathways of the LS, OS, and TP outflows are explored by a Lagrangian particle tracking analysis based on a ~3 km regional ocean model simulation. The Makassar Strait transport contributes to ~80%, ~75%, and ~45% of the LS, OS, and TP outflows, respectively. However, ~41% and ~19% of the TP and OS outflows stem from the Lifamatola Passage, which largely feeds the upper and intermediate layers of the outflows. The role of Karimata Strait is quite limited and restricted to the upper layer. It takes 1-2 years and 2-6 years for the Makassar Strait water to reach the OS and TP, respectively, whereas the Lifamatola Passage water passes through the OS (2-6 years) and TP (3-9 years) on a prolonged transit time. In the Banda Sea, the western boundary current is the main pathway toward the OS, while the waters to the TP tend to take a basin interior route.

The seasonality of mesoscale eddy intensity in the southeastern tropical Indian Ocean (SETIO) is investigated using the latest eddy dataset and marine hydrological reanalysis data. The results show that the eddy intensity in an area to the southwest coast of the Java Island has prominent seasonality-eddies in this area are relatively weak during the first half of the year but tend to enhance in August and peak in October. Further analysis reveals that the strong eddies in October are actually developed from the ones mainly formed in July to September, and the barotropic instability and baroclinic instability are the key dynamics for eddy development, but each plays a different role at different development stages. The barotropic instability resulting from the horizontal shear of surface current plays an important role in the early stage of eddy development. However, in the late development stage, the baroclinic instability induced by the sloping pycnocline becomes the major energy contributor of eddy development.

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

Most climate forecast agencies failed to make successful predictions of the strong 2020/2021 La Niña event before May 2020. The western equatorial Pacific warm water volume (WWV) before the 2020 spring failed to predict this La Niña event because of the near neutral state of the equatorial Pacific Ocean in the year before. A strong Indian Ocean Dipole (IOD) event took place in the fall of 2019, which is used as a precursor for the La Niña prediction in this study. We used observational data to construct the precursory relationship between negative sea level anomalies (SLA) in the southeastern tropical Indian Ocean (SETIO) in boreal fall and negative Niño 3.4 sea surface temperature anomalies index one year later. The application of the above relation to the prediction of the 2020/2021 La Niña was a great success. The dynamics behind are the Indo-Pacific "oceanic channel" connection via the Indian Ocean Kelvin wave propagation through the Indonesian seas, with the atmospheric bridge playing a secondary role. The high predictability of La Niña across the spring barrier if a positive IOD should occur in the previous year suggests that the negative SETIO SLA in fall is a much better and longer predictor for this type of La Niña prediction than the WWV. In comparison, positive SETIO SLA lead either El Niño or La Niña by one year, suggesting uncertainty of El Niño predictions.