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IntroductionThe Indonesian Throughflow (ITF) connects the Pacific Ocean and the Indian Ocean. It plays an important role in the global ocean circulation system. The interannual variability of ITF transport is largely modulated by climate modes, such as Central-Pacific (CP) and Eastern-Pacific (EP) El Niño and Indian Ocean Dipole (IOD). However, the relative importance of these climate modes importing on the ITF is not well clarified.MethodsDominant roles of the climate modes on ITF in specific periods are quantified by combining a machine learning algorithm of the random forest (RF) model with a variety of reanalysis datasets.ResultsThe results reveal that during the period from 1993 to 2019, the average ITF transport derived from high-resolution reanalysis datasets is -14.97 Sv with an intensification trend of -0.06 Sv year-1, which mainly occurred in the upper layer. Four periods, which are 1993–2000, 2002–2008, 2009–2012 and 2013–2019, are identified as Niño 3.4, Dipole Mode Index (DMI), no significant dominant index, and DMI dominated, respectively.DiscussionThe corresponding sea surface height differences between the Northwest Tropical Pacific Ocean (NWP) and Southeast Indian Ocean (SEI) in these three periods when exist dominant index are -0.50 cm, 0.99 cm and -3.22 cm, respectively, which are responsible for the dominance of the climate modes. The study provides a new insight to quantify the response of ITF transport to climate drivers.

Long-term change in the timing of coastal upwelling due to climate variations alters the heat budget and biogeochemical balance in the regional ocean and is an important issue in local fisheries. In this study, we investigated decadal changes in the onset of coastal upwelling along the southern coast of Java over the past two decades (2003–2020) based on the timing of chlorophyll-a (Chl-a) bloom. We estimated the bloom from satellite Chl-a concentration data. On average, the onset of coastal upwelling observed (the first Chl-a bloom of the year) was around mid-June. In the most recent decade (2011–2020), earlier-onset upwelling (before early June) was observed frequently, and the linear trend for the onset date during 2003–2020 was about 2 weeks earlier/decade. To explore the causes of the change in the timing of the upwelling, we focused on the season (April–June) during which these earlier upwelling onsets occurred, and investigated decadal changes in atmosphere and ocean conditions associated with climate change. While sea surface temperature (SST) trends reflected a basin-wide warming pattern in the Indian Ocean, warming was not significant in the southeastern Indian Ocean. During the onset period of coastal upwelling, significant SST warming trends were also observed west of Sumatra. In association with the SST warming pattern, enhanced convective activity and convergent zonal winds around Sumatra were observed. Atmospheric forcing revealed trends favoring Ekman downwelling in the equatorial eastern Indian Ocean and upwelling in the southeastern Indian Ocean, which was consistent with the trends in thermocline depth. This study provides the first results regarding the recent decadal shift in the onset timing of coastal upwelling. Ongoing monitoring is needed to better understand the long-term change of the upwelling system in the eastern tropical Indian Ocean.

Using reanalysis data from the ECMWF and Hadley Centre of the UK Meteorological Office, the present study analyzes the relationship between Indian Ocean Dipole (IOD) in the previous autumn (September–November) and South China Sea Summer (June–August) Monsoon (SCSSM) from 1951 to 2021. It is found that the relationship between the two indices was significantly enhanced from the late 1970s to the beginning of the 21st century, with a significant resonance cycle of 2–4a. The previous autumn IOD forced the Gill pattern in the upper troposphere by changing the Sea Surface Temperature Anomaly (SSTA) of the following summer in the Tropical Indian Ocean (TIO), resulting in the anticyclonic circulation anomaly in the Philippine Sea. It eventually alters the southwest monsoon near the South China Sea and the Philippine Sea. However, for a decade or more, as the Pacific Decadal Oscillation (PDO) phase turns from warm to cold and the relationship between El Niño–Southern Oscillation (ENSO) and IOD weakens, the impact of the previous autumn IOD on Indian Ocean SSTA in the summer of the following year weakens, resulting in a decrease in the correlation between the previous autumn IOD and SCSSM. The impact of the previous autumn’s Indian Ocean SSTA on the following year’s SCSSM is more considered in terms of the eastern Bay of Bengal and the Somali Sea basin. In addition, during the warm phase of PDO, the IOD of the previous autumn can cause abnormal summer precipitation in South China by strengthening or weakening the Western Pacific Subtropical High (WPSH) of the following summer, but there is no significant effect in the cold phase of PDO.

The Indian Ocean Dipole (IOD) is one of the dominant interannual variabilities in the Indian Ocean (IO), and an extreme IOD, in particular, has dramatic effects on the weather, agriculture, and ecosystem around it. Therefore, the formation of an extreme IOD has been a worldwide research focus. Among 13 positive IOD (PIOD) events, two type-east and two type-comparable PIODs developed into extreme events during the 1960–2020 period. This investigation focuses on the cause of the formation of the type-east extreme PIOD, as previous studies have discussed the origin of the type-comparable extreme PIOD. Composite analysis showed that, as an entity, the strong East Asian and Australian monsoon (EAAM) may result in an evident easterly wind anomaly around the Indonesian region of the Marine Continent during May to August of the years when type-east PIODs occurred. The easterly wind anomaly associated with the EAAM was stronger in the extreme IOD group, whereas it was relatively weak in the regular group. The difference in the easterly wind anomaly between the extreme and regular groups could result in a vertical motion anomaly by enhancing the anomalous westward current. The stronger vertical motion created an upwelling in the deep-layer cold water, resulting in a more distinct difference in the vertical temperature gradient. All these conditions promoted the transformation of the type-east PIODs that occurred in 1961 and 1994 into extreme events and are indicative of the importance of vertical advection terms in the formation of type-east extreme PIODs. This study reveals the cause of the formation of type-east extreme PIODs, which will be helpful in understanding IOD diversity.

Existed studies have suggested a precursory relation between Indian Ocean Dipole (IOD) and El Niño and the Southern Oscillations (ENSO) with 1-year time lag. The underlying mechanisms were attributed to atmospheric bridge and/or oceanic channel processes. In this study, the oceanic channel dynamics in 23 climate models of the Coupled Model Intercomparison Project phase 5 (CMIP5) are assessed by correlation analyses in comparison with observations. The results show that the lag correlations between the IOD and ENSO anomalies associated with oceanic channel are significant, suggesting important role of oceanic channel dynamics in the cross-basin teleconnection in the analyzed CMIP5 models, consistent with observational analyses. In comparison, the correlations associated with atmospheric bridge are highly dispersive among the models and generally inconsistent with the observational analyses, suggesting model deficiencies. In a single climate model, the lag correlations associated with oceanic channel dynamics are consistent among different ensemble experiments, whereas those associated with atmospheric bridge processes are dispersive.

The recharge oscillator mechanism suggests that a strong El Niño event can trigger a following La Niña event that sometimes lasts for two or even three years through warm water volume preconditioning within the tropical Pacific. However, a prominent and persistent “double-dip” La Niña event appeared in the boreal winters of 2020/2021 and 2021/2022 without any significant El Niño preconditioning. Here we explore the possibility that tropical basin interactions may have initiated and helped to prolong La Niña conditions over the 2-year period 2020–2022. This period was preceded by a strong positive Indian Ocean Dipole (IOD) during the boreal fall of 2019 that gave way to basin-scale warming in the Indian Ocean in early 2020 and a notable tropical Atlantic warming in the boreal winter of 2019/2020. Later, a strong Atlantic Niño developed in the boreal summer of 2021. Using composite analyses to characterize earlier double-dip La Niñas, we argue the unusual sequence of events in 2019–2021 in the Indian and Atlantic Oceans may have energized and sustained the 2020–2022 La Niña event without any significant warm water volume preconditioning within the tropical Pacific.

In this study, we train a convolutional neural network (CNN) model using a selection of Coupled Model Intercomparison Project (CMIP) phase 5 and 6 models to investigate the predictability of the sea surface temperature (SST) variability off the Sumatra-Java coast in the tropical southeast Indian Ocean, the eastern pole of the Indian Ocean Dipole (IOD). Results show that the CNN model can beat the persistence of the interannual SST variability, such that the eastern IOD (EIOD) SST variability can be forecast up to 6 months in advance. Visualizing the CNN model using a gradient weighted class activation map shows that the strong positive IOD events (cold EIOD SST anomalies) can stem from different processes: internal Indian Ocean dynamics were associated with the 1994 positive IOD, teleconnection from the equatorial Pacific was important in 1997, and cooling off the Australian coast in the southeast Indian Ocean contributed to the 2019 positive IOD. The CNN model overcomes the winter prediction barrier of the IOD, to a large extent due to the frequent transition from a warm state of the Indian Ocean to a negative IOD condition (warm EIOD SST anomalies) over the boreal winter to the following spring period. The forecasting skills of the CNN model are on par with predictions from a coupled seasonal forecasting model (ACCESS-S2), even outperforming this dynamic model in seasons leading to the IOD peaks. The ability of the CNN model to identify key dynamic drivers of the EIOD SST variability suggests that the CMIP models can capture the internal Indian Ocean variability and its teleconnection with the Pacific climate variability.

Marine ecosystems are experiencing rapid shifts under climate change scenarios and baleen whales are vulnerable to environmental change, although not all impacts are yet clear. We identify how the migration behaviour of the Chagos whale, likely a pygmy blue whale (Balaenoptera musculus brevicauda), has changed in association with shifts in environmental factors. We used up to 18 years of continuous underwater acoustic recordings to analyse the relationships between whale acoustic presence and sea surface temperature (SST), chlorophyll-a concentration, El-Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). We compared these relationships between two independent sites Diego Garcia southeast (DGS) and Diego Garcia northwest (DGN) where Chagos whales are detected and are suspected to move interannually across the Chagos-Laccadive ridge. We showed that the number of whale songs detected increased on average by 7.7% and 12.6% annually at DGS and DGN respectively. At the DGS site, Chagos whales shifted their arrival time earlier by 4.2 ± 2.0 days/year ± SE and were detected for a longer period by 7.3 ± 1.2 days/year ± SE across 18 years. A larger number of songs were detected during periods of higher chlorophyll-a concentration, and with positive IOD phases. At the DGN site, we did not see an earlier shift in arrival and songs were not detected for a longer period across the 13 years. Whale presence at DGN had a weaker but opposite relationship with chlorophyll-a and IOD. The oceanic conditions in the Indian Ocean are predicted to change under future climate scenarios and this will likely influence Chagos whale migratory behaviour. Understanding how environmental factors influence whale movement patterns can help predict how whales may respond to future environmental change. We demonstrate the value of long-term acoustic monitoring of marine fauna to determine how they may be affected by changing environmental conditions.