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Both the tropical Indian and tropical Pacific Oceans are active atmosphere-ocean interactive regions with robust interannual variability, which also constitutes a linkage between the two basins in the mode of variability. Using a global atmosphere-ocean coupled model, we conducted two experiments (CTRL and PC) to explore the contributions of Indian Ocean interannual sea surface temperature (SST) modes to the occurrence of El Niño events. The results show that interannual variability of the SST in the Indian Ocean induces a rapid growth of El Niño events during the boreal autumn in an El Niño developing year. However, it weakens El Niño events or even promotes cold phase conversions in an El Niño decaying year. Therefore, the entire period of the El Niño is shortened by the interannual variations of the Indian Ocean SST. Specifically, during the El Niño developing years, the positive Indian Ocean Dipole (IOD) events force an anomalous Walker circulation, which then enhances the existing westerly wind anomalies over the west Pacific. This will cause a warmer El Niño event, with some modulations by ocean advection and oceanic Rossby and Kelvin waves. However, with the onset of the South Asian monsoon, the Indian Ocean Basin (IOB) warming SST anomalies excite low level easterly wind anomalies over the west tropical Pacific during the El Niño decaying years. As a result, the El Niño event is prompted to change from a warm phase to a cold phase. At the same time, an associated atmospheric anticyclone anomaly appears and leads to a decreasing precipitation anomaly over the northwest Pacific. In summary, with remote forcing in the atmospheric circulation, the IOD mode usually affects the El Niño during the developing years, whereas the IOB mode affects the El Niño during the decaying years. [ABSTRACT FROM AUTHOR]

The Indian Ocean Dipole (IOD) can affect the El Niño-Southern Oscillation (ENSO) state of the following year, in addition to the well-known preconditioning by equatorial Pacific Warm Water Volume (WWV), as suggested by a study based on observations over the recent satellite era (1981-2009). The present paper explores the interdecadal robustness of this result over the 1872-2008 period. To this end, we develop a robust IOD index, which well exploits sparse historical observations in the tropical Indian Ocean, and an efficient proxy of WWV interannual variations based on the temporal integral of Pacific zonal wind stress (of a historical atmospheric reanalysis). A linear regression hindcast model based on these two indices in boreal fall explains 50 % of ENSO peak variance 14 months later, with significant contributions from both the IOD and WWV over most of the historical period and a similar skill for El Niño and La Niña events. Our results further reveal that, when combined with WWV, the IOD index provides a larger ENSO hindcast skill improvement than the Indian Ocean basin-wide mode, the Indian Monsoon or ENSO itself. Based on these results, we propose a revised scheme of Indo-Pacific interactions. In this scheme, the IOD-ENSO interactions favour a biennial timescale and interact with the slower recharge-discharge cycle intrinsic to the Pacific Ocean. [ABSTRACT FROM AUTHOR]

This work attempts to reconcile in a common and comprehensive framework the various conflicting results found in the literature regarding Indian Summer Monsoon (ISM) rainfall-Sea Surface Temperature (SST) relationships, especially the links with El-Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). To do so, we first examine the linear relationships between ISM rainfall and global SST anomalies during 1950-1976 and 1979-2006 periods. Our results highlight the existence of significant modulations in SST teleconnections and precursory patterns between the first (June-July, JJ) and second part (August-September, AS) of the monsoon. This JJ-AS rainfall dichotomy is more pronounced after the 1976-1977 climate regime shift and tends to blur the global ISM-ENSO signal during the recent period, leading to an apparent weakening of this relationship at the seasonal time scale. Although ISM rainfall in JJ and AS is still strongly linked to ENSO over both periods, the lead-lag relationships between ENSO and AS Indian rainfall have changed during recent decades. Indeed, ENSO variability in the preceding boreal winter has now a significant impact on rainfall variability during the second half of ISM. To evaluate in more details the impact of this JJ-AS dichotomy on the ISM-ENSO-IOD relationships, ISM correlations are also examined separately during El Niño and La Niña years. Results indicate that the early onset of El Niño during boreal spring causes deficient monsoon rainfall in JJ. In response to weaker monsoon winds, warm SST anomalies appear in the west equatorial IO, generating favorable conditions for the development of a positive IOD in AS. Local air-sea processes triggered by the SST anomalies in the eastern node of IOD seem, in turn, to have a more active role on AS rainfall variability, as they may counteract the negative effect of El Niño on ISM rainfall via a modulation of the local Hadley circulation in the eastern IO. The JJ-AS rainfall dichotomy and its recent amplification may then result from an enhancement of these IO feedbacks during recent El Niño years. This explains why, although El Niño events are stronger, a weakening of the ISM-ENSO relationship is observed at the seasonal scale after 1979. Results during La Niña years are consistent with this hypothesis although local processes in the southeast IO now play a more prominent role and act to further modulate ISM rainfall in AS. Finally, our results highlight the existence of a biennal rhythm of the IOD-ENSO-ISM system during the recent period, according to which co-occurring El Niño and positive IOD events tend to be followed by a warming of the IO, a wet ISM during summer and, finally, a La Niña event during the following boreal winter. [ABSTRACT FROM AUTHOR]

This work reports a new methodology for deriving monthly averages of temperature ( T) and salinity ( S) fields for the Indian Ocean based on the use of an artificial neural network (ANN). Investigation and analysis were performed for this region with two distinct datasets: (1) monthly climatological data for T and S fields (in 1° × 1° grid boxes) at standard depth levels of the World Ocean Atlas 1994 (WOA94), and; (2) heterogeneous randomly distributed in situ ARGO, ocean station data (OSD) and profiling (PFL) floats. A further numerical experiment was conducted with these two distinct datasets to train the neural network model. Nonlinear regression mapping utilizing a multilayer perceptron (MLP) is employed to tackle nonlinearity in the data. This study reveals that a feed-forward type of network with a resilient backpropagation algorithm is best suited for deriving T and S fields; this is demonstrated by independently using WOA94 and in situ data, which thus tests the robustness of the ANN model. The suppleness of the T and S fields derived from the ANN model provides the freedom to generate a new grid at any desired level with a high degree of accuracy. Comprehensive training, testing and validation exercises were performed to demonstrate the robustness of the model and the consistency of the derived fields. The study points out that the parameters derived from the ANN model using scattered, inhomogeneous in situ data show very good agreement with state-of-the-art WOA climatological data. Using this approach, improvements in ocean climatology can be expected to occur in a synergistic manner with in situ observations. Our investigation of the Indian Ocean reveals that this approach can be extended to model global oceans. [ABSTRACT FROM AUTHOR]