ALBERT

Description: Recent studies arising from both statistical analysis and dynamical disease models indicate that there is a link between the incidence of cholera, a paradigmatic waterborne bacterial illness endemic to Bangladesh, and the El Niño–Southern Oscillation (ENSO). Cholera incidence typically increases following boreal winter El Niño events for the period 1973–2001. Observational and model analyses find that Bangladesh summer rainfall is enhanced following winter El Niño events, providing a plausible physical link between El Niño and cholera incidence. However, rainfall and cholera incidence do not increase following every winter El Niño event. Substantial variations in Bangladesh precipitation also occur in simulations in which identical sea surface temperature (SST) anomalies are prescribed in the central and eastern tropical Pacific. Bangladesh summer precipitation is thus not uniquely determined by forcing from the tropical Pacific, with significant implications for predictions of cholera risk. Nonparametric statistical analysis is used to identify regions of SST anomalies associated with variations in Bangladesh rainfall in an ensemble of pacemaker simulations. The authors find that differences in the response of Bangladesh summer precipitation to winter El Niño events are strongly associated with the persistence of warm SST anomalies in the central Pacific. Also there are significant differences in the SST patterns associated with positive and negative Bangladesh rainfall anomalies, indicating that the response is not fully linear. SST anomalies in the Indian Ocean also modulate the influence of the tropical Pacific, with colder Indian Ocean SST tending to enhance Bangladesh precipitation relative to warm Indian Ocean SST for identical conditions in the central and eastern tropical Pacific. This influence is not fully linear. Forecasts of Bangladesh rainfall and cholera risk may thus be improved by considering the Niño-3 and Niño-4 indices separately, rather than the Niño-3.4 index alone. Additional skill may also be gained by incorporating information on the southeast Indian Ocean and by updating the forecast with information on the evolution of the SST anomalies into spring.

Description: A complex empirical orthogonal function analysis was applied to sea surface temperature data in the southern high-latitude Pacific to identify and isolate primary processes related to the onset of El Niño (EN) events. Results were compared to those of a lead–lag composite analysis of a new tracer of EN events in the southern high-latitude Pacific, the Ross–Bellingshausen (RB) dipole. Both techniques successfully isolate the main low-frequency features in the interaction among the tropical and southern extratropical Pacific during the onset of recent eastward-propagating EN events. Particularly, positive RB peaks were followed by EN events around 9 months later, on average. In turn, RB maxima were anticipated by local warm anomalies in the western tropical Pacific a year in advance, which enhance local convection and upper-troposphere divergence and generate an anomalous wave train extending eastward and poleward in the southern extratropics. In addition, circulation changes lead to a warm SST region in the central tropical Pacific, which is then strengthened by suppressed equatorial easterlies. Convection thus starts to move to the central Pacific and so the Walker circulation weakens, activating the positive Bjerknes feedback that ultimately leads to the development of an EN event. These results highlight the enormous potential of the interaction between the tropics and this high-latitude region in the Southern Hemisphere to increase El Niño–Southern Oscillation understanding and to improve the long-lead prediction skill of EN phenomenon.

Description: The theoretical predictability limit of El Niño–Southern Oscillation has been shown to be on the order of years, but long-lead predictions of El Niño (EN) and La Niña (LN) are still lacking. State-of-the-art forecasting schemes traditionally do not predict beyond the spring barrier. Recent efforts have been dedicated to the improvement of dynamical models, while statistical schemes still need to take full advantage of the availability of ocean subsurface variables, provided regularly for the last few decades as a result of the Tropical Ocean–Global Atmosphere Program (TOGA). Here we use a number of predictor variables, including temperature at different depths and regions of the equatorial ocean, in a flexible statistical dynamic components model to make skillful long-lead retrospective predictions (hindcasts) of the Niño-3.4 index in the period 1970–2016. The model hindcasts the major EN episodes up to 2.5 years in advance, including the recent extreme 2015/16 EN. The analysis demonstrates that events are predicted more accurately after the completion of the observational array in the tropical Pacific in 1994, as a result of the improved data quality and coverage achieved by TOGA. Therefore, there is potential to issue long-lead predictions of this climatic phenomenon at a low computational cost.