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A phenomenon referred to here as Java–Sumatra Niño/Niña (JSN or JS Niño/Niña) is characterized by the appearance of warm/cold sea surface temperature anomalies (SSTAs) in the coastal upwelling region off Java–Sumatra in the southeastern equatorial Indian Ocean. JSN develops in July–September and sometimes as a precursor to the Indian Ocean dipole, but often without corresponding SSTAs in the western equatorial Indian Ocean. Although its spatiotemporal evolution varies considerably between individual events, JSN is essentially an intrinsic mode of variability driven by local atmosphere–ocean positive feedback, and thus does not rely on remote forcing from the Pacific for its emergence. JSN is an important driver of climate variability over the tropical Indian Ocean and the surrounding continents. Notably, JS Niña events developing in July–September project onto the South and Southeast Asian summer monsoons, increasing the probability of heavy rainfall and flooding across the most heavily populated regions of the world. [ABSTRACT FROM AUTHOR]

When record-breaking climate and weather extremes occur, decision-makers and planners want to know whether they are random natural events with historical levels of reoccurrence or are reflective of an altered frequency or intensity as a result of climate change. This paper describes a method to attribute extreme weather and climate events to observed increases in atmospheric CO2 using an initialized subseasonal to seasonal coupled global climate prediction system. Application of this method provides quantitative estimates of the contribution arising from increases in the level of atmospheric CO2 to individual weather and climate extreme events. Using a coupled subseasonal to seasonal forecast system differs from other methods because it has the merit of being initialized with the observed conditions and subsequently reproducing the observed events and their mechanisms. This can aid understanding when the reforecasts with and without enhanced CO2 are compared and communicated to a general audience. Atmosphere–ocean interactions are accounted for. To illustrate the method, we attribute the record Australian heat event of October 2015. We find that about half of the October 2015 Australia-wide temperature anomaly is due to the increase in atmospheric CO2 since 1960. This method has the potential to provide attribution statements for forecast events within an outlook period (i.e., before they occur). This will allow for informed messaging to be available as required when an extreme event occurs, which is of particular use to weather and climate services. [ABSTRACT FROM AUTHOR]

El Niño and La Niña, the warm and cold phases of El Niño–Southern Oscillation (ENSO), cause significant year-to-year disruptions in global climate, including in the atmosphere, oceans, and cryosphere. Australia is one of the countries where its climate, including droughts and flooding rains, is highly sensitive to the temporal and spatial variations of ENSO. The dramatic impacts of ENSO on the environment, society, health, and economies worldwide make the application of reliable ENSO predictions a powerful way to manage risks and resources. An improved understanding of ENSO dynamics in a changing climate has the potential to lead to more accurate and reliable ENSO predictions by facilitating improved forecast systems. This motivated an Australian national workshop on ENSO dynamics and prediction that was held in Sydney, Australia, in November 2017. This workshop followed the aftermath of the 2015/16 extreme El Niño, which exhibited different characteristics to previous extreme El Niños and whose early evolution since 2014 was challenging to predict. This essay summarizes the collective workshop perspective on recent progress and challenges in understanding ENSO dynamics and predictability and improving forecast systems. While this essay discusses key issues from an Australian perspective, many of the same issues are important for other ENSO-affected countries and for the international ENSO research community. [ABSTRACT FROM AUTHOR]

The development of a dynamical model seasonal prediction service for island nations in the tropical South Pacific is described. The forecast model is the Australian Bureau of Meteorology's Predictive Ocean-Atmosphere Model for Australia (POAMA), a dynamical seasonal forecast system. Using a hindcast set for the period 1982-2006, POAMA is shown to provide skillful forecasts of El Niño and La Niña many months in advance and, because the model faithfully simulates the spatial and temporal variability of rainfall associated with displacements of the southern Pacific convergence zone (SPCZ) and ITCZ during La Niña and El Niño, it also provides good predictions of rainfall throughout the tropical Pacific region. The availability of seasonal forecasts from POAMA should be beneficial to Pacific island countries for the production of regional climate outlooks across the region. [ABSTRACT FROM AUTHOR]

Forecast skill for seasonal mean rainfall across northern Australia is lower during the summer monsoon than in the premonsoon transition season based on 25 years of hindcasts using the Predictive Ocean Atmosphere Model for Australia (POAMA) coupled model seasonal forecast system. The authors argue that this partly reflects an intrinsic property of the monsoonal system, whereby seasonally varying air-sea interaction in the seas around northern Australia promotes predictability in the premonsoon season and demotes predictability after monsoon onset. Trade easterlies during the premonsoon season support a positive feedback between surface winds, SST, and rainfall, which results in stronger and more persistent SST anomalies to the north of Australia that compliment the remote forcing of Australian rainfall from El Niño in the Pacific. After onset of the Australian summer monsoon, this local feedback is not supported in the monsoonal westerly regime, resulting in weaker SST anomalies to the north of Australia and with lower persistence than in the premonsoon season. Importantly, the seasonality of this air-sea interaction is captured in the POAMA forecast model. Furthermore, analysis of perfect model forecasts and forecasts generated by prescribing observed SST results in largely the same conclusion (i.e., significantly lower actual and potential forecast skill during the monsoon), thereby supporting the notion that air-sea interaction contributes to intrinsically lower predictability of rainfall during the monsoon. [ABSTRACT FROM AUTHOR]