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Monsoonal rainfall in northern Australia (AUMR) varies substantially on interannual, decadal, and longer time scales, profoundly impacting natural systems and agricultural communities. Some of this variability arises in response to sea surface temperature (SST) variability in the Indo-Pacific linked to both El Niño–Southern Oscillation (ENSO) and the interdecadal Pacific oscillation (IPO). Here we use observations to investigate unresolved issues regarding the influence of the IPO and ENSO on AUMR. Specifically, we show that during negative IPO phases, central Pacific (CP) El Niño events are associated with below-average rainfall over northeast Australia, an anomalous anticyclonic pattern to the northwest of Australia, and eastward moisture advection toward the date line. In contrast, CP La Niña events (distinct from eastern Pacific La Niña events) during negative IPO phases drive significantly wet conditions over much of northern Australia, a strengthened Walker circulation, and large-scale moisture flux convergence. During positive IPO phases, the impact of CP El Niño and CP La Niña events on AUMR is weaker. The influence of central Pacific SSTs on AUMR has been stronger during the recent (post-1999) negative IPO phase. The extent to which this strengthening is associated with climate change or merely natural internal variability is not known.

This research analyses the observed relationship between eastern and central Pacific El Niño Southern Oscillation (ENSO) events and Australian monsoon rainfall (AUMR) on a decadal timescale during the December to March monsoon months. To assess the decadal influence of the different flavours of ENSO on the AUMR, we focus on the phases of the Interdecadal Pacific Oscillation (IPO) over the period 1920 to 2020. The AUMR is characterized by substantial decadal variability, which appears to be linked to the positive and negative phases of the IPO. During the past two historical negative IPO phases, significant correlations have been observed between central Pacific sea surface temperature (SST) anomalies and AUMR over both the northeast and northwest of Australia. This central Pacific SST-AUMR relationship has strengthened from the first negative IPO phase (mid-1940s to the mid-1970s) to the second (late 1990s to mid-2010s), while the eastern Pacific SST-AUMR influence has weakened. Composite rainfall anomalies over Australia reveal a different response of AUMR to central Pacific El Niño/La Niña and eastern Pacific La Niña events during positive IPO and negative IPO phases. This research clearly shows that ENSO's influence on AUMR is modulated by Pacific decadal variability, however this teleconnection, in itself, can change between similar decadal Pacific states. Going forward, as decadal prediction systems improve and become more mainstream, the IPO phase could be used as a potential source for decadal predictability of the tendency of AUMR.

The development of the Australian Community Climate and Earth-System Simulator-Seasonal prediction system version 1 (ACCESS-S1) signifies a major step towards addressing predictive limitations in multi-week to seasonal forecasting throughout Australia. It is anticipated that moving to ACCESS-S1 will provide improved skill in rainfall prediction during the dry to wet season transition period across tropical northern Australia. This is an important time for northern Australian livestock producers in terms of the decisions they make around pasture and livestock management. This study quantifies the hindcast skill of ACCESS-S1 for the northern rainfall onset (NRO), defined as the date when 50 mm of precipitation has accumulated at a given location from the 1st of September, heralding the shift towards greener pastures. We evaluate the raw model hindcasts, and compare them to hindcasts corrected for mean biases and those calibrated against observations. It is found that the raw ACCESS-S1 hindcasts broadly replicate the observed median NRO over the period 1990–2012, despite a ten- dency for earlier than observed onsets. In terms of forecasting the interannual variability of the NRO, the ca- librated hindcasts show the greatest skill, with the largest improvements over a climatological forecast in their probabilistic forecasts of an earlier or later than usual onset, with a large portion of northern Australian showing more than 10% improvement. With real-time NRO forecasts now generated by ACCESS-S1, it is expected that the calibrated predictions will help northern Australian graziers make better informed decisions around livestock management prior to the wet season.

The development of the Australian Community Climate Earth-System Simulator-Seasonal version 1 (ACCESS-S1) prediction system signifies a major step in addressing predictive limitations in multi-week to seasonal forecasting. It is anticipated that ACCESS-S1 will provide greater skill in its prediction of the wet season onset and intensity, which are crucial to the viability of cattle grazing across northern Australia. We evaluate the hindcast skill of the ACCESS-S1 for the northern rainfall onset, defined as the date when 50 mm of precipitation has accumulated at a given location from the 1st of September, heralding the start of the seasonal dry-to-wet transition over northern Australia. We show that the raw ACCESS-S1 hindcasts, regridded to a 5 km observed grid, capture the broad-scale features of the median onset, including an early October onset over the western Top End and southeast Queensland. However, the hindcasts fail to capture the later December onsets over central Australia. The greatest improvement in onset skill comes from first calibrating the hindcasts using observations, which outperform the raw model and bias corrected hindcasts over central Australia and the far west in the Pilbara-Gascoyne basin. Based on its simulation of realistic northern rainfall onset dates and variability alone, ACCESS-S1’s prediction performance can be considered an improvement over the older predictive system. As the real-time onset forecasts have were issued using ACCESS-S1 in July 2019, it is expected that the calibrated predictions will help improve the resilience of cattle producers and graziers to drought across northern Australia.

Operational El Nino-Southern Oscillation (ENSO)-based statistical forecasting of seasonal rainfall has been undertaken in Australia now for the best part of two decades. This article compares the performance in recent years (1997 to 2009) of the two major governmental programs currently forecasting seasonal rainfall across Australia, through the verification of independent forecasts. These programs are run by the Australian Bureau of Meteorology and the Queensland Government's Department of Environment and Resource Management (and its predecessors). In this study, the Queensland Government's Southern Oscillation Index (SOI) phase-based forecasts are not verified directly. Instead, the SOI-phase system methodology is reconstructed using the Bureau's monthly rainfall analyses and those reconstructed forecasts verified. Verification techniques employed include linear error in probability space (LEPS2) skill scores, per cent consistent rates, and reliability statistics. Over recent years, the Bureau forecasts and the SOI-phase forecasts have performed comparably when measured by the per cent consistent statistic, although with successes and failures in different parts of the country. Both sets of forecasts show widespread forecast skill in excess of climatology, the Bureau forecasts performing better in the west of the country and the SOI-phase forecasts performing better in the centre and east. The generally more emphatic SOI-phase forecasts translated into higher LEPS2 skill scores, but the Bureau forecasts showed greater reliability.

The monthly anomaly of the latitude of the subtropical ridge over eastern Australia (L) is a major regulator of synoptic-scale influences on Australia’s climate. Three datasets have been used to calculate L: observed Australia coastal mean sea level pressure (MSLP), Hadley and NCEP Reanalysis datasets. The choice of datasets used in this study appears to strongly influence results. Changes in the Southern Hemisphere Hadley cell and meridional circulation changes are associated with changes in L and it is shown that (1) L has statistically and physically significant relationships with seasonal rainfall in Australia; (2) the Antarctic Oscillation Index (AOI) has statistically significant relationships with Australian seasonal rainfall; and (3) L and Southern Annular Mode (SAM) are significantly related. Longer-term latitudinal shifts in L are also discussed.

The El Niño–Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Niño events are often associated with severe drought conditions. However, El Niño events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Niño are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (“footprints”) found in the El Niño impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean–atmosphere dynamics identifies three types of El Niño differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Niño events on agricultural systems.

Rainfall variability occurs over a wide range of temporal scales. Knowledge and understanding of such variability can lead to improved risk management practices in agricultural and other industries. Analyses of temporal patterns in 100 yr of observed monthly global sea surface temperature and sea level pressure data show that the single most important cause of explainable, terrestrial rainfall variability resides within the El Niño–Southern Oscillation (ENSO) frequency domain (2.5–8.0 yr), followed by a slightly weaker but highly significant decadal signal (9–13 yr), with some evidence of lesser but significant rainfall variability at interdecadal time scales (15–18 yr). Most of the rainfall variability significantly linked to frequencies lower than ENSO occurs in the Australasian region, with smaller effects in North and South America, central and southern Africa, and western Europe. While low-frequency (LF) signals at a decadal frequency are dominant, the variability evident was ENSO-like in all the frequency domains considered. The extent to which such LF variability is (i) predictable and (ii) either part of the overall ENSO variability or caused by independent processes remains an as yet unanswered question. Further progress can only be made through mechanistic studies using a variety of models.

Climate is a key driver of sugarcane productivity. Advance knowledge of the likely climate and its impact on production could add value to production, harvest and marketing efforts. A climate forecast system that incorporates five patterns or phases of the southern oscillation index is used to assess whether an early indication of sugarcane yield anomalies in Australia can be produced. Results indicate that, for certain sugarcane growing regions, the climate forecast system offers better estimates of the direction of the anomaly when compared with no climate forecast system. Improved results of the direction of the yield anomaly can be obtained some 7 months prior to the commencement of harvest. This information can then be used by marketers to plan better the customer allocations, shipping schedules and storage requirements for the next season. Advance knowledge of the crop size can also assist industry decision makers in scheduling when the harvest season should commence. Further research is required to determine if other climatic indices, such as sea surface temperatures, can improve yield estimation. Consideration also needs to be given to determining whether the magnitude and the direction of the yield anomaly can be more accurately forecasted with varying lead times.

The El Niño/Southern Oscillation phenomenon strongly influences rainfall distribution around the world. Using phases of the Southern Oscillation Index (SOI) allows a probabilistic forecast of future rainfall that can be useful to managers of agricultural systems. Using wheat as an example, we show in this study how the SOI phase system, when combined with a cropping systems simulation capability, can be used operationally to Improve tactical crop management and hence increase farm profits and/or decrease production risks. We show the validity of the approach for two contrasting locations, namely Dalby in Northern Australian and Piracicaba in Brazil At Dalby, highest median yields were achieved following a rapidly rising SOI phase in April/May and lowest median yields following a consistently negative phase. Conversely, highest median yields at Piracicaba followed a near zero April/May phase and lowest median yields when the phase was consistently positive. We show how tactical management options can range from crop or cultivar choice to nitrogen management and marketing of the future wheat crop.

A forecast method capable of estimating date of last frost and number of frosts per season in northeastern Australia some months in advance is described. Forecast “skill” is achieved using either Southern Oscillation index (SOI) patterns (phases) during the previous austral autumn or a linear discriminant approach and the SOI. When applying these systems, it is possible to provide significantly different probability distributions of day of last frost and number of frosts, depending on the SOI patterns observed during the previous season. An analysis of the time series of frost frequency and date of last frost suggests an apparent warming trend in the data, resulting in a trend toward earlier dates of last frost and fewer numbers of frosts at many of the locations analyzed. The beneficial implications of the proposed frost forecasting system for enterprises such as winter agriculture in the region are believed to be significant.

Phases of the Southern Oscillation Index (SOI) have been identified using cluster analysis. The monthly phases are associated with amounts of rainfall expressed in terms of probability distributions for various locations in eastern Australia. The phase representing rapid rise in SOI indicates above median rainfall during Southern Hemisphere autumn and spring at the locations analysed. The phase representing consistently positive SOI generally corresponds with above median rainfall amounts while the phase representing consistently negative SOI corresponds with below median rainfall amounts. As SOI phases relate to actual rainfall amounts, expressed in terms of probability distributions, the phases have, for instance, direct application to agricultural decision support programmes.

The accuracy of synoptic-based weather forecasting deteriorates rapidly after five days and is not routinely available beyond 10 days. Conversely, climate forecasts are generally not feasible for periods of less than 3 months, resulting in a weather-climate gap. The tropical atmospheric phenomenon known as the Madden-Julian Oscillation (MJO) has a return interval of 30 to 80 days that might partly fill this gap. Our near-global analysis demonstrates that the MJO is a significant phenomenon that can influence daily rainfall patterns, even at higher latitudes, via teleconnections with broadscale mean sea level pressure (MSLP) patterns. These weather states provide a mechanistic basis for an MJO-based forecasting capacity that bridges the weather-climate divide. Knowledge of these tropical and extra-tropical MJO-associated weather states can significantly improve the tactical management of climate-sensitive systems such as agriculture.

Atmospheric and oceanic teleconnections link the Antarctic Circumpolar Wave (ACW) in the Southern Ocean [White and Peterson, 1996] and the global El Nino Southern Oscillation (ENSO) wave (GEW) in the tropical Indo-Pacific Ocean [White and Cayan, 2000], both signals characterized by eastward phase propagation and 3- to 5-year-period variability. We extend the tropical standing mode of ENSO into the extratropics by regressing the Nino-3 sea surface temperature (SST) index against sea level pressure (SLP) anomalies over the globe, finding the Pacific-South America (PSA) pattern in SLP anomaly [Cai and Baines, 2001] straddling Drake Passage in the Southern Ocean. The amplitude of this PSA pattern is similar to1/3 that of the ACW in this domain and thus cannot be considered its principal driver. On the other hand, suppressing the tropical standing mode of ENSO in interannual ST (surface temperature) and SLP anomalies over the globe allows the GEW to be observed much more readily, whereupon its eastward phase propagation across the Warm Pool is found to remotely force the ACW in the eastern Pacific and western Atlantic sectors of the Southern Ocean through atmospheric teleconnections [Sardeshmukh and Hoskins, 1988] which propagate along with it. Subsequently, the ACW propagates this imposed GEW signal throughout the remainder of the Southern Ocean as a coupled wave in covarying ST and SLP anomalies, whereupon entering the Indian sector 1.5 to 2.5 years later it spawns a northern branch which takes another 1.5 to 2.5 years to propagate the ACW signal equatorward into the Warm Pool south of Indonesia. There it interferes constructively with the GEW. Thus the two forms of teleconnection, one fast and directed from the tropics to the high southern latitudes via the atmosphere and the other slow and directed from the high southern latitudes to the tropics via the ocean, complete a global circuit of 3- to 5-year duration that reinforces both the ACW and GEW and influences the tropical standing mode of ENSO.

A method for developing a ‘practical’ climate forecast system from the output of a General Circulation Model (GCM) is described. This forecast system is compatible with input needs of agricultural simulation and decision analysis models. The method uses principal components and cluster analysis of GCM-generated forecasts of the time series of the Southern Oscillation Index (SOI) to create SOI ‘types’ or ‘phases’. The SOI predictions were derived from a long-term GCM simulation that was forced with historical sea-surface temperature (SST) data. The GCM-derived ‘SOI phases’ (g-phases) could thus be associated with historical analogue years in a manner similar to SOI phases that have been developed from historical SOI data. Rainfall probability distributions associated with g-phases were calculated from actual rainfall amounts in the analogue year sets associated with each phase. These rainfall probabilities were compared with the currently available distributions that have been derived using lag relationships between SOI phases and rainfall. In addition, the historical SST data was analysed in a similar way so that analogue year sets could be formed. Empirical orthogonal function (EOF) analysis and cluster analysis were used to derive SST ‘EOF-types’ that depended on the temporal dynamics of spatial patterns in the Pacific Ocean and Indian Ocean SST data. Lag relationships between the SST/EOF types and rainfall distributions could then also be derived for comparison. The results show that both the g-phases derived from the GCM forecast of SOI and the SST/EOF-types generally provide larger shifts in rainfall distributions than are currently available, especially at longer lead times. The methods outlined above may facilitate more practical output from General Circulation Models and analyses of SST data so that connections to agricultural simulation models and software packages are enhanced.