In the latter half of 2016 Indonesia and Australia experienced extreme wet conditions and East Africa suffered devastating drought, which have largely been attributed to the occurrence of strong ...negative Indian Ocean Dipole (IOD) and weak La Niña. Here we examine the causes and predictability of the strong negative IOD and its impact on the development of La Niña in 2016. Analysis on atmosphere and ocean reanalyses and forecast sensitivity experiments using the Australian Bureau of Meteorology's dynamical seasonal forecast system reveals that this strong negative IOD, which peaked in July-September, developed primarily by the Indian Ocean surface and subsurface conditions. The long-term trend over the last 55 years in sea surface and subsurface temperatures, which is characterised by warming of the tropical Indian and western Pacific and cooling in the equatorial eastern Pacific, contributed positively to the extraordinary strength of this IOD. We further show that the strong negative IOD was a key promoter of the weak La Niña of 2016. Without the remote forcing from the IOD, this weak La Niña may have been substantially weaker because of the extraordinarily long-lasting warm surface condition over the dateline from the tail end of strong El Niño of 2015-16.
After exhibiting an upward trend since 1979, Antarctic sea ice extent (SIE) declined dramatically during austral spring 2016, reaching a record low by December 2016. Here we show that a combination ...of atmospheric and oceanic phenomena played primary roles for this decline. The anomalous atmospheric circulation was initially driven by record strength tropical convection over the Indian and western Pacific Oceans, which resulted in a wave-3 circulation pattern around Antarctica that acted to reduce SIE in the Indian Ocean, Ross and Bellingshausen Sea sectors. Subsequently, the polar stratospheric vortex weakened significantly, resulting in record weakening of the circumpolar surface westerlies that acted to decrease SIE in the Indian Ocean and Pacific Ocean sectors. These processes appear to reflect unusual internal atmosphere-ocean variability. However, the warming trend of the tropical Indian Ocean, which may partly stem from anthropogenic forcing, may have contributed to the severity of the 2016 SIE decline.
Seasonal climate prediction to date has largely focussed on probabilistic forecasts for above‐ and below‐average conditions in climate means. Here, we examine the possibility of making sub‐seasonal ...to seasonal outlooks for daily‐scale precipitation extremes in Australia. We first use observational data to show that significant relationships exist between climate modes, such as the El Niño–Southern Oscillation, and indices representing rainfall extremes across much of Australia. The strong observed teleconnections between climate modes and daily rainfall extremes suggest the potential for predictability on seasonal scales. The current Australian Bureau of Meteorology seasonal prediction system (ACCESS‐S1) is examined for performance in predicting rainfall extreme indices using a range of measures. Ensemble hindcasts, consisting of 11 members initialised every month during 1990–2012, perform well for some extreme rainfall indices on short lead‐times (up to 1 month). We note that at short lead‐times, forecasts are aided by skilful weather prediction, so forecast performance drops at lead‐times of a week or more. Forecast performance is lower in austral summer than other seasons and greater in the north and interior of the continent, particularly in the dry season, than elsewhere. The ACCESS‐S1 ensemble is overconfident but exhibits some reliability in probabilistic forecasts of above‐ or below‐average number of wet days and intensity of the highest daily maximum precipitation, especially in northern Australia. ACCESS‐S1 captures the broad pattern of relationships between climate modes and rainfall extremes that are observed. For two case‐studies of unusually extreme precipitation, ACCESS‐S exhibits contrasting performance for forecasts of extreme rainfall anomalies beyond the first month. These results suggest that ACCESS‐S1 may be used to produce outlooks for some rainfall indices, such as the number of wet days and the intensity of the wettest day, for the month ahead.
Bar graphs showing the area of Australia with significant concurrent Spearman rank correlations (p‐value < .05) between mean and extreme rainfall indices and (a) Niño‐3.4, (b) DMI, (c) north Australian SSTs, and (d) any of the three climate modes. The legend on the right indicates which bars correspond to which indices with mean rainfall in black, intensity‐based indices in red, frequency‐based indices in orange and a contribution‐based index in blue.
A robust positive trend in the Southern Annular Mode (SAM) is projected for the end of the 21st century under the Representative Concentration Pathway 8.5 scenario, which results in rainfall ...decreases in the midlatitudes and increases in the high latitudes in the Southern Hemisphere (SH). We find that this SAM trend also increases rainfall over the SH subtropics in austral summer but not in winter, leading to a pronounced wintertime poleward expansion of the subtropical dry zone. These dynamically driven rainfall changes by the SAM appear to oppose the thermodynamically driven projected rainfall changes in the SH subtropics and midlatitudes, whereas the two components reinforce each other in the high latitudes. However, we show that most climate models fall short in capturing the observed SAM component driven by the El Niño–Southern Oscillation and associated rainfall in the austral warm seasons, which limits our confidence in quantifying the contribution of the SAM to projected rainfall changes.
Key Points
SAM‐driven rainfall will mitigate the drying of the SH subtropics in summer under RCP8.5
A poleward expansion of the SH subtropics due to the positive trend in the SAM is a winter phenomenon
CMIP5 models poorly simulate the observed relationship between the SAM and El Nino‐Southern Oscillation
The maximum Eady growth rate measure of baroclinic instability is very commonly used in the literature. Its average is usually calculated directly from the time‐mean flow. It is suggested here that ...this approach is not entirely suitable, but rather one should obtain the Eady growth rates at all relevant synoptic times and average these. It is found at the 850 hPa level in the Southern Hemisphere that the time‐mean of the instantaneous rates exceed those calculated from the time‐mean field over much of the mid and high latitudes, and the difference is even more marked at 500 hPa. At both levels the axes of the maxima Eady growth rates are displaced to the south. Some implications are discussed, including the need for caution when diagnosing changes in cyclone properties from changes in Eady growth rate calculated directly from the time‐mean flow in climate change model simulations.
The stratosphere can have a significant impact on winter surface weather on subseasonal to seasonal (S2S) timescales. This study evaluates the ability of current operational S2S prediction systems to ...capture two important links between the stratosphere and troposphere: (1) changes in probabilistic prediction skill in the extratropical stratosphere by precursors in the tropics and the extratropical troposphere and (2) changes in surface predictability in the extratropics after stratospheric weak and strong vortex events. Probabilistic skill exists for stratospheric events when including extratropical tropospheric precursors over the North Pacific and Eurasia, though only a limited set of models captures the Eurasian precursors. Tropical teleconnections such as the Madden‐Julian Oscillation, the Quasi‐Biennial Oscillation, and El Niño–Southern Oscillation increase the probabilistic skill of the polar vortex strength, though these are only captured by a limited set of models. At the surface, predictability is increased over the United States, Russia, and the Middle East for weak vortex events, but not for Europe, and the change in predictability is smaller for strong vortex events for all prediction systems. Prediction systems with poorly resolved stratospheric processes represent this skill to a lesser degree. Altogether, the analyses indicate that correctly simulating stratospheric variability and stratosphere‐troposphere dynamical coupling are critical elements for skillful S2S wintertime predictions.
Key Points
Tropospheric precursors of SSW events are better represented for the North Pacific than for Eurasia
Teleconnections from the tropics add probabilistic skill but are only represented by a few models
Weak and strong vortex events in the NH stratosphere can contribute to surface skill 3–4 weeks later
Abstract
Southeastern Australia experienced an extreme heatwave event from 27 January to 8 February 2009, which culminated in the devastating “Black Saturday” bushfires that led to hundreds of human ...casualties and major economic losses in the state of Victoria. This study investigates the causes of the heatwave event, its prediction, and the role of anthropogenic climate change using a dynamical subseasonal-to-seasonal (S2S) forecast system. We show that the intense positive temperature anomalies over southeastern Australia were associated with the persistent high pressure system over the Tasman Sea and a low pressure anomaly over southern Australia, which favored horizontal warm-air advection from the lower latitudes to the region. Enhanced convection over the tropical western Pacific and northern Australia due to weak La Niña conditions appear to have played a role in strengthening the high pressure anomalies over the Tasman Sea. The observed climate conditions are largely reproduced in the hindcast of the Australian Community Climate and Earth System Simulator–Seasonal prediction system version 1 (ACCESS-S1). The model skillfully predicts the spatial characteristics and relative intensity of the heatwave event at a 10-day lead time. A climate attribution forecast experiment with low atmospheric CO
2
and counterfactual cold ocean–atmospheric initial conditions suggests that the enhanced greenhouse effect contributed about 3°C warming of the predicted event. This study provides an example of how a S2S prediction system can be used not only for multiweek prediction of an extreme event and its climate drivers, but also for the attribution to anthropogenic climate change.
The stratosphere has been identified as an important source of predictability for a range of processes on subseasonal to seasonal (S2S) time scales. Knowledge about S2S predictability within the ...stratosphere is however still limited. This study evaluates to what extent predictability in the extratropical stratosphere exists in hindcasts of operational prediction systems in the S2S database. The stratosphere is found to exhibit extended predictability as compared to the troposphere. Prediction systems with higher stratospheric skill tend to also exhibit higher skill in the troposphere. The analysis also includes an assessment of the predictability for stratospheric events, including early and midwinter sudden stratospheric warming events, strong vortex events, and extreme heat flux events for the Northern Hemisphere and final warming events for both hemispheres. Strong vortex events and final warming events exhibit higher levels of predictability as compared to sudden stratospheric warming events. In general, skill is limited to the deterministic range of 1 to 2 weeks. High‐top prediction systems overall exhibit higher stratospheric prediction skill as compared to their low‐top counterparts, pointing to the important role of stratospheric representation in S2S prediction models.
Key Points
High‐top models have more skill in the stratosphere and the troposphere compared to low‐top models
Extreme stratospheric events are predictable at 1‐ to 2‐week lead times in S2S models
SSW events tend to be less predictable than strong vortex events or final warming events
Observational records show that occurrences of the negative polarity of the Southern Annular Mode (low SAM) is significantly linked to El Niño during austral spring and summer, potentially providing ...long-lead predictability of the SAM and its associated surface climate conditions. In this study, we explore how this linkage may change under a scenario of a continuation of the ocean temperature trends that have been observed over the past 60 years, which are plausibly forced by increasing greenhouse gas concentrations. We generated coupled model seasonal forecasts for three recent extreme El Niño events by initialising the forecasts with observed ocean anomalies of 1 September 1982, 1997 and 2015 added into (1) the current ocean mean state and into (2) the ocean mean state updated to include double the recent ocean temperature trends. We show that the strength of extreme El Niño is reduced with the warmer ocean mean state as a result of reduced thermocline feedback and weakened rainfall-wind-sea surface temperature coupling over the tropical eastern Pacific. The El Niño-low SAM relationship also weakens, implying the possibility of reduced long-lead predictability of the SAM and associated surface climate impacts in the future.
Australian maximum temperatures have reached record values in recent austral springs and are projected to increase further in a warming world. We focus on three record spring heat events in September ...2013, October–November 2014 and October 2015, and examine the anomalous atmospheric circulation associated with these events in reanalysis and a sub-seasonal to seasonal prediction system, POAMA, to identify factors contributing to extreme heat over Australia. We find that an anomalous equivalent barotropic cyclonic circulation southwest of Australia and a quasi-stationary wave train formed by an upper-troposphere anticyclonic circulation over southern Australia and barotropic cyclone southeast of Australia are important features in these heat events, though the wave train was only observed in the latter two events. This wave train appears to be linked to the tropics, and particularly the tropical Indian Ocean, suggesting that teleconnections to the tropical Indian Ocean can be important for monthly spring extreme heat formation in Australia. However, the forecast relationship with the tropical Pacific Ocean was over-represented at the cost of the relationship between the Indian Ocean and upper-troposphere anomaly, limiting the ability of POAMA to forecast the full extent of the month- or 2 month-long heat extremes at zero lead time. This means that the model might underestimate the magnitude of future extreme heat events in spring, a factor that should be assessed in the next generation of seasonal forecast models.
PDF
