Madden–Julian Oscillation teleconnections to Australian springtime temperature extremes and their prediction in ACCESS-S1

Marshall, Andrew G.; Wang, Guomin; Hendon, Harry H.; Lin, Hai

doi:10.1007/s00382-022-06586-6

Cited by 6 publications

(5 citation statements)

References 68 publications

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“…Recent research has found a strong relationship between temperature extremes in southeast Australia and phases 2 and 3 of the MJO in SON (Marshall et al 2022). We find a modest response of the chance of extreme weekly PFI in southeast Australia to phase 3 in SON (Fig.…”

Section: Event Probabilities Associated With Concurrent Mjomentioning

confidence: 42%

“…Temperature is only one component determining PFI. Additionally, Marshall et al (2022) analyse data from 1990 to 2012 whereas we use data from 2003 to 2013. Given that the southeast Australian rainfall suppression associated with phases 2 and 3 of the MJO in SON has weakened over the past 15 years (Cowan et al 2023), it could be expected that using only data from 2003 to 2013 as opposed to data starting in 1990, we would find a weaker overall relationship between this phase and PFI in SON.…”

Section: Event Probabilities Associated With Concurrent Mjomentioning

confidence: 99%

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Associations between Australian climate drivers and extreme weekly fire danger

Taylor,

Marshall,

Crimp

et al. 2023

Int. J. Wildland Fire

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Aims We investigate the associations between major Australian climate drivers and extreme weekly fire danger throughout the year. Methods We use a composite-based approach, relating the probability of top-decile observed potential fire intensity to the positive and negative modes of the El Niño Southern Oscillation, Indian Ocean Dipole, Madden–Julian Oscillation, Southern Annular Mode, split-flow blocking and Subtropical Ridge Tasman Highs, both concurrently and at a variety of lag times. Key results The chance of extreme fire danger increases over broad regions of the continent in response to El Niño and positive Indian Ocean Dipole events, the negative mode of the Southern Annular Mode, split-flow Blocking Index and Subtropical Ridge Tasman High, and Madden–Julian Oscillation phases 5, 6, 2 and 8 in Austral summer, autumn, winter and spring respectively. These relationships exist not only concurrently, but also when a climate event occurs up to 6 months ahead of the season of interest. Conclusions These findings highlight the importance of considering the influence of diverse climate drivers, at a range of temporal lag periods, in understanding and predicting extreme fire danger. Implications The results of this study may aid in the development of effective fire management strategies and decision-making processes to mitigate the impacts of fire events in Australia.

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Section: Event Probabilities Associated With Concurrent Mjomentioning

confidence: 42%

Section: Event Probabilities Associated With Concurrent Mjomentioning

confidence: 99%

Associations between Australian climate drivers and extreme weekly fire danger

Taylor,

Marshall,

Crimp

et al. 2023

Int. J. Wildland Fire

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“…These, in turn, are influenced by large-scale climate drivers such as the El Niño Southern Oscillation, Indian Ocean Dipole, and Southern Annular Mode, among others [15]. These drivers have been shown to impact the extremes of temperature [16][17][18], fire weather, and fire risk [19,20]. As many of these drivers can be forecast in advance [21], knowledge of their states could be utilised in forecasting severe fire danger.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia

Taylor,

Marshall,

Crimp

et al. 2024

Atmosphere

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The increasing frequency and duration of severe fire events in Australia further necessitate accurate and timely forecasting to mitigate their consequences. This study evaluated the performance of two distinct approaches to forecasting extreme fire danger at two- to three-week lead times for the period 2003 to 2017: the official Australian climate simulation dynamical model and a statistical model based on climate drivers. We employed linear logistic regression to develop the statistical model, assessing the influence of individual climate drivers using single linear regression. The performance of both models was evaluated through case studies of three significant extreme fire events in Australia: the Canberra (2003), Black Saturday (2009), and Pinery (2015) fires. The results revealed that ACCESS-S2 generally underestimated the spatial extent of all three extreme FBI events, but with accuracy scores ranging from 0.66 to 0.86 across the case studies. Conversely, the statistical model tended to overpredict the area affected by extreme FBI, with high false alarm ratios between 0.44 and 0.66. However, the statistical model demonstrated higher probability of detection scores, ranging from 0.57 to 0.87 compared with 0.03 to 0.57 for the dynamic model. These findings highlight the complementary strengths and limitations of both forecasting approaches. Integrating dynamical and statistical models with transparent communication of their uncertainties could potentially improve accuracy and reduce false alarms. This can be achieved through hybrid forecasting, combined with visual inspection and comparison between the statistical and dynamical forecasts. Hybrid forecasting also has the potential to increase forecast lead times to up to several months, ultimately aiding in decision-making and resource allocation for fire management.

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“…The MJO is known to have impacts on Australian climate across the continent and in all phases and seasons [16,27,50]. However, phases highlighted by Taylor et al [46] have been shown to have particularly strong relationships with chances of extreme FBI over specific areas of interest.…”

Section: Prediction Associated With the Madden-julian Oscillationmentioning

confidence: 99%

“…In JJA, phase 2 (Figure 12) of the MJO is relevant for forecasting the chance of extreme FBI, as a higher frequency of extreme FBI events is observed across much of Northern Australia where JJA is the dry season [46]. Phase 2 in this season is associated with enhanced convection over Africa and the Indian Ocean, with higher geopotential height over Eastern Australia but increased maximum and minimum temperatures in parts of Northern Australia [24,50]. In the northern desert/southern Cape York regions where maximum temperature is found to increase in phase 2, ACCESS-S2 shows decreased predictive skill for forecasting extreme FBI.…”

Section: Prediction Associated With the Madden-julian Oscillationmentioning

confidence: 99%

Climate Driver Influences on Prediction of the Australian Fire Behaviour Index

Taylor,

Marshall,

Crimp

et al. 2024

Atmosphere

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Fire danger poses a pressing threat to ecosystems and societies worldwide. Adequate preparation and forewarning can help reduce these threats, but these rely on accurate prediction of extreme fire danger. With the knowledge that climatic conditions contribute heavily to overall fire danger, this study evaluates the skill with which episodes of extreme fire danger in Australia can be predicted from the activity of large-scale climate driver patterns. An extremal dependence index for extreme events is used to depict the historical predictive skill of the Australian Bureau of Meteorology’s subseasonal climate prediction system in replicating known relationships between the probability of top-decile fire danger and climate driver states at a lead time of 2–3 weeks. Results demonstrate that the El Niño Southern Oscillation, Southern Annular Mode, persistent modes of atmospheric blocking, Indian Ocean Dipole and Madden-Julian Oscillation are all key for contributing to predictability of fire danger forecasts in different regions during critical fire danger periods. Northwest Australia is found to be particularly predictable, with the highest mean index differences (>0.50) when certain climate drivers are active, compared with the climatological index mean. This integrated approach offers a valuable resource for decision-making in fire-prone regions, providing greater confidence to users relying on fire danger outlooks for key management decisions, such as those involved in the sectors of national park and forest estate management, agriculture, emergency services, health and energy. Furthermore, the results highlight strengths and weaknesses in both the Australian Fire Danger Rating System and the operational climate model, contributing additional information for improving and refining future iterations of these systems.

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Madden–Julian Oscillation teleconnections to Australian springtime temperature extremes and their prediction in ACCESS-S1

Cited by 6 publications

References 68 publications

Associations between Australian climate drivers and extreme weekly fire danger

Associations between Australian climate drivers and extreme weekly fire danger

A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia

Climate Driver Influences on Prediction of the Australian Fire Behaviour Index

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