Australian Rainfall Increases During Multi‐Year La Niña

Huang, Ashley T.; Gillett, Zoe E.; Taschetto, Andréa S.

doi:10.1029/2023gl106939

Geophysical Research Letters

2024

DOI: 10.1029/2023gl106939

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Australian Rainfall Increases During Multi‐Year La Niña

Ashley T. Huang,

Zoe E. Gillett,

Andréa S. Taschetto

Abstract: Australia is one of the regions strongly affected by the El Niño‐Southern Oscillation (ENSO). The recent 2020–2023 La Niña event was marked by record‐breaking rainfall and flooding across eastern Australia. The continuous wet conditions during the triple La Niña motivated us to explore the impacts of single‐year and multi‐year ENSO events on Australian rainfall using observational data sets. We find that, while there is no difference in the rainfall impacts during single or double El Niño events, Australian ra… Show more

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Cited by 5 publications

References 52 publications

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Projected Changes to Characteristics of El Niño‐Southern Oscillation, Indian Ocean Dipole, and Southern Annular Mode Events in the CMIP6 Models

Chung,

Power,

Boschat

et al. 2024

Earth's Future

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In this study we analyse projections of future changes to the El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Southern Annular Mode (SAM) using the latest generation of climate models. Multiple future scenarios are considered. We quantify the fraction of models that project future increases or decreases in the frequency and amplitude of ENSO, IOD, and SAM events in the late 21st century. Changes to the frequency of co‐occurring and consecutive driver phases are also examined. We find that while there is large inter‐model spread, the most common pathways correspond to more frequent ENSO events; weaker, less frequent IOD events; and stronger, but less frequent austral spring SAM events. There is no clear consensus on the change to the frequency of concurrent events, though we find a significant increase in La Niña‐ and El Niño‐only events occurring with neutral IOD and SAM. We also find a significant increase to the frequency of consecutive positive IOD events under a high emissions scenario, but no significant change to the frequency of consecutive ENSO or negative IOD events. In most models, the correlation between drivers, that is, ENSO and IOD, and ENSO and SAM, does not significantly change between the late 20th and late 21st century. These results indicate a high degree of internal variability in the models.

show abstract

Projected Changes to Characteristics of El Niño‐Southern Oscillation, Indian Ocean Dipole, and Southern Annular Mode Events in the CMIP6 Models

Chung,

Power,

Boschat

et al. 2024

Earth's Future

View full text Add to dashboard Cite

show abstract

Contrasting impact of single-year and multi-year El Niño on the Pacific-North American teleconnection pattern

Liu,

Tao,

Huang

et al. 2024

Environ. Res. Lett.

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Distinct Pacific-North American (PNA) teleconnection patterns and their associated climate impacts in North America have been observed during single-year El Niño events, as well as during both the first and second years of multi-year El Niño events. The research highlights the critical role of PNA-related North Pacific circulation anomalies, which shift northwestward during multi-year events and reach their peak intensity in the second year. Multiple dynamical diagnostic methods are employed to elucidate the reasons behind the diverse subtropical circulation responses. Differences in the anomalous tropical heat sources influence the formation of Rossby wave sources through adjustments in divergent wind anomalies, subsequently modulating the position and intensity of the PNA patterns. Additionally, variations in the meridional range of sea surface temperature anomalies affect the edge of tropospheric temperature through moist-adiabatic adjustment, leading to distinct subtropical jet stream responses. This, in turn, modifies the position of North Pacific circulation anomalies through the advection of anomalous kinetic energy. Furthermore, synoptic-scale transient eddies act as a feedback mechanism, helping to maintain and intensify these diverse atmospheric anomalies.

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Predicting Australian energy demand variability using weather data and machine learning

Richardson,

Hobeichi,

Sweet

et al. 2024

Environ. Res. Lett.

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Managing energy systems requires understanding the variability of energy demand, which on daily timescales is driven primarily by the weather. Historical records of demand typically cover 1-2 decades which may be too short to capture the range of possible demand, particularly for a climate with high interannual variability such as that of Australia. Predicting demand using long records of weather data opens the possibility of more robustly estimating true demand variability. We estimate daily energy demand between 2010 and 2019 for Australian states in the National Electricity Market using machine learning with reanalysis weather variables as predictors. We assess the performance of these models and examine their behaviour to identify which weather variables are most important for predicting demand. We then use the models to estimate demand for the period 1959-2022. We use this 63-year record to quantify how the probability of high demand days can change compared to individual 10-year periods and when conditioned by the phase of the El Niño Southern Oscillation (ENSO). Energy demand can be accurately predicted with weather, with median errors of 2-4% on years omitted from the training. We show that the probability of extreme demand over different 10-year periods can vary from half to twice as likely, depending on the decade. When further conditioned on ENSO phase, the probabilities can be up to 7 times higher than when using the 63-year period, implying a risk of overestimating weather-related energy demand if shorter records are used. We conclude that machine learning methods can accurately predict energy demand using only weather data, enabling us to estimate demand variability over longer time horizons than is possible with demand observations. These longer records are important when attempting to quantify tail risks of demand, and so can help to inform the design of energy systems.

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Australian Rainfall Increases During Multi‐Year La Niña

Cited by 5 publications

References 52 publications

Projected Changes to Characteristics of El Niño‐Southern Oscillation, Indian Ocean Dipole, and Southern Annular Mode Events in the CMIP6 Models

Projected Changes to Characteristics of El Niño‐Southern Oscillation, Indian Ocean Dipole, and Southern Annular Mode Events in the CMIP6 Models

Contrasting impact of single-year and multi-year El Niño on the Pacific-North American teleconnection pattern

Predicting Australian energy demand variability using weather data and machine learning

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