The Australian Climate Service (ACS) was established in 2021 to uplift Australian climate science and services to support resilience in the face of increasing impacts from weather and climate extremes. Formed as a partnership across the Federal Government of Australia, it consists of four Partner Organisations: The Australian Bureau of Statistics (ABS), the Bureau of Meteorology (BoM), Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Geoscience Australia (GA) forming a virtual agency. Here, we describe the approach taken by the BoM and CSIRO to develop the climate parts of this service, provide examples of successful delivery, and reflect on lessons learnt. The approach to development borrowed from practices established for seasonal climate prediction services, drawing on the Global Framework for Climate Services and World Meteorological Organization good practice guidance documents. In establishing the service, we found that many concepts and frameworks mapped readily from other timescales. For example, the concept of operations, used for BoM weather services and methods benchmarking, was applied to areas such as climate model selection and calibration. Having modellers, scientists, and climate services staff from multiple agencies collaborate within activities as part of a single value chain was critical for delivery, and readily achievable through an end-to-end program approach.

In the aftermath of extreme weather, policy makers, contingency planners and insurers often seek to understand the likelihood of experiencing such events. The most common tool for this is extreme value analysis (EVA), but likelihood estimates based on observed or reanalysis data can be highly uncertain due to the relatively short observational record. Substantially larger samples of plausible extreme weather events can be obtained using the UNprecedented Simulated Extremes using ENsembles (UNSEEN) approach, which involves applying EVA to large forecast/hindcast ensembles. While larger sample sizes generally reduce the uncertainty associated with EVA, using seasonal or decadal forecast data introduces additional uncertainties related to model bias and model diversity. In this study, a multi-model ensemble of hindcast data from the CMIP6 Decadal Climate Prediction Project was analysed to quantify these additional uncertainties in the context of extreme temperature and rainfall across Australia. Factoring in model bias and diversity dramatically increased the uncertainty associated with estimated event likelihoods from the UNSEEN approach, to the point that it equaled or exceeded the uncertainty from an observation-based approach at most locations. Model diversity tended to be the largest source of uncertainty (60-70% of the total). Bias correction was also a significant source of uncertainty (30-40%), while the uncertainty associated with EVA was trivial. Our results suggest that an UNSEEN-based approach to estimating the likelihood of climate extremes should be understood as an approach that has different uncertainty characteristics to an observation-based approach, as opposed to less uncertainty.

This past year, 2024, is on track to be the warmest year, joining 2023 as the two hottest years on record. With the exceptional heat, weather and climate extremes were common across the world. In particular, 2024 has seen a remarkable run of extreme precipitation events and resulting impacts. Here, we provide an overview of the most notable extreme events of the year, including extreme precipitation and floods, tropical cyclones, and droughts. The characteristics and impacts of these extreme events are summarized, followed by discussion on the physical drivers and the role of global warming. Finally, we also discuss the future prospects in extreme event studies, including impact-based perspectives, challenges in attribution of precipitation extremes, and the existing gap to minimize impacts from climate extremes.

Extreme weather far beyond what has been experienced in recent memory can be especially dangerous and costly. Proactively identifying locations at high risk of experiencing unprecedented weather can assist with disaster preparedness. Such locations can be referred to as having soft records, meaning the most extreme event in observational records is not particularly severe compared to what is possible. In previous studies, the systematic identification of soft records over a large spatial domain involves applying extreme value analysis to gridded observational or reanalysis data. A limitation of these studies is the small sample size, which we propose can be addressed by adapting the UNprecedented Simulated Extremes using ENsembles (UNSEEN) approach that is commonly used to estimate event likelihood in the aftermath of isolated unprecedented events. The UNSEEN approach makes use of seasonal or decadal forecast/hindcast ensembles, which provide a large sample of plausible events over recent decades. To demonstrate the utility of applying the UNSEEN approach to a large spatial grid, we assessed record daily maximum temperatures across Australia using gridded observations and data from 10 different decadal forecasting systems. The observation-based results highlighted broad areas of soft records in the south-east of mainland Australia, extending north into south-west and western Queensland. The UNSEEN-based analysis also identified soft records in western Queensland, but not in the south-east where the underlying positive trends in extreme temperature were far less severe in the models than in observations. We suggest that the use of large model ensembles (i.e., an UNSEEN-based approach) can complement an observation-based approach to identifying soft records over large gridded spatial domains.

We study the hemispheric to continental scale regimes that lead to summertime heatwaves in the Northern Hemisphere. By using a powerful data mining methodology - archetype analysis - we identify characteristic spatial patterns consisting of a blocking high pressure systems embedded within a meandering upper atmosphere circulation that is longitudinally modulated by coherent Rossby Wave Packets. Periods when these atmospheric regimes are strongly expressed correspond to large increases in the likelihood of extreme surface temperature. Most strikingly, these regimes are shown to be typical of surface extremes and frequently reoccur. Three well publicised heatwaves are studied in detail - the June-July 2003 western European heatwave, the August 2010 "Russian" heatwave, and the June 2021 "Heatdome" event across western North America, and are shown to be driven by blocking high pressure systems linked to stalled Rossby Wave Packets. We discuss the implications of our work for long-range prediction or early warning, climate model assessment and post-event diagnosis.

When a record hot month occurs, timely and credible attribution and contextualisation information can enhance public understanding and future preparedness. This is particularly effective if provided in real time by a National Meteorological and Hydrological Service (NMHS). Many NMHSs are working to integrate research-based attribution methods into their operational services. In this study, researchers and climate service staff collaborated to assess the feasibility of delivering such information swiftly and aligned with standard NMHS data and procedures. The record warm July (winter) temperatures of Tasmania, Australia in 2023 were chosen to illustrate the trial. Rapid results were available three days after the event. Approximately half of the unusual warmth was attributed to climate change, with the likelihood of breaking the previous record at least 17 times higher in the current climate compared to a stationary pre-industrial climate (14% vs. 0.4%). The warming trend became evident in the 1980s, and by 2060, average July temperatures in Tasmania match the record temperature of July 2023 under a high emissions scenario. However, average July minimum temperatures were not well modelled, necessitating the addition of a higher-resolution forecast-based attribution method. In subsequent analysis, almost all the forecast temperature anomaly, and reduced storm activity, was attributable to climate change. Statistical analysis revealed that a weak El Ni & ntilde;o partly offset the unusual warmth. To expedite these additional approaches, information drawn from real-time forecasts could be used. Lessons learnt from this trial include technical improvements to align better with NMHS protocols including using consistent datasets and baselines, and refining and automating the method suite. Logistical and communication enhancements included training staff to run the suite, improving communication materials, and developing delivery channels. These learnings provide key considerations for NMHSs as they move towards providing timely and credible climate attribution and contextualisation information as part of their operational services.

AbstractA large stretch of the east coast of Australia experienced unprecedented rainfall and flooding over a two‐week period in early 2022. It is difficult to reliably estimate the likelihood of such a rare event from the relatively short observational record, so an alternative is to use data from an ensemble prediction system (e.g., a seasonal or decadal forecast system) to obtain a much larger sample of simulated weather events. This so‐called ‘UNSEEN’ method has been successfully applied in several scientific studies, but those studies typically rely on a single prediction system. In this study, we use data from the Decadal Climate Prediction Project to explore the model uncertainty associated with the UNSEEN method by assessing 10 different hindcast ensembles. Using the 15‐day rainfall total averaged over the river catchments impacted by the 2022 east coast event, we find that the models produce a wide range of likelihood estimates. Even after excluding a number of models that fail basic fidelity tests, estimates of the event return period ranged from 320 to 1814 years. The vast majority of models suggested the event is rarer than a standard extreme value assessment of the observational record (297 years). Such large model uncertainty suggests that multi‐model analysis should become part of the standard UNSEEN procedure.

We summarise the contributions to the Topical Collection on quality of climate information for adaptation decision support. Based on these contributions, we draw some further lessons for the development of high-quality climate information and services, bridging between a “credibility-first” paradigm (exemplified by top-down information provision from systematic downscaling or impact projections) and a “salience-first” paradigm (exemplified by user-led tailored information products or storylines) by looking to identify their respective strengths and use cases. We emphasise that a more nuanced collective understanding of the dimensions of information quality in climate information and services would be beneficial to users and providers and ultimately support more confident and effective climate adaptation decisions and policy-making.

Extreme event attribution (EEA) information is increasingly in demand from climate services. EEA messages can: raise awareness about the effect climate change has already imposed, inform climate change liability conversations, and be combined with climate projections to inform adaptation. However, due to limitations in observations, models and methods, there are barriers towards operationalising EEA in practice. Operational services will need EEA to be done transparently and using preset formats. Here we review recent experience and practice in EEA in Australia and New Zealand with a view to inform the design of an EEA component of climate services. We present a flow chart of the processes involved, noting particular care is needed on the trigger, event definition, and climate model evaluation, with effective stage gates. We also promote the use of tailored causal network diagrams as a standard tool to inform an EEA study and communicate results, with particular care needed for messages on events with lower confidence or complex sets of influences, including tropical cyclones and extratropical cyclones. We suggest that extending EEA to impact attribution is essential for making EEA messages salient but requires an uplift in forming interdisciplinary teams and in granular exposure and vulnerability datasets and is likely to raise new interdisciplinary methodological questions. Finally, we suggest communication of EEA messages can learn more from its origins in medical epidemiology.

Diverse characteristics of El Niño Southern Oscillation (ENSO) events challenge the traditional view of tropical coupled ocean-atmosphere systems. The probability of a transition from one type of event to another is influenced by multiple factors of which many are projected to change. Here we assess the likelihood of ENSO transitions in observations and climate models, including a distinction between events that peak in the Eastern Pacific (EP) and Central Pacific (CP). We find that the initial ENSO state influences the likelihood of certain transitions and that some transitions are not physically possible or stochastically likely. For example, transitions to CP events are more likely than EP events except from a neutral state. We also find that El Niños tend to occur as singular events compared to La Niñas. While consecutive El Niño and La Niña events of EP type are possible, opposing EP events do not occur in succession. We identify several transitions likely driven by internal dynamical processes including neutral conditions to El Niño, CP El Niño to another El Niño, EP El Niño to CP La Niña, CP La Niña to CP El Niño and La Niña, and EP La Niña to neutral and CP El Niño. Projections of future transitions show an increased probability of transitions to CP El Niño events while transitions to EP La Niña events become less frequent under a high-emissions scenario. Accordingly, transitions to these events become more and less likely, respectively. We also find changes in the likelihood of specific transitions in a warming world: consecutive CP El Niño events become more likely while EP El Niño events become less likely to transition into CP La Niña events. These changes are expected to occur as early as 2050 with some changes to be accelerated by the end of the 21st century.

El Niño-Southern Oscillation global (ENSO) imprint on sea surface temperature comes in many guises. To identify its tropical fingerprints and impacts on the rest of the climate system, we propose a global approach based on archetypal analysis (AA), a pattern recognition method based on the identification of extreme configurations in the dataset under investigation. Relying on detrended sea surface temperature monthly anomalies over the 1982 to 2022 period, the technique recovers central and eastern Pacific ENSO types identified by more traditional methods and allows one to hierarchically add extra flavours and nuances to both persistent and transient phases of the phenomenon. Archetypal patterns found compare favorably to phase identification from K-means, fuzzy C-means and recently published network-based machine-learning algorithms. The AA implementation is modified for the identification of ENSO phases in sub-seasonal-to-seasonal prediction systems and complements current alert systems in characterising the diversity of ENSO and its teleconnections. Tropical and extra-tropical teleconnection composites from various oceanic and atmospheric fields derived from the analysis are shown to be robust and physically relevant. Extending AA to sub-surface ocean fields improves the discrimination between phases when the characterisation of ENSO based on sea surface temperature is uncertain. We show that AA on detrended sea-level monthly anomalies provides a clearer expression of ENSO types.

Despite common background La Ni & ntilde;a conditions, Australia was very dry in November 2020 and wet in November 2021. This paper aims to provide an explanation for this difference. Large-scale drivers of Australian rainfall, including El Ni & ntilde;o-Southern Oscillation, Indian Ocean dipole, Southern Annular Mode, and Madden-Julian oscillation, were examined but did not provide obvious clues for the differences. We found that the absence (in 2020) or presence (in 2021) of an enhanced thermal wind and subtropical jet over the Australian continent contributed to the rainfall anomalies. In general, La Ni & ntilde;a sets up warm sea surface temperatures around northern Australia, which enhances the meridional temperature gradient over the continent and hence thermal wind and subtropical jet. In November 2021, these warm sea surface temperatures, coupled with a persistent midlatitude trough, which advected cold air over the Australian continent, led to an enhanced meridional temperature gradient and subtropical jet over Australia. The enhanced jet provided favorable conditions for the development of rain-bearing weather systems across Australia. In 2020, the continent was warm, displacing the latitude of maximum meridional temperature gradient south of the continent, resulting in fewer instances of the subtropical jet over Australia, and little development of weather systems over the continent. We highlight that although La Ni & ntilde;a tilts the odds to wetter conditions for Australia, in any given month, variability in temperatures over the continent can contribute to subtropical jet variability and resulting rainfall in ways which confound the normal expectation from La Ni & ntilde;a. SIGNIFICANCE STATEMENT: Forecasts of El Ni & ntilde;o-Southern Oscillation are eagerly awaited, as the state of this climate driver has profound impacts on the likelihood of rainfall in regions around the world. While El Ni & ntilde;o and La Ni & ntilde;a do change rainfall likelihoods, the actual outcomes of these events are sometimes counter to expectation. This work explores one of the confounding factors to those expectations in the Australian context}the role of the meridional temperature gradient over the continent in modifying the storm track over Australia, which can disrupt the expected El Ni & ntilde;o and La Ni & ntilde;a teleconnections. We present case studies for two La Ni & ntilde;a springs, highlighting that the Australian continent can help shape its own weather toward wetter or drier outcomes.

<p>Diverse characteristics of ENSO events occurring either in the Eastern Pacific (EP) and Central Pacific (CP), have led to a shift in the traditional view of tropical coupled ocean- atmosphere systems and present a challenge in light of recent changes and possible future ENSO behaviour. The probability of a transition from one type of event to another is influenced by multiple factors of which many are projected to change with global warming. Here we assess the likelihood of transition from one ENSO state to another in long instrumental records and climate models. We find specific transitions are more likely than others. For example, strong EP El Niño events are more likely to transition to La Niña events than to El Niño events. The strength of an El Niño event is found to be an important factor. Consecutive CP El Niño events, unlike consecutive EP El Niño events, are more likely to occur after a strong CP event. Under a high emission scenario, we find significant changes of transitions.</p>

Global coffee production is at risk from synchronous crop failures, characterised by widespread concurrent reductions in yield occurring in multiple countries at the same time. For other crops, previous studies have shown that synchronous failures can be forced by spatially compounding climate anomalies, which in turn may be driven by large-scale climate modes such as the El Niño Southern Oscillation (ENSO). We provide a systematic analysis of spatially compounding climate hazards relevant to global coffee production. We identify 12 climate hazards from the literature, and assess the extent to which these hazards occur and co-occur for the top 12 coffee producing regions globally. We find that the number of climate hazards and compound events has increased in every region between 1980 and 2020. Furthermore, a clear climate change signature is evident, as the type of hazard has shifted from overly cool conditions to overly warm. Spatially compounding hazards have become particularly common in the past decade, with only one of the six most hazardous years occurring before 2010. Our results suggest that ENSO is the primary mode in explaining annual compound event variability, both globally and regionally. El Niño-like sea-surface temperatures in the Pacific Ocean are associated with decreased precipitation and increased temperatures in most coffee regions, and with spatially compounding warm and dry events. This relationship is reversed for La Niña-like signatures. The Madden Julian Oscillation also shows a strong association with climate hazards to coffee, with increased activity in the Maritime Continent related to a global increase in the number of cold or wet hazards and a decrease in the number of warm or dry hazards. With climate change projections showing a continued rise in temperatures in the tropics is likely, we suggest that coffee production can expect ongoing systemic shocks in response to spatially compounding climate hazards.

In eastern Australia we expect to experience wet conditions during La Niña and dry during El Niño events. We explore how well these expectations match historical outcomes by assessing, for spring, how much rain fell during past La Niña and El Niño events. We use a tercile framing and find that for rainfall averaged across eastern Australia, La Niña approximately doubles the chance of spring rainfall being in the wet tercile whereas El Niño approximately doubles the chance of a dry spring. Also of note is that during La Niña, the dry tercile is mostly vacant and during El Niño, the wet tercile is mostly vacant, indicating that one should not expect dry conditions in La Niña or wet in El Niño for eastern Australia as a whole. At individual locations across Australia, the results vary, and in some cases, including the eastern seaboard, La Niña or El Niño events do not change the odds of wet and dry springs significantly beyond chance expectations. For example, in the Sydney region, the normal chance of experiencing a wet tercile spring is 33% and this increases only slightly in a La Niña to 38%, suggesting that La Niña is not a strong indicator for wet conditions in this region. These outcomes may help to manage our expectations for the likely rainfall outcomes during future El Niño–Southern Oscillation (ENSO) events.

The record-shattering hot day in the Pacific Northwest in June 2021 is used to motivate a study of record-shattering temperature extremes in a very large hindcast ensemble. The hottest days in the Pacific Northwest in the large ensemble have similar large scale and synoptic patterns to those associated with the observed event. From the perspective of a fixed location, the hottest ensemble days are acutely sensitive to the chance sequencing of a dry period with a precisely positioned weather pattern. These days are thus rare and require very large samples (tens of thousands of years) to capture. The enduring nature of record-shattering heat records can be understood through this lens of weather ‘noise’ and sampling. When a record-shattering event occurs due to chance alignment of weather systems in the optimal configuration, any small sample of years subsequent to the (very unlikely) record event has an extremely low chance of finding yet another chance extreme. While warming of the baseline climate can narrow the gap between more regular extremes and record-shattering extremes, this can take many decades depending on the pace of climate change. Climate models are unlikely to capture record-shattering extremes at fixed locations given by observations unless the model samples are large enough to provide enough weather outcomes to include the optimal weather alignments. This underscores the need to account for sampling in assessing models and changes in weather-sensitive extremes. In particular, climate models are not necessarily deficient in representing extremes if that assessment is based on their absence in undersize samples.

The World Climate Research Programme (WCRP) envisions a world “that uses sound, relevant, and timely climate science to ensure a more resilient present and sustainable future for humankind.” This bold vision requires the climate science community to provide actionable scientific information that meets the evolving needs of societies all over the world. To realize its vision, WCRP has created five Lighthouse Activities to generate international commitment and support to tackle some of the most pressing challenges in climate science today. The overarching goal of the Lighthouse Activity on Explaining and Predicting Earth System Change is to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth system, including capabilities for early warning of potential high impact changes and events. This article provides an overview of both the scientific challenges that must be addressed, and the research and other activities required to achieve this goal. The work is organized in three thematic areas: (i) monitoring and modeling Earth system change; (ii) integrated attribution, prediction, and projection; and (iii) assessment of current and future hazards. Also discussed are the benefits that the new capability will deliver. These include improved capabilities for early warning of impactful changes in the Earth system, more reliable assessments of meteorological hazard risks, and quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization.

Marine heatwaves can have disastrous impacts on ecosystems and marine industries. Given their potential consequences, it is important to understand how broad-scale climate variability influences the probability of localised extreme events. Here, we employ an advanced data-mining methodology, archetype analysis, to identify large scale patterns and teleconnections that lead to marine extremes in certain regions. This methodology is applied to the Australasian region, where it identifies instances of anomalous sea-surface temperatures, frequently associated with marine heatwaves, as well as the broadscale oceanic and atmospheric conditions associated with those extreme events. Additionally, we use archetype analysis to assess the ability of a low-resolution climate model to accurately represent the teleconnection patterns associated with extreme climate variability, and discuss the implications for the predictability of these impactful events.

The ability to find and recognize patterns in high-dimensional geophysical data is fundamental to climate science and critical for meaningful interpretation of weather and climate processes. Archetypal analysis (AA) is one technique that has recently gained traction in the geophysical science community for its ability to find patterns based on extreme conditions. While traditional empirical orthogonal function (EOF) analysis can reveal patterns based on data covariance, AA seeks patterns from the points located at the edges of the data distribution. The utility of any objective pattern method depends on the properties of the data to which it is applied and the choices made in implementing the method. Given the relative novelty of the application of AA in geophysics it is important to develop experience in applying the method. We provide an assessment of the method, implementation, sensitivity, and interpretation of AA with respect to geophysical data. As an example for demonstration, we apply AA to a 39-yr sea surface temperature (SST) reanalysis dataset. We show that the decisions made to implement AA can significantly affect the interpretation of results, but also, in the case of SST, that the analysis is exceptionally robust under both spatial and temporal coarse graining. Significance Statement Archetypal analysis (AA), when applied to geophysical fields, is a technique designed to find typical configurations or modes in underlying data. This technique is relatively new to the geophysical science community and has been shown to be beneficial to the interpretation of extreme modes of the climate system. The identification of extreme modes of variability and their expression in day-to-day weather or state of the climate at longer time scales may help in elucidating the interplay between major teleconnection drivers and their evolution in a changing climate. The purpose of this work is to bring together a comprehensive report of the AA methodology using an SST reanalysis for demonstration. It is shown that the AA results are significantly affected by each implementation decision, but also can be resilient to spatiotemporal averaging. Any application of AA should provide a clear documentation of the choices made in applying the method.