James S. Risbey’s research while affiliated with The Royal Hobart Hospital and other places

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.

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 Niño 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.

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.

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.

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.

Despite common background La Niña 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 Niño–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 Niña 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 Niña 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 Niña. Significance Statement Forecasts of El Niño–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 Niño and La Niña 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 Niño and La Niña teleconnections. We present case studies for two La Niña springs, highlighting that the Australian continent can help shape its own weather toward wetter or drier outcomes.

El Ni\~no-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.

A 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.

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.

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.