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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.

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.

With increased interest in climate forecasts and projections, it is important to understand more about their sources and levels of skill. A starting point here is to describe the nature of the skill associated with forecasts and projections. Climate forecasts and projections typically both include time varying forcing of the climate, but only forecasts have initial conditions set close to the observed climate state. Climate forecasts therefore derive skill from both initial conditions and from forcing. The character of the initial condition skill and forcing skill is different. Skill from initial conditions results in a narrowing of expectations relative to a climatological distribution and points toward a more favoured part of the distribution. Forcing skill could result from a shift in the preferred parts of the climatological distribution in response to forcing, or it could result from a shift in the entire distribution, or both. Assessments of forcing skill require time averages of the target variable that are long enough so that the contributions from internal variations are small compared to the forced response. The assessment of skill of climate forecasts and projections is inherently partial because of the small number of repeated trials possible on typical climate time scales but is nonetheless the only direct measure of their performance.

The joint WWRP–WCRP Subseasonal to Seasonal Prediction Project (e.g., Robertson et al. 2014) created a global repository of experimental or operational near-real-time S2S forecasts and reforecasts (hindcasts) from 11 international meteorological institutions, cohosted by ECMWF and CMA (Vitart et al. 2017). These data are publicly accessible by researchers and users (https://apps.ecmwf.int/datasets/data/s2s and http://s2s.cma.cn/index). With the exception of the fourth case study, which uses GloSea5 forecasts (MacLachlan et al. 2015), all case studies use selected S2S forecasts and reforecasts that are available from this repository, providing a consistent basis for S2S forecast skill assessment and evaluation of their utility. The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale. DD gratefully acknowledges support from the Swiss National Science Foundation through project PP00P2_170523. For case study 1, ACP and WTKH were funded by the U.K. Climate Resilience Programme, supported by the UKRI Strategic Priorities Fund. RWL was funded by NERC Grant NE/P00678/1 and by the BER DOE Office of Science Federal Award DE-SC0020324. TS was funded by NERC Independent Research Fellowship (NE/P018637/1). CMG and DB were funded by the Helmholtz Young Investigator Group “SPREADOUT” Grant VH-NG-1243. Case study 2 was supported by the U.K. Global Challenges Research Fund NE/P021077/1 (GCRF African SWIFT) and the Tertiary Education Trust Fund (TETFUND) of Nigeria TETFund/DR&D/CE/NRF/STI/73/VOL.1. EO thanks Adrian Tomkins of ICTP, Italy, for his contribution. Case study 3 was undertaken as part of the Columbia World Project, ACToday, Columbia University (https://iri.columbia.edu/actoday/). Case study 4 was supported by the ForPAc (Towards Forecast-based Preparedness Action) project within the NERC/FCDO SHEAR Programme NE/P000428/1, NE/P000673/1, and NE/P000568/1. Case study 5 was undertaken as part of the International Research Applications Project, funded by the U.S. National Oceanic and Atmospheric Administration. EO thanks IRAP project colleagues at The University of Arizona, Indian Meteorological Department, Regional Integrated Multi-Hazard Early Warning System for Africa and Asia, and two of Bihar’s State Agricultural Universities for their contributions. For case study 6, CASC thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico Process 305206/2019-2 and Fundação de Amparo à Pesquisa do Estado de São Paulo Process 2015/50687-8 (CLIMAX Project) for their support. For case study 7, DW’s contributions were carried out under contract with the National Aeronautics and Space Administration. Case study 8 was funded by the EU Horizon 2020 Research and Innovation Programme Grant 7767874 (S2S4E). We also acknowledge the Subseasonal-to-Seasonal Project’s Real-Time Pilot Initiative for providing access to real-time forecasts. For case study 9, TIC-LCPE Hydro-04 was funded by the University of Strathclyde’s Low Carbon Power and Energy program. JB was supported by EPSRC Innovation Fellowship EP/R023484/1. We thank Andrew Low and Richard Hearnden from SSE Renewables for their input. Case study 10 was supported by the Earth Systems and Climate Change Hub under the Australian Government’s National Environmental Science Program, and the Decadal Climate Forecasting Project (CSIRO). Case study 11 was funded by the Technologies for Sustainable Built Environments Centre, Reading University, in conjunction with the EPSRC Grant EP/G037787/1 and BT PLC. Case study 12 was funded through the framework service contract for operating the EFAS Computational Center Contract 198702 and the Copernicus Fire Danger Computations Contract 389730 295 in support of the Copernicus Emergency Management Service and Early Warning Systems between the Joint Research Centre and ECMWF. Peer Reviewed "Article signat per 60 autors/es: Christopher J. White, Daniela I. V. Domeisen, Nachiketa Acharya, Elijah A. Adefisan, Michael L. Anderson, Stella Aura, Ahmed A. Balogun, Douglas Bertram, Sonia Bluhm, David J. Brayshaw, Jethro Browell, Dominik Büeler, Andrew Charlton-Perez, Xandre Chourio, Isadora Christel, Caio A. S. Coelho, Michael J. DeFlorio, Luca Delle Monache, Francesca Di Giuseppe, Ana María García-Solórzano, Peter B. Gibson, Lisa Goddard, Carmen González Romero, Richard J. Graham, Robert M. Graham, Christian M. Grams, Alan Halford, W. T. Katty Huang, Kjeld Jensen, Mary Kilavi, Kamoru A. Lawal, Robert W. Lee, David MacLeod, Andrea Manrique-Suñén, Eduardo S. P. R. Martins, Carolyn J. Maxwell, William J. Merryfield, Ángel G. Muñoz, Eniola Olaniyan, George Otieno, John A. Oyedepo, Lluís Palma, Ilias G. Pechlivanidis, Diego Pons, F. Martin Ralph, Dirceu S. Reis Jr., Tomas A. Remenyi, James S. Risbey, Donald J. C. Robertson, Andrew W. Robertson, Stefan Smith, Albert Soret, Ting Sun, Martin C. Todd, Carly R. Tozer, Francisco C. Vasconcelos Jr., Ilaria Vigo, Duane E. Waliser, Fredrik Wetterhall, and Robert G. Wilson"

Large-scale cloud features referred to as cloudbands are known to be related to widespread and heavy rain via the transport of tropical heat and moisture to higher latitudes. The Australian northwest cloudband is such a feature that has been identified in simple searches of satellite imagery but with limited investigation of its atmospheric dynamical support. An accurate, long-term climatology of northwest cloudbands is key to robustly assessing these events. A dynamically based search algorithm has been developed that is guided by the presence and orientation of the subtropical jet stream. This jet stream is the large-scale atmospheric feature that determines the development and alignment of a cloudband. Using a new 40-yr dataset of cloudband events compiled by this search algorithm, composite atmospheric and ocean surface conditions over the period 1979–2018 have been assessed. Composite cloudband upper-level flow revealed a tilted low pressure trough embedded in a Rossby wave train. Composites of vertically integrated water vapor transport centered around the jet maximum during northwest cloudband events reveal a distinct atmospheric river supplying tropical moisture for cloudband rainfall. Parcel backtracking indicated multiple regions of moisture support for cloudbands. A thermal wind anomaly orientated with respect to an enhanced sea surface temperature gradient over the Indian Ocean was also a key composite cloudband feature. A total of 300 years of a freely coupled control simulation of the ACCESS-D system was assessed for its ability to simulate northwest cloudbands. Composite analysis of model cloudbands compared reasonably well to reanalysis despite some differences in seasonality and frequency of occurrence.

The CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CAFE60v1) provides a large (96 member) ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatio-temporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades. For the atmosphere, we evaluate CAFE60v1 in comparison to empirical indices of the major climate teleconnections and blocking with various reanalysis products. Estimates of the large scale ocean structure, transports and biogeochemistry are compared to those derived from gridded observational products and climate model projections (CMIP). Sea ice (extent, concentration and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model and observational products. Our results show that CAFE60v1 is a useful, comprehensive and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multi-year to decadal time scales.

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.

Subseasonal forecast skill is not homogeneous in time, and prior assessment of the likely forecast skill would be valuable for end-users. We propose a method for identifying periods of high forecast confidence using atmospheric circulation patterns, with an application to southern Australia precipitation. In particular, we use archetypal analysis to derive six patterns, called archetypes, of daily 500-hPa geopotential height (Z500) fields over Australia. We assign Z500 reanalysis fields to the closest-matching archetype and subsequently link the archetypes to precipitation for three key regions in the Australian agriculture and energy sectors: the Murray Basin, southwest Western Australia, and western Tasmania. Using a 20-yr hindcast dataset from the European Centre for Medium-Range Weather Forecasts subseasonal-to-seasonal prediction system, we identify periods of high confidence as when hindcast Z500 fields closely match an archetype according to a distance criterion. We compare the precipitation hindcast accuracy during these confident periods compared to normal. Considering all archetypes, we show that there is greater skill during confident periods for lead times of less than 10 days in the Murray Basin and western Tasmania, and for greater than 6 days in southwest Western Australia, although these conclusions are subject to substantial uncertainty. By breaking down the skill results for each archetype individually, we highlight how skill tends to be greater than normal for those archetypes associated with drier-than-average conditions.

Recent studies have shown that regardless of model configuration, skill in predicting El Niño–Southern Oscillation (ENSO), in terms of target month and forecast lead time, remains largely dependent on the temporal characteristics of the boreal spring predictability barrier. Continuing the 2019 study by O’Kane et al., we compare multiyear ensemble ENSO forecasts from the Climate Analysis Forecast Ensemble (CAFE) to ensemble forecasts from state-of-the-art dynamical coupled models in the North American Multimodel Ensemble (NMME) project. The CAFE initial perturbations are targeted such that they are specific to tropical Pacific thermocline variability. With respect to individual NMME forecasts and multimodel ensemble averages, the CAFE forecasts reveal improvements in skill when predicting ENSO at lead times greater than 6 months, in particular when predictability is most strongly limited by the boreal spring barrier. Initial forecast perturbations generated exclusively as disturbances in the equatorial Pacific thermocline are shown to improve the forecast skill at longer lead times in terms of anomaly correlation and the random walk sign test. Our results indicate that augmenting current initialization methods with initial perturbations targeting instabilities specific to the tropical Pacific thermocline may improve long-range ENSO prediction.

We assess the representation of multiday temperature and rainfall extremes in southeast Australia in three coupled general circulation models (GCMs) of varying resolution. We evaluate the statistics of the modeled extremes in terms of their frequency, duration, and magnitude compared to observations, and the model representation of the midtropospheric circulation (synoptic and large scale) associated with the extremes. We find that the models capture the statistics of observed heatwaves reasonably well, though some models are “too wet” to adequately capture the observed duration of dry spells but not always wet enough to capture the magnitude of extreme wet events. Despite the inability of the models to simulate all extreme event statistics, the process evaluation indicates that the onset and decay of the observed synoptic structures are well simulated in the models, including for wet and dry extremes. We also show that the large-scale wave train structures associated with the observed extremes are reasonably well simulated by the models although their broader onset and decay is not always captured in the models. The results presented here provide some context for, and confidence in, the use of the coupled GCMs in climate prediction and projection studies for regional extremes.

Attribution of an extreme magnitude 1-day rainfall event in Hobart is inhibited by small sample size. For moderate magnitude Hobart daily rainfall extremes, models suggest that the associated extratropical lows will deliver more rainfall with weaker pressure anomalies in a warmer world.

There is evidence suggesting that fire activity in southeast Australia has increased in the past two decades. There is also anecdotal evidence that pyrocumulonimbus events have become more common, although the observed record is short. We explore the extent to which possible changes in fire and pyrocumulonimbus occurrence in southeast Australia can be explained by changes in the number of extreme (above the 95th percentile) days per year of two fire-weather indices, the Forest Fire Danger Index (FFDI) and the continuous Haines index (C-Haines). For the period 1958 through 2020, we show that there is a dependence between the number of extreme FFDI days per year and burned area for forested parts of southeast Australia. To a lesser extent, there is a relationship between the FFDI and pyrocumulonimbus occurrences. We find an increase in the occurrence of extreme FFDI days from the late 1990s, with up to 24 more extreme days per year. In the southwest of the state of Victoria, there are concomitant increases in temperature and wind speed, and decreases in relative humidity. We show that the structure of C-Haines has not changed, but extreme days more frequently coincide with extreme FFDI days. This suggests that possible observed increases in pyrocumulonimbus occurrences may partly be explained as a consequence of the increases in co-occurring extreme FFDI and C-Haines days, rather than due to changes in the atmospheric stability. We also find that changes in the atmospheric circulation are consistent with those seen in the fire weather indices. Increased surface and mid-tropospheric pressure since 1998 are consistent with a poleward shift of the storm track. In particular, an increase in anticyclonic anomalies in the Tasman Sea is indicative of enhanced blocking activity. Although blocking frequency in these longitudes has decreased overall, the proportion of extreme FFDI days that are considered blocked has increased.

When do probability distribution functions (PDFs) about future climate misrepresent uncertainty? How can we recognise when such misrepresentation occurs and thus avoid it in reasoning about or communicating our uncertainty? And when we should not use a PDF, what should we do instead? In this paper, we address these three questions. We start by providing a classification of types of uncertainty and using this classification to illustrate when PDFs misrepresent our uncertainty in a way that may adversely affect decisions. We then discuss when it is reasonable and appropriate to use a PDF to reason about or communicate uncertainty about climate. We consider two perspectives on this issue. On one, which we argue is preferable, available theory and evidence in climate science basically exclude using PDFs to represent our uncertainty. On the other, PDFs can legitimately be provided when resting on appropriate expert judgement and recognition of associated risks. Once we have specified the border between appropriate and inappropriate uses of PDFs, we explore alternatives to their use. We briefly describe two formal alternatives, namely imprecise probabilities and possibilistic distribution functions, as well as informal possibilistic alternatives. We suggest that the possibilistic alternatives are preferable.

Assessments of climate forecast skill depend on choices made by the assessor. In this perspective, we use forecasts of the El Niño-Southern-Oscillation to outline the impact of bias-correction on skill. Many assessments of skill from hindcasts (past forecasts) are probably overestimates of attainable forecast skill because the hindcasts are informed by observations over the period assessed that would not be available to real forecasts. Differences between hindcast and forecast skill result from changes in model biases from the period used to form forecast anomalies to the period over which the forecast is made. The relative skill rankings of models can change between hindcast and forecast systems because different models have different changes in bias across periods., Many different methods have been developed to forecast climate phenomena like the El Nino-Southern Oscillation (ENSO) which makes a fair comparison of their capabilities crucial. In this perspective, the authors discuss how choices in the evaluation method can lead to an overestimated perceived skill of ENSO forecasts.

Persistent shifts in mean rainfall have wide-ranging impacts to hydrology and water availability, and a reliable set of climate projections of change to mean rainfall is a useful tool for future planning. Most climate models project a decrease in winter rainfall in southern Australia, however there is a wide model range and there is not yet a robust assessment of underlying physical processes that can inform and constrain projections. Here, a multiple linear regression model between indices of atmospheric circulation and gridded rainfall in observations and in CMIP5 climate models is developed for July, representing the peak of winter. The regression is used as an evaluation tool for models and a basis to select models. Spatial distributions of the coefficients from the regression illustrate the relative important of different circulation features for rainfall across the region, and illustrate where climate models have deficiencies. As an additional check of projections, historical years that are an analogue for the projected future mean state of the atmospheric circulation are identified and the rainfall anomaly during those years is examined. Both approaches broadly agree and support previous work in suggesting a constraint on rainfall change to a decrease only. The regression analysis also suggests that the median projection for southwest Western Australia should be revised lower than the median of all climate models. The results demonstrate value in applying statistical techniques to understand relationships of rainfall to circulation and to refine confidence in regional climate projections.

We define and examine extreme frost events at three station locations across southern Australia. A synoptic assessment of the events shows that they are generally characterized by passage of a front or trough followed by a developing blocking high. Frost typically occurs at the leading edge of the block. The very cold air pool leading to the frost event is the result of descent of cold, dry midtropospheric air parcels from regions poleward of the station. The air is exceptionally cold because it is advected across the strong meridional temperature gradients in the storm track. The air is dry because this equatorward meridional pathway requires descent and so must have origins well above the surface in the dryer midtroposphere. The position of the block and location of the dry descent are dynamically determined by large-scale waveguide modes in the polar jet waveguide. The role of the waveguide modes is deduced from composites of midtropospheric flow anomalies over the days preceding and after the frost events. These show organized wavenumber 3 or 4 wave trains, with the block associated with the frost formed as a node of the wave train. The wave trains resemble known waveguide modes such as the Pacific–South America mode, and the frost event projects clearly onto these modes during their life cycle. The strong interannual and decadal variability of extreme frost events at a location can be understood in light of event dependence on organized waveguide modes.

At the global scale, reconstructions of cool season temperature over past centuries are relatively rare. Here we present 277-year reconstructions of cool season (July–October) temperatures for southern Australia based on three different data sets: a spatial field reconstruction based on highly resolved temperature data from the Australian Water Availability Product data; reconstructions for the four southeast Australian states based on the Berkeley Earth mean temperature data for each state; and reconstructions for individual stations in southeastern Australia from the Australian Bureau of Meteorology’s Australian Climate Observations Reference Network–Surface Air Temperature data. Our reconstructions typically capture 25–50% of the variation over the late twentieth Century calibration period and are strongest for the southern state of Tasmania and the southeastern part of mainland Australia. All three use Tasmanian tree-rings sensitive to cool season temperatures and display similar variability. In the context of our reconstructions, the persistent warming in the observed record since ~ 1950 is unprecedented. While the low frequency variability of winter temperatures is generally in step with that in summer (December–February) temperatures, high frequency variability is not, illustrating the need for seasonal reconstructions to help improve understanding of variability in inter-seasonal dynamics and the historical importance of this on the environment. The reconstructions covary with changes in the Southern Annular Mode and may be useful for future reconstructions of this phenomenon.

From time to time atmospheric flows become organized and form coherent long-lived structures. Such structures could be propagating, quasi-stationary, or recur in place. We investigate the ability of Principal Components Analysis (PCA) and Archetypal Analysis (AA) to identify long-lived events, excluding propagating forms. Our analysis is carried out on the Southern Hemisphere mid-tropospheric flow represented by geopotential height at 500hPa (Z500). The leading basis patterns of Z500 for PCA and AA are similar and describe structures representing (or similar to) the Southern Annular Mode (SAM) and Pacific South American (PSA) pattern. Long-lived events are identified here from sequences of 8 days or longer where the same basis pattern dominates for PCA or AA. AA identifies more long-lived events than PCA using this approach. The most commonly occurring long-lived event for both AA and PCA is the annular SAM-like pattern. The second most commonly occurring event is the PSA-like Pacific wavetrain for both AA and PCA. For AA the flow at any given time is approximated as weighted contributions from each basis pattern, which lends itself to metrics for discriminating among basis patterns. These show that the longest long-lived events are in general better expressed than shorter events. Case studies of long-lived events featuring a blocking structure and an annular structure show that both PCA and AA can identify and discriminate the dominant basis pattern that most closely resembles the flow event.

Climate forecast skill for the El Nino-Southern Oscillation (ENSO) is better than chance, but has increased little in recent decades. Further, the relative skill of dynamical and statistical models varies in skill assessments, depending on choices made about how to evaluate the forecasts. Using a suite of models from the North American Multi-Model Ensemble (NMME) archive we outline the consequences for skill of how the bias corrections and forecast anomalies are formed. We show that the method for computing forecast anomalies is such a critical part of the provenance of a skill score that any score for forecast anomalies lacking clarity about the method is open to wide interpretation. Many assessments of hindcast skill are likely to be overestimates of attainable forecast skill because the hindcast anomalies are informed by observations over the period assessed that would not be available to a real forecast. The relative skill rankings of forecast models can change between hindcast and forecast systems because the impact of model bias on skill is sensitive to the ways in which forecast anomalies are formed. Dynamical models are found to be more skillful than simple statistical models for forecasting the onset of El Nino events.

Subseasonal prediction skill of precipitation is typically low. Sometimes, however, forecasts are accurate and it would be useful to end-users to assess a priori if this might be the case. We use a 20-year hindcast data set of the ECMWF S2S prediction system and identify periods of high forecast confidence, evaluating model skill of precipitation forecasts for these periods compared to lower confidence predictions.From reanalysis data, we derive a set of circulation patterns, called archetypes, that represent the broad-scale atmospheric circulation over Australia. These archetypes are combinations of ridges and troughs, and yield different precipitation patterns depending on the location of these features. In the literature, a typical application of circulation patterns is assigning daily reanalysis fields to the closest-matching pattern, thus obtaining conditional distributions of precipitation corresponding to key modes of atmospheric variability. A problem common to such analyses is that the precipitation distributions associated with the circulation patterns can be too similar; distinct distributions are required in order for the patterns to be useful in estimating precipitation. We show that by subsampling the archetype occurrences only when they are particularly well-matched to the underlying field, the conditional precipitation distributions become more distinct.We subsample hindcast fields in the same way, obtaining a sample of periods when the model is confident about its prediction of the upcoming archetype. We then calculate model skill in predicting precipitation for three regions in southern Australia during such periods compared to when the model is not confident about the predicted archetype. Our results suggest that during periods of forecast confidence, precipitation skill is greater than normal for shorter leads (up to ten days) in two of the three regions (the Murray Basin and Western Tasmania). Skill for the third region (Southwest Western Australia) is greater during confident periods for lead times greater than one week, although this is marginal.

We assess the large-scale atmospheric dynamics influencing rainfall extremes in Tasmania, located within the Southern Hemisphere storm track. We characterize wet and dry multiday rainfall extremes in western and eastern Tasmania, two distinct climate regimes, and construct atmospheric flow composites around these extreme events. We consider the onset and decay of the events and find a link between Rossby wave trains propagating in the polar jet waveguide and wet and dry extremes across Tasmania. Of note is that the wave trains exhibit varying behavior during the different extremes. In the onset phase of rainfall extremes in western Tasmania, there is a coherent wave train in the Indian Ocean, which becomes circumglobal in extent and quasi-stationary as the event establishes and persists. Wet and dry extremes in this region are influenced by opposite phases of this circumglobal wave train pattern. In eastern Tasmania, wet extremes relate to a propagating wave train, which is first established in the Indian Ocean sector and propagates eastward to the Pacific Ocean sector as the event progresses. During dry extremes in eastern Tasmania, the wave train is first established in the Pacific Ocean, as opposed to Indian Ocean, and persists in this sector for the entire event, with a structure indicative of the Pacific–South American pattern. The findings regarding different wave train forms and their relationship to rainfall extremes have implications for extreme event attribution in other regions around the globe.

Climate and weather have profound effects on economies, the food security and livelihoods of communities throughout the Pacific Island region. These effects are particularly important for small-scale fisheries and occur, for example, through changes in sea surface temperature, primary productivity, ocean currents, rainfall patterns, and through cyclones. This variability has impacts over both short and long time scales. We differentiate climate predictions (the actual state of climate at a particular point in time) from climate projections (the average state of climate over long time scales). The ability to predict environmental conditions over the time scale of months to decades will assist governments and coastal communities to reduce the impacts of climatic variability and take advantage of opportunities. We explore the potential to make reliable climate predictions over time scales of six months to 10 years for use by policy makers, managers and communities. We also describe how climate predictions can be used to make decisions on short time scales that should be of direct benefit to sustainable management of small-scale fisheries, and to disaster risk reduction, in Small-Island Developing States in the Pacific.

This work examines summer heat wave events in four different regions of Australia (southwest, central, southeast, and northeast) to assess similarities and differences in the circulations that precede, accompany, and follow the heat wave events. A series of circulation composites are constructed for days from 10 days prior to 5 days following onset of each heat wave event. The composites of geopotential height anomalies and wave activity flux vectors show that heat waves in southwest and southeast Australia are preceded by coherent wave train structures in the Indian Ocean region, accompanied by blocking in the Australian region (as an amplified node of the wave train structure), and followed by coherent responses of wave train patterns in the Pacific and South America regions. The heat wave blocking high is maintained by convergence of wave activity in a well-defined wave channel. The concentration of wave activity in the block is aided by the formation of a subtropical jet branch and wave barrier on the equatorward side of the block. Heat waves in central and northeast Australia show similar wave train life cycle responses, but with a proximate ridge in the midtroposphere and a trough in the nearby waveguide region. Heat waves in Australia can be viewed as an element of successive expression of the planetary waveguide modes in the Southern Hemisphere and serve as signifiers of organized, active phases of these modes.

This study documents the importance, and changes in the importance, of a suite of synoptic to large-scale drivers of minimum temperature variability across the Australian region. The drivers investigated are the El Nino – Southern Oscillation (ENSO) as measured by the Southern Oscillation Index, atmospheric blocking, the Southern Annular Mode, and the position and intensity of the subtropical ridge. In most regions, individual drivers generally account for between about 5 and 10% of the interannual variability across Australia, although in some seasons and regions these drivers can collectively account for more than 60% of the observed variability. The amount of minimum temperature variance explained by individual drivers is highest in south-eastern Australia in summer (December–February), where the drivers collectively account for 67% of the variance, due primarily to the relationships with blocking and ENSO. The varying importance of the drivers of minimum temperature variability between seasons and between two discrete periods (i.e. 1960–1984 and 1985–2015) has been investigated. In the more recent period the intensity of the subtropical ridge has played a more important role in minimum temperature variability, particularly in the south-western and south-eastern parts of Australia in summer (December–February), with the position of the subtropical ridge a feature of greater importance over much of Victoria in spring (September–November). For the more recent period the intensity of the subtropical ridge and the southern annular mode have been more important drivers of minimum temperature variability for autumn (March–May) and winter (June–August), respectively, across southern New South Wales and northern Victoria.

The Earth has warmed over the past century. The warming rate (amount of warming over a given period) varies in time and space. Observations show a recent increase in global mean warming rate, which is initially maintained in model projections, but which diverges substantially in future depending on the emissions scenario followed. Scenarios that stabilize forcing lead to much lower warming rates, as the rate depends on the change in forcing, not the amount. Warming rates vary spatially across the planet, but most areas show a shift toward higher warming rates in recent decades. The areal distribution of warming rates is also changing shape to include a longer tail in recent decades. Some areas of the planet are already experiencing extreme warming rates of about 1 °C/decade. The fat tail in areal distribution of warming rates is pronounced in model runs when the forcing and global mean warming rate is increasing, and indicates a climate state more prone to regime transitions. The area-proportion of the Earth displaying warming/cooling trends is shown to be directly related to the global mean warming rate, especially for trends of length 15 years and longer. Since the global mean warming rate depends on the forcing rate, the proportion of warming/cooling trend areas in future also depends critically on the choice of future forcing scenario. Keywords: Climate variability, Climate projection, Transient response, Extreme warming

Policy- and decision-makers require assessments of status and trends for marine species, habitats, and ecosystems to understand if human activities in the marine environment are sustainable, particularly in the face of global change. Central to many assessments are statistical and dynamical models of populations, communities, ecosystems, and their socioeconomic systems and management frameworks. The establishment of a national system that could facilitate the development of such model-based assessments has been identified as a priority for addressing management challenges for Australia’s marine environment. Given that most assessments require cross-scale information, individual models cannot capture all of the spatial, temporal, biological, and socioeconomic scales that are typically needed. Coupling or integrating models across scales and domains can expand the scope for developing comprehensive and internally consistent, system-level assessments, including higher-level feedbacks in social–ecological systems. In this article, we summarize: (i) integrated modelling for marine systems currently being undertaken in Australia, (ii) methods used for integration and comparison of models, and (iii) improvements to facilitate further integration, particularly with respect to standards and specifications. We consider future needs for integrated modelling of marine social–ecological systems in Australia and provide a set of recommendations for priority focus areas in the development of a national approach to integrated modelling. These recommendations draw on—and have broader relevance for—international efforts around integrated modelling to inform decision-making for marine systems.

Atmospheric circulation change is likely to be the dominant driver of multidecadal rainfall trends in the midlatitudes with climate change this century. This study examines circulation features relevant to southern Australian rainfall in January and July and explores emergent constraints suggested by the intermodel spread and their impact on the resulting rainfall projection in the CMIP5 ensemble. The authors find relationships between models’ bias and projected change for four features in July, each with suggestions for constraining forced change. The features are the strength of the subtropical jet over Australia, the frequency of blocked days in eastern Australia, the longitude of the peak blocking frequency east of Australia, and the latitude of the storm track within the polar front branch of the split jet. Rejecting models where the bias suggests either the direction or magnitude of change in the features is implausible produces a constraint on the projected rainfall reduction for southern Australia. For RCP8.5 by the end of the century the constrained projections are for a reduction of at least 5% in July (with models showing increase or little change being rejected). Rejecting these models in the January projections, with the assumption the bias affects the entire simulation, leads to a rejection of wet and dry outliers.

The authors undertake a multiscale spectral reexamination of the variability of the Pacific–South American (PSA) pattern and the mechanisms by which this variability occurs. Time scales from synoptic to interannual are investigated, focusing on the means by which tropical variability is communicated to the midlatitudes and on in situ forcing within the midlatitude waveguides. Particular interest is paid to what fraction of the total variability associated with the PSA, occurring on interannual time scales, is attributable to tropical forcing relative to that occurring on synoptic and intraseasonal time scales via internal waveguide dynamics. In general, it is found that the eastward-propagating wave train pattern typically associated with the PSA manifests across time scales from synoptic to interannual, with the majority of the variability occurring on synoptic-to-intraseasonal time scales largely independent of tropical convection. It is found that the small fraction of the total variance with a tropical signal occurs via the zonal component of the thermal wind modulating both the subtropical and polar jets. The respective roles of the Hadley circulation and stationary Rossby wave sources are also examined. Further, a PSA-like mode is identified in terms of the slow components of higher-order modes of tropospheric geopotential height. This study reestablishes the multiscale nonlinear nature of the PSA modes arising largely as a manifestation of internal midlatitude waveguide dynamics and local disturbances.

Using reanalysed atmospheric data and applying a data-driven multiscale approximation to non-stationary dynamical processes, we undertake a systematic examination of the role of memory and dimensionality in defining the quasi-stationary states of the troposphere over the recent decades. We focus on the role of teleconnections characterised by either zonally-oriented wave trains or meridional dipolar structures. We consider the impact of various strategies for dimension reduction based on principal component analysis, diagonalization and truncation.We include the impact of memory by consideration of Bernoulli, Markovian and non-Markovian processes. We a priori explicitly separate barotropic and baroclinic processes and then implement a comprehensive sensitivity analysis to the number and type of retained modes. Our results show the importance of explicitly mitigating the deleterious impacts of signal degradation through ill-conditioning and under sampling in preference to simple strategies based on thresholds in terms of explained variance. In both hemispheres, the results obtained for the dominant tropospheric modes depend critically on the extent to which the higher order modes are retained, the number of free model parameters to be fitted, and whether memory effects are taken into account. Our study identifies the primary role of the circumglobal teleconnection pattern in both hemispheres for Bernoulli and Markov processes, and the transient nature and zonal structure of the Southern Hemisphere patterns in relation to their Northern Hemisphere counterparts. For both hemispheres, overfitted models yield structures consistent with the major teleconnection modes (NAO, PNA and SAM), which give way to zonally oriented wavetrains when either memory effects are ignored or where the dimension is reduced via diagonalising. Where baroclinic processes are emphasised, circumpolar wavetrains are manifest.

This work reviews the literature on an alleged global warming 'pause' in global mean surface temperature (GMST) to determine how it has been defined, what time intervals are used to characterise it, what data are used to measure it, and what methods used to assess it. We test for 'pauses', both in the normally understood meaning of the term to mean no warming trend, as well as for a 'pause' defined as a substantially slower trend in GMST. The tests are carried out with the historical versions of GMST that existed for each pause-interval tested, and with current versions of each of the GMST datasets. The tests are conducted following the common (but questionable) practice of breaking the linear fit at the start of the trend interval ('broken' trends), and also with trends that are continuous with the data bordering the trend interval. We also compare results when appropriate allowance is made for the selection bias problem. The results show that there is little or no statistical evidence for a lack of trend or slower trend in GMST using either the historical data or the current data. The perception that there was a 'pause' in GMST was bolstered by earlier biases in the data in combination with incomplete statistical testing.

The perception of the accuracy of regional climate projections made in the early 1990s about climate change by 2030 may be influenced by how the temperature trend has changed in the 25 years since their publication. However, temperature trends over this period were influenced not only by external forcings such as greenhouse gases but also natural variations. The temperature of Southern Australia, the Sahel, South Asia and Southern Europe are currently within the warming estimates from statements in the early 1990s from the IPCC and CSIRO, assuming a linear trend between 1990 and 2030. However, northern Australia and central North America are currently at the lower limit or below these projections, having featured areas of multi-year regional cooling during global warming, sometimes called ‘warming holes’. Recent climate model simulations suggest that cooling can be expected in the recent past and near future in some regions, including in Australia and the US, and that cooling is less likely over 1990–2030 than in 1990–2015, bringing observations closer to the IPCC and CSIRO warming estimates by 2030. Cooling at the 25-year scale in some regions can be associated with cyclic variability such as the Inter-decadal Pacific Oscillation, or low trend such as in the Southern Ocean. Explicitly communicating the variability in regional warming rates in climate projections, including the possibility of regional warming ‘holes’ (or the opposite of ‘surges’ or ‘peaks’) would help to set more reliable expectations by users of those projections.

There has been much recent published research about a putative “pause” or “hiatus” in global warming. We show that there are frequent fluctuations in the rate of warming around a longer-term warming trend, and that there is no evidence that identifies the recent period as unique or particularly unusual. In confirmation, we show that the notion of a pause in warming is considered to be misleading in a blind expert test. Nonetheless, the most recent fluctuation about the longer-term trend has been regarded by many as an explanatory challenge that climate science must resolve. This departs from long-standing practice, insofar as scientists have long recognized that the climate fluctuates, that linear increases in CO2 do not produce linear trends in global warming, and that 15-yr (or shorter) periods are not diagnostic of long-term trends. We suggest that the repetition of the “warming has paused” message by contrarians was adopted by the scientific community in its problem-solving and answer-seeking role and has led to undue focus on, and mislabeling of, a recent fluctuation. We present an alternative framing that could have avoided inadvertently reinforcing a misleading claim.

August 2014 saw very strong monthly positive mean sea level pressure (MSLP) anomalies and intense daily to multiday MSLP events south of Australia and in the Tasman Sea (Fig. 32.1a). To the west of Tasmania there were monthly anomalies of over 10 hPa (2.4 standard deviations from the mean), the highest on record since 1979 using ERA-Interim reanalysis (ERAint; Dee et al. 2011), or from 1850 using the Hadley Centre Sea Level Pressure analysis (HadSLP2r; Allan and Ansell 2006). Atmospheric blocking west of Tasmania on 10–15 August (Figs. 32.1b,c) featured the highest daily August MSLP anomaly in either record in that location. Blocking was seen in the south Tasman Sea later in the month, including the highest daily MSLP anomaly on record at that location. The spatial distribution of the monthly MSLP anomalies resembles a wave-3 pattern (Fig. 32.1a).

Short-term climate trends are sensitive to definitions, data and testing. This sensitivity underlies an alleged pause in global warming, and highlights the need for meaningful definitions to sustain claims that it was real. See Analysis p.41 After a spike in global-mean temperature associated with the 1998 El Nino, the climate system experienced several years of reduced warming, and perhaps even slight cooling. This period, variously termed the 'hiatus', 'pause' or 'slowdown', should have come as no surprise given our understanding of El Nino and natural climate variability. However, soon after the recognition of the reduced warming, it appeared that models and observations were diverging, raising the question of whether the models were missing important processes. Although global warming has since recommenced, the hiatus sparked an enormous research effort. Iselin Medhaug et al. synthesize the literature and reassess the model and observational evidence. Their assessment reconciles the apparent contradictions between models and data and obviates the need to revise our understanding of the underlying physics of climate systems. The hiatus was an episode of natural variability after all.

Some well-established scientific findings may be rejected by vocal minorities because the evidence is in conflict with political views or economic interests. For example, the tobacco industry denied the medical consensus on the harms of smoking for decades, and the clear evidence about human-caused climate change is currently being rejected by many politicians and think tanks that oppose regulatory action. We present an agent-based model of the processes by which denial of climate change can occur, how opinions that run counter to the evidence can affect the scientific community, and how denial can alter the public discourse. The model involves an ensemble of Bayesian agents, representing the scientific community, that are presented with the emerging historical evidence of climate change and that also communicate the evidence to each other. Over time, the scientific community comes to agreement that the climate is changing. When a minority of agents is introduced that is resistant to the evidence, but that enter into the scientific discussion, the simulated scientific community still acquires firm knowledge but consensus formation is delayed. When both types of agents are communicating with the general public, the public remains ambivalent about the reality of climate change. The model captures essential aspects of the actual evolution of scientific and public opinion during the last 4 decades.

Changes over the scale of decades in oceanic environments present a range of challenges for management and utilisation of ocean resources. Here we investigate sources of global temporal variation in Sea Surface Temperature (SST) and Ocean Colour (Chl-a) and their co-variation, over a 14 year period using statistical methodologies that partition sources of variation into inter-annual and annual components and explicitly account for daily auto-correlation. The variation in SST shows bands of increasing variability with increasing latitude, while the analysis of annual variability in Chl-a shows mostly mid-latitude high variability bands. Covariation patterns of SST and Chl-a suggests several different mechanisms impacting Chl-a change and variance. Our high spatial resolution analysis indicates these are likely to be operating at relatively small spatial scales. There are large regions showing warming and rising of Chl-a, contrasting with regions that show warming and decreasing Chl-a. The covariation pattern in annual variation in SST and Chl-a reveals broad latitudinal bands. On smaller scales there are significant regional anomalies where upwellings are known to occur. Over decadal time scales both trend and variation in SST, Chl-a and their covariance is highly spatially heterogeneous, indicating that monitoring and resource management must be regionally appropriate.

Human influence may have enhanced the circulation pattern that drives cold outbreaks and frost risk over southwest Western Australia in September 2016, but larger thermodynamic changes may have still made these events less likely.

A critical question in the global warming debate concerns the causes of the observed trends of the Southern Hemisphere (SH) atmospheric circulation over recent decades. Secular trends have been identified in the frequency of occurrence of circulation regimes, namely the positive phase of the Southern Annular Mode (SAM) and the hemispheric wave-3 pattern which is associated with blocking. Previous studies into the causes of these secular trends have either been purely model based, have not included observational forcing data or have mixed external forcing with indices of internal climate variability impeding a systematic and unbiased attribution of the causes of the secular trends. Most model studies also focused mainly on the austral summer season. However, the changes to the storm tracks have occurred in all seasons and particularly in the austral winter and early spring when midlatitude blocking is most active and stratospheric ozone should not play a role. Here we systematically attribute the secular trends over the recent decades using a non-stationary clustering method applied to both reanalysis and observational forcing data from all seasons. While most previous studies emphasized the importance of stratospheric ozone depletion in causing austral summer SH circulation trends, we show observational evidence that anthropogenic greenhouse gas concentrations have been the major driver of these secular trends in the SAM and blocking when all seasons are considered. Our results suggest that the recovery of the ozone hole might delay the signal of global warming less strongly than previously thought and that effects from all seasons are likely crucial in understanding the causes of the secular trends.

We identify the dynamical drivers of systematic changes in persistent quasi-stationary states (regimes) of the Southern Hemisphere troposphere and their secular trends. We apply a purely data-driven approach, whereby a multiscale approximation to nonstationary dynamical processes is achieved through optimal sequences of locally stationary fast vector autoregressive factor processes, to examine a high resolution atmospheric reanalysis over the period encompassing 1958–2013. This approach identifies regimes and their secular trends in terms of the predictability of the flow and is Granger causal. A comprehensive set of diagnostics on both isentropic and isobaric surfaces is employed to examine teleconnections over the full hemisphere and for a set of regional domains. Composite states for the hemisphere obtained from nonstationary nonparametric cluster analysis reveal patterns consistent with a circumglobal wave 3 (polar)–wave 5 (subtropical) pattern, while regional composites reveal the Pacific South American pattern and blocking modes. The respective roles of potential vorticity sources, stationary Rossby waves and baroclinic instability on the dynamics of these circulation modes are shown to be reflected by the seasonal variations of the waveguides, where Rossby wave sources and baroclinic disturbances are largely contained within the waveguides and with little direct evidence of sustained remote tropical influences on persistent synoptic features. Warm surface temperature anomalies are strongly connected with regions of upper level divergence and anticyclonic Rossby wave sources. The persistent states identified reveal significant variability on interannual to decadal time scales with large secular trends identified in all sectors apart from a region close to South America.

Vested interests and political agents have long opposed political or regulatory action in response to climate change by appealing to scientific uncertainty. Here we examine the effect of such contrarian talking points on the scientific community itself. We show that although scientists are trained in dealing with uncertainty, there are several psychological reasons why scientists may nevertheless be susceptible to uncertainty-based argumentation, even when scientists recognize those arguments as false and are actively rebutting them. Specifically, we show that prolonged stereotype threat, pluralistic ignorance, and a form of projection (the third-person effect) may cause scientists to take positions that they would be less likely to take in the absence of outspoken public opposition. We illustrate the consequences of seepage from public debate into the scientific process with a case study involving the interpretation of temperature trends from the last 15 years. We offer ways in which the scientific community can detect and avoid such inadvertent seepage.

Attribution of cause of climate change is hindered by our ability to separate internal low-frequency variability from the forced response in the climate system. We characterize the spatiotemporal characteristics of internal variability by comparing ensemble averages of in-band fractional variances in Coupled Model Intercomparison Project Phase 5 (CMIP5) preindustrial control simulations to estimates from observations and reanalyses. For sea surface temperature and sea level height anomalies both models and observations show that variability on time scales less than 5 years is predominantly in the tropics and has the spatial signature of El Nino–Southern Oscillation. On progressively longer time scales the variance moves to the extratropics and from middle to higher latitudes while displaying spatially coherent features. The CMIP5 models show good agreement in the spatial and temporal apportioning of in-band variance when the variances are normalized.