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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 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.

Subseasonal-to-seasonal (S2S) forecasts are bridging the gap between weather forecasts and long-range predictions. Decisions in various sectors are made in this forecast timescale, therefore there is a strong demand for this new generation of predictions. While much of the focus in recent years has been on improving forecast skill, if S2S predictions are to be used effectively, it is important that along with scientific advances, we also learn how best to develop, communicate and apply these forecasts.In this presentation, we present recent progress in the applications of S2S forecasts. We summarise case studies from a recently-published applications community review paper in the Bulletin of the American Meteorological Society (BAMS), covering sectoral applications of S2S predictions from around the world, including public health, disaster preparedness, water management, telecommunications, energy and agriculture. Involving over 60 authors and drawing from the recent advances and experience of researchers and users working with S2S forecasts globally, we explore the value of applications-relevant S2S predictions through a series of sectoral cases where uptake is starting to occur.From across 12 case studies, we show that:The S2S forecasting timescale is a new concept for many users. While the additional value of S2S forecasts for decision-making is increasingly gaining interest among users, incorporating probabilistic ensemble S2S forecasts into existing operations is not trivial. Barriers to widespread adoption of S2S forecasts include lack of access to the forecasts and the co-production to tailor forecasts to user needs, as well as varying ‘in house’ expertise in how to interpret and effectively apply them. This can create a ‘knowledge-value’ gap in some instances. S2S forecasts do not produce a ‘go/no go’ answer of how a user should respond to a potential hazard; instead they provide additional, supplementary ‘situational awareness’ information that can be used to support decision-making on S2S timescales. While S2S forecasting is still a maturing discipline globally, this publication marks a significant step forward in moving from potential to actual S2S forecasting applications – a collective body of evidence demonstrating both skill and utility across sectors that places user needs and applications at the forefront of S2S forecast development.Our paper, ‘Advances in the application and utility of subseasonal-to-seasonal predictions’, is available from BAMS as an open access publication: https://doi.org/10.1175/BAMS-D-20-0224.1.

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.

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.

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.

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.

We present an ice-penetrating radar data set which consists of 26 internal reflecting horizons (IRHs) that cover the entire Dome C area of the East Antarctic plateau, the most extensive to date in the region. This data set uses radar surveys collected over the space of 10 years, starting with an airborne international collaboration in 2008 to explore the region, up to the detailed ground based surveys in support of the Beyond EPICA – Oldest Ice (BE-OI) European Consortium. Through direct correlation with the EPICA-DC ice core, we date 19 IRHs that span the past four glacial cycles, from the beginning of the Holocene to over 350 ka. We indirectly date and provide stratigraphic information for seven older IRHs using an 1-D ice flow inverse model, going back to an estimated 700 ka. Depth and age uncertainties are quantified for all IRHs and provided as part of the data set. The IRH data set presented in this study is available at the U.S. Antarctic Program Data Center (USAP-DC) (https://doi.org/10.15784/601411, Cavitte et al., 2020) and represents a contribution to the SCAR AntArchitecture program.

Knowledge of past, current and future hydroclimatic risk is of great importance. However, like many other countries, Australia's observed hydroclimate records are at best only ~ 120 years long (i.e. from ~ 1900 to present) but are typically less than ~ 50 years long. Therefore, recent research has focused on developing longer hydroclimate records based on palaeoclimate information from a variety of different sources. Here we review and compare the insights emerging from 11 published palaeoclimate records that are relevant to annual to sub-decadal hydroclimatic variability in eastern Australia over the last ~ 1000 years. The sources of palaeoclimate information include ice cores, tree rings, cave deposits and lake sediment deposits. The published palaeoclimate information was then analysed to determine when (and where) there was agreement (or uncertainty) about the timing of wet and dry epochs in the pre-instrumental period (1000–1899). The occurrence, frequency, duration and spatial extent of pre-instrumental wet and dry epochs was then compared to wet and dry epochs since 1900. The results show that instrumental records (~ 1900–present) underestimate (or at least misrepresent) the full range of rainfall variability that has occurred, and is possible, in eastern Australia. Even more disturbing is the suggestion, based on insights from the published palaeoclimate data analysed, that 71 % of the pre-instrumental period appears to have no equivalent in the instrumental period. This implies that the majority of the past 1000 years was unlike anything encountered in the period that informs water infrastructure, planning and policy in Australia. A case study, using a typical water storage reservoir in eastern Australia, demonstrates that current water resource infrastructure and management strategies would not cope under the range of pre-instrumental conditions that this study suggests has occurred. When coupled with projected impacts of climate change and growing demands, these results highlight some major challenges for water resource management and infrastructure. Though our case study location is eastern Australia, these challenges, and the limitations associated with current methods that depend on instrumental records that are too short to realistically characterise interannual to multidecadal variability, also apply globally.

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis. Fil: Merryfield, William J.. Canadian Centre For Climate Modelling And Analysis; Canadá Fil: Baehr, Johanna. Universitat Hamburg; Alemania Fil: Batté, Lauriane. Centre National de Recherches Météorologiques; Francia Fil: Becker, Emily J.. No especifíca; Fil: Butler, Amy H.. State University of Colorado at Boulder; Estados Unidos Fil: Coelho, Caio A.S.. Instituto Nacional de Pesquisas Espaciais; Brasil Fil: Danabasoglu, Gokhan. National Center for Atmospheric Research; Estados Unidos Fil: Dirmeyer, Paul A.. No especifíca; Fil: Doblasreyes, Francisco. No especifíca; Fil: Domeisen, Daniela I.V.. No especifíca; Fil: Ferranti, Laura. No especifíca; Fil: Ilynia, Tatiana. No especifíca; Fil: Kumar, Arun. No especifíca; Fil: Müller, Wolfgang A.. No especifíca; Fil: Rixen, Michel. World Meteorological Organization; Estados Unidos Fil: Robertson, Andrew W.. International Research Institute For Climate And Society; Estados Unidos Fil: Smith, Doug M.. No especifíca; Fil: Takaya, Yuhei. Japan Meteorological Agency; Japón Fil: Tuma, Matthias. World Meteorological Organization; Estados Unidos Fil: Vitart, Frederic. No especifíca; Fil: White, Christopher J.. University of Strathclyde; Reino Unido Fil: Alvarez, Mariano Sebastián. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Ardilouze, Constantin. No especifíca; Fil: Attard, Hannah. No especifíca; Fil: Baggett, Cory. State University of Colorado - Fort Collins; Estados Unidos Fil: Balmaseda, Magdalena A.. No especifíca; Fil: Beraki, Asmerom F.. University Of Pretoria; Sudáfrica Fil: Bhattacharjee, Partha S.. National Oceanic And Atmospheric Administration; Estados Unidos Fil: Bilbao, Roberto. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; España Fil: De Andrade, Felipe M.. University of Reading; Reino Unido Fil: DeFlorio, Michael J.. No especifíca; Fil: Díaz, Leandro Baltasar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Ehsan, Muhammad Azhar. The Abdus Salam. International Centre for Theoretical Physics; Italia. The Abdus Salam; Italia Fil: Fragkoulidis, Georgios. Johannes Gutenberg Universitat Mainz; Alemania Fil: Grainger, Sam. No especifíca; Fil: Green, Benjamin W.. State University of Colorado at Boulder; Estados Unidos Fil: Hell, Momme C.. No especifíca; Fil: Infanti, Johnna M.. National Oceanic And Atmospheric Administration; Estados Unidos Fil: Isensee, Katharina. No especifíca; Fil: Kataoka, Takahito. No especifíca; Fil: Kirtman, Ben P.. No especifíca; Fil: Klingaman, Nicholas P.. University of Reading; Reino Unido Fil: Lee, June Yi. Pusan National University; Corea del Sur Fil: Mayer, Kirsten. State University of Colorado at Boulder; Estados Unidos Fil: McKay, Roseanna. Monash University; Australia Fil: Mecking, Jennifer V.. University of Southampton; Reino Unido Fil: Miller, Douglas E.. University of Illinois. Urbana - Champaign; Estados Unidos Fil: Neddermann, Nele. No especifíca; Fil: Ng, Ching Ho Justin. University of Princeton; Estados Unidos Fil: Ossó, Albert. University of Graz; Austria Fil: Pankatz, Klaus. Max Planck Institute For Meteorology; Estados Unidos Fil: Peatman, Simon. No especifíca; Fil: Pegion, Kathy. George Mason University; Estados Unidos Fil: Perlwitz, Judith. State University of Colorado at Boulder; Estados Unidos Fil: Recalde Coronel, G. Cristina. Escuela Superior Politecnica del Litoral ; Ecuador Fil: Reintges, Annika. Geomar-Helmholtz Centre for Ocean Research Kiel; Alemania Fil: Renkl, Christoph. Dalhousie University Halifax; Canadá Fil: Solaraju-Murali, Balakrishnan. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; España Fil: Spring, Aaron. No especifíca; Fil: Stan, Cristiana. George Mason University; Estados Unidos Fil: Sun, Y. Qiang. University of Princeton; Estados Unidos Fil: Tozer, Carly R.. No especifíca; Fil: Vigaud, Nicolas. No especifíca; Fil: Woolnough, Steven. University of Reading; Reino Unido Fil: Yeager, Stephen. National Center for Atmospheric Research; Estados Unidos

* This essay stems from international conferences on subseasonal to seasonal and seasonal to decadal prediction jointly convened by WWRP and WCRP in September 2018 in Boulder, Colorado: (www.wcrp-climate.org/s2s-s2d-2018-home). Adapted from “Current and Emerging Developments in Subseasonal to Decadal Prediction,”. Published Online in BAMS, June 2020. For the full, citable article, see DOI:10.1175 /BAMS-D-19-0037.1. © Copyright 2020 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). Tremendous recent progress in climate prediction on subseasonal to decadal time scales has been enabled by better observations, data assimilation, and models originating from the weather prediction and climate simulation communities together with ever-increasing computational power. World Climate Research Program (WCRP) efforts led initially to predictions one to two seasons ahead becoming part of the WMO operational infrastructure. More recently, a joint World Weather Research Program (WWRP) and WCRP Subseasonal to Seasonal Prediction Project has started tackling the weather–climate gap (from two weeks to a season). The NOAA-led Subseasonal Experiment project has similar aims. New frontiers have been enabled by Earth system models that represent the carbon and other biogeochemical cycles in addition to the physical climate system. As a result, skillful multiyear prediction is likely achievable for biogeochemical and ecological Earth system components. The ultimate collective subseasonal to seasonal (S2S; 2 weeks to season) and seasonal to decadal (S2D) endeavor is to improve the prediction of the spatial–temporal continuum connecting weather to climate through a coordinated, seamless, and integrated Earth system approach. The S2S and S2D communities share common scientific and technical challenges. This essay* synthesizes those commonalities across time scales and Earth system components, and from basic research to operational delivery. Peer Reviewed Article signat per 65 autors/es: William J. Merryfield, Johanna Baehr, Lauriane Batté, Emily J. Becker, Amy H. Butler, Caio A. S. Coelho, Gokhan Danabasoglu, Paul A. Dirmeyer, Francisco J. Doblas-Reyes, Daniela I. V. Domeisen, Laura Ferranti, Tatiana Ilynia, Arun Kumar, Wolfgang A. Müller, Michel Rixen, Andrew W. Robertson, Doug M. Smith, Yuhei Takaya, Matthias Tuma, Frederic Vitart, Christopher J. White, Mariano S. Alvarez, Constantin Ardilouze, Hannah Attard, Cory Baggett, Magdalena A. Balmaseda, Asmerom F. Beraki, Partha S. Bhattacharjee, Roberto Bilbao, Felipe M. de Andrade, Michael J. DeFlorio, Leandro B. Díaz, Muhammad Azhar Ehsan, Georgios Fragkoulidis, Sam Grainger, Benjamin W. Green, Momme C. Hell, Johnna M. Infanti, Katharina Isensee, Takahito Kataoka, Ben P. Kirtman, Nicholas P. Klingaman, June-Yi Lee, Kirsten Mayer, Roseanna McKay, Jennifer V. Mecking, Douglas E. Miller, Nele Neddermann, Ching Ho Justin Ng, Albert Ossó, Klaus Pankatz, Simon Peatman, Kathy Pegion, Judith Perlwitz, G. Cristina Recalde-Coronel, Annika Reintges, Christoph Renkl, Balakrishnan Solaraju-Murali, Aaron Spring, Cristiana Stan, Y. Qiang Sun, Carly R. Tozer, Nicolas Vigaud, Steven Woolnough, and Stephen Yeager.

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.

An important step in developing climate forecasts is the identification of an appropriate set of predictors. Seasonal rainfall forecasts for South Australia (SA) currently have low predictive skill. It is hypothesised that this is because the large-scale climate processes influencing SA's rainfall variability have yet to be completely identified and therefore are not adequately represented in forecast models. Here, relationships between large-scale climate influences and rainfall in SA are assessed with a focus on methods that account for non-linearities inherent in the climate system. A threshold method and also a novel method for climate predictor selection, Partial Mutual Information, are used to establish the hierarchy of importance of the key influences. From a large suite of potential predictors, variability in the Subtropical Ridge intensity, blocking and the gradient of sea surface temperatures (SSTs) in the tropical central and eastern Indian Ocean are found to be key indicators of seasonal rainfall variability in SA. Interactions between processes are shown to increase the amount of variability accounted for and therefore need to be better considered when producing seasonal forecasts. Notably, the view of which climate process(es) is(are) important changes depending on the input variables used, suggesting that it is not wise to make a priori assumptions about what is or is not important. Furthermore, even a perfect forecast of El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), the focus of forecasting efforts in Australia, will not necessarily lead to an improvement to forecast skill in SA given that these processes are not key for the region. The results of this analysis can inform the development of existing and new statistical and dynamical seasonal forecasting systems for SA, and for other regions where the impacts of climate variability are significant but not necessarily dominated by ENSO and IOD.

Seasonal rainfall forecasts are an important tool for risk management across many sectors. However, significant challenges arise in the development of skilful and practically useful seasonal forecasts for regions where the temporal and spatial variability of rainfall is large and/or knowledge about what causes this variability is in its infancy. This is evident in the state of South Australia (SA), where seasonal rainfall currently has low predictive skill. The key climate processes have yet to be fully identified in SA and therefore may not be adequately represented in forecast models. The aim of this paper is to identify and quantify relationships between large-scale ocean-atmospheric processes and seasonal rainfall variability across SA. We identify two distinct climate zones: (1) the arid northern region, where rainfall is mostly influenced by climate processes stemming from the tropical Indian and/or Pacific Oceans and (2) southern SA, which is dominated by Southern Ocean processes. The average percent of variability of SA rainfall accounted for by any single large-scale climate process (i.e. linear regression using a single predictor) is 8% in summer, 19% in autumn, 33% in winter and 24% in spring. However, when two or more processes are considered in combination (through multiple linear regression), this rises to 13, 26, 46, and 33%, respectively, highlighting the importance of capturing the interaction among multiple climate processes. Importantly, the findings from this study provide a set of metrics against which existing statistical and dynamical forecasting schemes can be tested and highlight processes that should be focused on in order to improve (or develop new) forecasting schemes. The study also recommends the need for further investigations into non-linear relationships between rainfall and large-scale ocean-atmospheric processes and the development of more objective methods for determining which climate process, or combination of processes, are most important for a certain season or location.

Reconstructing past hydroclimatic variability using climate-sensitive paleoclimate proxies provides context to our relatively short instrumental climate records and a baseline from which to assess the impacts of human-induced climate change. However, many approaches to reconstructing climate are limited in their ability to address sampling variability inherent in different climate proxies. We iteratively optimise an ensemble of possible reconstruction data series to maximise the Gaussian kernel correlation of Rehfeld et al. (2011) which reconciles differences in the temporal resolution of both the target variable and proxies or covariates. The reconstruction method is evaluated using synthetic data with different degrees of sampling variability and noise. Two examples using paleoclimate proxy records and a third using instrumental rainfall data with missing values are used to demonstrate the utility of the method. While the Gaussian kernel correlation method is relatively computationally expensive, it is shown to be robust under a range of data characteristics and will therefore be valuable in analyses seeking to employ multiple input proxies or covariates.

Ice cores provide some of the best-dated and most comprehensive proxy records, as they yield a vast and growing array of proxy indicators. Selecting a site for ice core drilling is nonetheless challenging, as the assessment of potential new sites needs to consider a variety of factors. Here, we demonstrate a systematic approach to site selection for a new East Antarctic high-resolution ice core record. Specifically, seven criteria are considered: (1) 2000-year-old ice at 300 m depth; (2) above 1000 m elevation; (3) a minimum accumulation rate of 250 mm years−1 IE (ice equivalent); (4) minimal surface reworking to preserve the deposited climate signal; (5) a site with minimal displacement or elevation change in ice at 300 m depth; (6) a strong teleconnection to midlatitude climate; and (7) an appropriately complementary relationship to the existing Law Dome record (a high-resolution record in East Antarctica). Once assessment of these physical characteristics identified promising regions, logistical considerations (for site access and ice core retrieval) were briefly considered. We use Antarctic surface mass balance syntheses, along with ground-truthing of satellite data by airborne radar surveys to produce all-of-Antarctica maps of surface roughness, age at specified depth, elevation and displacement change, and surface air temperature correlations to pinpoint promising locations. We also use the European Centre for Medium-Range Weather Forecast ERA 20th Century reanalysis (ERA-20C) to ensure that a site complementary to the Law Dome record is selected. We find three promising sites in the Indian Ocean sector of East Antarctica in the coastal zone from Enderby Land to the Ingrid Christensen Coast (50–100° E). Although we focus on East Antarctica for a new ice core site, the methodology is more generally applicable, and we include key parameters for all of Antarctica which may be useful for ice core site selection elsewhere and/or for other purposes.

Study region South East Queensland (SEQ), Australia. Study focus Decision makers in the water sector need to deal with uncertainty about the impacts of climate variability and change. Identifying solutions for hydroclimatic risk adaptation strategies that are both optimal and robust in the presence of this uncertainty presents a difficult challenge. The instrumental hydroclimatic record in Australia is short (∼60−120 years depending on location and variable), and fails to encompass enough climate variability to allow the calculation of robust statistics around the baseline risk of extreme events (i.e. multi-year droughts, decadal periods with clustering of major flood events). This paper (i) demonstrates how palaeoclimate data can be used to better understand what is possible with respect to drought frequency and duration in South East Queensland (SEQ), Australia and (ii) investigates some implications from palaeoclimate data for drought planning, drought management and water security decision making. New hydrological insights for the region The instrumental period is not representative of the full range of past climate variability. Droughts worse than those in the instrumental record are not only possible, but likely, and the probability of conditions drier than the worst on instrumental record is not zero. This means that current drought risk estimates are at best misleading and probably convey a false sense of security that is not justified given the insights now available from palaeoclimate data.

Here we present a revised Law Dome, Dome Summit South (DSS) ice core age model (denoted LD2017) that significantly improves the chronology over the last 88 ka. An ensemble approach was used, allowing for the computation of both a median age and associated uncertainty as a function of depth. The revised chronology incorporates extended continuous annual layer counting to 853 m using chemical species with seasonally-varying behaviours. The annual layer counted age at 853 m is 2332 years before 2000 (y b2k) with an error of +13/−7 y, i.e. 2345–2325 y b2k . Below this depth, non-linear interpolation between age ties using a probability density function for age/depth is used to constrain and model the age of the ice. The ice-based age ties below the annual layer counted section are based on matching volcanic event markers, methane (CH4) gas concentration, isotopic composition of ice (δ18O) and the Last Glacial Maximum (LGM) dust peak to other records. For consistency, the timescale used for all matching is the AICC2012 timescale (Veres et al., 2013). The first ice-based age tie is the base of the annual layer counting record (2332 y b2k) and the age ties from ~ 2400–4000 y b2k are volcanic synchronised ice-based age ties. The detection of abrupt changes in CH4 gas concentrations within the DSS record provides further independent gas-based age ties, including the tightly constrained 8200 y b2k event. The improved age control between 9000 and 21000 y b2k is supplemented by CH4 and δ18O ice measurements (Pedro et al., 2011). Over the period 16600 to 18600 y b2k large changes in dust concentration, matched to the EDC dust record, are used to constrain two ice-based age ties. Unlike previous studies, where the modelling was used to simultaneously infer both age and snow accumulation rate, we made an independent estimate of the snow accumulation rate, where required, for the use of gas based age ties.

Since its establishment in 1865, Goyder’s Line in South Australia has represented the division between land suitable for cropping and land seen as viable for grazing. Despite this, current cropping areas extend beyond Goyder’s Line. In this study, the 220 mm growing season (April to October) rainfall isohyet is used as a proxy for Goyder’s Line to assess its temporal and spatial variability. Using indices of the El Nino Southern Oscillation, Indian Ocean sea surface temperature variability, Southern Annular Mode and subtropical ridge, it is shown that climate state significantly influences the location of the 220 mm growing season rainfall isohyet. This implies that the boundary between viable and non-viable cropping areas (i.e. Goyder’s Line or ‘nature’s limit’) is non- stationary. These results also indicate the key influences on South Australia’s climate and have important implications globally for agricultural practices operating in or bordering semi-arid environments.

Estimation of correlation with appropriate uncertainty limits for scientific data that are potentially serially correlated is a common problem made seriously challenging especially when data are sampled unevenly in space and/or time. Here we present a new, robust method for estimating correlation with uncertainty limits between autocorrelated series that does not require either resampling or interpolation. The technique employs the Gaussian kernel method with a bootstrapping resampling approach to derive the probability density function and resulting uncertainties. The method is validated using an example from radar geophysics. Autocorrelation and error bounds are estimated for an airborne radio-echo profile of ice sheet thickness. The computed limits are robust when withholding 10%, 20%, and 50% of data. As a further example, the method is applied to two time-series of methanesulphonic acid in Antarctic ice cores from different sites. We show how the method allows evaluation of the significance of correlation where the signal-to-noise ratio is low and reveals that the two ice cores exhibit a significant common signal. HighlightsCorrelation confidence limits can be calculated for unevenly sampled data.Employs Gaussian kernel method used with bootstrapping resampling.Two different studies using highly autocorrelated data validates method.

Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods). This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets – the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. The results of the monthly, seasonal and annual comparisons show that not only are the three gridded datasets different relative to each other, there are also marked differences between the gridded rainfall data and the rainfall observed at gauges within the corresponding grids – particularly for extremely wet or extremely dry conditions. Also important is that the differences observed appear to be non-systematic. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia initially using gauged data as the source of rainfall input and then gridded rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged data. Rather, the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.

In the version of this Perspective originally published, affiliations 1 and 4 ware incorrect, and should have read: “1Antarctic Climate & Ecosystems CRC, University of Tasmania, Hobart, Tasmania, Australia” and “4Centre for Water, Climate and Land (CWCL), University of Newcastle, Callaghan, NSW, Australia”. These have been corrected in the online versions of this Perspective.