This study uses a novel approach to investigate the covariability among key modes of climate variability, El Ni & ntilde;o-Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and Southern Annular Mode (SAM), and quantifies their impact on Australian precipitation variability. Unlike traditional approaches that treat these climate modes independently, we apply principal component analysis to the indices of these climate modes to better understand their covariability and independence. We also use cross validation to validate and improve our estimates of the individual and collective impact of these climate modes on Australian precipitation. The first and third principal components, which collectively explain-50% of the variance across climate modes, correspond to ENSO variability and a large fraction of IOD variability that is coherent with ENSO. The second principal component, explaining-20% of the variance, represents SAM. While much of the IOD signal covaries with ENSO, we identify a component of IOD variability that is independent of ENSO and has a modest but discernible impact on southern Australian precipitation variability during austral winter. When acting in concert, these climate modes explain up to 40%-45% of precipitation variance in local parts of eastern Australia during spring. These findings offer new insights for climate model evaluation and demonstrate the value of jointly considering the covariability and independence of climate modes to improve the interpretation and communication of seasonal forecasts that consider these climate modes. SIGNIFICANCE STATEMENT: This study provides new quantitative estimates of the degree to which large-scale climate modes, including El Ni & ntilde;o-Southern Oscillation, Indian Ocean dipole, and Southern Annular Mode, influence year-to-year variations in Australian seasonal precipitation. We show that their combined impact is greatest during austral spring in parts of eastern Australia and explains up to 40%-45% of variations in precipitation in this region. This suggests that smaller-scale processes and random weather and climate phenomena account for most of the remaining precipitation variability. Our statistically robust approach provides a clear basis for quantifying how climate modes influence regional precipitation variability and sets realistic bounds for the predictability they can provide in seasonal forecasts. This objective framework is also easily adaptable to other regions affected by climate modes.

Climate change projections depend on future climatic conditions and a historical reference period. A key uncertainty in estimating precipitation projections stems from the uncertain net contribution of internal variability to the value of precipitation averaged over the reference period, δ_1 . Here we derive equations that, when used with observational data, climate model output, and Bayes’ Theorem, can estimate this contribution. The resulting estimate depends on how accurately the ensemble mean is assumed to simulate externally forced change (EFC). Solutions are obtained for cases where the ensemble mean is perfectly accurate, and where it under- or overestimates EFC. The method is applied in a Case Study on precipitation change in south-east Australia. If the ensemble accurately simulates EFC from 1900–1999 to 2000–2021 then the CMIP5-based estimate of δ_1 has a mean of -31.2 mm, which is -8.1 δ_1 impacts estimates of future change via a process called “reversion to the mean”.

Future change in precipitation driven by anthropogenic influences on the Earth’s radiative balance will further affect ecosystems, water resources, agriculture, economies, lives and livelihoods. Increased clarity on anthropogenically forced precipitation change can assist adaptation in some contexts. Climate scientists typically quantify precipitation change in models using the average value of the percentage change evident in many different models, i.e., %ΔP^j=100( P_2^j-P_1^j/P_1^j) , where P_i^j is the average value of precipitation over Period i in model j . Here we use theory and results from CMIP6 climate models under preindustrial, historical and future forcing to assess the accuracy of this approach. We show that this standard approach inaccurately estimates precipitation change evident in models, even in infinitely large ensembles. Under a wide variety of circumstances, the discrepancy is approximated by 100(μ_2/μ_1)/(m/CoV^2-1) , where μ_i is the population mean for Period i , m is the number of years in the reference period, and CoV is the Coefficient of Variation (i.e., the standard deviation of precipitation variability divided by the mean). The discrepancy is therefore greater for shorter reference periods and is greatest where the CoV is large (which tends to occur in dry regions) and anthropogenic forcing increases precipitation. The discrepancy using climate model output under SSP370 forcing has an average value of 5.7

During the 2017-2019 period, a large region of the Murray Darling Basin in Australia received the lowest three-year rainfall resulting in an unprecedented drought, known as the Tinderbox Drought. The cool season (Apr-Sep) rainfall declined by more than 54% of the 1901-1960 average. An analysis of the observed rainfall records (1900 – 2020) shows that it was exceptionally unlikely that a decline of this magnitude could occur from internal climate variability alone. In this study, we analysed outputs from CMIP5 and CMIP6 climate models under different forcing conditions (i.e., pre-industrial, historical-all forcings and different future emissions pathways) to estimate the relative contribution of anthropogenic forcing and internal variability to the observed 2017-2019 cool season rainfall reduction and the future likelihood of three-year rainfall change as dry or drier than the Tinderbox Drought under different emission pathways. According to the models, taken at face value, the Tinderbox Drought is an extremely unlikely event, but the likelihood of its occurrence is being increased from virtually impossible to extremely unlikely by the anthropogenic forcing. This suggests that the Tinderbox Drought was largely dominated by internal climate variability, however, it would not have been as dry without the influence of anthropogenic forcing. We found that the likelihood of a three-year drought as dry or drier than the Tinderbox Drought is going to increase by 15% towards the end of the twenty-first century under a high-emission scenario. Even with a marked reduction in emissions, its likelihood will be still around 5 % which is 10 times higher than the pre-industrial climate. Only a few ensemble members simulate a drying as large or larger than the observed 2017-2019 drying. The inability of most models to fully replicate the large drying seen so far leads to two possible conclusions: the rainfall in this region is more sensitive to greenhouse gas concentrations than is currently modelled, or factors other than climate change have coincidentally reduced rainfall during the recent period of anthropogenic climate change.

Multi-year droughts (MYDs) can have major impacts on ecosystems, agriculture, water resources, economies, people and societies. Here, we examine statistical properties of MYDs consisting of uninterrupted sequences of years in which annual precipitation falls below a given threshold. We examine statistics including the proportion of years that are part of droughts of duration ≥ n years, the proportion of droughts that have duration ≥ n years, and both the duration and the number of droughts with duration ≥ n years, in thirty-eight 200-year-long simulations of CMIP6 coupled global climate models under pre-industrial control conditions. We also derive formulae that approximate the average value of these and other statistics using simple stochastic models. The theoretical values obtained agree reasonably well with their Multi-Model Mean (MMM) climate model counterparts over the globe. Regional contrasts in the value of the statistics for MYDs consisting of uninterrupted sequences of years with below-average precipitation can be largely explained by spatial variations in the percentile of the mean. Corresponding formulae that account for non-zero temporal autocorrelation tend to agree somewhat more closely with the MMM values. MYD statistics are estimated using observational data and compared with theoretical and climate model estimates. The formulae incorporating non-zero autocorrelation provide a better estimate of the observational values than do MMM values or formulae derived assuming zero autocorrelation.

Sea surface temperature (SST) patterns in the Pacific Ocean cause climate variability in many parts of the world. This is due to the El Niño-Southern Oscillation (ENSO) on interannual timescales and the Interdecadal Pacific Oscillation (IPO) acting on decadal to interdecadal timescales, modifying ENSO teleconnections. However, how both ENSO, ENSO diversity and the IPO interact with each other still requires further clarification. In this study, we use observations of Pacific Ocean SSTs from 1920 to 2022 to explore the statistical relationships between decadal ENSO variability and the IPO. More specifically, we show how ENSO event characteristics of both central and eastern Pacific El Niño, as well as all La Niña events varies between their occurrence in warm (positive), compared to cool (negative) phases of the IPO. We further show that up to 60% of the variability in the IPO Tripole Index can be reconstructed by using simple ENSO metrics such as the relative frequency of El Niño and La Niña events. While statistically a clear relationship between ENSO and the IPO exists, some of the IPO’s key features, especially North Pacific SSTs, cannot be explained by decadal ENSO variability.

We synthesise advances in the understanding of the physical processes that play a role in developing, intensifying, and terminating meteorological droughts. We focus on Australia, where new understanding of drought drivers across different climate regimes provides insights into drought processes elsewhere in the world. Drawing on observational, climate model and machine learning-based research, we conclude that meteorological drought develops and intensifies largely through an absence of synoptic processes responsible for strong moisture transport and heavy precipitation. The subsequent presence of these synoptic processes is key to drought termination. Large-scale modes of climate variability modulate drought through teleconnections, which alter drought-determining synoptic behaviour. On local scales, land surface processes play an important role in intensifying dry conditions and propagating meteorological drought through the hydrological cycle. In the future, Australia may experience longer and more intense droughts than have been observed in the instrumental record, although confidence in drought projections remains low. We propose a research agenda to address key knowledge gaps to improve the understanding, simulation and projection of drought in Australia and around the world. Australia experiences meteorological droughts due to insufficient moisture transport and heavy precipitation, which are influenced by climate variability and land processes, and are expected to become longer and more frequent, according to a review of observational and model-based studies.

The climate of the Pacific Ocean varies on interannual, decadal, and longer timescales. This variability is dominated by the El Niño–Southern Oscillation (ENSO) and the Interdecadal Pacific Oscillation (IPO), both of which have profound impacts on countries within and well beyond the Pacific. To date, previous studies have only examined a small subset of the possible links between ENSO, its diversity, and the IPO. Here we focus on the statistical relationship between decadal variability in ENSO properties and the IPO, testing the null hypothesis that the IPO arises from random decadal changes in ENSO activity, including ENSO diversity. We use observed sea surface temperature (SST) records since 1920 to investigate how the timing, structure, frequency, duration, and magnitude of El Niño and La Niña events differ between IPO phases. We find that using the relative frequency of El Niño and La Niña events and either the mean event duration or SST magnitude can reproduce up to 60% of the IPO Tripole Index timeseries. While the spatial SST patterns that represent the IPO and ENSO are similar, the IPO is meridionally broader in the central to eastern Pacific, which may be caused by a lagged relationship with low-frequency SST variability in the equatorial Pacific. In addition, North Pacific SST anomalies of opposite sign to the tropical Pacific SST anomalies is a unique feature of the IPO that cannot be explained by decadal ENSO variability. This suggests a clear IPO and ENSO relationship, but also independence in some of the IPO’s characteristics.

We examine the characteristics and causes of southeast Australia's Tinderbox Drought (2017 to 2019) that preceded the Black Summer fire disaster. The Tinderbox Drought was characterized by cool season rainfall deficits of around -50% in three consecutive years, which was exceptionally unlikely in the context of natural variability alone. The precipitation deficits were initiated and sustained by an anomalous atmospheric circulation that diverted oceanic moisture away from the region, despite traditional indicators of drought risk in southeast Australia generally being in neutral states. Moisture deficits were intensified by unusually high temperatures, high vapor pressure deficits, and sustained reductions in terrestrial water availability. Anthropogenic forcing intensified the rainfall deficits of the Tinderbox Drought by around 18% with an interquartile range of 34.9 to -13.3% highlighting the considerable uncertainty in attributing droughts of this kind to human activity. Skillful predictability of this drought was possible by incorporating multiple remote and local predictors through machine learning, providing prospects for improving forecasting of droughts.

CONTEXT: Understanding climate change impacts on beef production enterprises in northern Australia is challenging due to the complexity of the system, which involves biophysical processes such as pasture and animal production, herd management, economics, emissions, and the interaction among these things. Modelling is a powerful tool to help the beef industry understand the impacts of climate change and to develop adaptation plans to help ensure enterprises remain economically viable over the long term.OBJECTIVE: We assess climate change impacts on a single specialised beef enterprise south-east of Moura (24.59 degrees S, 150.09 degrees E) in northern Australia's Central Queensland region. This is achieved by comparing enterprise performances during a 2030s climate and a 1975-2013 baseline period.METHODS: We used a calibrated pasture model called GRASP to simulate the farm's pasture growth under multiple scenarios for land conditions, grass basal areas and stocking rates for the baseline and 2030s climates and to construct pasture growth databases - known as datacubes. The datacubes provide a simplified representation of mean coupling between the herd dynamics and livestock production model (Crop Livestock Enterprise Model). The coupled whole-of-farm model was parameterised using enterprise data collected through

During the 2017-2019 period, a large region of the Murray Darling Basin in Australia received the lowest three-year rainfall resulting in an unprecedented drought, known as the Tinderbox Drought. The cool season (Apr-Sep) rainfall declined by more than 54% of the 1901-1960 average. An analysis of the observed rainfall records (1900 – 2020) shows that it was exceptionally unlikely that a decline of this magnitude could occur from internal climate variability alone. In this study, we analysed outputs from CMIP5 and CMIP6 climate models under different forcing conditions (i.e., pre-industrial, historical-all forcings and different future emissions pathways) to estimate the relative contribution of anthropogenic forcing and internal variability to the observed 2017-2019 cool season rainfall reduction and the future likelihood of three-year rainfall change as dry or drier than the Tinderbox Drought under different emission pathways. According to the models, taken at face value, the Tinderbox Drought is an extremely unlikely event, but the likelihood of its occurrence is being increased from virtually impossible to extremely unlikely by the anthropogenic forcing. This suggests that the Tinderbox Drought was largely dominated by internal climate variability, however, it would not have been as dry without the influence of anthropogenic forcing. We found that the likelihood of a three-year drought as dry or drier than the Tinderbox Drought is going to increase by 15% towards the end of the twenty-first century under a high-emission scenario. Even with a marked reduction in emissions, its likelihood will be still around 5 % which is 10 times higher than the pre-industrial climate. Only a few ensemble members simulate a drying as large or larger than the observed 2017-2019 drying. The inability of most models to fully replicate the large drying seen so far leads to two possible conclusions: the rainfall in this region is more sensitive to greenhouse gas concentrations than is currently modelled, or factors other than climate change have coincidentally reduced rainfall during the recent period of anthropogenic climate change.

The Australian monsoon delivers seasonal rain across a vast area of the continent stretching from the far northern tropics to the semi-arid regions. This article provides a review of advances in Australian monsoon rainfall (AUMR) research and a supporting analysis of AUMR variability, observed trends, and future projections. AUMR displays a high degree of interannual variability with a standard deviation of approximately 34% of the mean. AUMR variability is mostly driven by the El Nino-Southern Oscillation (ENSO), although sea surface temperature anomalies in the tropical Indian Ocean and north of Australia also play a role. Decadal AUMR variability is strongly linked to the Interdecadal Pacific Oscillation (IPO), partially through the IPO's impact on the strength and position of the Pacific Walker Circulation and the South Pacific Convergence Zone. AUMR exhibits a century-long positive trend, which is large (approximately 20 mm per decade) and statistically significant over northwest Australia. The cause of the observed trend is still debated. Future changes in AUMR over the next century remain uncertain due to low climate model agreement on the sign of change. Recommendations to improve the understanding of AUMR and confidence in AUMR projections are provided. This includes improving the representation of atmospheric convective processes in models, further explaining the mechanisms responsible for AUMR variability and change. Clarifying the mechanisms of AUMR variability and change would aid with creating more sustainable future agricultural systems by increasing the reliability of predictions and projections.This article is categorized under:Paleoclimates and Current Trends > Modern Climate Change

Following efforts from leading centres for climate forecasting, sustained routine operational near-term climate predictions (NTCP) are now produced that bridge the gap between seasonal forecasts and climate change projections offering the prospect of seamless climate services. Though NTCP is a new area of climate science and active research is taking place to increase understanding of the processes and mechanisms required to produce skillful predictions, this significant technical achievement combines advances in initialisation with ensemble prediction of future climate up to a decade ahead. With a growing NTCP database, the predictability of the evolving externally-forced and internally-generated components of the climate system can now be quantified. Decision-makers in key sectors of the economy can now begin to assess the utility of these products for informing climate risk and for planning adaptation and resilience strategies up to a decade into the future. Here, case studies are presented from finance and economics, water management, agriculture and fisheries management demonstrating the emerging utility and potential of operational NTCP to inform strategic planning across a broad range of applications in key sectors of the global economy.

Pacific Island countries are vulnerable to climate variability and change. Developing strategies for adaptation and planning processes in the Pacific requires new knowledge and updated information on climate science. In this paper, we review key climatic processes and drivers that operate in the Pacific, how they may change in the future and what the impact of these changes might be. In particular, our emphasis is on the two major atmospheric circulation patterns, namely the Hadley and Walker circulations. We also examine climatic features such as the South Pacific Convergence Zone and Intertropical Convergence Zone, as well as factors that modulate natural climate variability on different timescales. It is anticipated that our review of the main climate processes and drivers that operate in the Pacific, as well as how these processes and drivers are likely to change in the future under anthropogenic global warming, can help relevant national agencies (such as Meteorological Services and National Disaster Management Offices) clearly communicate new information to sector-specific stakeholders and the wider community through awareness raising.

We examine rainfall variability and change in three sub-regions of the state of Victoria in Australia: the Murray Basin Victoria (MBVic), southeast Victoria (SEVic), and southwest Victoria (SWVic). These sub-regions represent three different hydrological super-catchments over Victoria and received average cool season rainfall for the 1997–2018 period, about 15%, 11%, and 8% less, respectively, than the 1900–1959 average. All three observed declines are shown to be very unusual in terms of historical variability. On analysing CMIP5 models under different forcing conditions (preindustrial, historical-all, historical-GHGs-only, historical-natural-only, RCP2.6, RCP4.5, and RCP8.5), we estimate that external forcing caused 30% of the observed drying in SWVic, 18% in MBVic, and 17% in SEVic. The external forcing contributions to the observed trend for the 1900–2018 period are estimated to be 56%, 17%, and 24% for SWVic, MBVic, and SEVic, respectively. Taken at face value, these figures suggest that only the 1900–2018 trend in SWVic was dominated by external forcing. Nearly all models underestimate the magnitude of the observed drying. This arises because models underestimate the magnitude of decadal variability, and because models might also underestimate externally forced drying, and/or the contribution of internal variability in the real world to the observed event was unusually large. By 2037, approximately 90% of the models simulate drying in SWVic, even under a low emissions scenario. Under a high emission scenario, the anthropogenically forced drying towards the late twenty-first century is so large in all three sub-regions that internal variability appears too small to offset it.

The cool-season (May to October) rainfall decline in southwestern Australia deepened during 2001–2020 to become 20.5% less than the 1901–1960 reference period average, with a complete absence of very wet years (i.e., rainfall > 90th percentile). CMIP5 and CMIP6 climate model simulations suggest that approximately 43% of the observed multi-decadal decline was externally-forced. However, the observed 20-year rainfall anomaly in 2001–2020 is outside the range of both preindustrial control and historical simulations of almost all climate models used in this study. This, and the fact that the models generally appear to simulate realistic levels of decadal variability, suggests that 43% might be an underestimate. A large ensemble from one model exhibits drying similar to the observations in 10% of simulations and suggests that the external forcing contribution is indeed larger (66%). The majority of models project further drying over the twenty-first century, even under strong cuts to greenhouse gas emissions. Under the two warmest scenarios, over 70% of the late twenty-first century years are projected to be drier than the driest year simulated during the 1901–1960 period. Our results suggest that few, if any, very wet years will occur during 2023–2100, even if strong cuts to global emissions are made.

The South Pacific convergence zone (SPCZ) is evaluated in simulations of historical climate from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and phase 6 (CMIP6) models, showing a modest improvement in the simulation of South Pacific precipitation (spatial pattern and mean bias) in CMIP6 models but little change in the overly zonal position of the SPCZ compared with CMIP5 models. A set of models that simulate a reasonable SPCZ are selected from both ensembles, and future projections under high emissions (RCP8.5 and SSP5-8.5) scenarios are examined. The multimodel mean projected change in SPCZ precipitation and position is small, but this multimodel mean response obscures a wide range of future projections from individual models. To investigate the full range of future projections a storyline approach is adopted, focusing on groups of models that simulate a northward-shifted SPCZ, a southward-shifted SPCZ, or little change in SPCZ position. The northward-shifted SPCZ group also exhibit large increases in precipitation in the equatorial Pacific, while the southward-shifted SPCZ group exhibit smaller increases in equatorial precipitation but greater increases within the SPCZ region. A moisture budget decomposition confirms the findings of previous studies: that changes in the mean circulation dynamics are the primary source of uncertainty for projected changes in precipitation in the SPCZ region. While uncertainty remains in SPCZ projections, partly due to uncertain patterns of sea surface temperature change and systematic coupled model biases, it may be worthwhile to consider the range of plausible SPCZ projections captured by this storyline approach for adaptation and planning in the South Pacific region. Significance StatementThe South Pacific convergence zone is a band of intense rainfall that influences the weather and climate of many Pacific Island communities. Future changes in the SPCZ will therefore impact these communities. We examine climate model representations of future climate to find out how the SPCZ might change in a warmer world. While the models disagree on future changes in the SPCZ, we suggest that it may be useful to consider groups of models with common "storylines" of future change. The changes in the position of the SPCZ in a warmer world correlate strongly to the amount of rainfall change locally. Some models suggest a northward movement of the SPCZ, while others suggest a southward movement. Consideration of the full range of possible future behavior of the SPCZ is needed to better prepare for the impacts of a warmer climate.

Climate sensitivity refers to the amount of global surface warming that will occur in response to a doubling of atmospheric CO2 concentrations when compared to pre-industrial levels. Understanding climate sensitivity and reducing uncertainty in the estimation of climate sensitivity are therefore critical to reducing spread in projected climate change under given scenarios. The aim of this study is to estimate real-world Equilibrium Climate Sensitivity (ECS) by exploiting relationships found between observable parameters and the magnitude of climate change. We develop an emergent constraint based on surface temperature variability, which we test using preindustrial control and historical simulations from CMIP5 and CMIP6 models. We estimate the relationship between model-to-model differences (M2MDs) in ECS and M2MDs in global, tropical and tropical Pacific temperature variability, using the various measures of variability on interannual through to multidecadal timescales. We find higher correlations between MDMDs in ECS and M2MDs in the standard deviation of temperature variability in the tropics, which peaks at the decadal timescale, with larger spread in CMIP6 models. These results are then optimally combined to constrain observed temperature decadal variability and provide a distribution of real-world ECS.

Here we use observations and simulations from 40 global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), under preindustrial, historical, and a high emission scenario (RCP8.5) to provide estimates of Victorian cool season (April–October) rainfall for the coming century. This includes a new method which exploits recent research that estimated the relative contribution of external forcing and natural variability to the observed multidecadal decline in cool season rainfall in Victoria from 1997. The new method is aimed at removing the influence of external forcing on Victoria’s cool-season rainfall, effectively rendering a stationary time-series. The resulting historical record is then modified by scaling derived from the mean projected change evident in climate models out to 2100. The results suggest that the median value of the All-Victoria rainfall PDF will decrease monotonically over the remainder of the twenty-first century under RCP8.5. The likelihood that All-Victoria rainfall in any given year from 2025 onward will be below the observed 5th percentile of the observations (291 mm) increases monotonically, becoming three times larger by the end of the century. The new method is assessed using cross-validation and its ability to hindcast observed multidecadal rainfall change. The latter indicates that CMIP5 models poorly replicate recent interdecadal rainfall change. So, while we have more confidence in the new method because it accounts for the non-stationarity in the observed climate, limitations in the CMIP5 models results in us having low confidence in the reliability of the estimated future rainfall distributions.