Rainfall In Australia Research Articles

Association of climate drivers with rainfall in New South Wales, Australia, using Bayesian Model Averaging

Rainfall in New South Wales (NSW), located in the southeast of the Australian continent, is known to be influenced by four major climate drivers: the El Nino/Southern Oscillation (ENSO), the Interdecadal Pacific Oscillation (IPO), the Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD). Many studies have shown the influences of ENSO, IPO modulation, SAM and IOD on rainfall in Australia and on southeast Australia in particular. However, only limited work has been undertaken using a multiple regression framework to examine the extent of the combined effect of these climate drivers on rainfall. This paper analysed the role of these combined climate drivers and their interaction on the rainfall in NSW using Bayesian Model Averaging (BMA) to account for model uncertainty by considering each of the linear models across the whole model space which is equal to the set of all possible combinations of predictors to find the model posterior probabilities and their expected predictor coefficients. Using BMA for linear regression models, we are able to corroborate and confirm the results from many previous studies. In addition, the method gives the ranking order of importance and the probability of the association of each of the climate drivers and their interaction on the rainfall at a site. The ability to quantify the relative contribution of the climate drivers offers the key to understand the complex interaction of drivers on rainfall, or lack of rainfall in a region, such as the three big droughts in southeastern Australia which have been the subject of discussion and debate recently on their causes.

Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals

Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill score. In general, ANFIS models show superior results in terms of correlation coefficient for the overall case study. As a pioneer study, it is proposed that ANFIS is a promising tool for the purpose of seasonal predictions in Australia as they produce comparable accuracy using minimal inputs, require less development time and they are less complex compared to dynamic models.

Contribution of tropical cyclones to extreme rainfall in Australia

ABSTRACTThe contribution by tropical cyclones (TCs) to extreme rainfall in Australia is examined using daily rainfall measurements from over 2000 rain gauges. Analyses focus on the period beginning with regular satellite monitoring of TCs (1969/1970) through the year 2012/2013 and consider daily and multi‐daily annual maximum rainfall series. Our results indicate that TCs play a prominent role in extreme rainfall over much Australia, with more than half of the highest annual rainfall events associated with these storms over the coastal regions and in particular over Western Australia. Moreover, the TC fractional contribution to extreme rainfall increases as we focus on the largest rainfall events, with approximately 66–100% of annual maxima in excess of 100 mm (∼4 inches) over Western Australia associated with TCs at over one third of the locations. Given the well‐established controls on Australian TC activity by the El Niño–Southern Oscillation (ENSO), we also examined the relationship between extreme rainfall associated with TCs and ENSO using logistic regression. A larger probability of having an annual rainfall maximum related to TCs occurs during La Niña years, consistent with enhanced Australian cyclogenesis during these phases of ENSO. We also highlighted regional differences in the link between ENSO and extreme rainfall events, highlighting the stronger connection along the coastal areas and in particular over Western Australia.

Rainfall in Australia's eastern seaboard: a review of confidence in projections based on observations and physical processes

Rainfall in Australia's eastern seaboard: a review of confidence in projections based on observations and physical processes

Modes of climate variability and heat waves in Victoria, southeastern Australia

AbstractSummertime heat waves in the southeastern state of Victoria, Australia, are associated with broad anticyclonic upper level potential vorticity (PV) anomalies. The current research establishes the relationship between heat waves, precipitation, and three modes of climate variability of importance for rainfall in Australia: the El Niño‐Southern Oscillation (ENSO), Madden‐Julian Oscillation (MJO), and Southern Annular Mode (SAM). Heat waves in Victoria in summer are more common during phases 3–6 of the MJO (when convection is enhanced over the eastern Indian Ocean, Maritime Continent, and western Pacific Ocean) and La Niña phases of ENSO. The PV‐Theta (Θ) structure of the heat wave and pattern of convection varies with the phase of each mode of variability. Enhanced tropical convection results in a heat wave characterized by a monopole of anticyclonic PV, whereas suppressed convection is associated with a dipole of anticyclonic and cyclonic PV.

Are estimates of anthropogenic and natural influences on Australia’s extreme 2010–2012 rainfall model-dependent?

Australia experienced much above average rainfall in association with strong, extended La Niña conditions during 2010–2012. Was the heavy Australian rainfall influenced by La Niña conditions and/or anthropogenic greenhouse gases? We investigate the relative contributions of anthropogenic climate change and natural climatic variability to the likelihood of heavy Australian rainfall using three distinct model datasets. Area-average rainfall anomalies for model simulations with natural forcings only were compared to simulations with both anthropogenic and natural forcings using 16 models participating in the Coupled Model Intercomparison Project Phase 5. Using fraction of attributable risk to compare the likelihood of unusual rainfall between the parallel experiments, we find attribution statements are uncertain, with FAR values sensitive to the attribution parameters considered, including thresholds, regions and seasons. When heavy rainfall probabilities were next investigated in ensembles of two atmospheric general circulation models, run with and without anthropogenically-induced sea surface temperature changes, results were model-dependent. Overall, the attribution of seasonal-scale heavy Australia rainfall to a particular cause is likely more complicated than for temperature extremes. As estimates of the greenhouse gas attributable change in rainfall risk may depend on the model datasets considered, it is also useful to consider model outputs from several datasets and using various estimates of counterfactual surface conditions to establish robust attribution statements for extreme rainfall events. In contrast, comparing the likelihoods of heavy rainfall during simulated La Niña years with El Niño/neutral years reveals a substantial La Niña influence on Australian rainfall during 2010–2012 that is robust to changes in the attribution framework.

A Bayesian Hierarchical Downscaling Model for South-West Western Australia Rainfall

SummaryDownscaled rainfall projections from climate models are essential for many meteorological and hydrological applications. The technique presented utilizes an approach that efficiently parameterizes spatiotemporal dynamic models in terms of the close association between mean sea level pressure patterns and rainfall during winter over south-west Western Australia by means of Bayesian hierarchical modelling. This approach allows us to understand characteristics of the spatiotemporal variability of the mean sea level pressure patterns and the associated rainfall patterns. An application is presented to show the effectiveness of the technique to reconstruct present day rainfall and to predict future rainfall.

Unusual rainfall in Australia explains global sea level drop in 2011

Since 1993, global mean sea level has been rising by about 3 millimeters per year on average, with some year‐to‐year variations. However, from mid‐2010 through 2011, the sea level unexpectedly dropped by 7 millimeters relative to this background trend. To investigate the cause of the unusual sea level drop, Fasullo et al. examined data from the Gravity Recovery and Climate Experiment (GRACE) satellite, which can determine water content on land and sea through gravity variations, as well as altimetry data, reanalyses of other previously collected data, global precipitation data, and data on continental river discharge rates.

Evaluating geostatistical methods of blending satellite and gauge data to estimate near real-time daily rainfall for Australia

Rain gauges provide valuable information about the amount and frequency of rainfall. In Australia, the majority of rain gauges are located in populated, wet coastal regions. Approximately 2000 gauges reporting within 24 h of a target day were used to make near real-time (NRT) estimates of daily precipitation. The remaining approximate to 4000 gauges for the same target day were used to evaluate bias and estimation performance using several traditional statistics. There is considerable potential to improve the estimation of rainfall in Australia using related ancillary data, particularly in sparsely gauged areas. The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA-RT) near real-time product (3B42RT) provided images (0.25 degrees resolution) of precipitation across Australia. Daily precipitation was estimated in 2009110 approximately every 5 km across Australia. This study evaluated selected geostatistical methods for estimating daily rainfall maps across Australia. It tackled the change of support problem and spatial intermittency of daily rainfall data in blending satellite and gauge data. Dissension occurred amongst traditional global statistical measures of performance which were compounded by extremes of gauge density. Overall, our assessment is that blending the 3B42RT satellite and rain gauge data was not worthwhile. However, the blending considerably reduced the estimation variance indicating that uncertainty of the map estimates was a neglected property necessary to detect change and difference in patterns. (C) 2013 Elsevier B.V. All rights reserved.

Nonlinear analysis of rainfall variability in Australia

This study examines the utility of nonlinear dynamic concepts for analysis of rainfall variability across Western Australia. The correlation dimension method, which involves data reconstruction and nearest neighbor concepts, is employed to estimate the dimensionality of rainfall time series and assess the degree of variability (or complexity) of rainfall dynamics. Monthly rainfall data observed over a period of 67 years (January 1937–December 2003) from each of 62 raingage stations across Western Australia are analyzed. The results indicate the utility of the dimensionality concept for identification of rainfall variability at point locations and rainfall patterns across space. The dimension estimates (ranging from 4.63 to 8.29) suggest a medium-level complexity in rainfall dynamics at each of the 62 stations and, hence, across Western Australia. Rainfall dynamics are generally more complex in the southwest part of Western Australia than in other parts of the state, with the far south exhibiting a greater complexity. Also, the mid-western region exhibits slightly more complex rainfall dynamics when compared to the northeast. The reliability of the correlation dimension estimates with respect to data size (i.e. number of data points in time series) is also discussed. The outcomes of the present study, the first ever based on nonlinear dynamic and chaos theories for rainfall in Australia and also across a far more extensive network of raingages, can provide new avenues for rainfall (and other hydrologic) studies in Australia and for spatial/spatio-temporal analysis.

Rainfall reductions over Southern Hemisphere semi-arid regions: the role of subtropical dry zone expansion

Since the late 1970s, Southern Hemisphere semi-arid regions such as southern-coastal Chile, southern Africa, and southeastern Australia have experienced a drying trend in austral autumn, predominantly during April and May. The rainfall reduction coincides with a poleward expansion of the tropical belt and subtropical dry zone by around 2°–3° in the same season. This has raised questions as to whether the regional rainfall reductions are attributable to this poleward expansion. Here we show that the impact of the poleward subtropical dry-zone shift is not longitudinally uniform: a clear shift occurs south of Africa and across southern Australia, but there is no evidence of a poleward shift in the southern Chilean sector. As such, a poleward shift of climatological April-May rainfall can explain most of the southeastern Australia rainfall decline, a small portion of the southern Africa rainfall trend, but not the autumn drying over southern Chile.

Interannual Variations of Wind Regimes off the Subtropical Western Australia Coast during Austral Winter and Spring

Abstract Off the Western Australia coast, interannual variations of wind regime during the austral winter and spring are significantly correlated with the Indian Ocean dipole (IOD) and the southern annular mode (SAM) variability. Atmospheric general circulation model experiments forced by an idealized IOD sea surface temperature anomaly field suggest that the IOD-generated deep atmospheric convection anomalies trigger a Rossby wave train in the upper troposphere that propagates into the southern extratropics and induces positive geopotential height anomalies over southern Australia, independent of the SAM. The positive geopotential height anomalies extended from the upper troposphere to the surface, south of the Australian continent, resulting in easterly wind anomalies off the Western Australia coast and a reduction of the high-frequency synoptic storm events that deliver the majority of southwest Australia rainfall during austral winter and spring. In the marine environment, the wind anomalies and reduction of storm events may hamper the western rock lobster recruitment process.

Hydraulic and treatment performance of pervious pavements under variable drying and wetting regimes

Pervious pavements are an effective stormwater treatment technology. However, their performance under variable drying and wetting conditions have yet to be tested, particularly under a continuous time scale. This paper reports on the clogging behaviour and pollutant removal efficiency of three pervious pavement types over 26 accelerated years. These pavements were monolithic porous asphalt (PA), Permapave (PP) and modular Hydrapave (HP). Over a cycle of 13 days, the period of which was equivalent to the average annual Brisbane, Australia rainfall (1,200 mm), the pavements were randomly dosed with four different flows. Drying events of 3 h duration were simulated during each flow. Inflow and outflow samples were collected and analysed for Total Suspended Solids (TSS), Total Phosphorus (TP) and Total Nitrogen (TN). To evaluate the rate of clogging, a 1 in 5 year Brisbane storm event was simulated in the 6th, 8th, 12th, 16th, 20th and 24th week. Under normal dosing conditions, none of the pavements showed signs of clogging even after 15 years. However, under storm conditions, both PA and HP started to clog after 12 years, while PP showed no signs of clogging after 26 years. The drying and various flow events showed no effects in TSS removal, with all systems achieving a removal of approximately 100%. The average TP removal was 20% for all flows except for low flow, which had a significant amount of leaching over time. Leaching from TN was also observed during all flows except high flow. The TSS, TP and TN results observed during storm events were similar to that of high flow.

The role of tropical modes of variability in recent rainfall deficits across the Murray‐Darling Basin

Southeastern Australia has been experiencing ongoing rainfall deficits since 1997. The spatial extent of the current drought closely corresponds to those regions where rainfall is influenced by interannual variations in the intensity of the subtropical ridge. An upward trend in the intensity of the subtropical ridge thus provides an explanation for the recent rainfall deficit. The possibility that slow variations in tropical modes of variability that are known to influence rainfall in the region, such as the El Niño–Southern Oscillation, may have contributed to southeastern Australia rainfall deficits is investigated using a tripole sea surface temperature index. This index takes into account key influences on Australian climate from variations in both the Indian and Pacific Oceans. The tripole index correlates well with rainfall variations across southeastern Australia on interannual to decadal time scales in winter and spring but not in autumn when most of the recent rainfall deficits have occurred. The influence of tropical sea surface temperature (SST) variations associated with the tripole on southeastern Australian rainfall is explained in terms of Rossby wave propagation, which helps us to understand why the impact is stronger in the southern half of the Murray‐Darling Basin and why there is no relationship in autumn. On the basis of the linear relationship between the tripole SST variation and rainfall across the Murray‐Darling Basin, we conclude that tropical SSTs have not contributed to the observed rainfall deficiencies in southeast Australia since 1997 but did contribute to the 1935–1945 rainfall deficits.

Teleconnection Pathways of ENSO and the IOD and the Mechanisms for Impacts on Australian Rainfall

Abstract Impacts of El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) on Australian rainfall are diagnosed from the perspective of tropical and extratropical teleconnections triggered by tropical sea surface temperature (SST) variations. The tropical teleconnection is understood as the equatorially trapped, deep baroclinic response to the diabatic (convective) heating anomalies induced by the tropical SST anomalies. These diabatic heating anomalies also excite equivalent barotropic Rossby wave trains that propagate into the extratropics. The main direct tropical teleconnection during ENSO is the Southern Oscillation (SO), whose impact on Australian rainfall is argued to be mainly confined to near-tropical portions of eastern Australia. Rainfall is suppressed during El Niño because near-tropical eastern Australia directly experiences subsidence and higher surface pressure associated with the western pole of the SO. Impacts on extratropical Australian rainfall during El Niño are argued to stem primarily from the Rossby wave trains forced by convective variations in the Indian Ocean, for which the IOD is a primary source of variability. These equivalent-barotropic Rossby wave trains emanating from the Indian Ocean induce changes to the midlatitude westerlies across southern Australia, thereby affecting rainfall through changes in mean-state baroclinicity, west–east steering, and possibly orographic effects. Although the IOD does not mature until austral spring, its impact on Australian rainfall during winter is also ascribed to this mechanism. Because ENSO is largely unrelated to the IOD during austral winter, there is limited impact of ENSO on rainfall across southern latitudes of Australia in winter. A strong impact of ENSO on southern Australia rainfall in spring is ascribed to the strong covariation of ENSO and the IOD in this season. Implications of this pathway from the tropical Indian Ocean for impacts of both the IOD and ENSO on southern Australian climate are discussed with regard to the ability to make seasonal climate predictions and with regard to the role of trends in tropical SST for driving trends in Australian climate.

Two Tweedie distributions that are near‐optimal for modelling monthly rainfall in Australia

AbstractStatistical models for total monthly rainfall used for forecasting, risk management and agricultural simulations are usually based on gamma distributions and variations. In this study, we examine a family of distributions (called the Tweedie family of distributions) to determine if the choice of the gamma distribution is optimal within the family. We restrict ourselves to the exponential family of distributions as they are the response distributions used for generalized linear models (GLMs), which has numerous advantages. Further, we restrict ourselves to distributions where the variance is proportional to some power of the mean, as these distributions also have desirable properties. Under these restrictions, an infinite number of distributions exist for modelling positive continuous data and include the gamma distribution as a special case. Results show that for positive monthly rainfall totals in the data history for a particular station, monthly rainfall is optimally or near‐optimally modelled using the gamma distribution by varying the parameters of the gamma distribution; using different distributions for each month cannot improve on this approach. In addition, under the same model restrictions, monthly rainfall totals that include zeros are also well modelled by the same family of distributions. Hence monthly rainfall can be suitably modelled using one of two Tweedie distributions depending on whether exact zeros appear in the rainfall history. We propose a slight variation of the gamma distribution for use in practice. This model fits the data almost as well as the gamma distribution but admits the possibility that future months may have zero rainfall. Copyright © 2010 Royal Meteorological Society

Dynamical, Statistical–Dynamical, and Multimodel Ensemble Forecasts of Australian Spring Season Rainfall

Abstract The prediction skill of the Australian Bureau of Meteorology dynamical seasonal forecast model Predictive Ocean Atmosphere Model for Australia (POAMA) is assessed for probabilistic forecasts of spring season rainfall in Australia and the feasibility of increasing forecast skill through statistical postprocessing is examined. Two statistical postprocessing techniques are explored: calibrating POAMA prediction of rainfall anomaly against observations and using dynamically predicted mean sea level pressure to infer regional rainfall anomaly over Australia (referred to as “bridging”). A “homogeneous” multimodel ensemble prediction method (HMME) is also introduced that consists of the combination of POAMA’s direct prediction of rainfall anomaly together with the two statistically postprocessed predictions. Using hindcasts for the period 1981–2006, the direct forecasts from POAMA exhibit skill relative to a climatological forecast over broad areas of eastern and southern Australia, where El Niño and the Indian Ocean dipole (whose behavior POAMA can skillfully predict at short lead times) are known to exert a strong influence in austral spring. The calibrated and bridged forecasts, while potentially offering improvement over the direct forecasts because of POAMA’s ability to predict the main drivers of springtime rainfall (e.g., El Niño and the Southern Oscillation), show only limited areas of improvement, mainly because strict cross-validation limits the ability to capitalize on relatively modest predictive signals with short record lengths. However, when POAMA and the two statistical–dynamical rainfall forecasts are combined in the HMME, higher deterministic and probabilistic skill is achieved over any of the single models, which suggests the HMME is another useful method to calibrate dynamical model forecasts.

Climate change in south-west Australia and north-west China: challenges and opportunities for crop production

Predictions from climate simulation models suggest that by 2050 mean temperatures on the Loess Plateau of China will increase by 2.5 to 3.75°C, while those in the cropping region of south-west Australia will increase by 1.25 to 1.75°C. By 2050, rainfall is not expected to change on the Loess Plateau of China, while in south-west Australia rainfall is predicted to decrease by 20 to 60 mm. The frequency of heat waves and dry spells is predicted to increase in both regions. The implications of rising temperatures are an acceleration of crop phenology and a reduction in crop yields, greater risk of reproductive failure from extreme temperatures, and greater risk of crop failure. The reduction in yield from increased phenological development can be countered by selecting longer-season cultivars and taking advantage of warmer minimum temperatures and reduced frost risk to plant earlier than with current temperatures. Breeding for tolerance of extreme temperatures will be necessary to counter the increased frequency of extreme temperatures, while a greater emphasis on breeding for increased drought resistance and precipitation-use efficiency will lessen the impact of reduced rainfall. Management options likely to be adopted in south-west Australia include the introduction of drought-tolerant perennial fodder species and shifting cropping to higher-rainfall areas. On the Loess Plateau of China, food security is paramount so that an increased area of heat-tolerant and high-yielding maize, mulching with residues and plastic film, better weed and pest control and strategic use of supplemental irrigation to improve rainfall-use efficiency are likely to be adopted.

Agave as a biofuel feedstock in Australia

The opportunity for commercial production of Agave in Australia stems from the substantial carbohydrate and fibre content of Agave, the nature of carbohydrates stored, the pre-existence of an Agave ethanol-producing industry in Mexico, demand for biofuel feedstocks, impressive water-use efficiencies of plants with the CAM pathway, and legislation mandating the ethanol content of fuel in Australia, where an estimated 748 ML will be required for blending with petrol by 2010–2011, compared with about 440 ML in 2009. Agave has potential as a crop for areas of seasonally limited rainfall in Australia, a judgement based upon desktop analyses and agronomic experience of growing Agave in Australia before 1915. Development of a viable Australian Agave farming system requires production be located in suitable regions, efficient propagation methods, mechanized production, and viable business plans at grower, processor and marketing levels. Growers and processors agree that Agave will not be grown commercially until plants are grown, maintained and harvested in Australia, product is produced and tested, and yield and risks are evaluated. To this end, Agave were imported into Australia from Mexico and a tissue culture propagation technique developed. A trial crop of Agave tequilana was planted in northern Queensland and CO2 exchange, nitrogen and water dynamics, carbohydrate content, and system inputs and outputs are being monitored. The experience will be used to fine-tune the farming system, assess production costs and develop robust life-cycle assessments. Processing of plants from trials will test harvesting and transport infrastructure and will provide material to processors for testing. Samples will be provided to potential producers of value-added products. An Australian Agave industry should provide opportunities for stimulating agronomic, scientific and commercial exchange between Australia and Mexico. Successful integration of Agave into Australian agriculture will require a biofuels-focussed breeding programme in collaboration with Mexican researchers.

Climate relationships with tree-ring width and δ13C of three Callitris species from semiarid woodlands in south-western Australia

Proxy measures of climate based on tree rings can allow reconstruction of climate back past the limit of instrumental records, thereby improving understanding of natural climate variability. We assessed the dendroclimatic potential of tree-ring widths and δ13C of three broadly co-occurring species of Callitris in south-western Western Australia. Ring width chronologies of C. columellaris F.Muell., C. canescens (Parl.) S.T. Blake and C. preissii Miq. met standard measures of dendrochronological acceptability. For all three species, the Expressed Population Signal (EPS) was >0.93 and mean correlations between series in each chronology was >0.79. In contrast, δ13C chronologies were of poorer statistical quality (EPS ranged 0.59 to 0.88, mean correlations ranged 0.33 to 0.65) with both less year-to-year and lower-frequency information (lower mean sensitivity and standard deviation values). The dominant climatic signal in the ring width chronologies was related to rainfall and was strongest over the March–September season (correlations ranged 0.27 to 0.70, all P < 0.05). Consistent with the poorer quality of the δ13C chronologies compared with those from ring widths, tree-ring δ13C was also less strongly correlated with rainfall and rarely significant (P = 0.05). The weaker δ13C correlations may be due to a strong water conservation strategy by Callitris. Our analysis of the whole ring rather than latewood and low sampling effort may also have dampened the δ13C response. However, combining the ring width and δ13C chronologies using Principal Components Analysis did not enhance the extraction of a climatic signal. While the variance explained by the first principal component (PC) was high for all three species (76 to 87%), correlations between the first PC and rainfall were not stronger than for ring widths alone. Tree-ring δ13C, in conjunction with δ18O in particular, may nevertheless provide insight into physiological responses of Callitris to climate variation. However, dendroclimatic studies using Callitris to develop past rainfall records should focus on developing chronologies from ring widths. Further effort to find sites with old trees (250 years or more) should be undertaken and are likely to provide much needed information on past rainfall in Australia.