Persistent extreme temperatures were observed in Australia during 2012–2014. We examine changes in the rate of hot and cold record breaking over the observational record for Australia‐ and State‐wide temperatures. The number of new hot (high‐maximum and high‐minimum temperatures) temperature records increases dramatically in recent decades, while the number of cold records decreases. In a stationary climate, cold and hot records are expected to occur in equal frequency on longer than interannual time scales; however, during 2000–2014, new hot records outnumber new cold records by 12 to one on average. Coupled Model Intercomparison Project phase 5 experiments reveal increased hot temperature record breaking occurs in simulations that impose anthropogenic forcings but not in natural forcings‐only experiments. This disproportionate hot to cold record breaking rates provides a useful indicator of nonstationarity in temperatures, which is related to the underlying mean observed Australian warming trend of 0.9°C since high‐quality records began in 1910.

1 Introduction Average Australian temperatures have warmed significantly (0.9°C) since high‐quality records began in 1910 [Bureau of Meteorology, 2014b]. Over the 10 year period 2004 to 2013, only 2011 recorded cooler‐than‐average conditions [Bureau of Meteorology, 2014a]. During this period of sustained warmth, and particularly from spring 2012 to 2014, observed temperature records were broken on daily to annual time scales and on spatial scales from individual stations to continent‐wide averages. In 2013, Australia‐wide area‐average temperatures records were set for the hottest day, week, month, and season observed, and it was the warmest year on record (Figure 1) [Bureau of Meteorology, 2014a]. Figure 1 Open in figure viewer PowerPoint T mean ) anomalies (K, relative to 1911–1940) for (a) annual, (b) DJF, and (c) January averages, from 1910 to 2014. The record anomalies of 2013 (red) are shown, together with spatial maps of average 2013 Australian conditions [Jones et al., 2009 Trewin, 2013 Observed Australian‐average mean temperature () anomalies (K, relative to 1911–1940) for (a) annual, (b) DJF, and (c) January averages, from 1910 to 2014. The record anomalies of 2013 (red) are shown, together with spatial maps of average 2013 Australian conditions []. Time series are derived from ACORN‐SAT []. Various extreme Australia‐wide temperatures during this period of persistent heat have been examined previously. Several studies have focused on probabilistic estimates of the contributing factors to specific extremes using Coupled Model Intercomparison Phase 5 (CMIP5) detection and attribution experiments [Taylor et al., 2012]. These studies demonstrate a substantial anthropogenic influence on the likelihood of recently observed summer, spring, and annual Australian mean and maximum temperatures [e.g., Lewis and Karoly, 2013, 2014; King et al., 2014]. A further study focused on Australian heat waves determined that anthropogenic influences significantly increased the risk of experiencing summer heat waves such as those observed in January 2013 [Perkins et al., 2014]. The mechanisms behind the 2013 record Australian September temperatures have further been explored using sensitivity experiments with a dynamical seasonal predication system, which found that background warming contributed to ~15% of the record temperature anomaly [Arblaster et al., 2014]. These studies investigate changes in the likelihood of individual observed Australian extremes and their driving processes but have not explored the unusualness of the rate of temperature record breaking observed during this period of persistent warmth. While estimates of changes in the frequency of record temperatures have been considered by Trewin and Vermont [2010] for Australia, and elsewhere [e.g., Meehl et al., 2009], these analyses extend only to 2009—prior to the recent period of persistent heat—and do not explore the contributing factors to changed frequencies. Characterizing changes in the rate of record‐setting temperatures, and their causes, is a significant research avenue, particularly as the frequency of extremes has important impacts on built infrastructure, natural ecosystems, and human health [Perkins et al., 2015]. For example, extreme temperature events of extended duration are associated with increased morbidity and mortality. Here we investigate the recent period of extreme Australian temperatures by examining the frequency of record breaking. We compare the relative frequency of cold and hot record breaking for State‐ and Australia‐wide minimum and maximum temperatures. Next, we investigate changes in the rate of record breaking through the observed record of 1910–2014 and its variability in relation to the El Niño–Southern Oscillation (ENSO) and the Interdecadal Pacific Oscillation (IPO). Finally, we examine the influence of various forcings (anthropogenic and natural) on the frequency of cold and hot record breaking in a suite of CMIP5 experiments.

2 Data and Methods 2.1 Observations We use data from observations and CMIP5 models. Observed data are derived from the high‐quality Australian Climate Observations Reference Network‐Surface Air Temperature (ACORN‐SAT) data set, which provides data from 1910 to 2014. [Trewin, 2013]. Area‐average ACORN‐SAT temperature time series (i.e., Figure 1) are provided by the Bureau of Meteorology and are calculated using a spatial averaging algorithm that accounts for area weighting [Trewin, 2012]. We use observed minimum (T min ) and maximum (T max ) temperatures, calculated as anomalies relative to a 1911–1940 climatology. We investigate annual (January–December, ANN), seasonal (December‐January‐February, DJF; March‐April‐May; June‐July‐August; September‐October‐November), and monthly average T max and T min for each region. As the number of stations varies through time, we focus on temperatures averaged over large areas. We expand on the approach used by Lewis et al. [2014] and calculate Australian area‐average (AUS) and State‐wide area‐average mean, maximum and minimum temperatures for Victoria, New South Wales (NSW), Queensland, the Northern Territory, South Australia, and Western Australia. The Australian Capital Territory is included within the defined NSW region, and Tasmania is excluded, as it is typically poorly resolved in global climate models. In order to investigate the long‐term warming trend on the rate of record breaking, we also examine detrended observational data. The long‐term warming signal is removed using a using a quadratic fit, which was found to be the best fit to Australian mean temperatures post‐1910 [Fawcett et al., 2012]. We investigate Australian temperature variability associated with Pacific climate variability, including the El Niño–Southern Oscillation and Interdecadal Pacific Oscillation. These large‐scale modes of variability are associated with temperature and rainfall anomalies in Australia [Risbey et al., 2009], affecting the interannual rate of temperature record breaking in Australia. Although other modes of variability (e.g., Southern Annular Mode and Indian Ocean Dipole) influence Australian climates, we focus on the principal modes of interannual and interdecadal variability that affect Australia's climate. Phases of ENSO were diagnosed using NINO3.4 surface temperature anomalies in the HadSST3 ensemble median gridded monthly temperature anomaly data set [Kennedy et al., 2011]. ENSO‐associated surface temperature anomalies were considered over the year of peak ENSO activity from April to March. Lastly, positive and negative IPO phases [Parker et al., 2007] were also derived from the IPO index calculated from HadSST3 anomalies. 2.2 Models We use a suite of CMIP5 detection and attribution experiments from 10 contributing models (supporting information Table S1). Models were included based on their skill [Perkins et al., 2007] in simulating Australian temperature variability, evaluated against observations [Lewis et al., 2014]. We use available data from the historical experiment, in which the climate of 1850–2005 is simulated with both anthropogenic (well‐mixed greenhouse gases, tropospheric aerosols, and ozone) and natural forcings (volcanic and solar) imposed (supporting information Table S2). For comparison, we use parallel simulations of the historicalNat experiment of the period 1850–2005, with only time‐varying solar and volcanic aerosols imposed. We calculate T max and T min anomalies for the historical and historicalNat experiments, relative to a 1911–1940 climatology. We consider only model years 1910–2005, as the period common to both observed and modeled data. CMIP5 data were regridded onto a 1.5° latitude by 1.5° longitude horizontal grid, and area‐average values were calculated using Australian Bureau of Meteorology standard regions. We apply an additional model criterion where at least 75% of the each model grid box must by land surface be included in area‐averaged temperatures. In both observational and model data, hot records are defined as the combined number of unprecedented high T max and high T min occurrences, and cold records as the combined number of unprecedented low T max and low T min , summed for all State‐ and Australia‐wide regions and all months and seasons considered.

3 Changes in the Observed Frequency of Record Breaking The total number of observed hot and cold records decreases rapidly from the beginning of observations (Figure 2a). This is expected in a stationary climate, where the probability of record setting decreases rapidly later in the sequence of temperatures [Coumou and Rahmstorf, 2012], and the expected ratio of hot and cold record breaking, averaged over a period of time, is one [Benestad, 2004]. Over the observed period from 1910 to 1960, the 20 year average ratio of hot/cold records is approximately equal to one, meaning that the frequency of hot record breaking is roughly equal to the frequency of cold record breaking. However, in the later part of the observed sequence (from 1960 to 2014), the rate of hot record breaking increases dramatically; and in the most recent observed 15 year period (from 2000 to 2014), the number of observed Australian hot records is over 12 times greater than the number of observed cold records. Over this later period, the probability of cold record breaking is generally low and the number of new cold temperature records set approaches zero. Figure 2 Open in figure viewer PowerPoint T max and high T min , red), observed cold records (low T max and low T min , blue) for all regions and all seasons and months. The 20 year average ratio of observed hot/cold records is also shown in black. (b) As for Figure Fawcett et al., 2012 T max records (red) and observed hot and cold T min records (blue) over the period 1950–2014. The least squares line of best fit is shown for T max and T min . (a) Number of observed hot records (highand high, red), observed cold records (lowand low, blue) for all regions and all seasons and months. The 20 year average ratio of observed hot/cold records is also shown in black. (b) As for Figure 2 a but showing records for observed data that has been nonlinearly detrended, whereby the warming signal has been removed using a quadratic fit []. (c) Difference in the number of observed hot and coldrecords (red) and observed hot and coldrecords (blue) over the period 1950–2014. The least squares line of best fit is shown forand There are several features of the frequency of observed hot and cold temperature record breaking. First, when T max and T min are considered separately over this period, there is a notable difference in the characteristics of observed T max and T min record breaking (Figure 2b). There is a statistically significant trend (as estimated using Sen's Kendall slope nonparametric method [Sen, 1968] and the statistical significance assessed using a t test at the 5% level) in the difference between hot and cold record numbers for both T min and T max . However, this calculated trend is larger for T min , indicating that high T min record breaking increases at a relatively faster rate than for high T max (Figure 2c). This differential rate of warming in minimum and maximum temperatures—diurnal temperature range trends—has previously been noted for Australia and other regions [Braganza et al., 2004; Karoly and Braganza, 2005; Fawcett et al., 2012]. Also, the rate of observed record breaking varies seasonally (between the warm and cool seasons). We define the austral warm season as October to March and the cool season as April to September and compare the rate of hot and cold record breaking (supporting information Figures S1 and S2). The rate of observed cold record breaking decreases more rapidly in the cool season, compared with the warm season (Figure S2). The increased rate of warm season hot record breaking is slightly greater than for the cool season, although this difference is not found to be statistically significant. Seasonal differences in the rate of hot and cold record breaking may be related to large‐scale modes. Specifically, differences in the rate of hot and cold record breaking over the most recent period may be partially affected by seasonal rainfall changes. For example, 2011 is unusual in recent times as a year of more cold than hot records, and coincided with two consecutive La Niña events (at the start and the end of the year) in association with heavy rainfall [Evans and Boyer‐Souchet, 2012]. We next explore Pacific variability and the frequency of temperature record breaking.

4 Influences on Observed Record Breaking 4.1 Pacific Variability and Frequency of Record Breaking ENSO is the main driver of Pacific climate variability [Philander, 1990] and is associated with temperature and rainfall anomalies in Australia [Power et al., 2006; Risbey et al., 2009; Arblaster and Alexander, 2012]. Decadal‐scale variability, such as from the Interdecadal Pacific Oscillation (IPO), which represents interdecadal ENSO‐like sea surface temperature variability, is linked to interdecadal changes in ENSO's impact on Australian climate [Power et al., 2006]. Internal climatic variability, including ENSO influences, may impact the interannual rate of temperature record breaking in Australia and observed cold and hot record breaking in recent decades. The influence of ENSO conditions on record breaking rates is regionally variable and dependent on the ENSO phase considered (Figure 3). The correlation between ENSO and extreme Australian temperatures is strongest for Australia and the northern States. The correlation between Victorian and South Australian hot record‐breaking temperatures and the NINO3.4 index is lower. Monthly to seasonal scale extreme temperature‐ENSO relationships do not necessarily hold for extremes at shorter time scales or for variables other than temperature. For example, ENSO is a strong determinant of interannual precipitation variability and considered a major contributor to bushfires and heat waves in southeastern Australia [Williams and Karoly, 1999; Nicholls and Lucas, 2007]. Figure 3 Open in figure viewer PowerPoint Correlation between annual average (April–March) NINO3.4 sea surface temperature anomalies (relative to 1961–1990) and (a) hot and (b) cold record breaking for Australian regions, for all seasons and months. The relationship between ENSO and extreme Australian temperatures is complex and also linked to rainfall variability [McBride and Nicholls, 1983]. Extreme maximum temperatures are significantly cooler over Australia during strong La Niña events than strong El Niño events [Arblaster and Alexander, 2012]. Hence, La Niña episodes may result in subdued hot record breaking but not necessarily increased cold record breaking. Overall, ENSO influences extreme temperatures in Australia but accounts for only part of the number of observed temperature extremes (see supporting information for further discussion). The IPO likely modulates ENSO's influence on Australian climate; during positive IPO conditions, the relationship between Australian climate variations and ENSO is subdued, while when the IPO is negative, year‐to‐year ENSO variability is closely associated with year‐to‐year variability in rainfall and surface temperature in Australia [Power et al., 1999]. However, multidecadal Pacific variability does not account for the observed trend in hot and cold Australia record breaking. We focus on rates of hot and cold record breaking during observed IPO negative (over period 1951–1975) and positive (1976–2000) conditions in detrended observed Australian temperatures. The rate of detrended observed record breaking during IPO negative and positive phases is similar for some regions (supporting information Figures S3 and S4), although for Australia‐wide average T max and T min , for example, there is a greater rate of both hot and cold record breaking during the IPO negative phase, as expected due to the nonlinearity of ENSO teleconnections to the Australian region associated with the phase of the IPO. Nonetheless, we would expect substantial differences in hot and cold record breaking in distinct IPO phases if this large‐scale mode of variability were a dominant determinant of the substantial increase through time in the observed rate of hot record breaking, relative to cold record breaking. 4.2 Anthropogenic Influences and Frequency of Record Breaking The number of observed hot records has increased in the recent period, although changes are not dominated by investigated modes of internal variability, such as ENSO. Furthermore, the number of observed Australian hot records is significantly greater than that expected under the assumption of stationarity. In a stationary climate, the expected ratio of hot and cold record breaking, averaged over a period of time, is one [Benestad, 2004]. The chance of seeing new record temperatures in a stationary time series can be described simply—the probability p of the last value n in a series observations being the highest (i.e., a new record) is p = 1/n (Figure 4a). This assumes that the observed sequence is identically distributed and independent. However, recent observational‐based analyses demonstrate that for heat records, the assumption of stationarity does not hold [Rahmstorf and Coumou, 2011]. When the ratio of observed to expected hot records is investigated, there is a significant increase after ~1960 in the number of observed new records, compared to the expected number (Figure 4b). Over the most recent period (2000–2014), the rate of observed Australian hot record setting is 5 times greater than that expected in a stationary climate. Figure 4 Open in figure viewer PowerPoint T max and T min ) in a stationary climate (blue) with observed hot records (red) for all regions and all seasons and months. (b) Ratio of expected and observed hot records (red), with 5 year smoothed series and trend line shown. The ratio of expected and observed hot records is also shown for detrended observed data (green), where the warming signal has been removed using a quadratic fit [Fawcett et al., 2012 (a) Comparison of the expected number of hot records (and) in a stationary climate (blue) with observed hot records (red) for all regions and all seasons and months. (b) Ratio of expected and observed hot records (red), with 5 year smoothed series and trend line shown. The ratio of expected and observed hot records is also shown for detrended observed data (green), where the warming signal has been removed using a quadratic fit []. We suggest that the dramatically increased rate of observed hot record breaking in Australian temperatures relates to the long‐term warming trend over Australia [Bureau of Meteorology, 2014b], which results in nonstationarities in the temperature time series. In the detrended observational time series (with the warming signal subtracted), the rate of record breaking follows what would be qualitatively expected in a stationary climate (Figure 2b). The number of new hot records does not increase through time in the observed detrended series, and the ratio of hot and cold record breaking is approximately one. In addition, the ratio of observed to expected hot records is not characterized by an increasing trend through time when detrended observed temperature data are considered (Figure 3b). CMIP5 detection and attribution experiments are used to investigate the impact of the underlying anthropogenic warming trend on record‐setting frequencies. First, the rate of hot record breaking (high T max and high T min ) and cold record breaking (low T max and low T min ) was calculated in each analyzed historical realization for all Australian regions and time periods. Multimodel mean hot and cold record numbers were calculated, together with ensemble 10th and 90th percentile values. Next, comparable hot and cold Australian record breaking rates were calculated for the parallel historicalNat simulations, where only natural climatic forcings (volcanic aerosols and solar) are imposed. In the anthropogenically forced case, the average number of hot records increases through time, while the number of cold records concomitantly decreases (Figure 4a). Hence, the ratio of hot record breaking to cold record breaking increases significantly, and particularly over the period of 1980–2005, when hot records occur more than 5 times more frequently than cold records. We note that CMIP5 historical simulations terminate in 2005 and do not provide for direct comparison with the instrumental record over the period of observed sustained heat in Australia during 2012–2014. In the historicalNat experiment, the multimodel mean for the ensemble utilized in this study does not reveal statistically significant trends in the number of hot or cold records through time. That is, the long‐term increase in the simulated rate of hot record setting in Australia‐ and State‐wide monthly, seasonal, and annual minimum and maximum temperatures is consistent with anthropogenic forcings. Both experiments included volcanic aerosol forcings and both reveal increases in the rate of cold record setting following the major eruptions of Agung, El Chichón, and Pinatubo (Figure 5). However, we did not isolate volcanic and ENSO anomalies, although observed temperature anomalies following El Chichón are likely related to both volcanic perturbations and the 1982–1983 El Niño episode. Figure 5 Open in figure viewer PowerPoint T max and high T min , red) and cold (low T max and low T min ) in 67 realizations of the CMIP5 historical experiment (red) for all regions and all seasons and months. The ensemble 10th and 90th percentile ranges are also shown for the historical experiments, and the timing of the Agung, El Chichón, and Pinatubo volcanic eruptions is indicated. The ~20 year average ratio of observed hot/cold records is also shown in black. (b) As for Figure (a) Multimodel mean number of hot records (highand high, red) and cold (lowand low) in 67 realizations of the CMIP5 historical experiment (red) for all regions and all seasons and months. The ensemble 10th and 90th percentile ranges are also shown for the historical experiments, and the timing of the Agung, El Chichón, and Pinatubo volcanic eruptions is indicated. The ~20 year average ratio of observed hot/cold records is also shown in black. (b) As for Figure 5 a but for 38 realizations of the CMIP5 historicalNat experiment.

5 Summary and Conclusions Australia experienced several hot years from spring 2012 to 2014, when many new hot temperature records were set. Throughout this period of persistent warmth, temperature records were broken on monthly, seasonal and annual time scales, and on spatial scales from individual stations to Australian area averages. In this current study, we assess the unusualness of the frequency of hot and cold record setting in recent observations, for Australian‐ and State‐wide minimum and maximum monthly, seasonal, and annual average temperatures. The rate of hot Australian temperature record breaking (high T max and high T min ) increases dramatically through the observed time series, while the number of new cold records (low T max and low T min ) set in the most recent decades decreases. In a stationary climate, the probability of setting new temperature records should decrease rapidly from the beginning of the sequence of observations [Benestad, 2004]. In addition, the expected ratio of hot to cold new temperature records should be approximately one. However, in the observed time series since ~1960, the ratio of observed hot records to cold records increases. In the recent period (2000–2014), the number of new hot records is over 12 times greater than new cold records. The observed time series has an increasing temperature trend of 0.9°C since high‐quality records began in 1910 [Bureau of Meteorology, 2014b]. Hence, the observed temperature is nonstationary, and the number of observed Australia hot records is significantly greater than that expected under the assumption of stationarity. When observed temperatures are investigated after the long‐term warming trend has been removed, the ratio of hot and cold records approximates that expected under the assumption of stationarity. In addition, the increased rate of hot record breaking cannot be explained by Pacific Ocean modes of variability exclusively but was shown to be qualitatively associated with the underlying anthropogenic trend of enhanced temperatures, although a formal attribution was not undertaken. The disproportionate hot to cold record‐breaking rates in Australian temperature provide a useful indicator of nonstationarity in temperatures, which is related to the underlying mean observed Australian warming trend of 0.9°C since high‐quality records began in 1910. This study focused on temperatures averaged over large spatial areas (Australia wide and State wide) and on monthly to annual average time scales. However, records are not necessarily independent of each other. Temperature anomalies have large spatial coherence, and record breaking temperatures are unlikely to affect a single state. The trends in hot temperature record breaking are largely consistent when each region is considered separately. In addition, the precise number of recent extreme temperature events in Australia should be interpreted carefully, as the number of records set in a short period may reflect natural climate processes, such as the El Niño–Southern Oscillation (ENSO) [Weaver et al., 2014] or tropical volcanic eruptions [e.g., Hansen et al., 1996], rather than long‐term trends. Our analysis points to a significant effect of ENSO on interannual variability in record breaking, particularly over northern Australia. However, the ENSO influences on rates of record breaking do not explain the increasing (decreasing) trend in hot (cold) records. These current findings on recent Australian hot temperature record setting are consistent with previous studies that demonstrate a tendency for low daily temperature records for Australian stations to outnumber high‐temperature records in the early part of the period and for high‐temperature records to outnumber low temperature records in the later part of the period [Trewin and Vermont, 2010]. The increased rate of recent hot record breaking in Australia is also consistent with observed global change [Seneviratne et al., 2014]. For example, a recent study demonstrates that climatic warming has increased the number of new global‐mean temperature records expected in the last decade from 0.1 to 2.8 [Rahmstorf and Coumou, 2011], and that the number of observed local monthly heat records around the globe is more than 3 times as high as expected in a stationary climate [Benestad, 2004; Coumou and Rahmstorf, 2012]. Overall, this simplified approach demonstrates the enhanced value of examining the frequency of extremes during the recent period of persistent heat, rather than focusing solely on individual records as recent studies of Australian extreme [Lewis and Karoly, 2013]. This demonstrates that the recent rate of hot record breaking dramatically exceeds that expected in a stationary climate, which has important consequences for impacts. For example, Perkins et al. [2015] note that both morbidity and mortality outcomes during heat waves are linked to the duration of high overnight temperatures. Also, the frequency of high temperatures impacts fire danger indices and ecosystem resilience. Given that the increased rate of observed hot record breaking is associated with the underlying anthropogenic warming trend, future changes in the frequency of hot temperatures record breaking under further greenhouse warming will likely have consequences on human and natural systems in Australia.

Acknowledgments This research was supported by the ARC Centre of Excellence for Climate System Science (grant CE 110001028) and the NCI National Facility. We thank the Bureau of Meteorology, the Bureau of Rural Sciences, and CSIRO for providing AWAP data. We acknowledge the WCRP's Working Group on Coupled Modelling, which is responsible for CMIP. The U.S. Department of Energy's PCMDI provides CMIP5 coordinating support. CMIP5 model data can be found at http://pcmdi9.llnl.gov/esgf‐web‐fe/; sea surface temperature data were downloaded from http://www.metoffice.gov.uk/hadobs/hadsst3/; and the Australian temperature data were obtained from http://www.bom.gov.au/climate/change. The Editor thanks Blair Trewin and an anonymous reviewer for their assistance in evaluating this paper.

Supporting Information Filename Description grl53430-sup-0001-supplementary.docWord document, 633 KB Figures S1–S8 and Tables S1 and S2 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.