1. Introduction
Increasing global emissions threaten to disproportionately impact the future of Greater Western Sydney (GWS) [
1], with some suburbs already experiencing temperatures 8 °C to 10.5 °C greater than the Sydney coastal region during heatwaves. While this disparity is partially underpinned by western Sydney’s inadequate urban planning and its geographical distance away from coastal sea breezes [
2], a more contentious point at present is whether the recent extreme temperatures are caused by climate change, natural variability, or both. GWS starts approximately 30 km inland from coastal Sydney. GWS extends about another 30 km inland and includes the suburbs of Penrith, Blacktown, Liverpool, and Richmond (
Figure 1).
The impacts of rising temperatures and extreme heat are contributing to longer summers, shorter winters, and decreasing time frames for implementing bushfire management strategies [
3]. The 2019/2020 Australian bushfires were a pronounced consequence of this warming, with a report by the Royal Commission into National Natural Disaster Arrangements [
4] highlighting that future fires will be more difficult to control as there is less time to take precautions such as prescribed burning. With 24 million hectares of land and ecosystems burnt, 3000 homes destroyed, and around three billion animals killed or displaced, the ramifications of the bushfires are captured in the quote, “…what was unprecedented is now our future” [
4].
The urban heat island effect occurs in areas where high concentrations of buildings and manufactured surfaces, which absorb and retain heat more readily, have replaced natural land cover. This effect is present in both coastal Sydney and GWS, exacerbating temperatures that push the limits of human endurance [
5]. The human body’s ability to cool itself reduces at temperatures above 35 °C [
6], especially if dewpoints are elevated, and the effects on vulnerable populations, such as children in classrooms without air-conditioning and low-income family households, contrasts with the effects on populations living near cooler coastal areas [
7]. A report titled, ‘HeatWatch: Extreme heat in western Sydney’ by [
2], highlights the ongoing trend in rising extreme temperature days, with analysis finding that western Sydney is projected to experience up to 46 days per year above 35 °C by 2090 [
2].
With longer, intense summers driving more prolonged heatwaves, droughts, and bushfires, the economic burden of dealing with these disastrous events is increasing [
5]. Climate Council statistics [
7] highlight that the costs associated with extreme weather events in Australia have more than doubled since the 1970s and that Australians are five times more likely to be displaced by such events than people living in Europe. GWS has a higher unemployment rate and the highest proportion of low-income families in the Sydney region [
8]. The escalating climate crisis threatens to exacerbate the socioeconomic divide and push more people into poverty. As this disparity, relative to coastal Sydney, increases in GWS, the consequences are striking, with regions such as the Blacktown city council implementing evacuation shelters to mitigate growing heatwave risks [
9].
Two-thirds of the population growth in Sydney, by 2036, is projected to occur in GWS, and with the increased population comes extensive infrastructure development and a growing economy [
8], one which is set to be disproportionately impacted by extreme temperatures. Given this alarming reality, there is an urgent need to understand how Sydney’s western suburbs differ from near-coastal suburbs in terms of temperature as well as factors that contribute to these differences.
Recent research highlights a growing interest in understanding the roles of Australian region climate drivers, particularly global warming, in influencing these changes. One study [
10] examined precipitation and temperature trends to understand drought conditions in Canberra, a rapidly growing region in inland southeast Australia. An increase in mean temperature was established for all seasons through permutation testing for two 20-year periods, starting in 1979. In addition, wavelet analysis was used to identify El Niño South Oscillation (ENSO) trends over time, providing a solid rationale to attribute climate drivers. However, the combination of linear and non-linear statistical models concluded that the most important drivers were the Indian Ocean Dipole (IOD) and the global warming temperature attributes. Another study investigating differences between inland and coastal locations [
11] performed a comparative study between inland Scone and coastal Newcastle, in the Hunter region of NSW. This research performed permutation testing on the differences in mean minimum and maximum temperature across three 20-year periods; it was concluded that temperatures have strongly increased in Newcastle since 1958. The inland location showed gradually increasing temperature but had weaker statistically significant evidence. This comparative study of coastal and inland locations did not attempt to link these changes to climate drivers, highlighting an overall gap in the literature.
The above-mentioned comparative studies did attempt to link the observed changes to climate drivers, highlighting a significant gap in the literature. Unlike those studies, the present research aims at assessing the impacts of climate change on rising maximum summer temperatures in Sydney, compared with those in western Sydney, with a focus on identifying links with large-scale Australian region climate drivers.
Hence, the aims of this study are twofold. First, the maximum temperature data for two locations, one in western Sydney and the other in coastal Sydney (see
Figure 1), are split into two 30-year periods (1962–1991 and 1992–2021). To quantify the rising temperatures over the most recent 30-year period relative to the first 30-year period, resampling permutation testing was employed to assess whether the observed changes are statistically significant. Second, the application of a range of machine learning (ML) techniques is employed to attribute the monthly maximum temperatures to identify the most significant Australian climate drivers for each of the two locations over the past 60 years. Additionally, it is of particular interest to determine how well the non-linear ML techniques, including Support Vector Machines (with a range of kernels) and Random Forests, performed compared with multiple linear regression.
5. Conclusions
This study analyzed changes in maximum summer temperatures for the Sydney coastal location of Observatory Hill when compared with the western inland location of Richmond, using statistical resampling methods, wavelets, and machine learning (ML) attribution techniques. Given that Sydney’s western suburbs are projected to absorb the majority of Sydney’s future population growth, rising temperatures will have increasing human health, socio-economic, and urban planning impacts. Warming temperatures also are driving significant differences in the number of extreme heat days, defined here as above the 95th percentile, being experienced in inland suburbs compared with coastal suburbs.
Analysis of the two most recent 30-year climate periods, 1962–1991 and 1992–2021, revealed increased medians and upper percentile values in the last 30 years for both locations. This was confirmed by permutation testing of the 4-month summer December to March periods. The results revealed significant increases in the median of maximum temperatures both for Observatory Hill, Sydney and Richmond, coastal and inland locations, respectively.
Various climate drivers and their two-way interactions with each other were considered for ML attribution. When employing methods of linear regression, SVM with the poly and RBF kernels, and random forests, it was SVM RBF and random forests that performed best. The results show common, highly influential drivers for both regions: Niño3.4, DMI, SAM * SOI, and GlobalT * SAM. Results for both Sydney and Richmond highlight the influence of global warming indicators, both individually and in combination with other climate drivers, in driving temperature changes.
For Sydney and Richmond, data analysis detected increasing median and 90th and 95th percentile values, suggesting that summers are becoming hotter and more extreme. January and February are notably the warmest months.
Permutation testing of differences in the median for individual months shows that Sydney has warmed for all months except December compared with the previous climate period. However, January was the only statistically significant increase for Richmond. When the entire summer period was analyzed, there is considerable evidence that median temperatures have risen for both Sydney and Richmond.
While these results show rapidly increasing maximum summer temperatures for Sydney compared to Richmond, the daily data analysis indicates that extreme temperature days have increased drastically for Richmond. Using the 90th and 95th percentiles from the first period as a baseline, Richmond experienced an increase of 120 and 64 days, respectively, in the second period. In contrast, for Sydney, the corresponding numbers of days shown decreases by 4 and 52 days. These findings underline the worsening situation in western Sydney compared with coastal Sydney.
A wavelet analysis was employed to understand periodic signals and both regions highlight significant ENSO influences on maximum temperatures from the mid-1980s onward. Applying ML techniques with expanding window cross-validation indicates that both Richmond and coastal Sydney share the common influential climate drivers of Niño3.4, SAM * SOI, IOD, and GlobalT * SAM. However, the Tasman Sea and Global SSTs have had far more influence on Sydney than Richmond. When investigating the performance of the ML techniques in this study, SVR with the polynomial function kernel and forward selection method performed the best for both sites, because it selected the most prominent attributes and produced lower standard deviations relative to other techniques.