As of December 2022, the highest recorded temperature in Australia was at Onslow Airport in Western Australia, where the temperature was **** degrees Celsius. This was matched by the highest temperature recorded at Oodnadatta Airport, South Australia, in 1960. What is causing increasing temperatures? The annual mean temperature deviation in the country has increased over the past century. In 2024, the annual national mean temperature was **** degrees Celsius above average. Climate experts agree that the major climate driver responsible for the heat experienced in Australia was a positive Indian Ocean Dipole (IOD). This is where sea surface temperatures are cooler in the eastern half of the Indian Ocean than the western half. The discrepancy in temperatures led to drier, warmer conditions across Australia. Global warming due to greenhouse gas emissions has been linked to the warming of sea surface temperatures and the IOD. Social change While the topic of global warming is undoubtedly controversial, many people perceive global warming as influencing Australia’s climate. In 2023, around ** percent of Australians believed climate change was occurring. Furthermore, around **** of Australians agreed that their government was not doing enough in terms of climate change action.

Temperature in Australia increased to 22.77 celsius in 2024 from 22.31 celsius in 2023. This dataset includes a chart with historical data for Australia Average Temperature.

This statistic displays the average minimum and maximum temperatures in Australia in 2015. According to the source, in Queensland, the hottest temperature was ***** degrees on average in 2015.

In 2024, the mean temperature deviation in Australia was 1.46 degrees Celsius higher than the reference value for that year, indicating a positive anomaly. Over the course of the last century, mean temperature anomaly measurements in Australia have exhibited an overall increasing trend. Temperature trending upwards Global land temperature anomalies have been fluctuating since the start of their measurement but show an overall upward tendency. Australian mean temperatures have followed this trend and continued to rise as well. Considered the driest inhabited continent on earth, this has severe consequences for the country. In particular, the south of Australia is predicted to become susceptible to drought, which could lead to an increase in bushfires as well. The highest temperatures recorded in Australia as of 2022 were measured in South Australia and Western Australia, both exceeding 50 degrees. The 2019/2020 bushfire season Already prone to wildfires due to its dry climate, the change in temperature has made Australia even more vulnerable to an increase in bushfires. One of the worst wildfires in Australia, and on a global level as well, happened during the 2019/2020 bushfire season. The combination of the hottest days and the lowest annual mean rainfall in 20 years resulted in a destruction of 12.5 million acres. New South Wales was the region with the largest area burned by bushfires in that year, a major part of which was conservation land.

IOCI3, a climate research collaboration between CSIRO, the Bureau of Meteorology (BoM) and the Western Australian Government, produced maps of mean hot spell intensity, frequency and duration for the 1958-2010 period using estimates derived from statistical models. They also produced maps of trends in hot spell intensity, frequency and duration for this time period. In addition they provided maps of mean hot spell thresholds, intensity, frequency and duration for the 1981-2010 period using estimates derived from statistical models, and projections of these characteristics for the 2070-2099 period under the A2 greenhouse gas (GHG) emissions scenario (described in the IPCC Special Report on Emissions Scenarios [SRES]), as well as the difference between these two periods." Results are provided in the JPEG file format. Lineage: High quality station data as well as quarter-degree gridded (0.25°× 0.25° resolution) daily maximum temperature data from BoM Australian Water Availability Project (AWAP) were used to produce these results. Hot spell temperature thresholds were selected using statistical methods. Hot spell occurrence (frequency) was modelled by a Poisson process, hot spell intensity by a generalized Pareto distribution, and hot spell duration through a geometric distribution. The Generalized Linear Model framework was used to estimate the parameters in the model for hot spells. This method was applied to daily maximum temperature data simulated from the CSIRO Cubic Conformal Atmospheric Model (CCAM) for both the present-day and possible future climate under the SRES A2 GHG emissions scenario. The CCAM was nested in the CSIRO Mk3.0 Global Climate Model host for the SRES A2 scenario. Caveats & limitations: The hot spell projections should be seen as initial estimates only, and they should not be used for making impact, vulnerability and risk assessments. They were made using only one climate model (CCAM); more work using an ensemble of global and regional climate model results is required to provide more robust projections of hot spells in Western Australia.

Extreme events are by definition rare, and analysis relies on partial (extreme) datasets (e.g., daily maximum temperatures higher 35 °C). In addition, estimating extremes necessitates extrapolating beyond such relatively small observed records. Consequently, the uncertainty associated with these projections of extremes is large, especially when extrapolating from a small dataset. To produce these projections we used AWAP data was used to overcome data shortages. However, the methods used to construct the AWAP dataset (interpolation) may smooth out some extreme values; this may lead to an underestimation of extremes in some cases. To these uncertainties are added the uncertainties inherent in the use of climate models.

The maximum temperature of the hottest month (the maximum temperature of any monthly maximum temperature)

Australia Percentage of Population Exposure to Hot Days data was reported at 54.800 % in 2021. This records a decrease from the previous number of 72.800 % for 2020. Australia Percentage of Population Exposure to Hot Days data is updated yearly, averaging 62.400 % from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 76.200 % in 1994 and a record low of 44.900 % in 2002. Australia Percentage of Population Exposure to Hot Days data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.GGI: Social: Air Quality and Health: OECD Member: Annual.

The development of the Australian geothermal industry over the last decade owes much to compilations of drill hole temperature data undertaken in the early 1990s in Canberra. The portrayal of this data on maps of predicted temperature at five kilometres depth, and contained heat resource calculations from this data, have shifted the perception that because Australia does not have significant current magmatic activity there is no geothermal potential. The Australian geothermal industry arguably now leads the world in terms of development of amagmatic geothermal systems for electricity generation. Work at the Bureau of Mineral Resources Geology and Geophysics (now Geoscience Australia) provided a brief compilation of open-file drill hole temperature data, and a map of thermal gradient (Nicholas et al. 1980). The work of Somerville et al. (1994) provided a much larger compilation, and included a significant study into the resource potential that could be accessed by Hot Dry Rock technology. Finally, Chopra and Holgate (2005 Austherm version) further extended the dataset and produced an image of the predicted temperature at 5 km that has become very widely distributed. (Figure 1). OZTEMP is the result of work undertaken to refine the Austherm database, and to utilise new datasets within a GIS for the extrapolation of temperature to 5 km depth and the interpolation between these datapoints. The method, which is largely derivative from that of Chopra and Holgate (2005), and the areas of new work, is described briefly below.

The monthly mean absolute maximum temperature derived from the hottest day of each month over 50-years (1955 to 2005) of 5km gridded daily climate (Jeffrey et al. 2001)

Given the rising frequency of thermal extremes (heatwaves and cold snaps) due to climate change, comprehending how a plant’s origin affects its thermal tolerance breadth becomes vital. We studied juvenile plants from three biomes: temperate coastal rainforest, desert, and alpine. In controlled settings, plants underwent hot days and cold nights in a factorial design to examine thermal tolerance acclimation. We assessed thermal thresholds (Tcrit-hot and Tcrit-cold) and thermal tolerance breadth (TTB). We hypothesised that: 1) desert species would show the highest heat tolerance, alpine the greatest cold tolerance, with temperate species intermediate; 2) all species would increase heat tolerance post hot days and cold tolerance after cold nights; 3) combined exposure would broaden TTB more than individual conditions, especially in the desert and alpine species. We found that biome responses were minor compared to the responses to the extreme temperature treatments. All plants increased thermal tolerance in response to hot 40°C days (Tcrit-hot increased by ~3.5°C) but there was minimal change in Tcrit-cold in response to the cold -2°C nights. In contrast, when exposed to both hot days and cold nights, on average plants exhibited an antagonistic response in TTB, where cold tolerance decreased and heat tolerance was reduced, and so we did not see the bi-directional expansion we hypothesised. There was, however, considerable variation among species in these responses. As climate change intensifies, plant communities, especially in transitional seasons, will regularly face such temperature swings. Our results shed light on potential plant responses under these extremes, emphasizing the need for deeper species-specific thermal acclimation insights, ultimately guiding conservation efforts. Methods Title: Methods for Assessing Thermal Tolerance in Plants from Different Australian Biomes Summary: This study compared the responses of plants from temperate rainforest, alpine, and desert biomes in Australia to hot days and cold nights using temperature-dependent increases in chlorophyll a fluorescence. For each biome, eight species were selected based on seed availability and family representation. Seeds were obtained from conservation seed banks, sown, and grown under common conditions in glasshouses. Some species were purchased from nurseries. A fully factorial experimental design was used with three biomes, eight species per biome, five replicates, and four temperature treatments (control, hot days, cold nights, and a combination of hot days and cold nights). Experiments were conducted in growth chambers, and plants were exposed to the temperature regimes for five days. Leaf temperatures were monitored using thermocouples. Thermal tolerance assays were performed on days three and five of the experiment using Maxi Pulse Amplitude Modulating (PAM) systems. Leaf discs were placed on Peltier plates and subjected to cooling (-25°C) and heating (65°C) ramps. The critical temperatures during heating (Tcrit-hot) and cooling (Tcrit-cold) were defined as the breakpoint between the slow and fast-rise phases of basal fluorescence.

The maximum temperature of the hottest month (the maximum temperature of any monthly maximum temperature)

(From http://www.worldclim.org/methods) - For a complete description, see:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as 1 km2 resolution). Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables.

The WorldClim interpolated climate layers were made using: * Major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. * The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km) * The ANUSPLIN software. ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing splines. We used latitude, longitude, and elevation as independent variables.

Australias vast hydrothermal and hot rock energy resources have the potential to become a very significant source of safe, secure, competitively-priced, emission free, renewable baseload power supplies for centuries to come. This potential combined with the evidence of risks posed by climate change is stimulating growth in geothermal energy exploration, proof-of-concept and demonstration power generation projects in Australia. In the six years since the grant of the first Geothermal Exploration Licence (GEL) in Australia in 2001, 19 companies have joined the hunt for renewable and emissions- free geothermal energy resources in 143 licence application areas covering approximately 149,000 km2. The associated work programs correspond to an investment of AUS$656 million (US$538 million), a tally which excludes up-scaling and deployment projects assumed in the Energy Supply Association of Australias scenario for 6.8% (~ 5.5 GWe) of Australias base-load power coming from geothermal resources by 2030. Most investment is focused on HFR for enhanced geothermal systems (EGS) to fuel binary power plants. At least two companies are also focused on hydrothermal resources, also to fuel binary power plants. The anticipated cost of EGS energy in Australia has been estimated at AUS$50$60 (US$40$50) per MWh. Without carbon pricing, many forms of conventional energy generation such as coal and natural gas are more cost effective. Geoscience Australias preliminary work suggests Australias hot rock energy between a minimum temperature of 150oC and at a maximum depth of 5 kilometres is roughly 1.2 billion PJ (roughly 20,000 years of Australias primary energy use in 2005), without taking account of the renewable characteristics of hot rock EGS plays.

This collection includes each of the climate variables (including quantile-based extremes) and predicted plant species distributions (37) generated as part of the manuscript titled 'Climate extreme variables generated using monthly time-series data improve predicted distributions of plant species' (Stewart et al. 2020a; doi: 10.1111/ecog.05253). Lineage: Climate variables are generated using 39 years of monthly maximum temperature (Stewart & Nitschke 2017), minimum temperature (Stewart & Nitschke 2018) and precipitation data (Stewart, et al. 2020b). Annual calculations for maximum temperature of the hottest month (BIO5), minimum temperature of the coldest month (BIO6), and precipitation of the driest quarter (BIO17) were used to quantify 'base climate' (long-term means), variability (standard deviations) and extremes of varying return intervals (defined using quantiles) based on historical observations. A tutorial, with R code, for producing these layers is provided in the supporting information to the manuscript (SDMExtremes_AppendixS2.pdf).

Species distribution models were fitted and predicted for 37 plant species across Victoria using boosted regression trees, following the procedures detailed in the published manuscript. Images are provided for base climate, variability and extreme (with 1 in 15 year return interval) models. All cross validation results are provided in the supporting information to the manuscript (SDMExtremes_Appendix_S4.xlsx).

Context

JJJ, a popular public radio station in Australia, runs an annual survey where the people vote on up to 10 of their favorite songs for the year. The votes are tallied and the 100 most popular songs are played on Australia day weekend.

Content

A simple dataset containing the top 100 songs for years 1993 through to 2017. The data was scraped from http://hottest100.org/ 🥁

The possible role of climate change in late Quaternary animal extinctions is hotly debated, yet few studies have investigated its direct effects on animal physiology to assess whether past climate changes might have had significant impacts on now-extinct species. Here we test whether climate change could have imposed physiological stress on the Tasmanian devil (Sarcophilus harrisii) during the mid-Holocene, when the species went extinct on mainland Australia. Physiological values for the devil were quantified using mechanistic niche models of energy and water requirements for thermoregulation, and soil-moisture-based indices of plant stress from drought to indirectly represent food and water availability. The spatial pervasiveness, extremity, and frequency of physiological stresses were compared between a period of known climatic and presumed demographic stability (8000-6010 BP) and the extinction period (5000-3010 BP). We found no evidence of widespread negative effects of climate on physiological parameters for the devil on the mainland during its extinction window. This leaves cultural and demographic changes in the human population or competition from the dingo (Canis dingo) as the main contending hypotheses to explain mainland loss of the devil in the mid-Holocene.

Methods Macroclimate

For paleoclimate assessment we downloaded bias-corrected data on monthly temperature (min and max), precipitation, and relative humidity using PaleoView, a free software that generates paleoclimate data at different temporal scales at a 2.5° × 2.5° resolution (Fordham et al. 2017). We selected an area cropped to Longitude 112.5°E- 155°E, Latitude 45°S to 10°S. We took ten-year averaged monthly climate variables centred on each ten-year period for 8000-2000 BP; i.e., the years 5010 BP (averaged from 5015 BP to 5006 BP) and 5000 BP (averaged from 5005 BP to 4996 BP) were taken. The PaleoView data were generated from the TRaCE21ka experiment (Liu et al. 2009; Otto-Bliesner et al. 2014) that uses the Community Climate System Model version 3 (Otto-Bliesner et al. 2006; Yeager et al. 2006), a global coupled atmosphere-ocean-sea ice-land general circulation model that includes a dynamic global vegetation module. The microclimate model that we used also requires cloud cover and wind speed data. This monthly data at yearly intervals—also generated by the TRaCE21ka experiment—was downloaded from the National Center for Atmospheric Research website (see Data Availability) and resampled from 3.75° × 3.75° to 2.5° × 2.5°, using the raster package (Hijmans 2019), then averaged over ten-year time periods to be consistent with the extracted PaleoView data. The cloud and wind data consist of 26 pressure levels representing slices of the atmosphere. We selected the lowest altitude level (~60 m) for wind speed, corrected to a reference height of 1.2 m by the equation:

v / vo = (h / ho)α

where v is the wind speed at height h (m/s), vo is the wind speed at height ho (m/s), and α is the wind shear exponent (0.15 to represent open grassland) (Campbell & Norman 1998). The mean value of mid-atmosphere cloud fraction (pressure levels 18-25 corresponding to 238- 6173 m altitude) was used for cloud cover and a diurnal pattern was imposed by multiplying by three to obtain daily maxima and by 0.5 such that cloud cover had an asymptotic relationship with rainfall at ~100% cloud cover. Diurnal variation in humidity was imposed by obtaining the daily minimum and maximum relative humidity from the mean daily humidity and air temperature via the WETAIR function in NicheMapR, on the assumption that the absolute relative humidity remained constant through the day.

Microclimate and animal models

Mechanistic niche models consist of two sub-models: a microclimate model and an animal model (Porter & Kearney 2009; Kearney et al. 2021a) (see Figure 1). We used the microclimate and endotherm model of the NicheMapR package (Kearney & Porter 2017; Kearney et al. 2021a) for the R programming environment (R Core Team 2018). The microclimate model downscales daily macroclimate data into hourly environmental conditions (air temperature, wind speed, relative humidity) at the height of the animal of interest as well as computing long and short-wave radiation fluxes and heat and water budgets for the substrate. The microclimatic variables are then used as the input by the animal (endotherm) model which solves the heat and mass balance equations for the animal given its functional physiological, morphological and behavioural traits (for example see (Kearney et al. 2021b)) .

We modified the micro_global function from NicheMapR to prepare the PaleoView/ TRaCE21ka data for input to the NicheMapR microclimate model. We interpolated (periodic spline) the monthly minimum and maximum air temperature, relative humidity and cloud cover for the decadal-averaged palaeoclimate data across 365 days and then ran the model for 730 days to allow sufficient spin-up time for the soil moisture calculations to reach steady state. For precipitation, we allocated monthly rainfall across the days of the month assuming the present-day pattern of rainy days per month (from New et al. (2002)), with the assumption that 50% of the rain fell on the first day of each month. This provided realistic annual cycles of soil moisture. Bulk density was set at a standard value for soil(1.3 g/cm³)(Campbell & Norman 1998) and hydraulic properties of a loam were assumed for soil moisture calculations. We simulated local air temperature, wind speed and relative humidity at a height of 30 cm – the approximate mid-point of a standing devil.

The environmentally imposed heat stress and associated energy and water requirements of the devil was simulated using the default version of the endoR function (Kearney et al. 2021a) which solves heat budgets for endotherms given their functional traits according to a specific sequence of morphological, behavioural and physiological thermoregulatory responses. The required parameters include the target core body temperature, basal metabolic rate, presence of fat, fur properties, and the size and shape of the species. These were obtained from the literature and the predictions compared against laboratory data on devil metabolic rates at different air temperatures (see Supplementary material for details). The resultant model is best considered as a general model of stress for an endothermic species of the devil’s size and shape because it does not include species-specific diet or behaviour besides denning behaviour. Values on the lower end of the weight range (6.5 kg) were chosen because stress on females, which are smaller than males, would likely have a greater negative effect on reproduction and therefore population growth and viability. The endoR model applies an ordered sequence of changes in behaviour (posture change) and physiology (piloerection, change flesh conductivity, allow core temperature to rise, pant, sweat) to maintain its specified core temperature given the minimum permissible metabolic heat production. Under cold conditions the model finds the metabolic rate required to maintain body temperature; under hot conditions the model finds the required water loss rate, contingent on the thermoregulatory options. The model thereby quantifies environmental stress in terms of energy and water requirements contingent on thermoregulatory responses. If the endotherm model cannot find a solution given the stated parameters, the model “fails”. This does not imply the conditions would have been certainly fatal, but rather that survival would be unlikely given the limited behaviours programmed in the model.

Microclimate inputs for our endotherm simulations made a distinction between foraging and denning environments. As devils are nocturnal and rest in burrows during the day (Rose et al. 2017), for night-time conditions (defined by the microclimate model predictions of solar zenith angle) the devils were assumed to experience conditions in the open at 30cm above the ground, and for day-time conditions we assumed that the den was a burrow at 50cm below ground with temperature-adjusted humidity and a wind speed of 0.1 m/s. Different sleeping conditions—a humid burrow (90% relative humidity and 0.1 m/s wind speed) and above ground at 30cm under the cover of thick vegetation (90% shade and half the wind speed at 30cm to simulate obstruction from the vegetation)—were also tested (see Supplementary material).

Stress indices

We formulated three stress metrics—plant, energy, and water stress—using the outputs from the microclimate model and animal model. Plant stress, as an indirect index of food availability for devils, was assumed to occur if the computed soil water potential at 10 cm below ground fell below -1500 J/kg in a given hour (a standard value for the permanent wilting point of plants (Campbell & Norman 1998)). Energy stress was defined as computed energy requirements being above the basal energy requirements multiplied by a conservative activity factor of 2x. Previous studies on mammals have used activity multipliers of 2.3 (Wang et al. 2018) and 2.25-4.5 (Mathewson et al. 2020). We chose a conservative value to compensate for our devil-specific behaviour dictating that devils remain active the entire night when they rest periodically during the night (Andersen et al. 2020). Water stress was assumed to occur when the panting multiplier exceeded one, indicating that the animal would need to pant (and thus lose water) to rid itself of excess heat. Hours when water stresses coincided with plant stress were also calculated, indicating a scenario of drought-like conditions with low food supply and high-water requirements. We calculated two composite stress metrics: physiological stress, the combined number of energy and water stress hours; and gross stress, physiological and plant stress combined. We quantified

In a survey conducted in 2024 about the Australian public's view on climate change, ** percent of respondents believed the melting of the polar ice caps was caused by global warming. More heatwaves and extremely hot days, as well as the bleaching of the Great Barrier Reef, also ranked high in the list of impacts that Australians think global warming is already causing.

Statistics illustrates the net export value of Machinery, plant and laboratory equipment; for treating materials by change of temperature, other than for making hot drinks or cooking or heating food in Australia from 2007 to 2024 by trade partner.

An interpretation of the crustal temperature at 5km depth, based on the OzTemp bottom hole temperature database and additional confidential company data. A simple two layer model has been used for the extrapolation of the temperature to 5km depth; where the data quality and availability has allowed a slightly more complex three layer model using heatflow and thermal conductivity data was used for the extrapolation.

The maximum temperature of any monthly minimum temperature