As of December 2022, the highest recorded temperature in Australia was at Onslow Airport in Western Australia, where the temperature was 50.7 degrees Celsius.

What is causing increasing temperatures?

The annual mean temperature deviation in the country has increased over the past century. In 2020, the annual national mean temperature was 1.15 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 perceived global warming as influencing Australia’s climate. In 2019, over 40 percent of young Australians believed climate change was the most pressing issue affecting their generation. This was a stark increase from the previous year. The majority of Australians agreed that their government should be taking some form of action on climate change. It seems that recent climate events have triggered a call for action by many Australians.

This statistic displays the average minimum and maximum temperatures in Australia in 2015. According to the source, in Queensland, the hottest temperature was 30.94 degrees on average in 2015.

In 2022, the observed annual average maximum temperature in Australia reached 28.8 degrees Celsius. Overall, the annual average maximum temperature had increased compared to the temperature reported for 1901.

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.

The maximum and minimum temperatures are the highest and lowest temperatures (respectively) which occurred throughout the 24 hours period up to 9am. The observed minimum daily temperature is assigned to the date the observation was made, as the diurnal cycle typically reaches its minimum at approximately 5am. The observed maximum daily temperature is assigned to the day prior to the date the observation was made, as the diurnal cycle typically reaches its maximum at approximately 3pm. If the data are not recorded daily (for example, the instrument malfunctioned), the first observation following the no-report period is flagged as an accumulation.

We must understand the natural cycles of the oceans to understand the evolution of our climate through geological time. Core MD 032607 was obtained in 2003 off the coast of Sumatra (36.9606 S, 137.4065 E). By investigating the properties and components of this core we are able to reveal some information regarding past oceanographic and climatic systems. Information obtained or inferred from the core include the isotopic composition of oxygen and carbon through time, an age vs. depth profile of the core (revealing sedimentation rates), the relative abundance of planktonic foraminifera over time, and estimates of historical sea-surface temperatures.

In 2022, the observed annual average mean temperature in Australia reached 21.96 degrees Celsius. Overall, the annual average temperature had increased compared to the temperature reported for 1901. Impact of climate change The rising temperatures in Australia are a prime example of global climate change. As a dry country, peak temperatures and drought pose significant environmental threats to Australia, leading to water shortages and an increase in bushfires. Western and South Australia reported the highest temperatures measured in the country, with record high temperatures of over 50°C in 2022. Australia’s emission sources While Australia has pledged its commitment to the Paris Climate Agreement, it still relies economically on a few high greenhouse gas emitting sectors, such as the mining and energy sectors. Australia’s current leading source of greenhouse gas emissions is the generation of electricity, and black coal is still a dominant source for its total energy production. One of the future challenges of the country will thus be to find a balance between economic security and the mitigation of environmental impact.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of maximum temperature (approx. 1.2 m from ground) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Surfaces are developed using ordinary trivariate splines (full spline dependence upon latitude, longitude and elevation).

Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated with full spline dependence upon latitude, longitude and elevation (Fenner School of Environment and Society & Geoscience Australia 2008) using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Daily anomalies were calculated by subtracting daily observations from climate normals and interpolated with full spline dependence upon latitude and longitude 5. Interpolated anomalies were added to interpolated climate normals to obtain the final daily surfaces. 6. Monthly surfaces are calculated as an aggregation of the daily product. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. C) Model performance (3DS): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly maximum temperature RMSE = 0.51 °C Daily maximum temperature RMSE = 1.20 °C

Please refer to the linked manuscript for further details.

The Bureau of Meteorology provides the Australian and international maritime communities with weather forecasts, warnings and observations for coastal waters areas and high seas around Australia. …Show full descriptionThe Bureau of Meteorology provides the Australian and international maritime communities with weather forecasts, warnings and observations for coastal waters areas and high seas around Australia. Generally most of these services are provided routinely throughout the day, while marine weather warnings may be issued at any time when the need becomes apparent. Because of the complex nature of the sea, the Bureau of Meteorology uses advanced computer models to predict the physical characteristics of the ocean. These computer forecasts are used by meteorologists in the preparation of marine forecasts and warnings. The forecasts include wind, weather, sea and swell and are intended to describe the average conditions over specified areas. Marine forecasts have been enhanced by the inclusion of ocean currents and sea-surface temperature forecasts through the BLUElink ocean forecasting initiative. The Sea Surface Temperature Browse Service provides access to browse images (1:5 resolution) of satellite derived Daily Sea Surface Temperature data available from 30 December 1998. The Bureau currently uses measurements from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) series of polar orbiting satellites to derive SSTs for the Australian region. The data is calibrated and quality controlled against SST data collected from ships and drifting buoys. The SSTs are used in real time operations and also archived as the data as part of Australia's National Climate Record. This record also provides links to BOM Ocean Analysis data including Daily/Weekly/Monthly records of Australian and Global Sea Surface and Subsurface Temperatures.

ACORN-SAT, the Australian Climate Observations Reference Network - Surface Air Temperature data set, is a homogenized daily maximum and minimum temperature data set containing data from 112 …Show full descriptionACORN-SAT, the Australian Climate Observations Reference Network - Surface Air Temperature data set, is a homogenized daily maximum and minimum temperature data set containing data from 112 locations across Australia extending from 1910 to the present.

Global Temperature: Daily Normal: Australia: Keith data was reported at 22.000 Degrees Celsius in 08 Feb 2024. This stayed constant from the previous number of 22.000 Degrees Celsius for 07 Feb 2024. Global Temperature: Daily Normal: Australia: Keith data is updated daily, averaging 22.000 Degrees Celsius from Feb 2024 (Median) to 08 Feb 2024, with 2 observations. The data reached an all-time high of 22.000 Degrees Celsius in 08 Feb 2024 and a record low of 22.000 Degrees Celsius in 08 Feb 2024. Global Temperature: Daily Normal: Australia: Keith data remains active status in CEIC and is reported by Climate Prediction Center. The data is categorized under Global Database’s Australia – Table AU.CPC.GT: Environmental: Global Temperature: Daily Normal.

This dataset contains the precipitation, mean maximum temperature and mean minimum temperature data used in the study Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia. This data was originally from the Australian Bureau of Meteorology Climate Data Online (http://www.bom.gov.au/climate/data/index.shtml), but has been updated to have missing values (1% of data) filled using a moving average centred on the year for which the data is missing.

Below is the abstract for the paper.

Southeast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn precipitation. However, annual precipitation remains stable as summer precipitation increased and the other seasons show no trend. Further, mean temperature increases in all seasons. These results suggest that Canberra is increasingly vulnerable to drought. Wavelet analysis suggests that the El-Niño Southern Oscillation (ENSO) influences precipitation and temperature in Canberra, although its impact on precipitation has decreased since the 2000s. Linear regression (LR) and support vector regression (SVR) were applied to attribute climate drivers of annual precipitation and mean maximum temperature (TMax). Important attributes of precipitation include ENSO, the southern annular mode (SAM), Indian Ocean Dipole (DMI) and Tasman Sea SST anomalies. Drivers of TMax included DMI and global warming attributes. The SVR models achieved high correlations of 0.737 and 0.531 on prediction of precipitation and TMax, respectively, outperforming the LR models which obtained correlations of 0.516 and 0.415 for prediction of precipitation and TMax on the testing data. This highlights the importance of continued research utilising machine learning methods for prediction of atmospheric variables and weather pattens on multiple time scales.

Australia Imports from New Zealand of Oils and Other Products of Distillation of High Temperature Coal Tar was US$3 Thousand during 2021, according to the United Nations COMTRADE database on international trade.

The data set derived from this project consists of the extraction of unusually cold days at Melbourne and Perth. (The basic source was the Bureau of Meteorology daily data records.) Another part of the data set is the points along the trajectories taken by the air to reach the cities as cold events.

From the abstracts of the referenced papers:

Cold air outbreaks, characterised by unseasonably low maximum temperatures, occurring over Melbourne between May 1972 and June 1991 have been identified and examined using an air parcel trajectory model and data from observations during the period of the outbreak events. Using a definition based on the long-term climatology of the region, thirteen outbreaks were identified during the study period.

The cold air pool source regions for each outbreak were examined via the use of the air parcel trajectory model using the assumption of travel along isobaric surfaces. Mean sea-level pressure patterns, the temporal behaviour of the maximum temperature surrounding an outbreak, three-hourly basic observational data and the determined isobaric trajectories were used to analyse the nature of each Melbourne outbreak.

It has emerged that air of recent Antarctic origin is not a feature common to the majority of outbreaks examined. It is also apparent that characteristic synoptic patterns are associated with cold outbreaks over the Melbourne region. These have been grouped into three categories, 'classic', warm front, and blocking anti-cyclone type. In the mean there is identifiable atmospheric organisation around the Antarctic continent associated with the events.

Unseasonably cold weather episodes have the potential to cause dislocation to many aspects of society, regardless of the season in which they occur. In this work we devise a method for quantitatively identifying extreme cold events in such a way that it is not biased to the winter season (as is usual in most other studies). We have applied this method to the daily maximum temperatures (over the period January 1972 to June 1991) in the southern Australian cities of Melbourne and Perth. We identify 10 cold events in winter and summer for the cities. Analyses were performed to determine the synoptic environment in which these events occurred. The most common synoptic type in these samples was the 'classic', which is characterised by, amongst other factors, the passage of a cold front over the city on the day of the outbreak, and the transport of air from subantarctic latitudes. Melbourne recorded five such events in summer and six in winter, while seven and eight occurred in the two seasons for Perth. The circulation features and characteristics of other synoptic types identified with these episodes is also examined.

The mean synoptic anomalies which are coincident with these cold events are analysed. For both cities and seasons there is a 'high-low' anomalous dipole in the regional MSLP pattern, with the high located in the 'upstream' quadrant from the anomalous cyclone. Having said this, the relative importance of the two features of the dipole in being associated with the cold event strongly depended on the city and season under consideration. The research shows that the regional structures associated with cold events in Melbourne and Perth bear some similarity, but also display a number of significant differences. These differences are associated partly with the different climatological and synoptic settings in which these cities find themselves, and the nature of their seasonality.

(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.

Temperature Max Average Annual | Australia | degC Temperature Max Average Annual | Australia | degC

Annual average daily maximum (max) and minimum (min) temperatures, smoothed with a 5-year running mean, for Australia’s Antarctic stations of Casey, Davis (from 1957) and Mawson (from 1954), and subantarctic Macquarie Island (from 1948). The record for Casey comprises measurements at Wilkes (February 1960 – January 1969), Casey Tunnel (February 1969 – December 1988) and the present Casey station (from January 1989).

Temperature data from Bureau of Meteorology. See http://www.bom.gov.au/climate/data/

Data used by the Department of Environment and Energy to produce graph at Figure ANT3 in the Antarctic environment theme of the 2016 State of the Environment Report, available at

https://soe.environment.gov.au/theme/antarctic-environment/topic/2016/physical-environment-atmosphere-climate-and-weather-patterns#Figure_ANT3

Dataset

This dataset was created by Josh Mills

Released under CC BY-SA 3.0

Contents

AbstractExtreme temperatures and heatwave events present challenging conditions for wildlife and are increasing in frequency and intensity in many regions due to climate change. High daily temperatures increase physiological stress and cause mortality in susceptible individuals (e.g., from poor health or exposure) but may also drive behavioural changes as individuals seek to thermoregulate (e.g., seeking shelter or water). As daily high temperatures accumulate into heatwave events, the ability of wildlife to tolerate conditions can diminish and exacerbate stress. Although climate change is well known to decouple species interactions, here we examine how extreme conditions may intensify interactions between predators and prey. In particular, we explore whether predators can exploit the thermoregulatory requirements of prey as they increasingly require access to water. We present evidence from the use of artificial waterpoints by dingoes and eastern grey kangaroos in Australia’s semi-arid rangelands, asking whether high temperatures and heatwaves alter species behaviour and interactions between them. Both species increasingly accessed waterpoints as daily maximum temperatures increased, however, the degree of co-occurrence at waterpoints increased significantly as temperatures became extreme and resulted in heatwaves. Not only did waterpoints become increasingly important for both species during heatwaves, dingo predation on kangaroos at these times also significantly increased, exploiting the apparent inability of kangaroos to avoid these water sources despite increased dingo presence. We found that 50 of 67 documented predation events at waterpoints occurred during heatwaves and that the predisposition of dingoes to hunt kangaroos of poor body condition was relaxed such that all kangaroos experienced predation at these times. Our results highlight the capacity of climate change to intensify predator-prey interactions in ways that may impact on persistence if prey are unable to adapt to predators exploiting their thermoregulatory requirements.GENERAL INFORMATION1. Corresponding Author Information Name:Loic Q. Juillard2. Date of data collection (single date, range, approximate date): November 2019 - April 20213. Geographic location of data collection: South-western Queensland, Australia.4. Information about funding sources that supported the collection of the data: Detroit Zoological Foundation (donations), Australia Zoo (in-kind)Sharing/Access information1. Links to publications that cite or use the data: To be updated post paper publication2. Links to other publicly accessible locations of the data: NA3. Links/relationships to ancillary data sets: NA4. Was data derived from another source? NA5. Recommended citation for this dataset: Cite the paper.DATA & FILE OVERVIEW1. File List:PredictedMourachan_BOM_Daily_Temp_1913_2021.xlsx - Predicted daily temperature of the Mourachan property based on BOM temperature data for the St George town from 1913 to 2021.Kangaroo_dingo_events_Heatwave_indices.xlsx - Kangaroo and dingo events at waterpoints split by camera and time of event, event durations as well as temperature and heatwave index data also present.All_Heatwave_Indices_Tested.xlsx - All heatwave indices (with daily values) created from the MMM found for the field site (32.102 degrees C)Calculating_Heatwave_Indices.R - R script used to calculate heatwave indices found in spreadsheet All_Heatwave_Indices_Tested.xlsx using mean daily temperature and MMM of 32.102 degrees C.METHODOLOGICAL INFORMATION1. Description of methods used for collection/generation of data: All animal-based data were collected from camera trapping, temperature data for the Mourachan property were collected using iButton temperature sensors. Past temperature data and heatwave conditions were collected using iButton data along with long-term BOM data for the nearest town (St George).2. Methods for processing the data:Read the full paper for details.3. Instrument- or software-specific information needed to interpret the data: N/A4. Standards and calibration information, if appropriate: NA5. Environmental/experimental conditions: NA6. Describe any quality-assurance procedures performed on the data: NA7. People involved with sample collection, processing, analysis and/or submission: All authorsDATA-SPECIFIC INFORMATION FOR: PredictedMourachan_BOM_Daily_Temp_1913_2021.xlsx1. Number of variables: 72. Number of cases/rows: 356683. Variable List:Date: Day/Month/YearMourachan_maximum_daily_temperature_C: Max daily temp for MourachanMourachan_minimum_daily_temperature_C: Min daily temp for MourachanMourachan_average_daily_temperature_C: Mean daily temp for MourachanStGeorge_maximum_daily_temperature_C: Max daily temp for St George (BOM)StGeorge_minimum_daily_temperature_C: Min daily temp for St George (BOM)StGeorge_average_daily_temperature_C: Mean daily temp for St George (BOM)DATA-SPECIFIC INFORMATION FOR: Kangaroo_dingo_events_Heatwave_indices.xlsx1. Number of variables: 182. Number of cases/rows: 289463. Variable List:Date: Day/Month/Year for each kangaroo and dingo eventsDam: Name of waterpoints where cameras were presentDateTime: Date and time of events observedTime: Time of events observednTime: nTime of events observedtimeSinceLastVisit_mins: Number of minutes between current and previous eventRoo_Duration: Length of events where kangaroos were observed (minutes)Dingo_Duration: Length of events where dingoes were observed (minutes)Dingo_pres_abs: Whether dingoes were present on a specific event (1 = yes)Kangaroo_pres_abs: Whether kangaroos were present on a specific event (1 = yes)Species: Species nameDaily_max_ibutton_temp: Daily maximum temperature for the Mourachan propertyMean_Daily_Adjusted_Temperature: Daily mean temperautre for the Mourachan propertyMMM_1971-2000: the Maximum Monthly Mean used by this study (32.102C) found from a 1971-2000 baselineHin_0_degree_threshold_13_day_window: Heatwave metric made and used for this study (negative values represent days where temperature was below the MMM, while positive values exceeded the MMM and represent the intensity of heatwave conditions)DATA-SPECIFIC INFORMATION FOR: All_Heatwave_Indices_Tested.xlsx1. Number of variables: 262. Number of cases/rows: 5153.Variable List:Date: List of days from start to end of the study periodMean_temperature: Mean daily temperature in degrees Celsius0_degree_threshold_1_day_window (onward): heatwave indices showing the threshold used as well as rolling average (day window) used.0_degree_threshold_13_day_window: heatwave index used in the research article

This dataset contains time series for monthly precipitation over six sites (Blackheath, Braidwood, Darkes Forest, Goulburn, Lithgow and Moss Vale) in the Sydney Catchment Area (SCA) and monthly mean maximum and mean minimum temperature for three sites (Goulburn, Lithgow, and Moss Vale) in the SCA. This data was used in the study Attribution and Prediction of Precipitation and Temperature Trends within the Sydney Catchment Using Machine Learning. The data was originally from the Australian Bureau of Meteorology Climate Data Online (http://www.bom.gov.au/climate/data/index.shtml), but has been updated to have missing values (8% of data) filled using a moving average centred on the year for which the data is missing.

Below is the abstract for the paper:

Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia's largest city. Increasing population, a warming climate, land surface changes, and expanded agricultural use increase water demand and reduce catchment runoff. Studying Sydney's water supply is necessary to manage water resources and lower the risk of severe water shortages. This study aims at understanding Sydney water supply by analysing precipitation and temperature trends across the catchment. A decreasing trend in annual precipitation was found across the Sydney catchment area. Annual precipitation also is significantly less variable, due to fewer years above the 80th percentile. These trends result from significant reductions in precipitation during spring and autumn, especially over the last 20 years. Wavelet analysis is applied to assess how the influence of climate drivers has changed over time. Attribute selection was carried out using linear regression and machine learning techniques including random forests and support vector regression. Drivers of annual precipitation included Niño3.4, SAM, DMI and measures of global warming such as the Tasman Sea Sea Surface temperature anomalies. The support vector regression model with a polynomial kernel achieved correlations of 0.921 and a skill score compared to climatology of 0.721. The linear regression model also performed well with a correlation of 0.815 and skill score of 0.567, highlighting the importance of considering both linear and non-linear methods when developing statistical models. Models were also developed on autumn and winter precipitation but performed worse than annual precipitation on prediction. For example, the best performing model on autumn precipitation, which accounts for approximately one quarter of annual precipitation, achieved an RMSE of 418.036 mm2 on the testing data while annual precipitation achieved an RMSE of 613.704 mm2. However, the seasonal models provided valuable insight into whether the season would be wet or dry compared to the climatology.