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.

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.

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.

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.

Three datasets containing climate data, compiled in April 2011, have been purchased from the Bureau of Meteorology. These datasets include observations from stations in all Australian States and Territories. Each dataset includes a file which gives details of the stations where observations were made and a file describing the data. AWS Hourly Data contains hourly records of precipitation, air temperature, wet bulb temperature, dew point temperature, relative humidity, vapour pressure, saturated vapour pressure, wind speed, wind direction, maximum wind gust, mean sea level pressure, station level pressure. Each record for each parameter is also flagged to indicate the quality of the value.Synoptic Data contains records of air temperature, dew point temperature, wet bulb temperature, relative humidity, wind speed, wind direction, mean sea level pressure, station level pressure, QNH pressure, vapour pressure and saturated vapour pressure. Each record for each parameter is also flagged to indicate the quality of the value.Daily Rainfall Data contains records precipitation in the 24 hours before 9 am, number of days of rain within the days of accumulation and the accumulated number of days over which the precipitation was measured. Each precipitation record is flagged to indicate the quality of the value.

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.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of minimum temperature (approx. 1.2 m from ground) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Surfaces are developed using trivariate splines (latitude, longitude and elevation) with partial dependence upon a topographic index of relative 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 using trivariate splines (latitude, longitude and elevation as spline variables) with partial dependence upon a topographic index of relative elevation. All models were fit and interpolated 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-T): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly minimum temperature RMSE = 0.80 °C Daily minimum temperature RMSE = 1.73 °C

Please refer to the linked manuscript for further details.

Australian Bureau of Meteorology assembled this dataset of 191 Australian rainfall stations for the purpose of climate change monitoring and assessment. These stations were selected because they are believed to be the highest quality and most reliable long-term rainfall stations in Australia. The longest period of record is August 1840 to December 1990, but the actual periods vary by individual station. Each data record in the dataset contains at least a monthly precipitation total, and most records also have daily data as well.

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.

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 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.

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.

Surface weather observations are recorded half hourly, primarily from aerodromes with some additional data coming from unmanned automatic weather stations. In special conditions, observations may be …Show full descriptionSurface weather observations are recorded half hourly, primarily from aerodromes with some additional data coming from unmanned automatic weather stations. In special conditions, observations may be made earlier and for cost savings, some stations may only report hourly. The following data are recorded: datetime, id_num (WMO index number, normally a unique id, but can be missing), id_name (abbreviated name, used to identify the observing site), date, time, wdir (wind direction, degrees from N), wspd (wind speed, knots), t_db (temperature dry bulb, degree C), dp (dew point, degree C), qnh (aircraft altimeter setting, hPa), rf9am (rainfall since 9am, mm), rf10m (rainfall last 10 minutes, mm), vic (visibility, m), avis (automatically measured visibility, m), gust (maximum wind gust last 10 minutes, knots), wx1int (first (most important) present weather intensity), wx1dsc (first (most important) present weather qualifier), wx1wx1 (first (most important) present weather type), wx1wx2 (additional weather type for mixed precipitation), wx1wx3 (additional weather type for mixed precipitation), wx2int (second (less important) present weather intensity), wx2dsc (second (less important) present weather qualifier), wx2wx1 (second (less important) present weather type), wx2wx2 (additional weather type for mixed precipitation), wx2wx3 (additional weather type for mixed precipitation), cld1amt (lowest cloud layer amount), cld1typ (lowest cloud layer type), cld1typ (lowest cloud layer base, m), cld2amt (second cloud layer amount), cld1typ (second cloud layer type), cld1base (second cloud layer base, m), cld3amt (third cloud layer amount), cld3typ (third cloud layer type), cld3base (lowest cloud layer base, m), cld4amt (fourth cloud amount), cld4typ (fourth cloud layer type), cld4base (fourth cloud layer base, m), ceil1amt (lowest cloud layer amount measured by ceilometer), ceil1base (lowest cloud layer base measured by ceilometer, m), ceil2amt (second cloud layer amount measured by ceilometer), ceil2base (second cloud layer base measured by ceilometer, m), ceil3amt (third cloud layer amount measured by ceilometer), ceil1base (third cloud layer base measured by ceilometer, m), rotation (required for rotation of wind barbs in MapServer), rh (relative humidity, %), stn_name (full station name). A record of the last 24 hours is available for each station. Information about codes can be found at [ http://www.bom.gov.au/weather-services/about/IDY03100.doc ].

\r \r This record links to Bureau of Meteorology metadata for each State's latest "Precis forecast" information, available through an ftp download.\r \r The Bureau of Meteorology's "Precis forecast" product (per State) contains the latest 7 day forecast, per location across that State, with daily projected values for temperature, rainfall and weather conditions.\r \r Data (7-day precis forecast data, for {{State}}) is available in XML format. (The plain text and html formats were withdrawn in Feb 2016) \r \r Place Names are the same, in the plain text, html and xml format files.\r \r The XML file uses the AAC location code (and location name), rather than the StationID code. \r The coordinates related to each AAC code/ location name, in the XML formatted file, are listed in the PointPlaces [IDM00013.*] data files, available from ftp://ftp.bom.gov.au/anon/home/adfd/spatial/IDM00013.dbf [open the dbf file, using Excel].\r \r Note that the precis forecasts relate to an area surrounding the nominated location, the coordinates of which are intended to be the "centre of town" for that location ( as derived from Geoscience Australia's placename Gazetteer)".\r \r

Data content \r

As well as forecast values [per day, across 7 days] for minimum and maximum temperature, rainfall (range and probability), and a precis of expected weather \r conditions, each file contains information on when the file was created, and the timespan that a value applies to. \r \r

User /Licence information\r

\r * The Bureau does not guarantee the availability of information on the ftp site.\r * Please read how to access the Bureau ftp site and its structure.\r * The Service Announcements page details changes to Bureau services.\r * Use of data should be in accordance with the copyright notice and disclaimer.\r \r * Secondary distribution of Bureau of Meteorology information currently freely\r available on the Bureau website and ftp sites requires formal permission.\r * Correct attribution of the Australian Bureau of Meteorology as the source of\r Bureau information is an important component of any secondary distribution\r permission that may be granted. Where Bureau information is to be used on a\r website, permission for use of that information should be applied for by the\r website owner.

A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers, with in situ data from ships, Argo floats, XBTs, CTDs, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the BoM Global Weekly 1 degree OI SST analysis, which relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.

This weather data package comprises weather data for automatic weather stations situated at 13 sites separated by distances of between 5 and 80 km. The weather stations record temperature and rainfall (in 2010, one weather station was set up so that it also began recording wind speed and direction). The air temperature, rainfall, wind speed and wind direction data are recorded in a data logger housed within the instrument stand. The network program uses a core of 12 sites and aims to quantitatively track long-term shifts in biodiversity and ecological processes in relation to key drivers, including unpredictable rainfall and droughts, fire, feral predators and grazing. A synopsis of related data packages which have been collected as part of the Desert Ecology Plot Network's full program is provided at http://www.ltern.org.au/index.php/ltern-plot-networks/desert-ecology

Changes in ocean temperature are of immense importance for global climate studies, marine ecology and fish recruitment, as well as being of interest to recreational swimmers. Because of the Leeuwin Current, water temperatures off Western Australia are some 4°C warmer than those at corresponding latitudes off the west coasts of southern Africa and South America. Water temperature measurements along the Western Australian coast are relatively sparse, although some intensive studies have been undertaken near cities and regional centres, as well as at selected sites important for fisheries.

Monthly temperature statistics have been compiled for a number of sites along the west coast of Australia, using a variety of data sources. To view monthly average temperature and minimum and maximum recorded at any site, click on the appropriate places on the map via the URL.

This record is the third in a series of heat flow determinations released by Geoscience Australia. Data in this record covers New South Wales, Northern Territory and Western Australia.

Sea surface temperatures in the Coral Sea and around northern Australia are about +1^oC warmer on average than 100 years ago, with the years 2009 through 2018 being the warmest 10-year period on record.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of minimum temperature (approx. 1.2 m from ground) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Surfaces are developed using bivariate splines (latitude and longitude) with partial dependence upon 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 using bivariate splines (latitude and longitude as spline variables) with partial dependence upon elevation. All models were fit and interpolated 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 (2DS-E): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly minimum temperature RMSE = 0.96 °C Daily minimum temperature RMSE = 1.81 °C

Please refer to the linked manuscript for further details.