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.

In 2023, the observed annual average mean temperature in Australia reached 22.32 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.

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.

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.

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.

Land surface temperature (LST) maps, and urban heat island (UHI) maps, for Australian urban centres, calculated over summer 2015/16. Generated as part of an investigation into changes in urban greenspace. Lineage: Land surface temperatures were calculated using data from the Landsat 8 thermal infrared sensor (TIRS) band 10. Each image was processed using the generalised single channel method of Jiménez-Muñoz et al. (2003, 2009). The required atmospheric parameters were obtained from publicly available observations by the Australian Government Bureau of Meteorology (BOM). The required land surface emissivity (LSE) values were estimated using the NDVI approach (Sobrino & Raissouni 2000). As many overpasses as possible during the summer of 2015/16 were processed, and the results averaged to obtain an estimate of typical summer LST. Urban Heat Island (UHI) was estimated by subtracting from the LST images an estimate of non-urban baseline temperature. This baseline was estimated by a first-order fit to the temperature of native vegetation within and around each urban centre.

The physical climate defines a significant portion of the habitats in which biological communities and species reside. It is important to quantify these environmental conditions, and how they have changed, as this will inform future efforts to study many natural systems. We present the results of a statistical summary of the variability in sea surface temperature (SST) time-series data for the waters surrounding Australia, from 1993 to 2013. We partition variation in the SST series into annual trends, inter-annual trends, and a number of components of random variation. We utilise satellite data and validate the statistical summary from these data to summaries of data from long-term monitoringstations and from the global drifter program. The spatially dense results show clear trends that associate with oceanographic features. Noteworthy oceanographic features include: average warming was greatest off southern West Australia and off eastern Tasmania where the warming was around 0.6 C per decade for a twenty year study period, and; insubstantial warming in areas dominated by the East Australian Current but this area did exhibit high levels of inter-annual variability (long-term trend increases and decreases but does not increase on average). The results of the analyses can be directly incorporated into (biogeographic) models that explain variation in biological data where both biological and environmental data are on a fine scale.

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.

Land surface temperature (LST) maps, and urban heat island (UHI) maps, for Australian capital cities, calculated over summer 2018-19. Lineage: Land surface temperatures were calculated using data from the Landsat 8 thermal infrared sensor (TIRS) band 10. Each image was processed using the generalised single channel method of Jiménez-Muñoz et al. (2003, 2009). The required atmospheric parameters were obtained from publicly available observations by the Australian Government Bureau of Meteorology (BOM). The required land surface emissivity (LSE) values were estimated using the NDVI approach (Sobrino & Raissouni 2000). Urban Heat Island (UHI) was estimated by subtracting from the LST images an estimate of non-urban baseline temperature. This baseline was estimated by a first-order fit to the temperature of native vegetation within and around each urban centre.

Heatwaves are defined as unusually high temperature events that occur for at least three consecutive days with major impacts to human health, economy, agriculture and ecosystems. This dataset provides time-series of heatwave characteristics such as peak temperature, number of events, frequency and duration from 1950 to 2016 in Australia. The analysis were based on daily minimum and maximum temperature obtained from the Australian Water Availability Project (AWAP). The data is available as spatial time-series (5km grid-cell) and aggregated time-series for all Local Government Areas in Australia.

The Australian geothermal industry is moving rapidly, and in that process requires a lot from geophysics to aid in characterising regional prospectivity for exploitable heat resources. Various groups are using hybrid methods to estimate Curie point temperatures at depth, or alternatively, the temperature at 5 kilometres below the surface. Deep drilling observations and airborne magnetic compilations are the key components, together with a basement geology interpretation. Several generations of this work are already published with more to come. A method to test these maps and also help characterise uncertainty is proposed based upon a deep 3D continental model scale, extending to the lithosphere. Variable surface temperature and heat flow grids, based upon remote sensing are used, together with a simple lithosphere boundary condition. The heat diffusion is then employed to test the temperature-depth maps. Progress on applying this method to Australia is reported.

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 Heat Index data was reported at 8.220 Day in 2020. This records a decrease from the previous number of 12.320 Day for 2019. Australia Heat Index data is updated yearly, averaging 4.760 Day from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 12.320 Day in 2019 and a record low of 0.490 Day in 1974. Australia Heat Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Environmental: Climate Risk. Total count of days per year where the daily mean Heat Index rose above 35°C. A Heat Index is a measure of how hot it feels once humidity is factored in with air temperature.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

To improve our understanding of transcriptomic stress responses in plants to hot and cold temperature shocks a selection of 20 native Australian species were sequenced. The 20 study species were selected from three ecosystems that represent the three contrasting biomes of arid, alpine, and coastal temperate environments in Australia (Harris et al., 2024). The arid biome represents an extreme environment that is known for its high maximum temperatures, but many regions also experience freezing nights during winter. The alpine biome represents extreme cold in the cooler months, but the exposed slopes of mountains can also experience high temperatures during summer. Coastal temperate environments represent a more stable climate, with lower temperature fluctuations within a day or across a year. The 20 study species spanned 13 families and 16 genera (see “DAP_LOTE_metadata.xlsx” for metadata tables).
Seeds for the study species were obtained from conservation seed banks, including the Australian National Botanic Gardens Seed Bank and the Australian Botanic Gardens Plant Bank. A single accession of seed was used for each species (Harris et al., 2024). Plants were grown in a temperature controlled glass house (25°C day 15°C night) before being moved to the Australian Plant Phenomics Facility at CSIRO Black Mountain laboratories, where they were kept in Conviron growth chambers for an acclimatisation period of 1-2 days prior to temperature treatments. Temperature treatments were run for three days with the heat treatment being 40°C days to 15°C nights and the cold treatment being 25°C days to -2°C nights, relative to a control group that experienced no change in temperature from benign growing conditions (25°C day 15°C night). On the morning after the third day, leaf samples were taken between 9:00am and 10:30am for RNA extraction (see below) and photosynthetic thermal tolerance measurements.

This collection contains Supplementarty information and RNA-seq libraries used for all data analyses presented in "LOTE_results_240703.html" and our paper. The Rmarkdown file "LOTE_Rcode_240703.Rmd" and the .rds file "LOTE_full_221026.rds" included here can be used to replicate the results. the .rds file includes binaries for the main data frames used for analyses. For more details on data files see “DAP_LOTE metadata.xlsx”.

Harris, R. J., Alvarez, P. R., Bryant, C., Briceño, V. F., Cook, A. M., Leigh, A., & Nicotra, A. B. (2024). Acclimation of thermal tolerance in juvenile plants from three biomes is suppressed when extremes co-occur. Conservation Physiology, 12(1). https://doi.org/10.1093/conphys/coae027

Lineage: Leaf samples for RNA extraction were taken the morning after three days of treatment. Leaf samples were snap frozen with liquid nitrogen before being transported on dry ice to a -80 °C freezer immediately after sampling. For extracting total RNA from leaf tissue, the best method for tissue homogenization proved to be grinding leaf samples with liquid nitrogen in a mortar and pestle. After grinding, samples were returned to dry ice until 16 samples were ready to start the RNA extraction. The mortar and pestle was cleaned with bleach between samples. The kit used for RNA extraction was the NucleoSpin RNA Plant and Fungi Kit (Macherey-Nagel, Germany) using the standard protocol except for an adjustment to the lysis buffer. The lysis buffer aliquot per sample included 400μl of PFL and 50μl PFR buffers from the NucleoSpin Kit, 100μl Fruit-mate for RNA Purification (Takara, Japan) and 5μl of ß-mercaptoethanol. After mRNA isolation with Oligo d(T)25 Magnetic Beads (New England BioLabs, Australia), strand specific RNA-seq libraries were prepared using an in-house template switching protocol. The protocol for library preps is fully described in Paten et al., (2022). Two plates of 96 libraries were prepared using custom barcodes. Samples were sequenced on a single NovaSeq S2 flowcell (300 cycles, 2x150bp), using a lane splitter kit to split the two sample pools onto one lanes each (i.e. set A on lane 1 and set B on lane 2). Sequencing was performed at the ACRF Biomolecular Resource Facility at John Curtin School of Medical Research at The Australian National University. Funding for sequencing costs was provided by Bioplatforms Australia.

Paten, A. M., Colin, T., Coppin, C. W., Court, L. N., Barron, A. B., Oakeshott, J. G., & Morgan, M. J. (2022). Non-additive gene interactions underpin molecular and phenotypic responses in honey bee larvae exposed to imidacloprid and thymol. Science of the Total Environment, 814. https://doi.org/10.1016/j.scitotenv.2021.152614

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.

INDICATOR DEFINITION Monthly means of three-hourly temperatures for Australian Antarctic stations Casey, Davis, Mawson, Macquarie Island and Heard Island.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: CONDITION

RATIONALE FOR INDICATOR SELECTION Global climate models show warming in response to increased greenhouse gas (carbon dioxide, methane etc) concentrations in the atmosphere; this is called the 'enhanced greenhouse effect'. Because of this, there is interest in observations of temperature across the globe, including Antarctica. Extensive high-quality observations from fixed locations are essential to serve as direct indicators of temperature changes and also confirm climate model output.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: Australian Antarctic stations: Casey (lat 66 degrees 16' 54.5" S, long 110 degrees 31' 39.4" E), Davis (lat 68 degrees 34' 35.8" S, long 77 degrees 58' 02.6" E), Mawson (lat 67 degrees 36' 09.7" S, long 62 degrees 52' 25.7" E), Macquarie Island (lat 54 degrees 37' 59.9" S, long 158 degrees 52' 59.9" E), Atlas Cove, Heard Island (lat 53 degrees 1' 8" S, long 73 degrees 23' 30" E) and Spit Bay, Heard Island (lat 53 degrees 6' 30" S, 73 degrees 43' 21" E).

Frequency: Monthly

Measurement Technique: Thermometry

RESEARCH ISSUES There is need to develop a high-quality data set from the available data, correcting erroneous data and estimating missing data. Adjustment may be necessary for changes in site location or exposure, and for changes in instrumentation or observing practices.

Some of these changes are documented in the station history files held by the Regional Observations Section. These history files are currently held as paper records, although more recent information is held electronically and there is an effort to digitise the older records.

Before the data can be used for the detection of change, a concerted effort will need to be made to identify deficiencies in the data, and then make compensations where possible. This is made more difficult by the lack of suitable comparison sites.

LINKS TO OTHER INDICATORS SOE Indicators 2 - Highest monthly record of daily maximum air temperatures SOE Indicators 3 - Lowest monthly record of daily minimum air temperatures SOE Indicators 4 - Monthly mean of daily radiosonde temperatures at the 100hPa level (deg C) SOE Indicators 5 - Monthly mean of daily radiosonde temperatures at the 500hPa level (deg C) SOE Indicators 6 - Daily mean 10m Firn Temperatures at AWS sites in the AAT (deg C) SOE Indicators 8 - Monthly mean of three-hourly mean sea level pressures (hPa) SOE Indicators 11 - Atmospheric concentrations of greenhouse gas species SOE Indicators 12 - Noctilucent cloud observations at Davis SOE Indicators 14 - Midwinter atmospheric temperature at altitude 87km SOE Indicators 16 - Extent of summer surface glacial melt (sq km) SOE Indicators 42 - Antarctic sea ice extent and concentration SOE Indicators 43 - Fast ice thickness at Davis and Mawson SOE Indicators 56 - Monthly fuel usage of the generator sets and boilers SOE Indicators 59 - Monthly electricity usage SOE Indicators 62 - Water levels of Deep Lake, Vestfold Hills

Note - Station codes in the data are as follows: 300000 - Davis 300001 - Mawson 300004 - Macquarie Island 300005 - Atlas Cove, Heard Island 300017 - Casey 300028 - Spit Bay, Heard Island

The fields for this dataset are: Temperature Mean Air Temperature Year Month Station Station Code Field Value Enough Oobservations Number Observations

An International 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 using optimal interpolation (OI) on a global 0.25 degree grid. This Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends infra-red SST observations from the Advanced Very High Resolution Radiometer (AVHRR) on NOAA and METOP polar-orbiting satellites, microwave SST observations from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on GCOM-W, and in situ data from ships, and drifting and moored buoys from the Global Telecommunications System (GTS). All SST observations are filtered for those values suspected to be affected by diurnal warming by excluding cases which have experienced recent surface wind speeds of below 6 m/s during the day and less than 2 m/s during the night, thereby resulting in daily foundation SST estimates that are largely free of diurnal warming effects.

USD 1393.88 Million in 2024; projected USD 2794.19 Million by 2033; CAGR 8.02%.

This dataset includes temperature and precipitation depth measurements in 10 min intervals taken in 2 locations and time periods after forest fires: - Madre del Agua (Tenerife, Spain): 17/11/20 to 19/11/2021 - Thompson reservoir (Victoria, Australia): 28/03/19 to 13/01/2020 Data was collected using RainWise Rainew raingauges coupled to Onset HOBO pendant dataloggers (UA-003-64) to monitor environmental parameters related to runoff occurrence. Full details about this dataset can be found at https://doi.org/10.5285/26774f3b-d535-4800-97e4-f2fc7cf9b2da

Proportionof the Australian Economic Zone experiencing record high sea surface temperatures 1981-2016. Includes three month running mean. Extreme SSTs calculated by comparing monthly SSTs with all other recorded monthly SSTs in the time series at a particular grid point. If the SST was higher than all other SSTs recorded for that grid point during that month, it was considered an extreme event.were Data used to re-create graph Figure MAR6 in Marine chapter of the 2016 State of the Environment Report. See; https://soe.environment.gov.au/theme/marine-environment/topic/2016/climate-change#marine-environment-figure-6

Data derived by CSIRO - metadata also at http://catalogue.aodn.org.au/geonetwork/srv/eng/metadata.show?uuid=cca8c5ce-4b21-406d-b20c-7b333f8e605c