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.

La temperatura in Australia è aumentata a 22,77 gradi Celsius nel 2024 rispetto ai 22,31 gradi Celsius del 2023. Questa pagina include un grafico con dati storici per la temperatura media in Australia.

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.

(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.

In 2023, the observed annual average maximum temperature in Australia reached 29.67 degrees Celsius. Overall, the annual average maximum temperature had increased compared to the temperature reported for 1901.

This statistic displays the average temperatures in Australia in 2015, by state. According to the source, in Queensland, the temperature reached approximately ***** degrees Celsius on average.

This data was used by the Department of Agriculture, Water and Environment to produce Figure 1 in the Climate chapter of the 2021 Australian State of the Environment Report.\r

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.

Climate by areas postcode (ABS Postal Areas 2001)

Background: To explain the possible effects of exposure to weather conditions on population health outcomes, weather data need to be calculated at a level in space and time that is appropriate for the health data. There are various ways of estimating exposure values from raw data collected at weather stations but the rationale for using one technique rather than another; the significance of the difference in the values obtained; and the effect these have on a research question are factors often not explicitly considered. In this study we compare different techniques for allocating weather data observations to small geographical areas and different options for weighting averages of these observations when calculating estimates of daily precipitation and temperature for Australian Postal Areas. Options that weight observations based on distance from population centroids and population size are more computationally intensive but give estimates that conceptually are more closely related to the experience of the population.

Results: Options based on values derived from sites internal to postal areas, or from nearest neighbour sites; that is, using proximity polygons around weather stations intersected with postal areas; tended to include fewer stations observations in their estimates, and missing values were common. Options based on observations from stations within 50 kilometres radius of centroids and weighting of data by distance from centroids gave more complete estimates. Using the geographic centroid of the postal area gave estimates that differed slightly from the population weighted centroids and the population weighted average of sub-unit estimates.

Conclusion: To calculate daily weather exposure values for analysis of health outcome data for small areas, the use of data from weather stations internal to the area only, or from neighbouring weather stations (allocated by the use of proximity polygons), is too limited. The most appropriate method conceptually is the use of weather data from sites within 50 kilometres radius of the area weighted to population centres, but a simpler acceptable option is to weight to the geographic centroid.

In 2024, the mean sea surface temperature in Australia was 0.89 degrees Celsius higher than the reference value for that year, indicating a positive anomaly. Over the course of the last century, mean sea surface temperature anomaly measurements in Australia have exhibited an overall increasing trend.

Dataset

This dataset was created by Josh Mills

Released under CC BY-SA 3.0

Contents

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of precipitation across Victoria at a spatial resolution of 9 seconds (approx. 250 m). 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) using a DEM smoothed (Gaussian filter with a standard deviation of 10 and a search radius of 0.0825°, optimised using cross validation) to account for the lack of strong correlation between elevation and precipitation at short distances (Hutchinson 1998; Sharples et al. 2005). All data was interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Monthly surfaces were interpolated directly from monthly station records using the methods described in step 3. 5. Daily anomalies were calculated as a proportion of monthly precipitation, and interpolated with full spline dependence on latitude and longitude. 6. Interpolated anomalies (constrained to values between 0 and 1) were multiplied by monthly precipitation to obtain the final daily surfaces. 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: Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly precipitation: RMSE = 7.65 mm (14.0% relative to mean) Monthly precipitation: RMSE = 13.12 mm (24.7% relative to mean) Daily precipitation: RMSE = 2.21 mm (26.3% relative to mean)

Temperature Min Average Annual | Australia | degC Temperature Min Average Annual | Australia | degC

(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.

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 trivariate splines (latitude, longitude and elevation) with partial dependence upon standardised day time MODIS land surface temperature. 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 standardised day time MODIS land surface temperature. 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. 2. Paget, MJ, King EA. 2008. MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research. Canberra, Australia. https://doi.org/10.4225/08/585c173339358 C) Model performance (3DS): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly maximum temperature RMSE = 0.48 °C Daily maximum temperature RMSE = 1.19 °C

Please refer to the linked manuscript for further details.

In 2023, the observed annual average minimum temperature in Australia reached 15.02 degrees Celsius. Overall, the annual average minimum temperature had increased compared to the temperature reported for 1901.

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.

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

This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/

The goal of this dataset is to predict the total daily rainfall using 24 hours of temperature measurements. This is useful as temperature sensors are much cheaper and easy to maintain as compared to rain gauges. This dataset contains 160,267 time series obtained from a dataset released by the Australian Bureau of Meteorology (BOM).The time series has 3 dimensions, measuring the average hourly temperature, minimum hourly temperature and maximum hourly temperature from 518 weather stations throughout all of Australia.

Please refer to https://data.gov.au/data/dataset/weather-forecasting-verification-data-2015-05-to-2016-04 for more details

The CSIRO versions of the BOM (Bureau of Meteorology Australia) Australian Temperature datasets are a concatenation of the individual monthly Temperature datasets into a single contiguous netcdf file for the time period 1900-2007 with a spatial resolution of .25° x .25°. The variables tav(Average Temperature), tmax (Maximum Temperature), tmin (Minimum Temperature) & tdtr (Diurnal Temperature Range) are available for the whole of Australia and also as a subset for the Murray Darling Basin. These have also been processed to include calculated Anomaly, Climatology, and Seasonal datasets available for Australia. There are approximately 17 files for Temperature data totalling 921.32 MB.