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.

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

Temperature in Australia increased to 22.32 celsius in 2023 from 21.93 celsius in 2022. This dataset includes a chart with historical data for Australia Average Temperature.

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.

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 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.

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.

(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.

Dataset

This dataset was created by Josh Mills

Released under CC BY-SA 3.0

Contents

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 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.

1) Data Introduction • The Rain in Australia Dataset is a tabular weather forecasting dataset, including daily weather information collected for approximately 10 years from various weather stations across Australia, and next-day precipitation (more than 1 mm, RainTomorrow).

2) Data Utilization (1) Rain in Australia Dataset has characteristics that: • Each row contains a variety of daily weather variables and target variables (RainTomorrow: Next Day RainTomorrow) such as date, region, highest/lowest temperature, precipitation, humidity, wind speed, and air pressure. • The data reflect multiple regions and various weather conditions, making them suitable for time series and spatial weather pattern analysis and the development of binary classification prediction models. (2) Rain in Australia Dataset can be used to: • Development of precipitation prediction models: Machine learning-based next-day precipitation prediction (whether an umbrella is required) models can be built using various weather variables and RainTomorrow labels. • Weather Patterns and Regional Analysis: By analyzing regional and seasonal weather variables and precipitation patterns, it can be used to establish customized weather strategies for each industry, such as climate change research and agriculture and tourism.

Australia Exports of oils and other products of distillation of high temperature coal tar to Thailand was US$105.13 Thousand during 2024, according to the United Nations COMTRADE database on international trade.

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.

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.

The "Weather in Australia" dataset encompasses weather observations collected from various locations across Australia from 2008 to 2018. This comprehensive dataset provides valuable insights into the diverse weather conditions experienced in different regions of the country. With a wide range of variables, it enables a detailed analysis of various meteorological factors and their impact on daily weather patterns. The dataset contains the following columns: Date: The date of the weather observation. MinTemp: The minimum temperature recorded on that day. MaxTemp: The maximum temperature recorded on that day. Rainfall: The amount of rainfall measured in millimeters. Evaporation: The amount of water evaporation measured in millimeters. Sunshine: The duration of sunshine in hours. WindGustDir: The direction of the strongest wind gust. WindGustSpeed: The speed of the strongest wind gust. WindDir9am: The wind direction at 9 am. WindDir3pm: The wind direction at 3 pm. WindSpeed9am: The wind speed at 9 am. WindSpeed3pm: The wind speed at 3 pm. Humidity9am: The humidity level at 9 am. Humidity3pm: The humidity level at 3 pm. Pressure9am: The atmospheric pressure at 9 am. Pressure3pm: The atmospheric pressure at 3 pm. Cloud9am: The fraction of sky covered by clouds at 9 am. Cloud3pm: The fraction of sky covered by clouds at 3 pm. Temp9am: The temperature at 9 am. Temp3pm: The temperature at 3 pm. RainToday: Whether there was rain recorded on that day (Yes/No). RainTomorrow: The target variable indicating whether it rained the following day (Yes/No). This dataset provides a wealth of information for studying weather patterns, exploring seasonal variations, and investigating the relationships between different meteorological factors. It serves as a valuable resource for conducting climate research, building predictive models, and gaining a deeper understanding of the weather phenomena experienced in Australia.

INDICATOR DEFINITION Monthly means of three-hourly pressures, reduced to mean sea level, 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 Measurement of the pressure over Antarctica and the Southern Ocean is considered important for monitoring behaviour of pressure systems on a local and global scale, which will help to interpret global climate change.

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: Barometry.

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 1 - Monthly mean air temperatures for Australian Antarctic Stations SOE Indicators 2 - Monthly highest temperatures for Australian Antarctic Stations SOE Indicators 3 - Monthly lowest temperatures for Australian Antarctic Stations SOE Indicators 4 - Monthly mean lower-stratospheric temperature above Australian Antarctic Stations SOE Indicators 5 - Monthly mean mid-tropospheric temperature above Australian Antarctic Stations SOE Indicators 38 - Mean sea level 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 in this dataset are: Mean MSL Pressure Year Month Station Station Code Field Value Enough Observations Number Observations

Max Temp of Warmest Month (Bio05) raster for southeastern Australia.Input file used to model the species distributions of 40 reptile species in Victoria, Australia.File obtained from the WorldClim (https://worldclim.org/) version 2 database, at a spatial resolution of ~1 km2Cell size - 250 x 250Original file has been clipped to southeastern Australia.Methods used to generate the input files and perform modelling are outlined in the methods section of the abovementioned publication.Citation - Fick, S.E. and Hijmans, R.J. (2017), WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol, 37: 4302-4315. https://doi.org/10.1002/joc.5086

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

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

The 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.