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.

In 2024, the mean temperature deviation in Australia was 1.46 degrees Celsius higher than the reference value for that year, indicating a positive anomaly. Over the course of the last century, mean temperature anomaly measurements in Australia have exhibited an overall increasing trend. Temperature trending upwards Global land temperature anomalies have been fluctuating since the start of their measurement but show an overall upward tendency. Australian mean temperatures have followed this trend and continued to rise as well. Considered the driest inhabited continent on earth, this has severe consequences for the country. In particular, the south of Australia is predicted to become susceptible to drought, which could lead to an increase in bushfires as well. The highest temperatures recorded in Australia as of 2022 were measured in South Australia and Western Australia, both exceeding 50 degrees. The 2019/2020 bushfire season Already prone to wildfires due to its dry climate, the change in temperature has made Australia even more vulnerable to an increase in bushfires. One of the worst wildfires in Australia, and on a global level as well, happened during the 2019/2020 bushfire season. The combination of the hottest days and the lowest annual mean rainfall in 20 years resulted in a destruction of 12.5 million acres. New South Wales was the region with the largest area burned by bushfires in that year, a major part of which was conservation land.

The 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.

(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 9am vapour pressure 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). All data was 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: Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly vapour pressure: RMSE = 0.38 hPA Daily vapour pressure: RMSE = 1.24 hPa

Mean monthly and mean annual 9am and 3pm relative humidity grids. The grids show the relative humidity values across Australia in the form of two-dimensional array data. The mean data are based on …Show full descriptionMean monthly and mean annual 9am and 3pm relative humidity grids. The grids show the relative humidity values across Australia in the form of two-dimensional array data. The mean data are based on the standard 30-year period 1976-2005. Relative Humidity (RH) data are recorded at a network of weather stations across Australia. The data from these weather stations are stored electronically in the Bureau of Meteorology's database called ADAM (Australian Data Archive for Meteorology). The average RH maps were based on daily 9am and 3pm RH data from about 530 weather stations. The daily data were extracted from ADAM and a number of quality control checks were then applied to the data. After the quality checks were applied to the data, monthly and annual 9am and 3pm averages were calculated for each of the weather stations. An objective analysis technique was then applied to these station averages to produce regular gridded datasets (each month and annual) covering Australia.

In 2023, the annual mean rainfall in Australia was 473.7 millimeters. Over the last twenty years, the mean area-average rainfall has fluctuated in Australia, with the lowest value recorded in 2019.

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.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Various climate variables summary for all 15 subregions based on Bureau of Meteorology Australian Water Availability Project (BAWAP) climate grids. Including

1. Time series mean annual BAWAP rainfall from 1900 - 2012.

2. Long term average BAWAP rainfall and Penman Potentail Evapotranspiration (PET) from Jan 1981 - Dec 2012 for each month

3. Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P (precipitation); (ii) Penman ETp; (iii) Tavg (average temperature); (iv) Tmax (maximum temperature); (v) Tmin (minimum temperature); (vi) VPD (Vapour Pressure Deficit); (vii) Rn (net radiation); and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend.

4. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009).

As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

There are 4 csv files here:

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset History

Dataset was created from various BAWAP source data, including Monthly BAWAP rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET, Correlation coefficient data. Data were extracted from national datasets for the GLO subregion.

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset Citation

Bioregional Assessment Programme (2014) GLO climate data stats summary. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/afed85e0-7819-493d-a847-ec00a318e657.

Dataset Ancestors

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive: •\tIncoming short-wave radiation on a sloping surface •\tShort-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface) •\tIncoming long-wave radiation •\tOutgoing long-wave radiation •\tNet long-wave radiation •\tNet radiation •\tSky view factor All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.

The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.

The monthly data in this collection are available at 1 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format. The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9632 .

The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.

The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18492 Lineage: Source data 1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016) 2. Aspect derived from the 1 arcsecond SRTM DEM-S 3. Slope derived from the 1 arcsecond SRTM DEM-S 4. Monthly cloud cover fraction (Jovanovic et al., 2011) 5. Monthly albedo derived from AVHRR (Donohue et al., 2010) 6. Monthly minimum and maximum air temperature (Bureau of Meteorology) 7. Monthly vapour pressure (Bureau of Meteorology) 8. Monthly fractional cover (Donohue et al., 2010) 9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013) 10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)

Solar radiation model Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for: \tAnnual variations in sun-earth distance \tSolar geometry based on latitude and time of year \tThe orientation of the land surface relative to the sun \tShadowing by surrounding topography \tClear-sky and cloud transmittance \tSunshine fraction (cloud-free fraction of the day) in morning and afternoon \tSurface albedo \tThe effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover \tAtmospheric emissivity based on vapour pressure

All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.

Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.

References Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.

Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.

Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.

Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.

NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota

Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar

Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.

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.

Abstract This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Köppen's scheme to classify world …Show full descriptionAbstract This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Köppen's scheme to classify world climates was devised in 1918 by Dr Wladimir Köppen of the University of Graz in Austria. Over the decades it has achieved wide acceptance amongst climatologists. However, the scheme has also had its share of critics, who have challenged the scheme's validity on a number of grounds. For example, Köppen's rigid boundary criteria often lead to large discrepancies between climatic subdivisions and features of the natural landscape. Furthermore, whilst some of his boundaries have been chosen largely with natural landscape features in mind, other boundaries have been chosen largely with human experience of climatic features in mind. The present paper presents a modification of Köppen's classification that addresses some of the concerns and illustrates this modification with its application to Australia. A modification of the Köppen classification of world climates has been presented. The extension has been illustrated by its application to Australian climates. Even with the additional complexity, the final classification contains some surprising homogeneity. For example, there is a common classification between the coastal areas of both southern Victoria and southern New South Wales. There is also the identical classification of western and eastern Tasmania. This arises due to the classification not identifying every climate variation because a compromise has to be reached between sacrificing either detail or simplicity. For example, regions with only a slight annual cycle in rainfall distribution do not have that variation so specified in the classification. Similarly, regions with only slightly different mean annual temperatures are sometimes classified as being of the same climate. The classification descriptions need to be concise, for ease of reference. As a result, the descriptions are not always complete. For example, the word "hot" is used in reference to those deserts with the highest annual average temperatures, even though winter nights, even in hot desert climates, can't realistically be described as "hot". In conclusion, the authors see the classification assisting in the selection of new station networks. There is also the potential for undertaking subsequent studies that examine climate change in the terms of shifts in climate classification boundaries by using data from different historical periods, and by using different characteristics to define climate type such as "inter-annual variability of precipitation". In the future, it is planned to prepare climate classification maps on a global scale, as well as on a regional-Australian scale. TABLE 1 Köppen's original scheme New scheme Tropical group Divided into equatorial & tropical groups Monsoon subdivision Becomes rainforest (monsoonal) subdivision Dry group Divided into desert & grassland groups Summer/winter drought subdivisions Now requires 30+mm in wettest month Temperate group Divided into subtropical & temperate groups Cold-snowy-forest group Cold group Dry summer/winter subdivisions Moderately dry winter subdivision added Polar group Maritime subdivision added Frequent fog subdivision Applies now only to the desert group Frequent fog subdivision Becomes high humidity subdivision High-sun dry season subdivision Absorbed into other subdivisions Autumn rainfall max subdivision Absorbed into other subdivisions Other minor subdivisions Absorbed into other subdivisions This dataset has been provided to the BA Programme for use within the programme only. For copyright information go to http://www.bom.gov.au/other/copyright.shtml. Information on how to request a copy of data can be found at www.bom.gov.au/climate/data. Dataset History Trewartha (1943) notes that Köppen's classification has been criticised from "various points of view" (Thornthwaite 1931, Jones 1932, Ackerman, 1941). Rigid boundary criteria often lead to large discrepancies between climatic subdivisions and features of the natural landscape. Some boundaries have been chosen largely with natural landscape features in mind (for example, "rainforest"), whilst other boundaries have been chosen largely with human experience of climatic features in mind (for example, "monsoon"). Trewartha (1943) acknowledges the validity of these criticisms when he writes that "climatic boundaries, as seen on a map, even when precisely defined, are neither better nor worse than the human judgements that selected them, and the wisdom of those selections is always open to debate". He emphasises, however, that such boundaries are always subject to change "with revision of boundary conditions ... (and that) ... such revisions have been made by Köppen himself and by other climatologists as well". Nevertheless, the telling evidence that the Köppen classification's merits outweigh its deficiencies lies in its wide acceptance. Trewartha (1943) observes that "its individual climatic formulas are almost a common language among climatologists and geographers throughout the world ... (and that) ... its basic principles have been ... widely copied (even) by those who have insisted upon making their own empirical classifications". Trewartha's (1943) comments are as relevant today as they were half a century ago (see, for example, Müller (1982); Löhmann et al. (1993)). For the above reasons, in modifying the Köppen classification (Figures 1 and 2), the authors have chosen to depart only slightly from the original. Nevertheless, the additional division of some of the Köppen climates and some recombining of other Köppen climates may better reflect human experience of significant features. In recognition of this, the following changes, which are also summarised in Table 1, have been adopted in this work: The former tropical group is now divided into two new groups, an equatorial group and a new tropical group. The equatorial group corresponds to the former tropical group's isothermal subdivision. The new tropical group corresponds to that remaining of the former tropical group. This is done to distinguish strongly between those climates with a significant annual temperature cycle from those climates without one (although this feature is not as marked in the Australian context, as elsewhere in the world). Under this definition some climates, distant from the equator, are classified as equatorial. This is considered acceptable as that characteristic is typical of climates close to the equator. Figure 1 shows that, in Australia, equatorial climates are confined to the Queensland's Cape York Peninsula and the far north of the Northern Territory. The equatorial and tropical group monsoon subdivisions are re-named as rainforest (monsoonal) subdivisions. This is done because, in these subdivisions, the dry season is so short, and the total rainfall is so great, that the ground remains sufficiently wet throughout the year to support rainforest. Figure 2 shows that, in Australia, rainforest subdivisions are found along parts of the northern part of Queensland's east coast. The former dry group is now divided into two new groups, a desert group and a grassland group. The new groups correspond to the former subdivisions of the dry group with the same name. This is believed necessary because of the significant differences between the types of vegetation found in deserts and grasslands. That there is a part of central Australia covered by the grassland group of climates (Figure 1) is a consequence of the higher rainfall due to the ranges in that region. The new desert and grassland winter drought (summer drought) subdivisions now require the additional criterion that there is more than 30 mm in the wettest summer month (winter month) to be so classified. This change is carried out because drought conditions may be said to prevail throughout the year in climates without at least a few relatively wet months. It should be noted that the original set of Köppen climates employed the phrases "winter drought" and "summer drought" to respectively describe climates that are seasonally dry. Figure 2 shows that the summer drought subdivisions are found in the southern half of the country, whilst the winter drought subdivisions are found in the northern half of the country. The former temperate group is divided into two new groups, a temperate group and a subtropical group. The new subtropical group corresponds to that part of the former temperate group with a mean annual temperature of at least 18°C. The new temperate group corresponds to that part of the former temperate group remaining. This is done because of the significant differences in the vegetation found in areas characterised by the two new groups, and in order that there is continuity in the boundary between the hot and warm desert and grassland climates where they adjoin rainy climates. Figure 1 shows that a large region, covering much of southeast Queensland and some elevated areas further north, is now characterised as subtropical. For simplicity, the former Köppen cold snowy forest group of climates is re-named as the cold group. Figure 1 shows that this climate is not found on the Australian mainland or in Tasmania. For the temperate, subtropical, and the cold groups, the distinctly dry winter subdivision requires the additional criterion of no more than 30 mm in the driest winter month to be so classified. In order that there be consistency between the criteria for the distinctly dry winter and the distinctly dry summer subdivisions, this is thought to be a worthwhile change. Figure 2 shows that, whereas that part of Western Australia characterised

This statistic displays the monthly mean rainfall in Perth, Australia, between 1993 and 2016. According to the source, 41 millimeters of rain fell on average in Perth in April.

Mean monthly and mean annual maximum, minimum & mean temperature grids. The grids show the temperature values across Australia in the form of two-dimensional array data. The mean data are based on the standard 30-year period 1961-1990. Gridded data were generated using the ANU (Australian National University) 3-D Spline (surface fitting algorithm). As part of the 3-D analysis process a 0.025 degree resolution digital elevation model (DEM) was used. The grid point resolution of the data is 0.025 degrees (approximately 2.5km). Approximately 600 stations were used in the analysis over Australia. All input station data underwent a high degree of quality control before analysis, and conform to WMO (World Meteorological Organisation) standards for data quality.

Annual mean precipitation of warmest quarter for the Australian continent for the baseline climatic average of monthly 1976-2005 data. Warmest quarter is the set of 3 consecutive months for which …Show full descriptionAnnual mean precipitation of warmest quarter for the Australian continent for the baseline climatic average of monthly 1976-2005 data. Warmest quarter is the set of 3 consecutive months for which the mean temperature over the selected period is higher than any other set of 3 consecutive months. Modelled using eMAST-R-Package 2.0

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.

Overview

The report is a quarterly report with a consistent and regular assessment of crop prospects for major field crops, forecasts of area, yield and production and a summary of seasonal conditions on a state by state basis.

Key issues • Condition of crops at the start of spring varied considerably between the states because of highly varied seasonal conditions over autumn and winter. ◦ Crops in Western Australia are generally in good to excellent condition with high yield prospects after a timely seasonal break and above average winter rainfall. ◦ Seasonal conditions in Victoria and South Australia were mixed and while crop prospects in some major growing regions are generally good, there are regions where crop prospects are generally below average. ◦ Seasonal conditions were very unfavourable in most cropping regions in New South Wales and Queensland and winter crop production in these states is forecast to be very much below average.

• Winter crop production will be heavily dependent on seasonal conditions during spring in regions in the eastern states (including South Australia) where soil moisture levels are low. • According to the latest three-month rainfall outlook (September to November), issued by the Bureau of Meteorology on 30 August 2018, spring rainfall will likely be below average in most cropping regions. Warmer than average temperatures in September are likely in Western Australia and some parts of Queensland. Temperatures in October are likely to be above average in most cropping regions in Australia. • Total winter crop production is forecast to decrease by 12% in 2018-19 to 33.2 million tonnes. • Winter crop production in 2018-19 is forecast to be 9% below the twenty-year average to 2017-18 but forecast production is 91% above the lowest production level during this period. Production in Queensland and New South Wales is forecast to be 38% and 46% below 2017-18 while production in Western Australia is forecast to be 12% above. • For the major winter crops, wheat production is forecast to decrease by 10% to 19.1 million tonnes, barley production is forecast to fall by 7% to around 8.3 million tonnes, and canola production is forecast to fall by 24% to around 2.8 million tonnes. • Area planted to summer crops is forecast to fall by 20% in 2018-19 to 1.1 million hectares, driven by forecast falls in area planted to rice and cotton. Area planted to grain sorghum is forecast to increase by 7% in response to favourable prices. Total summer crop production is forecast to fall by 16% to 3.5 million tonnes.

This statistic displays the monthly mean rainfall in Sydney, Australia, between 1929 and 2016. According to the source, 106 millimeters of rain fell on average in Sydney in April.

This statistic displays the monthly mean rainfall in Brisbane, Australia, between 1949 and 2000. According to the source, 90 millimeters of rain fell on average in Brisbane in April.

This statistic displays the monthly mean rainfall in Melbourne, Australia, between 1855 and 2016. According to the source, 58 millimeters of rain fell on average in Melbourne in April.