Australia Population Projection: Mid Year: Growth data was reported at 0.300 % in 2100. This stayed constant from the previous number of 0.300 % for 2099. Australia Population Projection: Mid Year: Growth data is updated yearly, averaging 0.750 % from Jun 1986 (Median) to 2100, with 115 observations. The data reached an all-time high of 2.230 % in 2008 and a record low of 0.300 % in 2100. Australia Population Projection: Mid Year: Growth data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s Australia – Table AU.US Census Bureau: Demographic Projection.

Official population projections for: • South Australia and regions for 2016 to 2041 • Local government areas (LGAs) and Statistical Areas level 2 (SA2s) for 2016 to 2036. Users should familiarise themselves with the assumptions, qualifications and background information provided on the DPTI population projections webpage at http://www.dpti.sa.gov.au/planning/population in order to choose the projection that best suits their needs. Updated every 5 years.

Statistically downscaled simulations of daily weather variables for station networks within South Australian Natural Resource Management regions for present and future (projected) climate conditions.

These population projections were prepared by the Australian Bureau of Statistics (ABS) for Geoscience Australia. The projections are not official ABS data and are owned by Geoscience Australia. These projections are for Statistical Areas Level 2 (SA2s) and Local Government Areas (LGAs), and are projected out from a base population as at 30 June 2022, by age and sex. Projections are for 30 June 2023 to 2032, with results disaggregated by age and sex.

Method The cohort-component method was used for these projections. In this method, the base population is projected forward annually by calculating the effect of births, deaths and migration (the components) within each age-sex cohort according to the specified fertility, mortality and overseas and internal migration assumptions. The projected usual resident population by single year of age and sex was produced in four successive stages – national, state/territory, capital city/rest of state, and finally SA2s. Assumptions were made for each level and the resulting projected components and population are constrained to the geographic level above for each year.
These projections were derived from a combination of assumptions published in Population Projections, Australia, 2022 (base) to 2071 on 23 November 2023, and historical patterns observed within each state/territory.

Projections – capital city/rest of state regions The base population is 30 June 2022 Estimated Resident Population (ERP) as published in National, state and territory population, June 2022. For fertility, the total fertility rate (at the national level) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, of 1.6 babies per woman being phased in from 2022 levels over five years to 2027, before remaining steady for the remainder of the projection span. Observed state/territory, and greater capital city level fertility differentials were applied to the national data so that established trends in the state and capital city/rest of state relativities were preserved. Mortality rates are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that mortality rates will continue to decline across Australia with state/territory differentials persisting. State/territory and capital city/rest of state differentials were used to ensure projected deaths are consistent with the historical trend. Annual net overseas migration (NOM) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, with an assumed gain (at the national level) of 400,000 in 2022-23, increasing to 315,000 in 2023-24, then declining to 225,000 in 2026-27, after which NOM is assumed to remain constant. State and capital city/rest of state shares are based on a weighted average of NOM data from 2010 to 2019 at the state and territory level to account for the impact of COVID-19. For internal migration, net gains and losses from states and territories and capital city/rest of state regions are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that net interstate migration will trend towards long-term historic average flows.

Projections – Statistical Areas Level 2 The base population for each SA2 is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. The SA2-level fertility and mortality assumptions were derived by combining the medium scenario state/territory assumptions from Population Projections, Australia, 2022 (base) to 2071, with recent fertility and mortality trends in each SA2 based on annual births (by sex) and deaths (by age and sex) published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. Assumed overseas and internal migration for each SA2 is based on SA2-specific annual overseas and internal arrivals and departures estimates published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. The internal migration data was strengthened with SA2-specific data from the 2021 Census, based on the usual residence one year before Census night question. Assumptions were applied by SA2, age and sex. Assumptions were adjusted for some SA2s, to provide more plausible future population levels, and age and sex distribution changes, including areas where populations may not age over time, for example due to significant resident student and defence force populations. Most assumption adjustments were made via the internal migration component. For some SA2s with zero or a very small population base, but where significant population growth is expected, replacement migration age/sex profiles were applied. All SA2-level components and projected projections are constrained to the medium series of capital city/rest of state data in Population Projections, Australia, 2022 (base) to 2071.

Projections – Local Government Areas The base population for each LGA is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. Projections for 30 June 2023 to 2032 were created by converting from the SA2-level population projections to LGAs by age and sex. This was done using an age-specific population correspondence, where the data for each year of the projection span were converted based on 2021 population shares across SA2s. The LGA and SA2 projections are congruous in aggregation as well as in isolation. Unlike the projections prepared at SA2 level, no LGA-specific projection assumptions were used.

Nature of projections and considerations for usage The nature of the projection method and inherent fluctuations in population dynamics mean that care should be taken when using and interpreting the projection results. The projections are not forecasts, but rather illustrate future changes which would occur if the stated assumptions were to apply over the projection period. These projections do not attempt to allow for non-demographic factors such as major government policy decisions, economic factors, catastrophes, wars and pandemics, which may affect future demographic behaviour. To illustrate a range of possible outcomes, alternative projection series for national, state/territory and capital city/rest of state areas, using different combinations of fertility, mortality, overseas and internal migration assumptions, are prepared. Alternative series are published in Population Projections, Australia, 2022 (base) to 2071. Only one series of SA2-level projections was prepared for this product. Population projections can take account of planning and other decisions by governments known at the time the projections were derived, including sub-state projections published by each state and territory government. The ABS generally does not have access to the policies or decisions of commonwealth, state and local governments and businesses that assist in accurately forecasting small area populations. Migration, especially internal migration, accounts for the majority of projected population change for most SA2s. Volatile and unpredictable small area migration trends, especially in the short-term, can have a significant effect on longer-term projection results. Care therefore should be taken with SA2s with small total populations and very small age-sex cells, especially at older ages. While these projections are calculated at the single year of age level, small numbers, and fluctuations across individual ages in the base population and projection assumptions limit the reliability of SA2-level projections at single year of age level. These fluctuations reduce and reliability improves when the projection results are aggregated to broader age groups such as the five-year age bands in this product. For areas with small elderly populations, results aggregated to 65 and over are more reliable than for the individual age groups above 65. With the exception of areas with high planned population growth, SA2s with a base total population of less than 500 have generally been held constant for the projection period in this product as their populations are too small to be reliably projected at all, however their (small) age/sex distributions may change slightly. These SA2s are listed in the appendix. The base (2022) SA2 population estimates and post-2022 projections by age and sex include small artificial cells, including 1s and 2s. These are the result of a confidentialisation process and forced additivity, to control SA2 and capital city/rest of state age/sex totals, being applied to their original values. SA2s and LGAs in this product are based on the Australian Statistical Geography Standard (ASGS) boundaries as at the 2021 Census (ASGS Edition 3). For further information, see Australian Statistical Geography Standard (ASGS) Edition 3.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is a key Australian Government initiative being led by Geoscience Australia, highlighted in the Data and Digital Government Strategy. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas of Australia.

Contact the Australian Bureau of Statistics If you have questions or feedback about this web service, please email geography@abs.gov.au. To subscribe to updates about ABS web services and geospatial products, please complete this form. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Population Projections, Australia, 2022 (base)

These population projections were prepared by the Australian Bureau of Statistics (ABS) for Geoscience Australia. The projections are not official ABS data and are owned by Geoscience Australia. These projections are for Statistical Areas Level 2 (SA2s) and Local Government Areas (LGAs), and are projected out from a base population as at 30 June 2022, by age and sex. Projections are for 30 June 2023 to 2032, with results disaggregated by age and sex.

Method The cohort-component method was used for these projections. In this method, the base population is projected forward annually by calculating the effect of births, deaths and migration (the components) within each age-sex cohort according to the specified fertility, mortality and overseas and internal migration assumptions. The projected usual resident population by single year of age and sex was produced in four successive stages – national, state/territory, capital city/rest of state, and finally SA2s. Assumptions were made for each level and the resulting projected components and population are constrained to the geographic level above for each year.
These projections were derived from a combination of assumptions published in Population Projections, Australia, 2022 (base) to 2071 on 23 November 2023, and historical patterns observed within each state/territory.

Projections – capital city/rest of state regions The base population is 30 June 2022 Estimated Resident Population (ERP) as published in National, state and territory population, June 2022. For fertility, the total fertility rate (at the national level) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, of 1.6 babies per woman being phased in from 2022 levels over five years to 2027, before remaining steady for the remainder of the projection span. Observed state/territory, and greater capital city level fertility differentials were applied to the national data so that established trends in the state and capital city/rest of state relativities were preserved. Mortality rates are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that mortality rates will continue to decline across Australia with state/territory differentials persisting. State/territory and capital city/rest of state differentials were used to ensure projected deaths are consistent with the historical trend. Annual net overseas migration (NOM) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, with an assumed gain (at the national level) of 400,000 in 2022-23, increasing to 315,000 in 2023-24, then declining to 225,000 in 2026-27, after which NOM is assumed to remain constant. State and capital city/rest of state shares are based on a weighted average of NOM data from 2010 to 2019 at the state and territory level to account for the impact of COVID-19. For internal migration, net gains and losses from states and territories and capital city/rest of state regions are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that net interstate migration will trend towards long-term historic average flows.

Projections – Statistical Areas Level 2 The base population for each SA2 is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. The SA2-level fertility and mortality assumptions were derived by combining the medium scenario state/territory assumptions from Population Projections, Australia, 2022 (base) to 2071, with recent fertility and mortality trends in each SA2 based on annual births (by sex) and deaths (by age and sex) published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. Assumed overseas and internal migration for each SA2 is based on SA2-specific annual overseas and internal arrivals and departures estimates published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. The internal migration data was strengthened with SA2-specific data from the 2021 Census, based on the usual residence one year before Census night question. Assumptions were applied by SA2, age and sex. Assumptions were adjusted for some SA2s, to provide more plausible future population levels, and age and sex distribution changes, including areas where populations may not age over time, for example due to significant resident student and defence force populations. Most assumption adjustments were made via the internal migration component. For some SA2s with zero or a very small population base, but where significant population growth is expected, replacement migration age/sex profiles were applied. All SA2-level components and projected projections are constrained to the medium series of capital city/rest of state data in Population Projections, Australia, 2022 (base) to 2071.

Projections – Local Government Areas The base population for each LGA is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. Projections for 30 June 2023 to 2032 were created by converting from the SA2-level population projections to LGAs by age and sex. This was done using an age-specific population correspondence, where the data for each year of the projection span were converted based on 2021 population shares across SA2s. The LGA and SA2 projections are congruous in aggregation as well as in isolation. Unlike the projections prepared at SA2 level, no LGA-specific projection assumptions were used.

Nature of projections and considerations for usage The nature of the projection method and inherent fluctuations in population dynamics mean that care should be taken when using and interpreting the projection results. The projections are not forecasts, but rather illustrate future changes which would occur if the stated assumptions were to apply over the projection period. These projections do not attempt to allow for non-demographic factors such as major government policy decisions, economic factors, catastrophes, wars and pandemics, which may affect future demographic behaviour. To illustrate a range of possible outcomes, alternative projection series for national, state/territory and capital city/rest of state areas, using different combinations of fertility, mortality, overseas and internal migration assumptions, are prepared. Alternative series are published in Population Projections, Australia, 2022 (base) to 2071. Only one series of SA2-level projections was prepared for this product. Population projections can take account of planning and other decisions by governments known at the time the projections were derived, including sub-state projections published by each state and territory government. The ABS generally does not have access to the policies or decisions of commonwealth, state and local governments and businesses that assist in accurately forecasting small area populations. Migration, especially internal migration, accounts for the majority of projected population change for most SA2s. Volatile and unpredictable small area migration trends, especially in the short-term, can have a significant effect on longer-term projection results. Care therefore should be taken with SA2s with small total populations and very small age-sex cells, especially at older ages. While these projections are calculated at the single year of age level, small numbers, and fluctuations across individual ages in the base population and projection assumptions limit the reliability of SA2-level projections at single year of age level. These fluctuations reduce and reliability improves when the projection results are aggregated to broader age groups such as the five-year age bands in this product. For areas with small elderly populations, results aggregated to 65 and over are more reliable than for the individual age groups above 65. With the exception of areas with high planned population growth, SA2s with a base total population of less than 500 have generally been held constant for the projection period in this product as their populations are too small to be reliably projected at all, however their (small) age/sex distributions may change slightly. These SA2s are listed in the appendix. The base (2022) SA2 population estimates and post-2022 projections by age and sex include small artificial cells, including 1s and 2s. These are the result of a confidentialisation process and forced additivity, to control SA2 and capital city/rest of state age/sex totals, being applied to their original values. SA2s and LGAs in this product are based on the Australian Statistical Geography Standard (ASGS) boundaries as at the 2021 Census (ASGS Edition 3). For further information, see Australian Statistical Geography Standard (ASGS) Edition 3.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is a key Australian Government initiative being led by Geoscience Australia, highlighted in the Data and Digital Government Strategy. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas of Australia.

Contact the Australian Bureau of Statistics If you have questions or feedback about this web service, please email geography@abs.gov.au. To subscribe to updates about ABS web services and geospatial products, please complete this form. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Population Projections, Australia, 2022 (base)

Graph and download economic data for Projection of General government gross debt for Australia (GGGDTPAUA188N) from 2025 to 2030 about Australia, projection, gross, debt, and government.

Climate projections for South Australia based on data from the New South Wales and Australian Regional Climate Modelling project (NARCliM 1.5) and intended for use in planning and risk assessments. Projections are provided for multiple temperature, rainfall and extreme temperatures variables, five time periods (baseline (1985-2005), 2020-2039, 2040-2059, 2060-2079 and 2080-2099) and two Representative Concentration Pathways (RCPs; RCP 4.5 and RCP 8.5). Projections are provided annually, as well as seasonally for temperature and rainfall variables.

Updated sea-level projections for Australia mainly based on the IPCC AR6 and CMIP6 climate models, under 5 Shared Socioeconomic Pathways (SSPs) over the period 2020-2150, as well as a few derived datasets such as sea-level projections along coastline and at tide gauge stations, sea-level projection increment under different global warming increment levels. Lineage: The regional sea-level projections for Australia were mainly based on the IPCC AR6 (Fox-Kemper et al. 2021), with two main updates: 1. The sea level component due to vertical land motion (VLM) is from the IPCC AR5 (Church et al. 2013), which only includes effects from Glacial Isostatic Adjustment (GIA); 2. Dynamic sea level component, related to ocean dynamics, is directly from CSIRO team’s processing of CMIP6 ensemble (Lyu et al. 2020), for better representing sea level dynamical features in coastal regions.

Updated to include UNCON079 This collection contains 9-second gridded datasets (ESRI binary float format in GDA94) showing the projected future (2050-centred) potential vegetation redistribution of 77 Major Vegetation Sub-groups (MVS classes) for continental Australia based on their pre-clearing distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. The training class data input to the kernel regression is provided with this package. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. Using the 1990 baseline training MVS class data, and without constraining the prediction to pre-existing map boundaries, the kernel regression projected to 2050 the distribution of the 77 Major Vegetation Sub-groups using 2050-centred (30 year average) future climates derived from the CanESM2 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. The kernel regression generates unconstrained probabilities varying in the range from 0 and up to 1 for each of the 77 MVS classes.

The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. Each class is denoted “UNCON###”, where the number refers to the code originally assigned to that MVS class by the supplier. A lookup table linking the MVS classes to the output codes and descriptive title is provided. Generalised representations of the vegetation classes derived from the individual class probabilities as the maximum probability in any grid cell are provided separately (see related information).

There are three dataset packages in this series: 1) 1990 predictions of MVS classes; 2) 2050 CanESM2 RCP 8.5 predictions of MVS classes; 3) 2050 MIROC5 RCP 8.5 predictions of MVS classes. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org Lineage: Predictive models of vegetation classes were derived using the two-step process originally developed for individual species distribution modelling with GDM (described in Elith et al. 2006). The first step uses a Generalised Dissimilarity Model (GDM) of vascular plants (VAS_V5_R11) to derive a set of scaled environmental variables for current (e.g. 1990 baseline) and future climates (e.g. 2050). The second step applies this data in a kernel regression to predict each vegetation class using training data derived from the pre-clearing mapping of 77 Major Vegetation Sub-groups. The training data comprised c.155,000 locations defined by randomly sampling within each vegetation class, proportional to their observed areal extent. These locations were then attributed with the baseline values of the GDM-scaled environmental variables. Separate kernel regressions were then run for the baseline and future climate scenarios using the baseline training data. In this way, the future distribution of each vegetation class was projected based on its affinity with present-day ecological environments.

At any location (grid cell), the kernel regression considers the surrounding relative density of training sites of the target vegetation class as a proportion of other types and generates a predicted probability for that class for the focal grid cell. A probability surface for the predicted proportions, varying from 0 to 1, is generated for each of the 77 mapped Major Vegetation Sub-groups. This method is infrequently used in ecology because of the need first to scale and reduce the dimensionality of the predictor variables (Lowe 1995). The GDM step reduces dimensionality (by choosing the variables to use) and scales the predictor variables using similarity-decay functions which equate to the multivariate distances expected by kernel regression. The kernel regression thus incorporates interactions by modelling ecological distances and vegetation class densities within a truly multivariate predictor space, with no assumption of additivity.

Kernel regression aims to optimise model performance in terms of the accuracy of predictions at any single location according to the area predicted for each class. The predicted proportions of common vegetation types are typically greater than for rarer vegetation types. Therefore, cell by cell, the class with the maximum probability selected to represent spatially varying vegetation class mosaics on a single map (essentially one dimension) will often be the common type, at the expense of locally rare and nationally rare types. Therefore, the best way to view the results, and to inform planning, is the individual probability surfaces. These properly reveal where the rarer vegetation types have a likelihood of persistence. Higher probabilities associated with other vegetation types at the same location can be viewed as a measure of the extent to which those other vegetation types may compete. However the outcome, at least in the medium term, may be more driven by the extant occurrence of ecosystems and their ability to persist under marginal conditions.

This workbook illustrates a national cohort-component population projection model using the example of Australia. It accompanies the paper "A brief guide to producing a national population projection" by Tom Wilson and Phil Rees.

Graph and download economic data for Projection of General government net lending/borrowing for Australia (GGNLBPAUA188N) from 2025 to 2030 about budget, Australia, projection, Net, and government.

Projections of rainfall, potential evapotranspiration (PET) and runoff for 1 degree and 2 degrees of global warming. Lineage: See associated technical report, "Projected changes in climate and runoff for south-eastern Australia..."

Australia Number of Deaths data was reported at 314,904.000 Person in 2050. This records an increase from the previous number of 311,860.000 Person for 2049. Australia Number of Deaths data is updated yearly, averaging 165,316.000 Person from Jun 1986 (Median) to 2050, with 65 observations. The data reached an all-time high of 314,904.000 Person in 2050 and a record low of 117,325.000 Person in 1987. Australia Number of Deaths data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s Australia – Table AU.US Census Bureau: Demographic Projection.

This is a sub-set of data released for GovHack 2017.The dataset includes simulations of daily weather variables for a small selection of weather stations (Adelaide, Coober Pedy, Mount Gambier, Port Pirie, Renmark) for historic and future (projected) climate conditions. The full dataset, which includes data for more than 200 weather stations and 15 climate models, can be found here https://data.sa.gov.au/data/dataset/goyder-institute-for-water-research-downscaled-climate-projections-for-south-australia

This record links to Bureau of Meteorology metadata for each State's latest "Precis forecast" information, available through an ftp download.

The Bureau of Meteorology's "Precis forecast" product (per State) contains the latest 7 day forecast, per location across that State, with daily projected values for temperature, rainfall and weather conditions.

Data (7-day precis forecast data, for {{State}}) is available in XML format. (The plain text and html formats were withdrawn in Feb 2016)

Place Names are the same, in the plain text, html and xml format files.

The XML file uses the AAC location code (and location name), rather than the StationID code. The coordinates related to each AAC code/ location name, in the XML formatted file, are listed in the PointPlaces [IDM00013.*] data files, available from ftp://ftp.bom.gov.au/anon/home/adfd/spatial/IDM00013.dbf [open the dbf file, using Excel].

Note that the precis forecasts relate to an area surrounding the nominated location, the coordinates of which are intended to be the "centre of town" for that location ( as derived from Geoscience Australia's placename Gazetteer)".

Data content

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

User /Licence information

• The Bureau does not guarantee the availability of information on the ftp site.
• Please read how to access the Bureau ftp site and its structure.
• The Service Announcements page details changes to Bureau services.

• Use of data should be in accordance with the copyright notice and disclaimer.

• Secondary distribution of Bureau of Meteorology information currently freely available on the Bureau website and ftp sites requires formal permission.

• Correct attribution of the Australian Bureau of Meteorology as the source of Bureau information is an important component of any secondary distribution permission that may be granted. Where Bureau information is to be used on a website, permission for use of that information should be applied for by the website owner.

This dataset contains population projections by sex and age group for SA3 areas of Australia (excluding a few areas with very small populations) for a 15 year projection horizon. The data are presented in both Excel and csv formats.

This is a three-dimensional forecast of ocean state from the sea surface to the deep ocean. The OceanMAPS model is a Bureau of Meteorology operational global ocean forecast system with 0.1 degree horizontal resolution and variable vertical resolution. This model was build with the support of the Bluelink research partnership.

This dataset captures a one-year period and is provided to support anyone wanting to better understand the model or how to work with this data.

LICENCE: The dataset referred to in this metadata record is available/ licenced under the “CC-BY-NC 3.0 au” license. The license summary may be found here: https://creativecommons.org/licenses/by-nc/3.0/au/
The full license text may be found here: https://creativecommons.org/licenses/by-nc/3.0/au/legalcode
~~~~~~~~~~~~~~~~~~~~~~~~~~
We request attribution as : Australian Bureau of Meteorology (2018), Australia Ocean 3D Bluelink Forecast Data Sample (2017-01 to 2017-12), {Point-of-truth authoritative version of metadata url : http://www.bom.gov.au/metadata/19115/ANZCW0503900704 } Downloaded from [url] on [date]
~~~~~~~~~~~~~~~~~~~~~~~~~~
Please refer to [ http://www.bom.gov.au/other/disclaimer.shtml ], for disclaimer details.

In 2030, the installed coal power generation capacity in Australia was projected to reduce to approximately ** gigawatts. This represented a projected ** percent reduction in the coal power generation capacity between 2020 and 2030.

This workbook contains projections of population ageing from 2016 to 2066 for Australia and the States and Territories. It includes both traditional and alternative measures of ageing, the latter being (1) the population with under 15 years of remaining life expectancy (RLE0.01).

This collection consists of application-ready climate projections based on observations, adjusted through a quantile-delta-change method to incorporate projected climate changes from CMIP6 Global Climate Models.

The collection consists of daily projections of precipitation, mean downwelling shortwave radiation, maximum and minimum near-surface temperature, maximum, mean and minimum near-surface relative humidity, and mean near-surface wind speed for 9 CMIP6 models. Two 30-year windows of projections are available (2035-2064 and 2070-2099) for four shared socio-economic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Data is available with two sources of historic climate observations for Australia: AGCD (for precipitation and temperature only) and BARRA-R2. The BARRA-R2 baseline data has been interpolated to the same resolution as AGCD (~5km).

In addition, historical baseline data for 1985-2014 from AGCD and BARRA-R2 used for the quantile-delta-change adjustment are also available in the collection.

More in-depth information on the dataset can be found in the technical report: https://doi.org/10.25919/03by-9y62

This collection is not updated frequently. Lineage: Daily data from 9 CMIP6 models (ACCESS-CM2, ACCESS-ESM1-5, CESM2, CMCC-ESM2, CNRM-ESM2-1, EC-Earth3, MPI-ESM1-2-HR, NorESM2-MM, UKESM1-0-LL) are used to calculate climate changes between a 1985-2014 baseline period and the two future projection periods for each SSP. The projected changes are then applied to 1985-2014 baseline data from AGCD and BARRA-R2 to produce the application-ready datasets. The quantile-delta-change method is used, which applies different climate changes to different parts (quantiles) of the distribution of daily data.

This process is done using python software available at https://github.com/AusClimateService/qq-workflows/tree/main/qdc-cmip6.

More in-depth information on the method can be found in the technical report: https://doi.org/10.25919/03by-9y62

The application-ready projections have also been subject to a quality-control and assurance check utilising a python script (https://github.com/climate-innovation-hub/qdc-cmip6-qaqc) to ensure full data coverage and compliance with metadata standards, with the process documented in the QAQC report: https://doi.org/10.25919%2F4n26-fh08