Data Order Online Help

Climate Perturbation Methods

Two methods are used to calculate the future climate scenarios.

1. Change factors: The term climate ‘change factor’ refers to the change in the climatological mean of a specific climate variable (e.g. temperature) between the current climate (defined in terms of a suitable 20th Century base period) and a projected time in the future (for example, the 30 years centred on 2050).
   
   Calculated on a monthly basis, ‘change factors’ may be used as scalars to transform historical daily climate time-series to produce time-series of projected future climate for use in biophysical models. Climate ‘change factors’ have been calculated based on the OzClim methodology (see http://www.csiro.au/ozclim/home.do), and applied to SILO historical baseline climate data to produce the Consistent Climate Scenarios CF projections datasets for 2030 and 2050.
   
   
2. Quantile-matching: The QM method produces bootstrapped daily data for the future by mapping historical cumulative distribution frequencies functions (CDFs), sourced from a 1957 to 2010 training period, to a supposed future CDF. For 2030, the future CDF is estimated by the forward projection of historical trends in monthly quantiles out to that year.  The projected data stream is then adjusted to confirm to the mean value indicated by the change factor associated with a specific GCM, emissions scenario and climate warming sensitivity.  The output should also honour the spread of values implied by the new future CDF.
   
   The Quantile-matched (QM) projections are only via the web-portal for 2030 and are not available for grid-points.

Baseline Start and End Years

Select the historical baseline years whose climate variables will be mapped to produce the future climate scenario data. The data for the historical baseline years are obtained from the SILO database.

To maximise data quality, the web-ordering system currently limits the earliest baseline start year to 1960.  Post 1960 data has the highest quality.

We recommend using at least a 1960 to 2010 set of data, as this will best capture the influence of inter-annual and decadal climate drivers (i.e. the El Niño-Southern Oscillation and the Pacific Decadal Oscillation). 

Projection Year

Select which year the baseline data will be projected into. For example, if you select 1960 to 2009 as the basline years, and 2030 as the projection year, you will receive data containing 50 years of data which are examples of what the year 2030 may look like. 

2050 data are not available for the Quantile-matched perturbation method.

Emissions Scenarios

Economic development, demographic and technological change play critical roles in the outcome of future greenhouse gas emissions and potentially climate change. As such, the IPCC documented a range of greenhouse gas emissions scenarios for the future in their ‘Special Report on Emissions Scenarios Summary for Policymakers’ (IPCC, 2000, see http://www.ipcc.ch/pdf/special-reports/spm/sres-en.pdf). The IPCC has grouped these AR4 SRES emissions scenarios into four categories termed ‘families’ (A1, A2, B1 and B2) each family having a storyline based on specific socio-economic and environmental characteristics. 

Select a maximum of three emission scenarios to model.

​SRES Emissions Scenario	​Remarks	​Storyline	​Energy use	​Median of projected CO2 concentrations, ppm (2030)	​Median of projected CO2 concentrations, ppm (2050)
​A1FI	​Most recommended. Represents the most extreme global warming risk analysed to date.  Observations suggest A1FI most closely represents the current trend in global CO2 emissions.  Only a few runs have been made.  Not available through PCMDI.  Data obtained by pattern-scaling.	​Very rapid economic growth. Global population that peaks around 2050 and declines thereafter. Rapid introduction of new and more efficient technologies.	​Fossil intensive	​449	​555
​​A2	​Preferred alternative to A1FI.  Similar to A1FI for the early 21st Century. Submitted to IPCC for PCMDI, but not as complete as A1B. Data obtained by pattern-scaling.	​Self-reliance and preservation of local identities. Continuously increasing global population. Economic development regionally oriented. Per capita economic growth and technological change more fragmented and slower than for other storylines.	​	​444​	​522​
​A1B	This model has the most variables. Submitted to IPCC for PCMDI.  Data obtained by pattern-scaling.	​Very rapid economic growth. Global population that peaks around 2050 and declines thereafter. Rapid introduction of new and more efficient technologies.	​Balance across all sources	​447	​522
​B2	​Submitted to IPCC for PCMDI, but not as complete as A1B. Data obtained by pattern-scaling.	​Emphasis on local solutions to economic, social, and environmental sustainability. Continuously increasing global population (rate lower than A2). Intermediate levels of economic development and less rapid and more diverse technological change than B1 and A1 storylines. Oriented toward environmental protection and social equity, but focused on local and regional levels.	​	425​	​473
​A1T	Not available through PCMDI.  Data obtained by pattern-scaling.	​Very rapid economic growth. Global population that peaks around 2050 and declines thereafter. Rapid introduction of new and more efficient technologies.	​Emphasis on non-fossil sources	​435	​496
B1	​Not available through PCMDI.  Data obtained by pattern-scaling.	​A convergent world with the same global population that peaks around 2050 and declines thereafter. Rapid changes in economic structures toward a service and information economy, with reductions in material intensity. Emphasis on global solutions to economic, social, and environmental sustainability, including improved equity, but without additional climate initiatives.	​Introduction of clean and resource-efficient technologies	432	482

 

​CO2-stabilisation scenario	​Storyline	​Remarks
​CO2-450	​CO2 emissions increase and then stabilise by 2100 (a ‘very low’ emissions scenario).	​Data for this project obtained by pattern-scaling.
​CO2-550	​CO2 emissions increase and then stabilise by 2150 (similar to B1).	​Data for this project obtained by pattern-scaling.

Warming Sensitivity

The projections data utilise climate warming sensitivities, a simple measure of the strength and potential uncertainty of the effect of Greenhouse gases on climate, particularly global temperature. The three warming sensitivities we use are calculated from estimates of global warming, due to the effect of CO2 concentrations at both 2030 and 2050.

• High (90th percentile)       
• Median (50th percentile)    
• Low (10th percentile)

You can select one, two or three sensitivities.

We recommend the combined use of all three climate warming sensitivities, as these will incorporate some of the uncertainty about the range of potential biosphere carbon feedbacks.

Climate Models

The climate scenarios data are available for a number of AR4 Global Climate Models (GCMs). Users must select at least one GCM.

An Expert Review Panel consisting of specialists from CSIRO, the Bureau of Meteorology and the former Queensland Department of Environment and Resource Management classified the available GCMs according to each model’s reliability in the Australian region. A summary of the classification is shown below, and more details are available from the User Guide.

​GCM	​Review panel recommendation
​UKMO-HadCM3	​More likely to produce credible projections
​MIROC3.3(hires)	​More likely to produce credible projections
​GFDL-CM2.1	​More likely to produce credible projections
​GFDL-CM2.0	​More likely to produce credible projections
​MIROC3.2(medres)	​More likely to produce credible projections
​ECHO-G	​More likely to produce credible projections
​UKMO-HadGEM1	​More likely to produce credible projections
​ECHAM5/MPI-OM	​More likely to produce credible projections
​MRI-CGCM2.3.2	​Likely to be less reliable
​CCSM3	​Likely to be less reliable
​CGCM3.1(T63)	​Likely to be less reliable
​GISS-AOM	​Likely to be less reliable
​INM-CM3.0	​Not recommended
​CGCM3.1(T47)	​Likely to be less reliable
FGOALS-g1.0	​Likely to be less reliable
​CSIRO-Mk3.0	​Consistently under performed
​CNRM-CM3	​Consistently under performed
​BCCR-BCM2.0	​Not recommended
​CSIRO-Mk3.5	​Not assessed, but expected to be better than CSIRO-Mk3.0

To assist in selecting appropriate models, work based on Watterson (2011) has been used. Watterson’s paper describes how projected Australian 21^st Century rainfall responses cluster, for the range of AR4 CMIP3 GCMs, according to global warming sensitivity and East Indian verses West Pacific Ocean temperature responses. The rainfall responses, which can be split into four Representative Future Climate (RFC) partitions (see figure), are based on: 

• HI: High global warming sensitivity and a warmer Eastern Indian than Western Pacific Ocean. In Australia, this produces a drier southwest and wetter northeast.
• HP: Larger global warming sensitivity and a warmer Western Pacific than Eastern Indian Ocean. In Australia, this produces a much drier continent than either of the HI, WP and WI scenarios.
• WI: Smaller global warming sensitivity and a warmer Eastern Indian than Western Pacific Ocean. In Australia, this produces a drier continent with wetter conditions in the north.
• WP: Smaller global warming sensitivity and a warmer Western Pacific than Eastern Indian Ocean. In Australia, this produces a much drier continent than the HI and WI scenarios, but not as severe as HP.

To see maps representing the mean change in a climate variable (i.e. increase/decrease) per degree of 21^st Century global warming, first click on the GCM name, then select the climate variable from the pull-down list.

Reference: Watterson, I.G. (2011).  Understanding and partitioning future climates for Australian regions from CMIP3 using ocean warming indices. Climatic Change.

Label for your data files

Enter a 1-8 character label for your ZIP archive data files. This label is used as an ID for your order on this web site, and is also added to the name of your ZIP archive data files that you will receive.

Type of operating platform

Select whether you require your data files to be compatible with Windows or Unix systems.

Output file format

Select the output format for your data files. Currently, there is a choice of two formats, both of which are ready to use in the most common biophysical models:

• APSIM - The APSIM Patched Point Data  format (.met) is used by the Agricultural Production Simulator (APSIM), developed by the Agricultural Production Systems Research Unit. An example of the format follows:
  

  year  day radn  maxt   mint  rain  evap    vp   code
   ()   () (mj/m2) (oC)  (oC)  (mm)  (mm) (hPa)     () 
  1970   1   21.0  19.0  14.5   0.0   8.8   9.0 222222
  1970   2   13.0  21.5  13.0   1.1   7.4  12.0 222222
  1970   3   28.0  26.5  13.0   0.0   8.2   9.0 222222
  1970   4   29.0  30.0  13.0   0.0   9.0  11.0 222222
  1970   5   31.0  33.0  15.5   0.0   9.2  12.0 222222
  1970   6   31.0  34.0  18.5   0.0  11.4  12.0 222222

• P51 -The P51 Patched Point Data format (.p51) is used by the Grass Production (GRASP) computer model, developed by the Queensland Government. An example of the format follows:
  

    date    jday  tmax  tmin  rain  evap   rad   vp  
   19700101    1  19.0  14.5   0.0   8.8  21.0   9.0 
   19700102    2  21.5  13.0   1.1   7.4  13.0  12.0 
   19700103    3  26.5  13.0   0.0   8.2  28.0   9.0 
   19700104    4  30.0  13.0   0.0   9.0  29.0  11.0 
   19700105    5  33.0  15.5   0.0   9.2  31.0  12.0 
   19700106    6  34.0  18.5   0.0  11.4  31.0  12.0 

Diagnostic charts included in the output data file package

The following location-specific charts will always be included with your order:

• Historical time series plots for the selected baseline period (i.e. 1960-2010), showing annual variability and long-term trend.
• Comparison of model projections plots, showing projected changes at 2030, from a 1960-2010 base period, for both annual mean temperature and rainfall.  Projected changes are presented for nineteen A1B forced GCMs and three climate warming sensitivities.

 

If this option is selected, the following plots for 2030 Quantile-matched projections are also included:

• Quantile trend plots showing trends used to estimate target cumulative distribution frequencies for 2030.some help info goes here
• Histograms of quantile-matched climate projections.  Plots arranged by month, showing frequency distributions for a range of climate variables in comparison to an observed 1957-2010 baseline climate.

Delivery method

The data will normally be delivered on an ftp site, and only in exceptional circumstances will the data be posted on a USB drive. You will be notified by email with a link when your data are ready to be picked up from the ftp site.