September 17, 2021
Melissa James
With support from the Australian Government’s Department of Industry, Science, Energy and Resources, the CSIRO has developed predictive weather data that can be used by building energy simulation software to explore the impact of climate change on building energy consumption.
Hourly weather data is available for 83 Australian locations for 4 future years (2030, 2050, 2070, or 2090) under 3 future climate scenarios (RCP2.6, RCP4.5, or RCP8.5). The Representative Concentration Pathway (RCP) climate projections reflect a range of possible climate futures with varying levels of anthropogenic greenhouse gas (GHG) emissions, atmospheric GHG concentrations, and the resultant radiative forcing (Figure 1). In RCP2.6, atmospheric GHG emissions peak around 2020 then rapidly decline in line with strong emission reduction measures. In RCP4.5, GHG emissions peak in 2040, while RCP8.5 reflects a future with little curbing of GHG emissions.
Figure 1. Resultant radiative forcing under different RCP scenarios. SOURCE: CSIRO and Bureau of Meteorology, Australia (2015)
The predictive weather data is based on a typical meteorological year of historical weather data drawn from the years 1990 to 2015. Global Climate Models and morphing were applied to this data to predict the future values under each climate scenario at each location.
The data can be downloaded for free as a zipfile from CSIRO’s Ag Climate Data Shop. It is available in two formats:
- In .epw format that can be used by building energy simulation software such as EnergyPlus, ESP-r, and IESVE
- In a weather file format suitable for building energy simulations using Nationwide House Energy Rating Scheme (NatHERS) software such as AccuRate, BERSPro, FirstRate5, and HERO in non-regulatory mode
Further information about the data is available here.
Also available via the Data Shop are Typical meteorological year (TMY) weather files in .epw format. These files contain hourly weather data for a typical meteorological year in 83 Australian locations. They are based on historical weather data from the years 1990 to 2015
