This dataset contains global gridded maps of Heating degree days (HDD) and Cooling degree days (CDD) for three climate scenarios: one historical scenario related to temperature observations from 2006 to 2016; and two climate projections considering a global mean temperature rise of 1.5°C and 2°C above pre-industrial levels, regardless of when these occur. HDD and CDD are widely used indicators to measure how much the mean temperature exceeds a reference temperature each day over a given period. They are widely used indicators to examine global temperature-related climate and quantify heating and cooling demand.
Five different maps of HDD and CDD are available for each scenario as NetCDF V4 files (*.nc). These maps relate to different annual statistical indices calculated using 70 climate simulations over a 10-year period: mean, median, 10th percentile, 90th percentile, and standard deviation. The novelty of this dataset lies in the combination of two factors: the representation of global mean temperature rise scenarios for 1.5°C and 2.0°C globally, regardless of when these occur; and the bias-corrected global climate dataset used to calculate HDD and CDD, which involves a large ensemble size at a high global spatio-temporal resolution.
Methods:
The global gridded statistical maps of HDD and CDD were calculated considering 18°C as the baseline temperature. First, the annual HDD and CDD were calculated for each simulated year of each scenario at all geographic locations (a total of 700 simulated years per scenario). Then, the statistical indices across this variability were obtained. Global gridded maps have a spatial resolution of 0.833° x 0.556° (longitude x latitude) over the land surface.
Climate data used:
These global gridded maps of CDD and HDD were calculated using bias-corrected global climate simulations for mean temperature generated using the HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Each scenario involved an ensemble of 70 individual members with 6-hourly mean temperatures at a horizontal resolution of 0.833 longitude and 0.556 latitude for a 10-year period (700 runs per scenario), aiming to ensure internal climate variability. These simulation experiments were run within the climateprediction.net (CPDN) climate simulation environment, using the Berkeley Open Infrastructure for Network Computing (BOINC) framework to distribute a large number of individual computational tasks. This system utilises the computational power of publicly volunteered computers that are globally distributed. The bias-corrected global climate dataset used to calculate these CDD and HDD maps is available at:
Lizana, J.; Miranda, N.D.; Sparrow, S.; Zachau-Walker, M.; Watson, P.; Wallom, D.C.H.; McCulloch, M. (2023): Large ensemble of global mean temperatures: 6-hourly HadAM4 model run data using the Climateprediction.net platform. NERC EDS Centre for Environmental Data Analysis, 28 June 2023. doi:10.5285/9c41e3aa67024bbdad796290a861e968
