|Historical Global-Gridded Degree-Days: A High Spatio-Resolution Database of CDD and HDD|
|Geoscience Data Journal , Online first - October 2019|
Degree-days have been routinely used by building designers and engineers to estimate indoor space cooling energy consumption and by policy makers and researchers for forecasting energy demand, consumption patterns and associated carbon emissions. This is partly rooted in their simplicity but yet powerful capability to represent a relationship between climate and cooling or heating requirements.
Degree-days are defined as monthly or annual sum of the difference between a base temperature (Tb) and daily mean outdoor air temperature (Td). The base temperature is also referred to as ‘threshold’ temperature or ‘set-point’ temperature, as it indicates the temperature at which the indoor cooling or heating systems do not need to operate in order to maintain human comfort levels.
This new open source dataset published today in Geoscience Data Journal represents a unique (first-ever) high-spatial resolution, global-gridded database of three types of degree-days, each with 6 threshold temperatures in °C: Cooling Degree Days (CDD), Heating degree Days (HDD), and a variant of Cooling Degree Days accounting for humidity, referred to as Wet Bulb Cooling Degree Days (CDD wetbulb).
In regions with high relative humidity, such as many coastal regions over the globe (in Australia, India, Brazil or China for instance), CDD wetbulb is a more suitable indicator than the conventional CDD. Technically, the temperature for wet bulb is the minimum temperature to which air can be cooled by evaporative cooling, and as such, contains information about air temperature as well as moisture content.
The degree-days included in this study are derived using meteorological variables from Global Land Data Assimilation System (GLDAS). It is a new generation global high-resolution reanalysis data product developed jointly by the National Aeronautics and Space Administration, Goddard Space Flight Center and the National Centers for Environmental Prediction. GLDAS incorporates satellite and ground-based observations, producing optimal fields of land surface states and fluxes in near-real-time, thus facilitating regular updates of the dataset presented in our study.
The scope and applications of this open source dataset are potentially very wide and diverse, depending on the focus you have. You could for instance perform empirical assessment of energy demands at local, regional and global scales, or analyze the implications of the efficiency of heating and cooling in buildings systems. You could also analyse clusters of grid-cells to identify regions with similar historical spatial-temporal patterns of degree-days.
Indeed, our dataset enables users to apply degree-days using various:
- spatial scales, by aggregating grid-cells to regional, national or user defined boundaries;
- temporal scales, by aggregating monthly degree-days to seasonal (e.g. winter months) or user-defined periods; and
- weighting options, e.g. population or other socio-economic indicator weighted degree-days, again at varying spatio-temporal scales.