Climate Design Days

Politicians “design” our outdoor climate by allowing further CO₂ emissions, thereby causing the Earth to continue heating up. Meteorologists have compiled today’s climate data from hourly measurements of past years, and projected future data under the influence of climate change.

The Climate Design Days (CDD) [CDD, 2025] method uses these hourly climate data to design the extreme winter and summer days, as well as the typical monthly days.

From the original 8,760 hourly climate data, Climate Design Days are generated: two for the extremes (winter and summer), and seven for normal average months. These are then recombined into a representative year, so that both the frequency distribution and the extreme peak values are accurately represented.

The advantage is that a planner only needs to focus on the 24 hours of the two extreme days, knowing that the peak values in the most extreme daily profile provide a reliable basis for decisions on the sizing of heating and cooling systems.

The CDD are generated for outdoor air temperature, global and direct solar horizontal irradiation, and air humidity.

This makes it possible, for example, to calculate the latent loads and energy for dehumidification in the cooling coils of air-handling systems. In addition, the solar irradiation data are available for the calculation of photovoltaic electricity generation.

The great advantage of the Climate Design Days method is that with only a few input values it compactly reveals the essential differences between various climate datasets. At the same time, the same parameters can be modified as “what-if” scenarios to instantly generate hourly datasets that are directly available for further calculations.

In the Climate Design Days method, it is possible to define a design version by adjusting a combination of selected input values (while keeping the original inputs unchanged). Comparing the design versions then shows the impact on selected output variables. For example, the lowest outdoor air temperature can be set 2 °C lower, and it can be observed how, on the winter design day, the room trend temperature with storage effect changes with an identically dimensioned heating capacity for the room heat transfer systems — and whether this becomes a problem. This way, the causal link between an increased input value and a directly increased heating load is eliminated.

Germany

The hourly climate data from the German Weather Service (DWD) were published in 2010 [DWD, 2010]. Climate data from the years 1988 to 2007 were analyzed. For Germany, there are Test Reference Years (TRY) for 15 regions, and for each region one normal year with average climate, one extreme winter year, and one extreme summer year. The extreme years were selected only from the period 1993 to 2007 [DWD, 2010, p. 41]. An extreme year contains climate data that occur in the respective season only once every 10 years. Using a tool [DWD, 2010, p. 69], it is also possible to generate climate data including climate change effects [DWD, 2010, pp. 42ff.] for a year in the period 2021 to 2050.

Our Approach: The three measured hourly climate datasets (original) were combined into one dataset. From the extreme winter year, the three months December, January, and February (12+1+2) were used; from the extreme summer year, the months June, July, and August (6+7+8); and the transition months in between were taken from the normal year. This produces one dataset (“climate today”) that enables the sizing of heating capacity in winter and cooling capacity in summer. In addition, it can simultaneously be used for energy-related evaluations and for energetic dimensioning, e.g. of geothermal probes or of part-load frequency distributions of heat pumps.

In the same way, a dataset (“climate future”) was compiled, with the year 2035 chosen for generation.

Using the Climate Design Days method, the parameters for generating the Climate Design Days were determined for the 15 regions, and for each region both the current climate and the future climate with climate change were created. The climate data are now available for calculations and for comparison between original data and design data.

In 2017, the German Weather Service (DWD) [DWD, 2017] analyzed the climate data from the years 1995 to 2012 and prepared new datasets. Climate data including climate change can now be generated for a year in the period 2031 to 2060. Via the website of the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [BBSR, 2017], climate data for today (2020) and for the future can be downloaded on a Germany map with a resolution of 1 km × 1 km. Here too, datasets are available for a normal average year, an extreme winter year, and an extreme summer year.

Using the Climate Design Days method, the downloaded datasets are analyzed and the parameters determined. The climate data (current or future) are then available for calculations and for comparison between original and design data.

Europe

The European Union PVGIS database [PVGIS, 2022] makes it possible to select a location on the map of Europe and then download the hourly climate data as a table.

The Climate Design Days method can read this format, automatically determine the parameters for generating the Climate Design Days, and present the results for comparison between original data and design data.

Worldwide

Globally available hourly climate data can be generated, for example, with the program Meteonorm [Meteonorm 8, 2020] for any location in the world. Both current climate data and future climate data with climate change effects can be created.

The Climate Design Days method can read a specified format of hourly climate data, automatically determine the parameters for generating the Climate Design Days, and present the results for comparison.