Resilient Urban Solutions
Our mission is to enhance climate change mitigation and adaptation in urban areas
Future weather data sets for engineering applications
Future climate data are generated by global climate models (GCMs), also known as general circulation models. GCMs contain atmospheric model, ocean model, land surface scheme and the sea ice model and simulate climatic conditions under different initial and boundary conditions. GCMs simulate future climatic conditions for the spatial resolution of 100–300 km2 which cannot be considered as weather and is coarse for the purpose of impact assessment. GCM data should be downscaled by means of statistical or dynamic downscaling techniques. One well-known statistical technique is morphing which combines present-day observed weather data with the GCM results. Morphing technique however reflects only changes in the average weather conditions and neglects changes in future weather sequences. For example it is not possible to see changes in extreme climatic conditions for the morphed data, though extremes will be more frequent and stronger in the future. Dynamic downscaling of GCMs by means of regional climate models (RCMs) has the advantage of generating physically consistent data sets across different variables. RCMs provide weather data with suitable temporal and spatial resolutions for direct use in engineering applications.
The major challenges with using future weather data sets for engineering applications are (1) synthesizing GCM or RCM data and generating suitable formats, (2) climate change uncertainties and the need for considering multiple long-term data sets, (3) dealing with big data sets, and (4) synthesizing representative data sets.
We have our pioneering and scientifically proven approaches in synthesizing future climate data sets and generating representative weather data sets for engineering applications.
Relevant publications
- Nik, V. M., “Application of typical and extreme weather data sets in the hygrothermal simulation of building components for future climate – A case study for a wooden frame wall”, Energy and Buildings, vol. 154, pp. 30–45, Nov. 2017, doi.org/10.1016/j.enbuild.2017.08.042.
- Nik, V. M., “Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)”, Applied Energy, vol. 177, pp. 204–226, Sep. 2016, doi:10.1016/j.apenergy.2016.05.107.
- Moazami, A., Nik, V.M. et al. “Impacts of future weather data typology on building energy performance – Investigating long-term patterns of climate change and extreme weather con-ditions”, Applied Energy, vol. 238, pp. 696–720, Mar. 2019, doi:10.1016/j.apenergy.2019.01.085.