Historical and future weather data for dynamic building simulations in Belgium using the regional climate model MAR: typical and extreme meteorological year and heatwaves

Abstract

Abstract. Increasing temperatures due to global warming will influence building, heating, and cooling practices. Therefore, this data set aims to provide formatted and adapted meteorological data for specific users who work in building design, architecture, building energy management systems, modelling renewable energy conversion systems, or others interested in this kind of projected weather data. These meteorological data are produced from the regional climate model MAR (Modèle Atmosphérique Régional in French) simulations. This regional model, adapted and validated over Belgium, is forced firstly, by the ERA5 reanalysis, which represents the closest climate to reality and secondly, by three Earth system models (ESMs) from the Sixth Coupled Model Intercomparison Project database, namely, BCC-CSM2-MR, MPI-ESM.1.2, and MIROC6. The main advantage of using the MAR model is that the generated weather data have a high resolution (hourly data and 5 km) and are spatially and temporally homogeneous. The generated weather data follow two protocols. On the one hand, the Typical Meteorological Year (TMY) and eXtreme Meteorological Year (XMY) files are generated largely inspired by the method proposed by the standard ISO15927-4, allowing the reconstruction of typical and extreme years, while keeping a plausible variability of the meteorological data. On the other hand, the heatwave event (HWE) meteorological data are generated according to a method used to detect the heatwave events and to classify them according to three criteria of the heatwave (the most intense, the longest duration, and the highest temperature). All generated weather data are freely available on the open online repository Zenodo (https://doi.org/10.5281/zenodo.5606983, Doutreloup and Fettweis, 2021) and these data are produced within the framework of the research project OCCuPANt (https://www.occupant.uliege.be/ (last access: 24 June 2022)​​​​​​​ – ULiège).

Impact of climate change on nearly zero-energy dwelling in temperate climate: Time-integrated discomfort, HVAC energy performance, and GHG emissions

“…It is necessary to transform them into compatible climate data using downscaling techniques (statistical or dynamical methods). For this aim, the regional climate model (MAR) "Modèle Atmosphérique Régional" was used in its version 3.11.14 [56], which is a dynamical method resulting in physically consistent climate parameters and extreme weather events. MAR is adapted and widely validated for Belgium region [57][58][59].…”

“…Second, MAR was forced every 6 h by Earth System Model (ESM) BCC-CSM2-MR (which approximately follow the mean temperature of all ESMs over Belgium up to 2100) from the Sixth Coupled Model Intercomparison Project (CMIP6) that represents the mean evolution of climate parameters between 1980 and 2014 with the historical scenario and 2015-2100 with the SSPs [62]. ESM forced MAR was then validated by comparing to MAR ERA5 simulations to verify whether it can be used to generate future climate data [56].…”

“…The weather data in this paper are derived for Brussels (Köppen-Geiger climate zone: Cfb, Marine west coast and warm summer) from Ref. [56]. The selection of the periods and emission scenarios (SSPs) for the weather files in this paper are inspired by the recommendations of the dynamic simulation guideline developed within International Energy Agency (IEA) EBC Annex 80 -"Resilient cooling of buildings" project [65] as well as a previous study in the literature [8].…”

Climate data for hygrothermal simulations of Brussels

“… In literature, there are studies on generating climate files for building simulations [5] , [6] , [7] . Generally, these studies focus on building energy simulations to compute heating/cooling loads, energy performance.…”

GSDM-WBT: global station-based daily maximum wet-bulb temperature data for 1981–2020

“…The trend of warming is threatening the climate system, terrestrial and marine ecosystems, and socio-economic development, resulting in the increase of the frequency and intensity of extreme climatic events, loss of biodiversity and protected areas, and human morbidity and mortality (Sun et al, 2014;Perkins-Kirkpatrick and Lewis, 2020). Long-term temperature datasets have become the basis for accurate assessment of global or local warming and its impacts, especially heat waves and their effects on health (Doutreloup et al, 2022;Fang et al, 2022). Previous studies on extreme heat mostly use near-surface air temperature directly based on observations from meteorological stations or numerical climate sim-ulations (Mazdiyasni et al, 2017;Dong et al, 2021;Fischer et al, 2021), but the intensity of air temperature is usually not equivalent to the human body's response to the thermal environment.…”