Regionalized dynamic climate series for ecological climate impact research in modern controlled environment facilities

Modern controlled environment facilities (CEFs) enable the simulation of dynamic microclimates in controlled ecological experiments through their technical ability to precisely control multiple environmental parameters. However, few CEF studies exploit the technical possibilities of their facilities, as climate change treatments are frequently applied by static manipulation of an inadequate number of climate change drivers, ignoring intra‐annual variability and covariation of multiple meteorological variables. We present a method for generating regionalized climate series in high temporal resolution that was developed to force the TUMmesa Model EcoSystem Analyzer with dynamic climate simulations. The climate series represent annual cycles for a reference period (1987–2016) and the climate change scenarios RCP2.6 and RCP8.5 (2071–2100) regionalized for a climate station situated in a forested region of the German Spessart mountains. Based on the EURO‐CORDEX and ReKliEs‐DE model ensembles, typical annual courses of daily resolved climatologies for the reference period and the RCP scenarios were calculated from multimodel means of temperature (t_a), relative humidity (rh), global radiation (R_g), air pressure (P), and ground‐level ozone and complemented by CO₂. To account for intra‐annual variation and the covariability of multiple climate variables, daily values were substituted by hourly resolved data resampled from the historical record. The resulting present climate Test Reference Year (TRY) well represented a possible annual cycle within the reference period, and expected shifts in future mean values (e.g., higher t_a) were reproduced within the RCP TRYs. The TRYs were executed in eight climate chambers of the TUMmesa facility and—accounting for the technical boundaries of the facility—reproduced with high precision. Especially, as an alternative to CEF simulations that reproduce mere day/night cycles and static manipulations of climate change drivers, the method presented here proved well suited for simulating regionalized and highly dynamic annual cycles for ecological CEF studies.