Indoor climate projections for European cattle barns
In the last decades, an exceptional global warming trend was observed. Modifications in the humidity and wind regime may amplify the regional and local impacts on livestock husbandry. Modifications in housing management are the main measures taken to improve the ability of livestock to cope with these conditions. Measures are, however, typically taken in reaction to uncomfortable conditions instead of in anticipation of a risk for climatic stress. Moreover, measures that balance welfare, environmental and economic issues are barely investigated in the context of climate change and are thus almost not available for commercial farms. In Europe, cows are economically highly relevant and are mainly kept in naturally ventilated buildings that are most susceptible to climate change. The high-yielding cows are particularly vulnerable to heat stress. We used nested models to estimate the future heat stress risk in such dairy cattle husbandry systems. The indoor climate was monitored inside three reference barns in Central Europe and in the Mediterranean region. An artificial neuronal network (ANN) was trained to relate the outdoor weather conditions provided by official meteorological weather stations to the measured indoor microclimate. Subsequently, this ANN model for the indoor microclimate was driven by an ensemble of regional climate model projections with three different greenhouse gas concentration scenarios. The data comprises: (1) Aggregated indoor measurements. Spatial averages for each barn are provided for zonal wind component (uw), meridional wind component (vw), air temperature (ta) and relative humidity (rh) as hourly averaged values. (2) Pictures of the naturally ventilated barns together with sketches of the measurement positions used for the averaging. (3) A map indicating the regional distribution of the data collection within the simulation domain. (4) Observations of the German Weather Service (DWD) and the National Climatic Data Center (NCDC) Archive of the National Oceanic and Atmospheric Administration (NOAA) aggregated to hourly values. Data for near-surface air temperature (tas), precipitation (pr), near-surface relative humidity (hurs), cloud coverage (cld), short wave downwelling radiation (rsds), sunshine duration (sdur), surface air pressure (ps), sea-level air pressure (psl), wind speed (wspd), wind direction (wdir), zonal wind component (uw), and meridional wind component (vw) are provided. Missing measurements were filled using a hot deck imputation method. (5) Simulated indoor climate conditions using trained artificial neuronal networks driven by historical respectively future (RCP 2.6, 4.5 and 8.5) climate model projections based on the CORDEX-EUR global-regional climate model ensemble. Time series of indoor averages for air temperature (ta), relative humidity (rh), zonal wind component (uw), meridional wind component (vw) and temperature humidity index (thi) are provided on an hourly base.