Coupled atmosphere-ocean general circulation models (AOGCMs, or just GCMs for short) simulate different realizations of possible future climates at global scale under contrasting scenarios of greenhouse gases emissions. While these datasets provide several meteorological variables as output, but two of the most important ones are air temperature at the Earth's surface and daily precipitation. GCMs outputs are spatially downscaled using different methodologies, but it is accepted that such data require further processing to be used in impact models, and particularly for crop simulation models. Daily values of solar radiation, wind, air humidity, and, at times, rainfall may have values which are not realistic, and/or the daily record of data may contain values of meteorological variables which are totally uncorrelated. Crop models are deterministic, but they are typicallyrun in a stochastic fashion by using a sample of possible weather time series that can be generated using stochastic weather generators. With their random variability, these multiple years of weather data can represent the time horizon of interest. GCMs estimate climate dynamics, hence providing unique time series for a given emission scenario; the multiplicity of years to evaluate a given time horizon is consequently not available from such outputs. Furthermore, if the time horizons of interest are very close (e.g. 2020 and 2030), averaging only the non-overlapping years of the GCM weather variables time series may not adequately represent the time horizon; this may lead to apparent inversions of trends, creating artefacts also in the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data covering Europe with a 25 km grid, which is adequate for crop modelling in the near-future. Climate data are derived from the ENSEMBLES downscaling of the HadCM3, ECHAM5, and ETHZ realizations of the IPCC A1B emission scenario, using for HadCM3 two different regional models for downscaling. Solar radiation, wind and relative air humidity weather variables where either estimated or collected from historical series, and derived variables reference evapotranspiration and vapour pressure deficit were estimated from other variables, ensuring consistency within daily records. Synthetic time series data were also generated using the weather generator ClimGen. All data are made available upon request to the European Commission Joint Research Centre's MARS unit.

DUVEILLER BOGDAN Grégory Henry E;  DONATELLI Marcello;  FUMAGALLI Davide;  ZUCCHINI Antonio;  NELSON Roger;  BARUTH Bettina; 

2016-04-05

SPRINGER WIEN

JRC97162

0177-798X,   

http://link.springer.com/article/10.1007%2Fs00704-015-1650-4,    https://publications.jrc.ec.europa.eu/repository/handle/JRC97162,   

10.1007/s00704-015-1650-4,