Seamlessly combined historical and projected daily meteorological datasets for impact studies in Central Europe: The FORESEE v4.0 and the FORESEE-HUN v1.0

• Published in: Climate services Vol. 33; p. 100443
• Main Authors: Kern, Anikó, Dobor, Laura, Hollós, Roland, Marjanović, Hrvoje, Torma, Csaba Zsolt, Kis, Anna, Fodor, Nándor, Barcza, Zoltán
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2024; Elsevier
• Subjects: Bias correction; Climate datasets; E-OBS; EURO-CORDEX; HUCLIM; Impact studies; HUCLIM; Impact studies; Bias correction; E-OBS; EURO-CORDEX; Climate datasets
• ISSN: 2405-8807; 2405-8807
• DOI: 10.1016/j.cliser.2023.100443

•FORESEE is a seamless climate database for Central Europe.•The two new datasets are based on 14 model and 2 RCP scenarios.•It enables the ensemble-based quantification of the projected climate change signal.•Two application examples are presented for probabilistic impact assessment. The FORESEE is an open access, climatological database for Central Europe containing observed and projected meteorological data for the 1951–2100 period. As a climate service, FORESEE disseminates basic meteorological variables at a daily time step with a 0.1° × 0.1° spatial resolution including maximum/minimum temperature, precipitation, incoming shortwave solar radiation and daylight vapour pressure deficit. The future climate in FORESEE v4.0 and FORESEE-HUN v1.0 is projected by 14 regional climate models from the EURO-CORDEX database using the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. Based on RCP4.5 the country-specific results indicate similar projected mean changes in annual mean temperature (1.5–1.7 °C) but considerable differences in precipitation (from −1.6 to 6.9%) in the region for 2071–2100 relative to 1991–2020. We present two case studies to demonstrate the applicability of FORESEE in climate change impact studies using the ensemble approach. Climate change induced negative weather effect (15.4% and 28.9% mean loss for 2071–2100 according to RCP4.5 and RCP8.5, respectively) might dominate the future winter wheat yields in Hungary that is superimposed to the overall trend determined by other factors. The projections provide consistent results about the mean advance in the start of the growing season for forests in Hungary up to 2100 with ensemble mean of 9.1 days (RCP4.5) and 19.8 days (RCP8.5). We also demonstrate that the representative model selection method might lead to misleading results in impact studies that should be considered. The updated FORESEE is a way forward in the dissemination of policy-relevant essential climate data in Central Europe.