CNR - Institute of Biosciences and BioResources (IBBR), Florence division, Via Madonna del Piano 10, I-50019, Sesto Fiorentino (Florence), Italy.
Department of Renewable Resources, University of Alberta, 751 General Services Building, Edmonton, AB, T6G 2H1, Canada.
Sci Data. 2020 Dec 4;7(1):428. doi: 10.1038/s41597-020-00763-0.
Interpolated climate data have become essential for regional or local climate change impact assessments and the development of climate change adaptation strategies. Here, we contribute an accessible, comprehensive database of interpolated climate data for Europe that includes monthly, annual, decadal, and 30-year normal climate data for the last 119 years (1901 to 2019) as well as multi-model CMIP5 climate change projections for the 21 century. The database also includes variables relevant for ecological research and infrastructure planning, comprising more than 20,000 climate grids that can be queried with a provided ClimateEU software package. In addition, 1 km and 2.5 km resolution gridded data generated by the software are available for download. The quality of ClimateEU estimates was evaluated against weather station data for a representative subset of climate variables. Dynamic environmental lapse rate algorithms employed by the software to generate scale-free climate variables for specific locations lead to improvements of 10 to 50% in accuracy compared to gridded data. We conclude with a discussion of applications and limitations of this database.
插补气候数据已成为区域或局地气候变化影响评估以及制定适应气候变化战略的重要基础。在这里，我们提供了一个易于访问的、全面的欧洲插补气候数据集，其中包括过去 119 年（1901 年至 2019 年）的逐月、逐年、十年和 30 年的正常气候数据，以及多模式 CMIP5 气候变率数据。该数据集还包括与生态研究和基础设施规划相关的变量，包含超过 20,000 个气候网格，可通过提供的 ClimateEU 软件包进行查询。此外，还可下载该软件生成的 1km 和 2.5km 分辨率的网格化数据。针对气候变量的代表性子集，我们评估了 ClimateEU 估算值与气象站数据的吻合程度。该软件用于为特定地点生成无标度气候变量的动态环境递减率算法，与网格化数据相比，其准确性提高了 10%至 50%。最后，我们讨论了该数据库的应用和局限性。
