BioVars - A bioclimatic dataset for Europe based on a large regional climate ensemble for periods in 1971-2098.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-05 DOI:10.1038/s41597-025-04507-w
Anne Reichmuth, Oldrich Rakovec, Friedrich Boeing, Sebastian Müller, Luis Samaniego, Andreas Marx, Hanna Komischke, Andreas Schmidt, Daniel Doktor
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Abstract

Ongoing ecological research is concerned with analysing climate-induced changes in species distribution. For this purpose, the projection must have high-quality bioclimatic variables from historical and future climatic periods for the projection. To date, there are many global bioclimatic variables on this topic. Nevertheless, a consistent dataset with identical model variables from historic and projected periods is rare. We present 26 bioclimatic variables that are calculated based on a large ensemble consisting of 70 bias-adjusted GCM-RCM simulations for 1971-2098. Both, the historic and the projection periods were calculated using the same models to ensure consistency between the periods. The variables are validated against E-OBS observations from which we calculated the same bioclimatic variables. For projection periods we chose 20 year ranges between 2021-2098. Here, we offer two versions of them: (1) variables separated into RCP 2.6, 4.5 and 8.5, including percentiles among the realisations and within the RCPs; and (2) variables per realisation separately. We then extracted the temporal 5th, 50th and 95th percentile per period as representing values.

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BioVars——基于1971-2098年期间大型区域气候集合的欧洲生物气候数据集。
正在进行的生态学研究关注于分析气候引起的物种分布变化。为此目的,预估必须有来自历史和未来气候时期的高质量生物气候变量。迄今为止,有许多关于这一主题的全球生物气候变量。然而,从历史和预估时期得到具有相同模型变量的一致数据集是罕见的。我们提出了26个生物气候变量,这些变量是基于1971-2098年70个经偏差调整的GCM-RCM模拟的大集合计算得出的。历史时期和预测时期都是使用相同的模型计算的,以确保时期之间的一致性。根据我们计算的相同生物气候变量的E-OBS观测验证了这些变量。对于预测期,我们选择了2021-2098年之间的20年区间。在这里,我们提供了它们的两个版本:(1)分为RCP 2.6, 4.5和8.5的变量,包括实现之间和RCP内部的百分位数;(2)每个实现的变量分别。然后,我们提取每个时期的第5、第50和第95个百分位数作为代表值。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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