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

作者信息

Reichmuth Anne, Rakovec Oldrich, Boeing Friedrich, Müller Sebastian, Samaniego Luis, Marx Andreas, Komischke Hanna, Schmidt Andreas, Doktor Daniel

机构信息

Helmholtz-Centre for Environmental Research - UFZ, Department Remote Sensing, Leipzig, 04318, Germany.

Remote Sensing Centre for Earth System Research, RSC4Earth, Leipzig University, Leipzig, Germany.

出版信息

Sci Data. 2025 Feb 5;12(1):217. doi: 10.1038/s41597-025-04507-w.

DOI:10.1038/s41597-025-04507-w
PMID:39910176
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11799319/
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.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/e2b737df225b/41597_2025_4507_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/946b54b1bacf/41597_2025_4507_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/e66c149f1dd9/41597_2025_4507_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/e9ee660abdd4/41597_2025_4507_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/5894340ebf74/41597_2025_4507_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/402077becb4a/41597_2025_4507_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/39ad2dffed0e/41597_2025_4507_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/0f0b3f038395/41597_2025_4507_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/ad665310334c/41597_2025_4507_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/af5a8fe113d2/41597_2025_4507_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/50370eedc37f/41597_2025_4507_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/762d865a4867/41597_2025_4507_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/43ef562ef28e/41597_2025_4507_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/6ac0a88fb37a/41597_2025_4507_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/306a7cd0a266/41597_2025_4507_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/e2b737df225b/41597_2025_4507_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/946b54b1bacf/41597_2025_4507_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/e66c149f1dd9/41597_2025_4507_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/e9ee660abdd4/41597_2025_4507_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/5894340ebf74/41597_2025_4507_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/402077becb4a/41597_2025_4507_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/39ad2dffed0e/41597_2025_4507_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/0f0b3f038395/41597_2025_4507_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/ad665310334c/41597_2025_4507_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/af5a8fe113d2/41597_2025_4507_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/50370eedc37f/41597_2025_4507_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/762d865a4867/41597_2025_4507_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/43ef562ef28e/41597_2025_4507_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/6ac0a88fb37a/41597_2025_4507_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/306a7cd0a266/41597_2025_4507_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11799319/e2b737df225b/41597_2025_4507_Fig15_HTML.jpg

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本文引用的文献

1
High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years.过去 12 万年的高分辨率陆地气候、生物气候和植被。
Sci Data. 2020 Jul 14;7(1):236. doi: 10.1038/s41597-020-0552-1.
2
Climatologies at high resolution for the earth's land surface areas.高分辨率地球陆地区域气候概况。
Sci Data. 2017 Sep 5;4:170122. doi: 10.1038/sdata.2017.122.
3
MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling.MERRAclim，一个高分辨率的全球遥感生物气候变量数据集，用于生态建模。
Sci Data. 2017 Jun 20;4:170078. doi: 10.1038/sdata.2017.78.