• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

一个新的生物气候指标全球数据集。

A new global dataset of bioclimatic indicators.

机构信息

Division on Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Viterbo, Italy.

Institute of Marine Sciences (ISMAR), Centro Nazionale delle Ricerche (CNR), Rome, Italy.

出版信息

Sci Data. 2020 Nov 16;7(1):398. doi: 10.1038/s41597-020-00726-5.

DOI:10.1038/s41597-020-00726-5
PMID:33199736
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7670417/
Abstract

This study presents a new global gridded dataset of bioclimatic indicators at 0.5° by 0.5° resolution for historical and future conditions. The dataset, called CMCC-BioClimInd, provides a set of 35 bioclimatic indices, expressed as mean values over each time interval, derived from post-processing both climate reanalysis for historical period (1960-1999) and an ensemble of 11 bias corrected CMIP5 simulations under two greenhouse gas concentration scenarios for future climate projections along two periods (2040-2079 and 2060-2099). This new dataset complements the availability of spatialized bioclimatic information, crucial aspect in many ecological and environmental wide scale applications and for several disciplines, including forestry, biodiversity conservation, plant and landscape ecology. The data of individual indicators are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format.

摘要

本研究提出了一个新的全球 0.5°×0.5°分辨率的生物气候指标栅格数据集,用于历史和未来条件。该数据集名为 CMCC-BioClimInd,提供了一组 35 个生物气候指数,以每个时间间隔的平均值表示,由历史时期(1960-1999 年)气候再分析的后处理和两种温室气体浓度情景下的 11 个集合偏差校正 CMIP5 模拟得出,用于未来气候预测的两个时期(2040-2079 年和 2060-2099 年)。这个新数据集补充了空间化生物气候信息的可用性,这在许多生态和环境大规模应用中以及包括林业、生物多样性保护、植物和景观生态学在内的多个学科中都是至关重要的。个别指标的数据以常用的网络通用数据格式 4(NetCDF4)格式公开提供下载。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/df5e455601f0/41597_2020_726_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/88a52fd09698/41597_2020_726_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/68d162713c46/41597_2020_726_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/96b95ebf82c9/41597_2020_726_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/62d5cf87d08d/41597_2020_726_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/df5e455601f0/41597_2020_726_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/88a52fd09698/41597_2020_726_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/68d162713c46/41597_2020_726_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/96b95ebf82c9/41597_2020_726_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/62d5cf87d08d/41597_2020_726_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/335d/7670417/df5e455601f0/41597_2020_726_Fig5_HTML.jpg

相似文献

1
A new global dataset of bioclimatic indicators.一个新的生物气候指标全球数据集。
Sci Data. 2020 Nov 16;7(1):398. doi: 10.1038/s41597-020-00726-5.
2
Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i.用于夏威夷气候变化研究的基线和未来气候情景的生物气候变量数据集。
Data Brief. 2022 Sep 2;45:108572. doi: 10.1016/j.dib.2022.108572. eCollection 2022 Dec.
3
Downscaled and bias-corrected bioclimatic dataset for the Mediterranean Sea (2005-2099).地中海地区(2005 - 2099年)降尺度和偏差校正后的生物气候数据集。
Data Brief. 2024 Aug 28;57:110846. doi: 10.1016/j.dib.2024.110846. eCollection 2024 Dec.
4
A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments.一个高分辨率的降尺度CMIP5预测数据集,包含全球基本地表气候变量,与ERA5再分析数据一致,用于气候变化影响评估。
Data Brief. 2021 Feb 21;35:106900. doi: 10.1016/j.dib.2021.106900. eCollection 2021 Apr.
5
CLIMBra - Climate Change Dataset for Brazil.CLIMBra - 巴西气候变化数据集。
Sci Data. 2023 Jan 20;10(1):47. doi: 10.1038/s41597-023-01956-z.
6
Comparing the Performance of CMCC-BioClimInd and WorldClim Datasets in Predicting Global Invasive Plant Distributions.比较CMCC - BioClimInd和WorldClim数据集在预测全球入侵植物分布方面的性能。
Biology (Basel). 2023 Apr 26;12(5):652. doi: 10.3390/biology12050652.
7
Bioclimatic dataset of Metropolitan France under current conditions derived from the WorldClim model.基于WorldClim模型得出的当前条件下法国大都市生物气候数据集。
Data Brief. 2020 Jun 4;31:105815. doi: 10.1016/j.dib.2020.105815. eCollection 2020 Aug.
8
Future battlegrounds for conservation under global change.全球变化下保护工作的未来战场。
Proc Biol Sci. 2008 Jun 7;275(1640):1261-70. doi: 10.1098/rspb.2007.1732.
9
Predicting the impacts of future sea-level rise on an endangered lagomorph.预测未来海平面上升对一种濒危兔形目动物的影响。
Environ Manage. 2007 Sep;40(3):430-7. doi: 10.1007/s00267-006-0204-z. Epub 2007 Jun 4.
10
High-resolution and bias-corrected CMIP5 projections for climate change impact assessments.高分辨率且有偏差纠正的 CMIP5 预估结果,可用于气候变化影响评估。
Sci Data. 2020 Jan 20;7(1):7. doi: 10.1038/s41597-019-0343-8.

引用本文的文献

1
Suppression of COVID-19 death incidence on open west coasts in the USA.美国西海岸开放地区新冠病毒死亡发生率的抑制情况。
Sci Rep. 2025 Aug 5;15(1):28542. doi: 10.1038/s41598-025-12972-x.
2
Thermal bioclimatic transformations in the coastal regions of Ganges delta: insights from CMIP6 multi-model ensemble.恒河三角洲沿海地区的热生物气候转变:来自CMIP6多模型集合的见解
Sci Rep. 2025 Jul 1;15(1):20569. doi: 10.1038/s41598-025-04149-3.
3
Dynamic interplay between niche variation and flight adaptability drove a hundred million years' dispersion in iconic lacewings.

本文引用的文献

1
Adjusting climate model bias for agricultural impact assessment: How to cut the mustard.调整气候模型偏差以进行农业影响评估:如何达到要求。
Clim Serv. 2019 Jan;13:65-69. doi: 10.1016/j.cliser.2019.01.004.
2
High-resolution monthly precipitation and temperature time series from 2006 to 2100.高分辨率月降水和温度时间序列,跨度为 2006 年至 2100 年。
Sci Data. 2020 Jul 23;7(1):248. doi: 10.1038/s41597-020-00587-y.
3
Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset.第四版 CRU TS 月高分辨率网格化多变量气候数据集。
生态位变异与飞行适应性之间的动态相互作用推动了标志性草蛉长达一亿年的扩散。
Proc Natl Acad Sci U S A. 2025 May 13;122(19):e2414549122. doi: 10.1073/pnas.2414549122. Epub 2025 May 2.
4
Continuous decline of climate fluctuations in the Kunlun-Pamir Plateau from the perspective of the bioclimatic variables.从生物气候变量角度看昆仑-帕米尔高原气候波动的持续下降
Sci Rep. 2025 Apr 17;15(1):13221. doi: 10.1038/s41598-025-97622-y.
5
Global Species Diversity Patterns of Polypodiaceae Under Future Climate Changes.未来气候变化下水龙骨科的全球物种多样性模式
Plants (Basel). 2025 Feb 26;14(5):711. doi: 10.3390/plants14050711.
6
Predicting climate-driven shift of the East Mediterranean endemic Lindl.预测气候驱动的东地中海特有植物林德利氏植物的迁移
Front Plant Sci. 2025 Feb 20;16:1461639. doi: 10.3389/fpls.2025.1461639. eCollection 2025.
7
South African indigenous chickens' genetic diversity, and the adoption of ecological niche modelling and landscape genomics as strategic conservation techniques.南非本土鸡的遗传多样性,以及采用生态位建模和景观基因组学作为战略保护技术。
Poult Sci. 2025 Jan;104(1):104508. doi: 10.1016/j.psj.2024.104508. Epub 2024 Nov 6.
8
Bioclimatic indicators dataset for the orographically complex Canary Islands archipelago.地形复杂的加那利群岛群岛的生物气候指标数据集。
Sci Data. 2024 Dec 4;11(1):1323. doi: 10.1038/s41597-024-04134-x.
9
Diversity and ecological niche model of malaria vector and non-vector mosquito species in Covè, Ouinhi, and Zangnanado, Southern Benin.贝宁南部科韦、乌因希和赞南纳多的疟疾媒介和非媒介蚊种的多样性和生态位模型。
Sci Rep. 2024 Jul 23;14(1):16944. doi: 10.1038/s41598-024-67919-5.
10
Habitat suitability of Opuntia ficus-indica (L.) MILL. (CACTACEAE): a comparative temporal evaluation using diverse bio-climatic earth system models and ensemble machine learning approach.仙人掌(Opuntia ficus-indica (L.) MILL.)适宜生境的时空评估:使用多种生物气候地球系统模型和集成机器学习方法的比较研究。
Environ Monit Assess. 2024 Feb 3;196(3):232. doi: 10.1007/s10661-024-12406-7.
Sci Data. 2020 Apr 3;7(1):109. doi: 10.1038/s41597-020-0453-3.
4
Likelihood of changes in forest species suitability, distribution, and diversity under future climate: The case of Southern Europe.未来气候条件下森林物种适宜性、分布和多样性变化的可能性:以欧洲南部为例。
Ecol Evol. 2017 Oct 7;7(22):9358-9375. doi: 10.1002/ece3.3427. eCollection 2017 Nov.
5
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.
6
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.
7
Extraordinary range expansion in a common bat: the potential roles of climate change and urbanisation.一种常见蝙蝠的非凡分布范围扩张:气候变化与城市化的潜在作用
Naturwissenschaften. 2016 Apr;103(3-4):15. doi: 10.1007/s00114-016-1334-7. Epub 2016 Feb 2.
8
Predicting changes in the distribution and abundance of species under environmental change.预测环境变化下物种分布和丰度的变化。
Ecol Lett. 2015 Mar;18(3):303-14. doi: 10.1111/ele.12410. Epub 2015 Jan 22.
9
Climate exposure of US national parks in a new era of change.美国国家公园在变革新时代的气候暴露情况。
PLoS One. 2014 Jul 2;9(7):e101302. doi: 10.1371/journal.pone.0101302. eCollection 2014.
10
Ensembles and probabilities: a new era in the prediction of climate change.集合与概率:气候变化预测的新时代。
Philos Trans A Math Phys Eng Sci. 2007 Aug 15;365(1857):1957-70. doi: 10.1098/rsta.2007.2068.