• 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

高分辨率(1 公里)的柯本-盖格尔气候分类图,时间范围为 1901 年至 2099 年,基于约束的 CMIP6 投影。

High-resolution (1 km) Köppen-Geiger maps for 1901-2099 based on constrained CMIP6 projections.

机构信息

King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

CSIRO Environment, Canberra, ACT, Australia.

出版信息

Sci Data. 2023 Oct 23;10(1):724. doi: 10.1038/s41597-023-02549-6.

DOI:10.1038/s41597-023-02549-6
PMID:37872197
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10593765/
Abstract

We introduce Version 2 of our widely used 1-km Köppen-Geiger climate classification maps for historical and future climate conditions. The historical maps (encompassing 1901-1930, 1931-1960, 1961-1990, and 1991-2020) are based on high-resolution, observation-based climatologies, while the future maps (encompassing 2041-2070 and 2071-2099) are based on downscaled and bias-corrected climate projections for seven shared socio-economic pathways (SSPs). We evaluated 67 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and kept a subset of 42 with the most plausible CO-induced warming rates. We estimate that from 1901-1930 to 1991-2020, approximately 5% of the global land surface (excluding Antarctica) transitioned to a different major Köppen-Geiger class. Furthermore, we project that from 1991-2020 to 2071-2099, 5% of the land surface will transition to a different major class under the low-emissions SSP1-2.6 scenario, 8% under the middle-of-the-road SSP2-4.5 scenario, and 13% under the high-emissions SSP5-8.5 scenario. The Köppen-Geiger maps, along with associated confidence estimates, underlying monthly air temperature and precipitation data, and sensitivity metrics for the CMIP6 models, can be accessed at www.gloh2o.org/koppen .

摘要

我们介绍了广泛使用的 1 公里柯本-盖格尔气候分类图的版本 2,用于历史和未来的气候条件。历史地图(涵盖 1901-1930 年、1931-1960 年、1961-1990 年和 1991-2020 年)基于高分辨率、基于观测的气候学,而未来地图(涵盖 2041-2070 年和 2071-2099 年)基于七个共享社会经济途径(SSP)的下转换和偏差校正的气候预测。我们评估了耦合模式比较计划第六阶段(CMIP6)的 67 个气候模型,并保留了一组 42 个具有最合理 CO 诱导变暖率的模型。我们估计,从 1901-1930 年到 1991-2020 年,全球约 5%的陆地表面(不包括南极洲)转变为不同的主要柯本-盖格尔类。此外,我们预计,从 1991-2020 年到 2071-2099 年,在低排放 SSP1-2.6 情景下,5%的陆地表面将转变为不同的主要类别,在中等排放 SSP2-4.5 情景下,8%将转变为不同的主要类别,在高排放 SSP5-8.5 情景下,13%将转变为不同的主要类别。柯本-盖格尔地图以及相关的置信估计、基础每月气温和降水数据以及 CMIP6 模型的敏感性指标,可在 www.gloh2o.org/koppen 上获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/91b6a76e0e7e/41597_2023_2549_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/4a18a4ac224c/41597_2023_2549_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/b41b569d4ffa/41597_2023_2549_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/03fc06a5e8fd/41597_2023_2549_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/1eb7f97273ab/41597_2023_2549_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/dad1545a20d8/41597_2023_2549_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/41c5779a0bcc/41597_2023_2549_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/91b6a76e0e7e/41597_2023_2549_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/4a18a4ac224c/41597_2023_2549_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/b41b569d4ffa/41597_2023_2549_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/03fc06a5e8fd/41597_2023_2549_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/1eb7f97273ab/41597_2023_2549_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/dad1545a20d8/41597_2023_2549_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/41c5779a0bcc/41597_2023_2549_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e144/10593765/91b6a76e0e7e/41597_2023_2549_Fig7_HTML.jpg

相似文献

1
High-resolution (1 km) Köppen-Geiger maps for 1901-2099 based on constrained CMIP6 projections.高分辨率(1 公里)的柯本-盖格尔气候分类图,时间范围为 1901 年至 2099 年,基于约束的 CMIP6 投影。
Sci Data. 2023 Oct 23;10(1):724. doi: 10.1038/s41597-023-02549-6.
2
Current and future trends in heat-related mortality in the MENA region: a health impact assessment with bias-adjusted statistically downscaled CMIP6 (SSP-based) data and Bayesian inference.当前和未来中东和北非地区热相关死亡趋势:基于贝叶斯推理和调整偏差的统计降尺度 CMIP6(SSP 为基础)数据的健康影响评估。
Lancet Planet Health. 2023 Apr;7(4):e282-e290. doi: 10.1016/S2542-5196(23)00045-1.
3
Present and future Köppen-Geiger climate classification maps at 1-km resolution.目前和未来的 1 公里分辨率柯本-盖格尔气候分类图。
Sci Data. 2018 Oct 30;5:180214. doi: 10.1038/sdata.2018.214.
4
Future precipitation and near surface air-temperature projection using CMIP6 models based on TOPSIS method: case study, Sistan-and-Baluchestan Province of Iran.利用基于 TOPSIS 方法的 CMIP6 模型对伊朗锡斯坦和俾路支省未来降水和近地表气温的预测:案例研究。
Environ Monit Assess. 2023 Nov 29;195(12):1548. doi: 10.1007/s10661-023-12084-x.
5
Extending the global high-resolution downscaled projections dataset to include CMIP6 projections at increased resolution coherent with the ERA5-Land reanalysis.扩展全球高分辨率降尺度预测数据集,以纳入与ERA5-Land再分析相一致的更高分辨率的CMIP6预测。
Data Brief. 2022 Oct 12;45:108669. doi: 10.1016/j.dib.2022.108669. eCollection 2022 Dec.
6
A 10-km CMIP6 downscaled dataset of temperature and precipitation for historical and future Vietnam climate.一个 10 公里分辨率的 CMIP6 降尺度数据集,包含历史时期和未来时期越南气候的温度和降水信息。
Sci Data. 2023 May 6;10(1):257. doi: 10.1038/s41597-023-02159-2.
7
Predicted changes in future precipitation and air temperature across Bangladesh using CMIP6 GCMs.利用CMIP6全球气候模式预测孟加拉国未来降水和气温的变化。
Heliyon. 2023 May 13;9(5):e16274. doi: 10.1016/j.heliyon.2023.e16274. eCollection 2023 May.
8
Historical global land surface air apparent temperature and its future changes based on CMIP6 projections.基于 CMIP6 预估的历史全球陆面空气地表气温及其未来变化。
Sci Total Environ. 2022 Apr 10;816:151656. doi: 10.1016/j.scitotenv.2021.151656. Epub 2021 Nov 15.
9
A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses.高分辨率日尺度全球数据集,对 CMIP6 模式进行统计降尺度,用于气候影响分析。
Sci Data. 2023 Sep 11;10(1):611. doi: 10.1038/s41597-023-02528-x.
10
Impacts of climate and land use change on groundwater recharge under shared socioeconomic pathways: A case of Siem Reap, Cambodia.在共享社会经济路径下,气候和土地利用变化对地下水补给的影响:以柬埔寨暹粒为例。
Environ Res. 2022 Aug;211:113070. doi: 10.1016/j.envres.2022.113070. Epub 2022 Mar 11.

引用本文的文献

1
Global thermal tolerance compilation for freshwater invertebrates and fish.淡水无脊椎动物和鱼类的全球热耐受性汇编
Sci Data. 2025 Aug 26;12(1):1488. doi: 10.1038/s41597-025-05832-w.
2
The Secret of Secrets: Carbonic Anhydrase Concentration in Lizards' Femoral Gland Secretions Is Tuned to Environmental Conditions.秘密中的秘密:蜥蜴股腺分泌物中的碳酸酐酶浓度与环境条件相适应。
Ecol Evol. 2025 Aug 21;15(8):e72023. doi: 10.1002/ece3.72023. eCollection 2025 Aug.
3
Wildfire legacies on pyrogenic carbon stocks in Amazonian peatlands.

本文引用的文献

1
Climate simulations: recognize the 'hot model' problem.气候模拟:认识到“热门模型”问题。
Nature. 2022 May;605(7908):26-29. doi: 10.1038/d41586-022-01192-2.
2
Making climate projections conditional on historical observations.使气候预测以历史观测为条件。
Sci Adv. 2021 Jan 22;7(4). doi: 10.1126/sciadv.abc0671. Print 2021 Jan.
3
An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence.利用多条证据评估地球气候敏感性
野火对亚马逊泥炭地中热解碳储量的影响。
Commun Earth Environ. 2025;6(1):678. doi: 10.1038/s43247-025-02674-7. Epub 2025 Aug 19.
4
Aedes albopictus Is Rapidly Invading Its Climatic Niche in France: Wider Implications for Biting Nuisance and Arbovirus Control in Western Europe.白纹伊蚊正在迅速侵入其在法国的气候适宜区:对西欧叮咬骚扰和虫媒病毒控制具有更广泛的影响。
Glob Chang Biol. 2025 Aug;31(8):e70414. doi: 10.1111/gcb.70414.
5
Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study.疑似登革热感染的儿科患者登革出血热的预后预测:一项多中心研究。
PLoS One. 2025 Aug 4;20(8):e0327360. doi: 10.1371/journal.pone.0327360. eCollection 2025.
6
Greenhouse Gas Mitigation Potential of Temperate Fen Paludicultures.温带沼泽地水产养殖的温室气体减排潜力
Glob Chang Biol. 2025 Aug;31(8):e70385. doi: 10.1111/gcb.70385.
7
Environmental Drivers of Trace Element Variability in Hedw.: A Cross-Regional Moss Biomonitoring Study in Georgia and the Republic of Moldova.赫德维希苔藓中微量元素变异性的环境驱动因素:格鲁吉亚和摩尔多瓦共和国的跨区域苔藓生物监测研究
Plants (Basel). 2025 Jul 3;14(13):2040. doi: 10.3390/plants14132040.
8
Analysing historical events and current management strategies of wildfires in Norway.分析挪威野火的历史事件和当前管理策略。
Sci Rep. 2025 Jul 10;15(1):24905. doi: 10.1038/s41598-025-08760-2.
9
Causal link between humid heatwaves and ischemic heart disease: assessing hospitalizations and economic burden across 955 Chinese counties.湿热热浪与缺血性心脏病之间的因果联系:评估中国955个县的住院情况及经济负担
BMC Med. 2025 Jul 1;23(1):359. doi: 10.1186/s12916-025-04133-8.
10
A global assemblage of regional prescribed burn records - GlobalRx.区域规定火烧记录的全球集合 - 全球处方(GlobalRx)。
Sci Data. 2025 Jul 1;12(1):1083. doi: 10.1038/s41597-025-04941-w.
Rev Geophys. 2020 Dec;58(4):e2019RG000678. doi: 10.1029/2019RG000678. Epub 2020 Sep 25.
4
Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models.从CMIP6地球系统模型解读平衡气候敏感度和瞬态气候响应的背景。
Sci Adv. 2020 Jun 24;6(26):eaba1981. doi: 10.1126/sciadv.aba1981. eCollection 2020 Jun.
5
Past warming trend constrains future warming in CMIP6 models.过去的变暖趋势限制了CMIP6模型中的未来变暖。
Sci Adv. 2020 Mar 18;6(12):eaaz9549. doi: 10.1126/sciadv.aaz9549. eCollection 2020 Mar.
6
SciPy 1.0: fundamental algorithms for scientific computing in Python.SciPy 1.0:Python 中的科学计算基础算法。
Nat Methods. 2020 Mar;17(3):261-272. doi: 10.1038/s41592-019-0686-2. Epub 2020 Feb 3.
7
Global warming without global mean precipitation increase?全球变暖但全球平均降水量未增加?
Sci Adv. 2016 Jun 24;2(6):e1501572. doi: 10.1126/sciadv.1501572. eCollection 2016 Jun.
8
The climate hazards infrared precipitation with stations--a new environmental record for monitoring extremes.气候危害与站点的红外降水——一种新的极端环境监测记录。
Sci Data. 2015 Dec 8;2:150066. doi: 10.1038/sdata.2015.66.
9
scikit-image: image processing in Python.scikit-image:在 Python 中进行图像处理。
PeerJ. 2014 Jun 19;2:e453. doi: 10.7717/peerj.453. eCollection 2014.