Spatially resolved emulated annual temperature projections for overshoot pathways.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-21 DOI:10.1038/s41597-024-04122-1
Jonas Schwaab, Mathias Hauser, Robin D Lamboll, Lea Beusch, Lukas Gudmundsson, Yann Quilcaille, Quentin Lejeune, Sarah Schöngart, Carl-Friedrich Schleussner, Shruti Nath, Joeri Rogelj, Zebedee Nicholls, Sonia I Seneviratne
{"title":"Spatially resolved emulated annual temperature projections for overshoot pathways.","authors":"Jonas Schwaab, Mathias Hauser, Robin D Lamboll, Lea Beusch, Lukas Gudmundsson, Yann Quilcaille, Quentin Lejeune, Sarah Schöngart, Carl-Friedrich Schleussner, Shruti Nath, Joeri Rogelj, Zebedee Nicholls, Sonia I Seneviratne","doi":"10.1038/s41597-024-04122-1","DOIUrl":null,"url":null,"abstract":"<p><p>Due to insufficient climate action over the past decade, it is increasingly likely that 1.5 °C of global warming will be exceeded - at least temporarily - in the 21<sup>st</sup> century. Such a temporary temperature overshoot carries additional climate risks which are poorly understood. Earth System Model climate projections are only available for a very limited number of overshoot pathways, thereby preventing comprehensive analysis of their impacts. Here, we address this issue by presenting a novel dataset of spatially resolved emulated annual temperature projections for different overshoot pathways. The dataset was created using the FaIR and MESMER emulators. First, FaIR was employed to translate ten different emission scenarios, including seven that are characterised by overshoot, into a large ensemble of forced global mean temperatures. These global mean temperatures were then converted into stochastic ensembles of local annual temperature fields using MESMER. To ensure an optimal tradeoff between accurate characterization of the ensemble spread and storage requirements for large ensembles, this procedure was accompanied by testing the sensitivity of sample quantiles to different ensemble sizes. The resulting dataset offers the unique opportunity to study local and regional climate change impacts of a range of overshoot scenarios, including the timing and magnitude of temperature thresholds exceedance.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1262"},"PeriodicalIF":5.8000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04122-1","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Due to insufficient climate action over the past decade, it is increasingly likely that 1.5 °C of global warming will be exceeded - at least temporarily - in the 21st century. Such a temporary temperature overshoot carries additional climate risks which are poorly understood. Earth System Model climate projections are only available for a very limited number of overshoot pathways, thereby preventing comprehensive analysis of their impacts. Here, we address this issue by presenting a novel dataset of spatially resolved emulated annual temperature projections for different overshoot pathways. The dataset was created using the FaIR and MESMER emulators. First, FaIR was employed to translate ten different emission scenarios, including seven that are characterised by overshoot, into a large ensemble of forced global mean temperatures. These global mean temperatures were then converted into stochastic ensembles of local annual temperature fields using MESMER. To ensure an optimal tradeoff between accurate characterization of the ensemble spread and storage requirements for large ensembles, this procedure was accompanied by testing the sensitivity of sample quantiles to different ensemble sizes. The resulting dataset offers the unique opportunity to study local and regional climate change impacts of a range of overshoot scenarios, including the timing and magnitude of temperature thresholds exceedance.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
超调路径的空间分辨率模拟年温度预测。
由于过去十年气候行动不足,21 世纪全球升温超过 1.5 °C--至少是暂时超过 1.5 °C--的可能性越来越大。这种暂时性的温度超调会带来更多的气候风险,而人们对这些风险的了解还很不够。地球系统模式气候预测仅适用于数量非常有限的超调途径,因此无法对其影响进行全面分析。为了解决这个问题,我们在这里提供了一个针对不同超调途径的空间分辨率模拟年度温度预测的新数据集。该数据集是利用 FaIR 和 MESMER 仿真器创建的。首先,利用 FaIR 将十种不同的排放情景(包括七种以超调为特征的情景)转化为大量的受迫全球平均气温集合。然后,利用 MESMER 将这些全球平均气温转换为当地年气温场的随机集合。为了确保在精确描述集合分布和大型集合的存储要求之间取得最佳平衡,在进行这一程序的同时,还测试了样本数量级对不同集合规模的敏感性。由此产生的数据集为研究一系列超调情景对当地和区域气候变化的影响提供了独特的机会,包括温度阈值超标的时间和幅度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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.
期刊最新文献
A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study. Chisco: An EEG-based BCI dataset for decoding of imagined speech. Database of surface water diversion sites and daily withdrawals for the Upper Colorado River Basin, 1980-2022. Dataset of Smartphone-Based Finger Tapping Test. Expanding the genome information on Bacillales for biosynthetic gene cluster discovery.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1