Yuanfang Chai, Yao Yue, Louise Slater, Chiyuan Miao
{"title":"Emergent constraints indicate slower increases in future global evapotranspiration","authors":"Yuanfang Chai, Yao Yue, Louise Slater, Chiyuan Miao","doi":"10.1038/s41612-025-00932-1","DOIUrl":null,"url":null,"abstract":"<p>Projections of global terrestrial evapotranspiration (ET) are plagued by sizeable uncertainties. Here, we uncover bivariate emergent constraint relationships between projected global ET trends (2015–2100) and historical vapour pressure deficit (VPD) trends (1980–2014) under the low emission scenario of SSP126 when water supply is sufficient, and with historical precipitation trends under the high emission scenarios of SSP370 and SSP585 when water availability is limited, across 28 CMIP6 models. Combining multiple observational datasets into a Hierarchical Emergent Constraint framework, we find the raw CMIP6 models overestimate future annual ET growth rates. The original projections of 0.233 ± 0.107 mm year<sup>−1</sup> (SSP126), 0.360 ± 0.244 mm year−<sup>1</sup> (SSP370) and 0.506 ± 0.365 mm year<sup>−1</sup> (SSP585) are adjusted downwards to 0.193 ± 0.074 mm year<sup>−1</sup>, 0.272 ± 0.184 mm year<sup>−1</sup> and 0.391 ± 0.299 mm year<sup>−1</sup>. The revised projection uncertainties are reduced by 18.1–31.1%. These findings highlight the value of incorporating observational constraints to improve the reliability of ET projections, which are critical for understanding the future global water cycle.</p>","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"15 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Climate and Atmospheric Science","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1038/s41612-025-00932-1","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Projections of global terrestrial evapotranspiration (ET) are plagued by sizeable uncertainties. Here, we uncover bivariate emergent constraint relationships between projected global ET trends (2015–2100) and historical vapour pressure deficit (VPD) trends (1980–2014) under the low emission scenario of SSP126 when water supply is sufficient, and with historical precipitation trends under the high emission scenarios of SSP370 and SSP585 when water availability is limited, across 28 CMIP6 models. Combining multiple observational datasets into a Hierarchical Emergent Constraint framework, we find the raw CMIP6 models overestimate future annual ET growth rates. The original projections of 0.233 ± 0.107 mm year−1 (SSP126), 0.360 ± 0.244 mm year−1 (SSP370) and 0.506 ± 0.365 mm year−1 (SSP585) are adjusted downwards to 0.193 ± 0.074 mm year−1, 0.272 ± 0.184 mm year−1 and 0.391 ± 0.299 mm year−1. The revised projection uncertainties are reduced by 18.1–31.1%. These findings highlight the value of incorporating observational constraints to improve the reliability of ET projections, which are critical for understanding the future global water cycle.
期刊介绍:
npj Climate and Atmospheric Science is an open-access journal encompassing the relevant physical, chemical, and biological aspects of atmospheric and climate science. The journal places particular emphasis on regional studies that unveil new insights into specific localities, including examinations of local atmospheric composition, such as aerosols.
The range of topics covered by the journal includes climate dynamics, climate variability, weather and climate prediction, climate change, ocean dynamics, weather extremes, air pollution, atmospheric chemistry (including aerosols), the hydrological cycle, and atmosphere–ocean and atmosphere–land interactions. The journal welcomes studies employing a diverse array of methods, including numerical and statistical modeling, the development and application of in situ observational techniques, remote sensing, and the development or evaluation of new reanalyses.