Emergent constraints on global soil moisture projections under climate change
Abstract Surface soil moisture is projected to decrease under global warming. Such projections are mostly based on climate models, which show large uncertainty (i.e., inter-model spread) partly due to inadequate observational constraint. Here we identify strong physically-based emergent relationship...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-025-02024-7 |
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| author | Lei Yao Guoyong Leng Linfei Yu Hongyi Li Qiuhong Tang Andre Python Jim W. Hall Xiaoyong Liao Ji Li Jiali Qiu Johannes Quaas Shengzhi Huang Yin Jin Jakob Zscheischler Jian Peng |
| author_facet | Lei Yao Guoyong Leng Linfei Yu Hongyi Li Qiuhong Tang Andre Python Jim W. Hall Xiaoyong Liao Ji Li Jiali Qiu Johannes Quaas Shengzhi Huang Yin Jin Jakob Zscheischler Jian Peng |
| author_sort | Lei Yao |
| collection | DOAJ |
| description | Abstract Surface soil moisture is projected to decrease under global warming. Such projections are mostly based on climate models, which show large uncertainty (i.e., inter-model spread) partly due to inadequate observational constraint. Here we identify strong physically-based emergent relationships between soil moisture change (2070–2099 minus 1980–2014) and recent air temperature and precipitation trends across an ensemble of climate models. We extend the commonly used univariate Emergent Constraints to a bivariate method and use observed temperature and precipitation trends to constrain global soil moisture changes. Our results show that the bivariate emergent constraints can reduce soil moisture change uncertainty by 7.87%, which is four times more effective than traditional temperature-based univariate constraints. The bivariate emergent constraints change the sign of soil moisture change from negative to positive for semi-arid, dry sub-humid and humid regions and global land as a whole, but exacerbates the drying trend in arid and hyper-arid regions. |
| format | Article |
| id | doaj-art-b860dc7ac86b45fe95d06b2bed51d104 |
| institution | Kabale University |
| issn | 2662-4435 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Earth & Environment |
| spelling | doaj-art-b860dc7ac86b45fe95d06b2bed51d1042025-01-26T12:54:05ZengNature PortfolioCommunications Earth & Environment2662-44352025-01-01611810.1038/s43247-025-02024-7Emergent constraints on global soil moisture projections under climate changeLei Yao0Guoyong Leng1Linfei Yu2Hongyi Li3Qiuhong Tang4Andre Python5Jim W. Hall6Xiaoyong Liao7Ji Li8Jiali Qiu9Johannes Quaas10Shengzhi Huang11Yin Jin12Jakob Zscheischler13Jian Peng14Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesCenter for Data Science, Zhejiang UniversityInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesCenter for Data Science, Zhejiang UniversityEnvironmental Change Institute, University of OxfordInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesLeipzig Institute for Meteorology, Leipzig UniversityState Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of TechnologyCenter for Data Science, Zhejiang UniversityDepartment of Compound Environmental Risks, Helmholtz Centre for Environmental Research—UFZDepartment of Remote Sensing, Helmholtz Centre for Environmental Research—UFZAbstract Surface soil moisture is projected to decrease under global warming. Such projections are mostly based on climate models, which show large uncertainty (i.e., inter-model spread) partly due to inadequate observational constraint. Here we identify strong physically-based emergent relationships between soil moisture change (2070–2099 minus 1980–2014) and recent air temperature and precipitation trends across an ensemble of climate models. We extend the commonly used univariate Emergent Constraints to a bivariate method and use observed temperature and precipitation trends to constrain global soil moisture changes. Our results show that the bivariate emergent constraints can reduce soil moisture change uncertainty by 7.87%, which is four times more effective than traditional temperature-based univariate constraints. The bivariate emergent constraints change the sign of soil moisture change from negative to positive for semi-arid, dry sub-humid and humid regions and global land as a whole, but exacerbates the drying trend in arid and hyper-arid regions.https://doi.org/10.1038/s43247-025-02024-7 |
| spellingShingle | Lei Yao Guoyong Leng Linfei Yu Hongyi Li Qiuhong Tang Andre Python Jim W. Hall Xiaoyong Liao Ji Li Jiali Qiu Johannes Quaas Shengzhi Huang Yin Jin Jakob Zscheischler Jian Peng Emergent constraints on global soil moisture projections under climate change Communications Earth & Environment |
| title | Emergent constraints on global soil moisture projections under climate change |
| title_full | Emergent constraints on global soil moisture projections under climate change |
| title_fullStr | Emergent constraints on global soil moisture projections under climate change |
| title_full_unstemmed | Emergent constraints on global soil moisture projections under climate change |
| title_short | Emergent constraints on global soil moisture projections under climate change |
| title_sort | emergent constraints on global soil moisture projections under climate change |
| url | https://doi.org/10.1038/s43247-025-02024-7 |
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