Added Values of CMIP5 Models in Reducing the Uncertainty Range of Future Projections over China Using Unequal-Weighted Average.
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| Title: | Added Values of CMIP5 Models in Reducing the Uncertainty Range of Future Projections over China Using Unequal-Weighted Average. |
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| Authors: | Fan, Wenlu1,2 (AUTHOR), Tian, Zhiping1 (AUTHOR), Jiang, Dabang1,2 (AUTHOR) jiangdb@mail.iap.ac.cn |
| Source: | Journal of Climate. Apr2026, Vol. 39 Issue 7, p1657-1667. 11p. |
| Subjects: | Climate change models, Ensemble learning, Climate change forecasts, Weather & climate change, Climate change |
| Geographic Terms: | China |
| Abstract: | To enhance the skill of climate projections, researchers have developed various weighting schemes based on the performance of models in simulating historical climate. However, the capacity of models with these weighting methods to accurately reproduce future climate change remains uncertain. In this study, models from the Coupled Model Intercomparison Project phase 5 (CMIP5) with reliability ensemble averaging (REA) scheme and performance and independence (PI)-weighted scheme are employed to project near-term temperature changes over China. By comparing observations with the weighted ensemble averages of models, we evaluate the projection skill of CMIP5 ensembles using these two weighting methods for the near-term projections. Our results indicate that, compared to the equal-weighted ensemble mean, PI-weighted projections based on the performance in reproducing historical temperature trends at the gridcell level display overall improvements in spring, summer, and autumn. Meanwhile, REA-weighted projections perform slightly worse on both annual and seasonal scales than the equal-weighted ensembles. Of importance is that the projection uncertainties, measured by the range of 5th–95th percentiles, estimated by the two unequal-weighted schemes are generally lower than those of the equal-weighted approach. However, the small projection uncertainty with large biases in REA-weighted ensembles increases the undesirable risk of observations falling outside the uncertainty range. As reducing the projection uncertainty also extends to the next two decades by weighting, the PI-weighted ensemble serves as a reliable alternative for future temperature projections over China. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Climate is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192792949 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Added Values of CMIP5 Models in Reducing the Uncertainty Range of Future Projections over China Using Unequal-Weighted Average. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fan%2C+Wenlu%22">Fan, Wenlu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tian%2C+Zhiping%22">Tian, Zhiping</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Dabang%22">Jiang, Dabang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jiangdb@mail.iap.ac.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Climate%22">Journal of Climate</searchLink>. Apr2026, Vol. 39 Issue 7, p1657-1667. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Climate+change+models%22">Climate change models</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change+forecasts%22">Climate change forecasts</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+%26+climate+change%22">Weather & climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To enhance the skill of climate projections, researchers have developed various weighting schemes based on the performance of models in simulating historical climate. However, the capacity of models with these weighting methods to accurately reproduce future climate change remains uncertain. In this study, models from the Coupled Model Intercomparison Project phase 5 (CMIP5) with reliability ensemble averaging (REA) scheme and performance and independence (PI)-weighted scheme are employed to project near-term temperature changes over China. By comparing observations with the weighted ensemble averages of models, we evaluate the projection skill of CMIP5 ensembles using these two weighting methods for the near-term projections. Our results indicate that, compared to the equal-weighted ensemble mean, PI-weighted projections based on the performance in reproducing historical temperature trends at the gridcell level display overall improvements in spring, summer, and autumn. Meanwhile, REA-weighted projections perform slightly worse on both annual and seasonal scales than the equal-weighted ensembles. Of importance is that the projection uncertainties, measured by the range of 5th–95th percentiles, estimated by the two unequal-weighted schemes are generally lower than those of the equal-weighted approach. However, the small projection uncertainty with large biases in REA-weighted ensembles increases the undesirable risk of observations falling outside the uncertainty range. As reducing the projection uncertainty also extends to the next two decades by weighting, the PI-weighted ensemble serves as a reliable alternative for future temperature projections over China. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Climate is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1175/JCLI-D-25-0443.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1657 Subjects: – SubjectFull: Climate change models Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Climate change forecasts Type: general – SubjectFull: Weather & climate change Type: general – SubjectFull: Climate change Type: general – SubjectFull: China Type: general Titles: – TitleFull: Added Values of CMIP5 Models in Reducing the Uncertainty Range of Future Projections over China Using Unequal-Weighted Average. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fan, Wenlu – PersonEntity: Name: NameFull: Tian, Zhiping – PersonEntity: Name: NameFull: Jiang, Dabang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08948755 Numbering: – Type: volume Value: 39 – Type: issue Value: 7 Titles: – TitleFull: Journal of Climate Type: main |
| ResultId | 1 |
