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  Anthropogenic influence on extreme temperature and precipitation in Central Asia

Fallah, B. H., Russo, E., Menz, C., Hoffmann, P., Didovets, I., Hattermann, F. F. (2023): Anthropogenic influence on extreme temperature and precipitation in Central Asia. - Scientific Reports, 13, 6854.
https://doi.org/10.1038/s41598-023-33921-6

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https://doi.org/10.5281/zenodo.7063876 (Supplementary material)
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 Creators:
Fallah, Bijan H.1, Author              
Russo, Emmanuele2, Author
Menz, Christoph1, Author              
Hoffmann, Peter1, Author              
Didovets, Iulii1, Author              
Hattermann, Fred Fokko1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: We investigate the contribution of anthropogenic forcing to the extreme temperature and precipitation events in Central Asia (CA) during the last 60 years. We bias-adjust and downscale two Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) ensemble outputs, with natural (labelled as hist-nat, driven only by solar and volcanic forcing) and natural plus anthropogenic forcing (labelled as hist, driven by all-forcings), to 0.25∘×0.25∘ spatial resolution. Each ensemble contains six models from ISIMIP, based on the Coupled Model Inter-comparison Project phase 6 (CMIP6). The presented downscaling methodology is necessary to create a reliable climate state for regional climate impact studies. Our analysis shows a higher risk of extreme heat events (factor 4 in signal-to-noise ratio) over large parts of CA due to anthropogenic influence. Furthermore, a higher likelihood of extreme precipitation over CA, especially over Kyrgyzstan and Tajikistan, can be attributed to anthropogenic forcing (over 100% changes in intensity and 20% in frequency). Given that these regions show a high risk of rainfall-triggered landslides and floods during historical times, we report that human-induced climate warming can contribute to extreme precipitation events over vulnerable areas of CA. Our high-resolution data set can be used in impact studies focusing on the attribution of extreme events in CA and is freely available to the scientific community.

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Language(s): eng - English
 Dates: 2022-11-162023-04-202023-04-262023-04-26
 Publication Status: Finally published
 Pages: 17
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41598-023-33921-6
MDB-ID: No MDB - stored outside PIK (see DOI)
Organisational keyword: RD2 - Climate Resilience
PIKDOMAIN: RD2 - Climate Resilience
Working Group: Hydroclimatic Risks
Research topic keyword: Attribution
Research topic keyword: Extremes
Research topic keyword: Climate impacts
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
Model / method: Model Intercomparison
Regional keyword: Asia
OATYPE: Gold - DEAL Springer Nature
 Degree: -

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Title: Scientific Reports
Source Genre: Journal, SCI, Scopus, p3, OA
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Pages: - Volume / Issue: 13 Sequence Number: 6854 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals2_395
Publisher: Nature