Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation
Mountains play a critical role in water cycles in semiarid regions by providing for the majority of the total runoff. However, hydroclimatic conditions in mountainous regions vary considerably in space and time, with high interannual fluctuations driven by large-scale climate oscillations. Here, we...
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Online Access: | http://hdl.handle.net/10419/251962 https://doi.org/10.1088/1748-9326/ac6229 |
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ftzbwkiel:oai:econstor.eu:10419/251962 2024-01-07T09:45:11+01:00 Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation Umirbekov, Atabek Peña-Guerrero, Mayra Daniela Müller, Daniel 2022 http://hdl.handle.net/10419/251962 https://doi.org/10.1088/1748-9326/ac6229 eng eng Bristol: IOP Publishing Journal: Environmental Research Letters ISSN: 1748-9326 Volume: 17 Year: 2022 Issue: 5 Bristol: IOP Publishing doi:10.1088/1748-9326/ac6229 http://hdl.handle.net/10419/251962 RePEc:zbw:espost:251962 http://www.econstor.eu/dspace/Nutzungsbedingungen https://creativecommons.org/licenses/by/4.0/ ddc:330 climate teleconnections Central Asia machine learning mountains seasonal forecasting precipitation doc-type:article 2022 ftzbwkiel https://doi.org/10.1088/1748-9326/ac6229 2023-12-11T00:45:14Z Mountains play a critical role in water cycles in semiarid regions by providing for the majority of the total runoff. However, hydroclimatic conditions in mountainous regions vary considerably in space and time, with high interannual fluctuations driven by large-scale climate oscillations. Here, we investigated teleconnections between global climate oscillations and the peak precipitation season from February to June in the Tian-Shan and Pamir Mountains of Central Asia. Using hierarchical climate regionalization, we identified seven subregions with distinct precipitation patterns, and assessed correlations with selected climate oscillations at different time lags. We then simulated the seasonal precipitation in each subregion from 1979 to 2020 using the most prevalent teleconnections as predictors with support vector regression (SVR). Our findings indicate that the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, and the Eastern Atlantic/West Russia pattern are among the major determinants of the seasonal precipitation. The dominant lead-lag times of these oscillations make them reliable predictors ahead of the season. We detected notable teleconnections with the North Atlantic Oscillation and Scandinavian Pattern, with their strongest associations emerging after onset of the season. While the SVR-based models exhibit robust prediction skills, they tend to underestimate precipitation in extremely wet seasons. Overall, our study highlights the value of appropriate spatial and temporal aggregations for exploring the impacts of climate teleconnections on precipitation in complex terrains. Article in Journal/Newspaper North Atlantic North Atlantic oscillation EconStor (German National Library of Economics, ZBW) Pacific Environmental Research Letters 17 5 055002 |
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Open Polar |
collection |
EconStor (German National Library of Economics, ZBW) |
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ftzbwkiel |
language |
English |
topic |
ddc:330 climate teleconnections Central Asia machine learning mountains seasonal forecasting precipitation |
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ddc:330 climate teleconnections Central Asia machine learning mountains seasonal forecasting precipitation Umirbekov, Atabek Peña-Guerrero, Mayra Daniela Müller, Daniel Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation |
topic_facet |
ddc:330 climate teleconnections Central Asia machine learning mountains seasonal forecasting precipitation |
description |
Mountains play a critical role in water cycles in semiarid regions by providing for the majority of the total runoff. However, hydroclimatic conditions in mountainous regions vary considerably in space and time, with high interannual fluctuations driven by large-scale climate oscillations. Here, we investigated teleconnections between global climate oscillations and the peak precipitation season from February to June in the Tian-Shan and Pamir Mountains of Central Asia. Using hierarchical climate regionalization, we identified seven subregions with distinct precipitation patterns, and assessed correlations with selected climate oscillations at different time lags. We then simulated the seasonal precipitation in each subregion from 1979 to 2020 using the most prevalent teleconnections as predictors with support vector regression (SVR). Our findings indicate that the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, and the Eastern Atlantic/West Russia pattern are among the major determinants of the seasonal precipitation. The dominant lead-lag times of these oscillations make them reliable predictors ahead of the season. We detected notable teleconnections with the North Atlantic Oscillation and Scandinavian Pattern, with their strongest associations emerging after onset of the season. While the SVR-based models exhibit robust prediction skills, they tend to underestimate precipitation in extremely wet seasons. Overall, our study highlights the value of appropriate spatial and temporal aggregations for exploring the impacts of climate teleconnections on precipitation in complex terrains. |
format |
Article in Journal/Newspaper |
author |
Umirbekov, Atabek Peña-Guerrero, Mayra Daniela Müller, Daniel |
author_facet |
Umirbekov, Atabek Peña-Guerrero, Mayra Daniela Müller, Daniel |
author_sort |
Umirbekov, Atabek |
title |
Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation |
title_short |
Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation |
title_full |
Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation |
title_fullStr |
Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation |
title_full_unstemmed |
Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation |
title_sort |
regionalization of climate teleconnections across central asian mountains improves the predictability of seasonal precipitation |
publisher |
Bristol: IOP Publishing |
publishDate |
2022 |
url |
http://hdl.handle.net/10419/251962 https://doi.org/10.1088/1748-9326/ac6229 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
Journal: Environmental Research Letters ISSN: 1748-9326 Volume: 17 Year: 2022 Issue: 5 Bristol: IOP Publishing doi:10.1088/1748-9326/ac6229 http://hdl.handle.net/10419/251962 RePEc:zbw:espost:251962 |
op_rights |
http://www.econstor.eu/dspace/Nutzungsbedingungen https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1088/1748-9326/ac6229 |
container_title |
Environmental Research Letters |
container_volume |
17 |
container_issue |
5 |
container_start_page |
055002 |
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1787426667709333504 |