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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Humboldt-Universität zu Berlin
2022
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fthuberlin:oai:edoc.hu-berlin.de:18452/25584 2023-12-03T10:27:05+01:00 Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation Umirbekov, Atabek Peña-Guerrero, Mayra Daniela Müller, Daniel 2022-04-19 application/pdf http://edoc.hu-berlin.de/18452/25584 https://nbn-resolving.org/urn:nbn:de:kobv:11-110-18452/25584-5 https://doi.org/10.1088/1748-9326/ac6229 https://doi.org/10.18452/24900 eng eng Humboldt-Universität zu Berlin http://edoc.hu-berlin.de/18452/25584 urn:nbn:de:kobv:11-110-18452/25584-5 doi:10.1088/1748-9326/ac6229 http://dx.doi.org/10.18452/24900 1748-9326 (CC BY 4.0) Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ climate teleconnections Central Asia machine learning mountains seasonal forecasting precipitation 550 Geowissenschaften ddc:550 article doc-type:article publishedVersion 2022 fthuberlin https://doi.org/10.1088/1748-9326/ac622910.18452/24900 2023-11-05T23:36:29Z 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. Volkswagen Foundationhttp://dx.doi.org/10.13039/501100001663 Peer Reviewed Article in Journal/Newspaper North Atlantic North Atlantic oscillation Open-Access-Publikationsserver der Humboldt-Universität: edoc-Server Pacific Environmental Research Letters 17 5 055002 |
institution |
Open Polar |
collection |
Open-Access-Publikationsserver der Humboldt-Universität: edoc-Server |
op_collection_id |
fthuberlin |
language |
English |
topic |
climate teleconnections Central Asia machine learning mountains seasonal forecasting precipitation 550 Geowissenschaften ddc:550 |
spellingShingle |
climate teleconnections Central Asia machine learning mountains seasonal forecasting precipitation 550 Geowissenschaften ddc:550 Umirbekov, Atabek Peña-Guerrero, Mayra Daniela Müller, Daniel Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation |
topic_facet |
climate teleconnections Central Asia machine learning mountains seasonal forecasting precipitation 550 Geowissenschaften ddc:550 |
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. Volkswagen Foundationhttp://dx.doi.org/10.13039/501100001663 Peer Reviewed |
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 |
Humboldt-Universität zu Berlin |
publishDate |
2022 |
url |
http://edoc.hu-berlin.de/18452/25584 https://nbn-resolving.org/urn:nbn:de:kobv:11-110-18452/25584-5 https://doi.org/10.1088/1748-9326/ac6229 https://doi.org/10.18452/24900 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
http://edoc.hu-berlin.de/18452/25584 urn:nbn:de:kobv:11-110-18452/25584-5 doi:10.1088/1748-9326/ac6229 http://dx.doi.org/10.18452/24900 1748-9326 |
op_rights |
(CC BY 4.0) Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1088/1748-9326/ac622910.18452/24900 |
container_title |
Environmental Research Letters |
container_volume |
17 |
container_issue |
5 |
container_start_page |
055002 |
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1784276674171371520 |