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: | https://www.econstor.eu/bitstream/10419/251962/1/Umirbekov_2022_Regionalization_climate_teleconnections.pdf |
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ftleibnizopen:oai:oai.leibnizopen.de:aG__PYkBdbrxVwz6XxxZ 2023-07-30T04:05:30+02:00 Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation Umirbekov, Atabek Peña-Guerrero, Mayra Daniela Müller, Daniel 2022 https://www.econstor.eu/bitstream/10419/251962/1/Umirbekov_2022_Regionalization_climate_teleconnections.pdf eng eng Bristol: IOP Publishing http://www.econstor.eu/dspace/Nutzungsbedingungen https://creativecommons.org/licenses/by/4.0/ Environmental Research Letters Vol. 17 Iss. 5 IOP Publishing Bristol ISSN 1748-9326 doi:10.1088/1748-9326/ac6229 climate teleconnections Central Asia machine learning mountains seasonal forecasting precipitation Article 2022 ftleibnizopen https://doi.org/10.1088/1748-9326/ac6229 2023-07-10T12:50:00Z 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 LeibnizOpen (The Leibniz Association) Pacific Environmental Research Letters 17 5 055002 |
institution |
Open Polar |
collection |
LeibnizOpen (The Leibniz Association) |
op_collection_id |
ftleibnizopen |
language |
English |
topic |
climate teleconnections Central Asia machine learning mountains seasonal forecasting precipitation |
spellingShingle |
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 |
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 |
https://www.econstor.eu/bitstream/10419/251962/1/Umirbekov_2022_Regionalization_climate_teleconnections.pdf |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Environmental Research Letters Vol. 17 Iss. 5 IOP Publishing Bristol ISSN 1748-9326 doi:10.1088/1748-9326/ac6229 |
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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1772817470436933632 |