Artikel

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 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.

Sprache
Englisch

Erschienen in
Journal: Environmental Research Letters ; ISSN: 1748-9326 ; Volume: 17 ; Year: 2022 ; Issue: 5 ; Bristol: IOP Publishing

Klassifikation
Wirtschaft
Thema
climate teleconnections
Central Asia
machine learning
mountains
seasonal forecasting
precipitation

Ereignis
Geistige Schöpfung
(wer)
Umirbekov, Atabek
Peña-Guerrero, Mayra Daniela
Müller, Daniel
Ereignis
Veröffentlichung
(wer)
IOP Publishing
(wo)
Bristol
(wann)
2022

DOI
doi:10.1088/1748-9326/ac6229
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Umirbekov, Atabek
  • Peña-Guerrero, Mayra Daniela
  • Müller, Daniel
  • IOP Publishing

Entstanden

  • 2022

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