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  Network-Based Approach and Climate Change Benefits for Forecasting the Amount of Indian Monsoon Rainfall

Fan, J., Meng, J., Ludescher, J., Li, Z., Surovyatkina, E., Chen, X., Kurths, J., Schellnhuber, H. J. (2022): Network-Based Approach and Climate Change Benefits for Forecasting the Amount of Indian Monsoon Rainfall. - Journal of Climate, 35, 3, 1009-1020.
https://doi.org/10.1175/JCLI-D-21-0063.1

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[15200442 - Journal of Climate] Network-Based Approach and Climate Change Benefits for Forecasting the Amount of Indian Monsoon Rainfall.pdf (Verlagsversion), 8MB
 
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Fan, Jingfang1, Autor              
Meng, Jun1, Autor              
Ludescher, Josef1, Autor              
Li, Zhaoyuan 2, Autor
Surovyatkina, Elena1, Autor              
Chen, Xiaosong 2, Autor
Kurths, Jürgen1, Autor              
Schellnhuber, Hans Joachim1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Zusammenfassung: Despite the development of sophisticated statistical and dynamical climate models, a relative long-term and reliable prediction of the Indian summer monsoon rainfall (ISMR) has remained a challenging problem. Toward achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near-surface air temperature field. We show that some characteristics of the directed and weighted climate networks can serve as efficient long-term predictors for ISMR forecasting. The developed prediction method produces a forecasting skill of 0.54 (Pearson correlation) with a 5-month lead time by using the previous calendar year’s data. The skill of our ISMR forecast is better than that of operational forecasts models, which have, however, quite a short lead time. We discuss the underlying mechanism of our predictor and associate it with network–ENSO and ENSO–monsoon connections. Moreover, our approach allows predicting the all-India rainfall, as well as the rainfall different homogeneous Indian regions, which is crucial for agriculture in India. We reveal that global warming affects the climate network by enhancing cross-equatorial teleconnections between the southwest Atlantic, the western part of the Indian Ocean, and the North Asia–Pacific region, with significant impacts on the precipitation in India. A stronger connection through the chain of the main atmospheric circulations patterns benefits the prediction of the amount of rainfall. We uncover a hotspot area in the midlatitude South Atlantic, which is the basis for our predictor, the southwest Atlantic subtropical index (SWAS index). Remarkably, the significant warming trend in this area yields an improvement of the prediction skill.

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 Datum: 2022-01-112022-01-112022-02-01
 Publikationsstatus: Final veröffentlicht
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1175/JCLI-D-21-0063.1
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
PIKDOMAIN: Director Emeritus / Executive Staff / Science & Society
PIKDOMAIN: RD1 - Earth System Analysis
Organisational keyword: RD4 - Complexity Science
Organisational keyword: RD1 - Earth System Analysis
Organisational keyword: Director Emeritus Schellnhuber
Research topic keyword: Monsoon
Research topic keyword: Climate impacts
 Art des Abschluß: -

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Titel: Journal of Climate
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Seiten: - Band / Heft: 35 (3) Artikelnummer: - Start- / Endseite: 1009 - 1020 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals254
Publisher: American Meteorological Society (AMS)