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  Generalized synchronization between ENSO and hydrological variables in Colombia: A recurrence quantification approach

Salas, H. D., Poveda, G., Mesa, O. J., Marwan, N. (2020): Generalized synchronization between ENSO and hydrological variables in Colombia: A recurrence quantification approach. - Frontiers in Applied Mathematics and Statistics, 6, 3.
https://doi.org/10.3389/fams.2020.00003

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Salas, H. D.1, Autor
Poveda, G.1, Autor
Mesa, O. J.1, Autor
Marwan, Norbert2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: We use Recurrence Quantification Analysis (RQA) to study features of Generalized Synchronization (GS) between El Niño-Southern Oscillation (ENSO) and monthly hydrological anomalies (HyAns) of rainfall and streamflows in Colombia. To that end, we check the sensitivity of the RQA concerning diverse HyAns estimation methods, which constitutes a fundamental procedure for any climatological analysis at inter-annual timescales. In general, the GS and its sensitivity to HyAns methods are quantified by means of time-lagged joint recurrence analysis. Then, we link the GS results with the dynamics of major physical mechanisms that modulate Colombia's hydroclimatology, including the Caribbean, the CHOCO and the Orinoco Low-Level Jets (LLJs), and the Cross-Equatorial Flow (CEF) over northwestern Amazonia (southern Colombia). Our findings show that RQA exhibits significant differences depending on the HyAns methods. GS results are similar for the HyAns methods with variable annual cycle but the time-lags seem to be sensitive. On the other hand, our results make evident that HyAns in the Pacific, Caribbean, and Andean regions of Colombia exhibit strong (weak) GS with the ENSO signal during La Niña (El Niño), when hydrological anomalies are positive (negative). Results from the GS analysis allow us to identify spatial patterns of non-linear dependence between ENSO and the Colombian's climatology. The mentioned moisture transport sources constitute the interdependence mechanism and contribute to explain hydrological anomalies in Colombia during the phases of ENSO. During La Niña (El Niño), GS is strong (weak) for the Caribbean and the CHOCO LLJs whereas GS is moderate (strong) for the Orinoco LLJ. Moreover, moisture advection by the Caribbean and CHOCO LLJs exhibit synchrony with HyAns at 0–2 (2–4) months-lags over north-western Colombia and the Orinoco LLJ moisture advection synchronizes with HyAns at similar month-lags over the Amazon region of Colombia. Furthermore, our results suggest a strong (weak) GS between negative (positive) Sea Surface Temperatures (SST) anomalies in the Eastern Pacific and rainfall anomalies in Colombia. In contrast, GS is strong (weak) for positive (negative) SST anomalies in the Central Pacific. Our GS results contribute to advance our understanding on the regional effects of both phases of ENSO in Colombia, whose socio-economical, environmental and ecological impacts cannot be overstated. This work provides a novel approach that reveals new insights into the impact of ENSO on northern South America.

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 Datum: 2020
 Publikationsstatus: Final veröffentlicht
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.3389/fams.2020.00003
PIKDOMAIN: RD4 - Complexity Science
eDoc: 8987
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Climate impacts
Research topic keyword: Atmosphere
Research topic keyword: Extremes
Research topic keyword: Weather
Model / method: Nonlinear Data Analysis
Regional keyword: South America
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
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Titel: Frontiers in Applied Mathematics and Statistics
Genre der Quelle: Zeitschrift, Scopus, oa
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Seiten: - Band / Heft: 6 Artikelnummer: 3 Start- / Endseite: - Identifikator: Anderer: Frontiers
Anderer: 2297-4687
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/frontiers-applied-mathematics_statistics
Publisher: Frontiers