European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation

Pakdaman, Morteza; Babaeian, Iman; Javanshiri, Zohreh; Falamarzi, Yashar

doi:10.1007/s11269-021-03042-8

Cited by 10 publications

(1 citation statement)

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“…They used machine learning techniques and showed that some unknown teleconnection patterns (e.g., South Pacific pattern in the Central America and the southwestern US sites in winter) had a much higher contribution to precipitation variability when compared to the known teleconnection patterns. Machine learning techniques have widespread applications in climate prediction (for example see , Pakdaman et al (2022) and ). Jiang et al (2014) studied the spatiotemporal variability of Alberta's seasonal precipitation, their teleconnection with largescale climate anomalies and sea surface temperature by using wavelet analysis, Wavelet-based Principal Component Analysis (WPCA), composite analysis and Scale-Averaged Wavelet Power (SAWP) of seasonal precipitation.…”

Section: Introductionmentioning

confidence: 99%

Influence of large-scale climate signals on theprecipitation variability over Iran

Javanshiri¹,

Babaeian²,

Pakdaman³

2022

Preprint

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This study investigates relationships between the twenty large-scale climatesignals and the precipitation variability during 1960–2018 in Iran.The twenty large-scale climate indicators include atmosphere-ocean teleconnectionsas well as El Ni˜no and Southern Oscillation (ENSO) signals,Pacific and Atlantic ocean Sea Surface Temperature (SST). WaveletCoherence Analysis (WCA) was applied to detect the impact of climatesignals on Iran’s monthly precipitation. The results revealed that (a) thesignificant wavelet coherence and the phase difference between monthlyprecipitation and climate signals were highly variable in time and periodicity,(b) at inter-annual scale, Iran’s precipitation had been linked moreto Extreme Eastern Tropical Pacific SST(0◦-10◦S, 90◦-80◦W) (Ni˜no1.2), Oceanic Ni˜no Index (ONI), and Western Hemisphere Warm Pool(WHWP), respectively, (c) decadal and inter-decadal precipitation variabilityare mainly associated with variability in Atlantic Meridional Mode(AMM), Western Pacific Index (WP) and Arctic Oscillation (AO), and(d) in the most recent decade, the coherence between precipitation andlarge-scale climate signals has been declined in decade and inter-decadescales, and an unstable coherence has been emerged in annual scale.

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Section: Introductionmentioning

confidence: 99%