Wang, J.J.; Shi, B.; Pan, X.Y., and Liu, L.F., 2020. Multi-scale characteristics of precipitation and its relationship with ENSO and AO. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 23–28. Coconut Creek (Florida), ISSN 0749-0208.
This study aims to reveal the multi-scale characteristics of precipitation and its response relationship with El Niño Southern Oscillation (ENSO) and Arctic Oscillation (AO) in Shandong province of China. Historical total monthly precipitation data of 21 meteorological stations in the research region from 1961 to 2017 were preprocessed firstly by wavelet power spectrum for analyzing the intensity of oscillations in different time-frequency scales. The results of wavelet power spectrum reveal that the 12-month periodicity was significant of precipitation. Then, the ensemble empirical mode decomposition (EEMD) was used to analyse the periodicity of precipitation, the precipitation time series was decomposed into 8 IMFs (Intrinsic Mode Function, IMF) and 1 residual, the significance test of IMFs shows that the annual periodicity oscillation is stronger than the interdecadal periodicity. The results reveal that the period of IMF2 was 12-month through the fast Fourier transform analysis, and the periodicity passed the significance test of 99%. Finally, the cross wavelet analysis and wavelet coherence were used to analyse the resonance periodicity and coherence of precipitation with ENSO and AO in different time-frequency scales. The results show that ENSO and precipitation were mainly in negative phase, and the resonance periodicity of 12-month and 32-64 months were strong. Simultaneously, the phase relationship between AO and precipitation is complicated, the resonance periodicity of 12 months was strong too. This research preliminarily explores of the multi-scale characteristics of precipitation in Shandong province of China, which can provide some references for precipitation prediction and water resources utilization in this region.