Application of Cross Wavelet Transformation to Analysis on Regional Climate Variations

By combining the cross spectrum with the wavelet transform, the cross wavelet transform method is adopted to analysis on variations of regional climate. Compared with the traditional cross spectrum method, more advantages for the correlation analysis of coupling oscillations between regional climate variation and atmospheric circulation system are provided by the cross wavelet transform method. Not only the limitation of the classical cross spectrum method is made up, but also the technical predominance of the wavelet transform method is exerted on displaying the localized distributing characteristics of climate signal in time and frequency space. This method possesses the ability to distinguish coupling signals of two time series and the excellence to describe the distributing of coupling signals in time-frequency space, it can be applied to diagnose and forecast the variations of regional climate in the work of provincial meteorological bureaus. As an example, this method is used to study the associated statistical characteristics and time-frequency correlations between the anomaly series of monthly Arctic Oscillation Indices (ΔI_AO) and monthly precipitation (ΔR), temperature(ΔT) in Henan Province in the last 56 years. The influences of Arctic Oscillation on climate variations in Henan Province are analyzed by means of wavelet cross-correlation coefficient, wavelet coherence spectrum and wavelet phase spectrum. The results show that there are significant correlative oscillations at multi-time scales between the variations of precipitation, temperature in Henan Province and Arctic Oscillation. The wavelet cross-correlations between ΔI_AO and ΔR, ΔI_AO and ΔT on inter-decadal timescale are obviously higherthan on inter-annual timescale in frequency space, and the correlation measure decreases with the increasing of coupling oscillation frequencies. In time domain, positive and negative correlations between ΔI_AO and ΔR, ΔI_AO and ΔT display the staggered distributing characteristics, and the correlation measure varies with the inter-annual and inter-decadal timescale. It is estimated that the negative correlation between ΔI_AO and ΔR, the positive correlation between ΔI_AO and ΔT will be maintained for 1—2 years by the varying tendency of wavelet cross-correlation coefficients. Analysis on the wavelet coherence spectrum shows that the coherent significances depend on the associated statistical characteristics of ΔI_AO and ΔR, ΔI_AO and ΔT in time and frequency space. There are localized distributing characteristics of wavelet coherence and phase in time space. The significant syntonic periods between ΔI_AO and ΔR are quasi-2-year, 3—5-year, 6—8-year and above 20-year timescales, interdecadal correlation distributes in all time space and inter-annual correlations presents at different stages in time space. The significant correlations between ΔI_AO and ΔT are showed on the periods of about 1a, 2—4-year, 6—8-year and above 16-year, the coherent significance increases obviously after 1974 for inter-decadal correlation and it is different for inter-annual correlations at different stages in time space. The cross-wavelet phases of correlative oscillation between ΔI_AO and ΔR, ΔI_AO and ΔT vary with the syntonic frequencies. The phase differences of correlative oscillation on inter-decadal timescale are lager than on inter-annual timescales, and the phase variations are inphase basically before 1975 and inverted clearly after 1975 in time space.It shows that Arctic Oscillation anomalies of inter-annual and inter-decadal timescale have significant influence on climate variations in Henan Province.The relationship and coherent significance between the variations of precipitation, temperature in Henan Province and Arctic Oscillation depend on their associated statistical characteristics in time-frequency space.