Bivariate and Partial Wavelet Coherence for Revealing the Remote Impacts of Large-Scale Ocean-Atmosphere Oscillations on Drought Variations in Xinjiang, China

Xinjiang, an arid area located in the central part of the Eurasian continent with high evaporation and low precipitation, experiences frequent droughts. This study builds on previous research by incorporating five key ocean-atmosphere oscillations and using the one-month SPEI as a meteorological drought indicator. Monthly time series of precipitation and temperature from 53 meteorological stations are utilized to calculate the monthly SPEI time series, and the seasonal Kendall test analyzes trends. Despite increased precipitation, the drought conditions in Xinjiang worsened due to increased temperatures, especially in the south, during 1961–2017. The 53 monthly SPEI time series are clustered using the agglomerative hierarchical method, basically reflecting Xinjiang’s topographical and climatic diversity. However, classical correlation methods show a weak or negligible overall correlation between the SPEI and large-scale ocean-atmosphere oscillators. Therefore, the partial wavelet coherence (PWC) method was used to detect the scale-specific correlations. Both bivariate wavelet coherence (BWC) and PWC detected significant correlations between the SPEI and the ocean-atmosphere oscillators at some specific time scales. Our analyses indicate that southern Xinjiang droughts are more influenced by Pacific or Indian Ocean oscillators, while northern droughts are affected by Atlantic or Arctic climate variations.