Identifying strong signals between low-frequency climate oscillations and annual precipitation using correlation analysis

摘要

Long-term changes in precipitation in California and North and South Carolina are correlated to low-frequency oscillations of several hydroclimate indices (HCIs) through correlation analysis that utilizes longer sliding window sizes compared to previous studies to reduce higher-frequency noise in each time series. HCIs that are considered include the El Nino/Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), the North Atlantic Oscillation, the Pacific-Decadal Oscillation, among others. Multi-year accumulations of precipitation at several point locations were correlated to these HCIs temporally averaged over the same period. The sliding window size, lag time, and beginning month were varied to optimize the correlation for each site and HCI; a 60-month window size and 12-month lag time were found to result in the highest correlation. Correlation strength was characterized by the Pearson's r statistic, while correlation significance was estimated through a permutation experiment that employs a bootstrapping technique, resulting in a p-value between 0 and 1. Using a 60-month sliding window size and 12-month lag time, it was found that the MJO exhibited the strongest and the most significant correlation with accumulated precipitation throughout California, whereas similar correlation was found with ENSO throughout the Carolinas; correlation strength exceeded a Pearson's r of .80, while correlation significance was p < .05 at several sites. Optimal beginning months ranged from December to March for a majority of sites. This study underscores the potential of low-frequency climate oscillations that manifest themselves in the long-range dependence of precipitation on tropical disturbances.