Author(s): Chi Hang Chung; Ji Chen
Linked Author(s): Ji Chen
Keywords: No Keywords
Abstract: Climate patterns like the El Nino–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Arctic Oscillation (AO) and Antarctic Oscillation (AAO) have great influence on the global precipitation, and the relationships between the climate patterns and the precipitation can vary significantly throughout the year. The global precipitation data used are from the Global Precipitation Climatology Project (GPCP), and the study period is from 1979 to 2011. In order to have a more detailed understanding of such relationships, this study focuses on exploration of monthly features of teleconnection between four key climate patterns (i. e., ENSO, IOD, AO and AAO) and precipitation anomaly over the globe. Further, this study also investigates the variation of the influence of the climate patterns and the change in the dominating climate patterns. This study develops a multiple linear regression method to simulate the global precipitation anomaly for each month in a year through using four key climate pattern indexes (namely, NINO3. 4, DMI, AO and AAO index). The change of the regression coefficients throughout the year can be regarded as the variation of the impact of the corresponding climate pattern on the related precipitation anomaly. By examining the regression coefficients of each predictor, the variation of the impact of each climate pattern on the precipitation at every grid can be found. Further, we analyze the dominant climate pattern which has the largest regression coefficient. Since the influences of the climate patterns are varying, the dominant climate patterns at each grid also vary. This variation in the dominating climate pattern indicates that the precipitations in different regions are affected by different global climate patterns in different seasons. This study also analyzes the mechanisms of the monthly teleconnections of these four key climate patterns. The study results are valuable for predication of monthly regional extreme precipitations through using the governing climate patterns as preliminary indicators.