This study aims at finding the dominant climate patterns that influence the precipitation anomalies for different regions over the world. To this end, a multiple linear regression model is employed to represent the im...This study aims at finding the dominant climate patterns that influence the precipitation anomalies for different regions over the world. To this end, a multiple linear regression model is employed to represent the impact of four major climate patterns(El Ni?o-Southern Oscillation(ENSO), Indian Ocean Dipole(IOD), Arctic Oscillation(AO) and Antarctic Oscillation(AAO)) on the global precipitation anomalies. The normalized climate pattern indexes and normalized precipitation anomalies are used in the regression model. For the Northern Hemisphere, the three predictors used are the normalized NINO3.4 index(representing ENSO), normalized DMI(representing IOD) and normalized AO index; for the Southern Hemisphere, also three indexes are used as three predictors, and the normalized AO index is replaced by the normalized AAO index. The influences brought by each climate pattern can be represented by the magnitude of the corresponding regression coefficients, and the dominant climate patterns are those with the largest magnitude. The study results show that the precipitation anomalies in the northern part of South America and the northwestern part of Southeast Asia are mainly influenced by ENSO. The precipitation anomalies in East Africa and the southwestern part of Southeast Asia are mainly influenced by IOD. The precipitation anomalies in Europe and west coast of North America are mainly influenced by AO; the precipitation anomalies in the eastern part and southern part of South America, southern part of Africa, and the northeastern Australia are mainly influenced by AAO. These findings are consistent with the general understanding on the teleconnection features of the four climate patterns. Further, the regression model can be used for predicting precipitation anomalies through use of these major climate patterns.展开更多
基金supported by Hong Kong RGC GRF projects(Grant Nos.HKU 710712E and 7109010E)NSFC project(Grant No.51479224)
文摘This study aims at finding the dominant climate patterns that influence the precipitation anomalies for different regions over the world. To this end, a multiple linear regression model is employed to represent the impact of four major climate patterns(El Ni?o-Southern Oscillation(ENSO), Indian Ocean Dipole(IOD), Arctic Oscillation(AO) and Antarctic Oscillation(AAO)) on the global precipitation anomalies. The normalized climate pattern indexes and normalized precipitation anomalies are used in the regression model. For the Northern Hemisphere, the three predictors used are the normalized NINO3.4 index(representing ENSO), normalized DMI(representing IOD) and normalized AO index; for the Southern Hemisphere, also three indexes are used as three predictors, and the normalized AO index is replaced by the normalized AAO index. The influences brought by each climate pattern can be represented by the magnitude of the corresponding regression coefficients, and the dominant climate patterns are those with the largest magnitude. The study results show that the precipitation anomalies in the northern part of South America and the northwestern part of Southeast Asia are mainly influenced by ENSO. The precipitation anomalies in East Africa and the southwestern part of Southeast Asia are mainly influenced by IOD. The precipitation anomalies in Europe and west coast of North America are mainly influenced by AO; the precipitation anomalies in the eastern part and southern part of South America, southern part of Africa, and the northeastern Australia are mainly influenced by AAO. These findings are consistent with the general understanding on the teleconnection features of the four climate patterns. Further, the regression model can be used for predicting precipitation anomalies through use of these major climate patterns.
