Stochastic Modeling of La Niña Influence on Meteorological Droughts in the South Cone of America

The objective of this work is to characterize and to establish quantitative relations between the properties of meteorological droughts in the geographic scope of the South Cone of America and the extreme phases of ENOS. From the quantitative point of view it also of interest to be appraised the existence of non linearities and effects of temporary phase angle between both phenomena. Stochastic models were used to relate the temporary structure of dependency between the field of Standardized Anomalies of the Sea Surface Temperature, SST, fields in the Pacific Ocean, and fields of monthly values of the Standardized Precipitation Index in those continental regions of South America, to the south of the Tropic of Capricorn, where the homogenous influence of ENOS has been previously identified, particularly La Niña events related with the occurrence of droughts. Transfer Function and NARMAX models were applied to model relationships between both phenomena, applied to all the phantom of both phenomena as a first approach. This sample that are no significant differences between both types of modeling, linear and non linear. In addition, since evidence exists of which the extreme events La Niña are associate with the occurrence of meteorological droughts in this region, with the intention of modeling the nonlinear relation presents between both types of events, these same models are applied in a threshold scheme, considering the percentile of the main component of the SST that identifies conditions of La Niña, like indicating variable. The modeling indicates that while these models applied to the whole set of the variables allow to explain not more of the 0.10 of the variance of the first main component of the monthly precipitation field in the analyzed continental zones, get to explain more of the 0.50 of her if they are applied as much considering only conditions of the colder extreme phase, corresponding to La Niña, like exogenous variable in the Transfer Function like in NARMAX models, and without significant differences among them are detected.