CONICET | Buscador de Institutos y Recursos Humanos

The objective of this work is to find atmosphere circulation and sea surface temperature (SST) patterns, which are able to explain the snow precipitation in Central Andes, in Mendoza, and to develop a model for predicting snowfall over the region. For this purpose, a series of regional annual average snowfall (RAS) was used. In this case the series is representative for the Andes Mountain between 30° and 37°S, during 1951-2010. Masiokas et al (2010] detailed the way this data set was constructed. Years were classified as ?dry? if the annual value of snow was below the first quartile and "wet" if it exceeded the third quartile. The ?wet? group was made up by 15 years, 9 years were Niño and 4 years were Niña, while the ?dry? group was made up by 15 years, only 6 years were Niña and 3 were Niño. For each group, geopotential height anomalies at low levels (1000 Hpa, G1000), middle levels (500 Hpa, G500) and high levels (300 Hpa, G300), zonal wind (U) and meridional (V) at 850hPa and SST fields for different seasons (summer, spring, winter and autumn) were made. The method proposed for predicting snow precipitation in Central Andes (30°-37°S) in Argentina is a regression technique which uses atmosphere and oceanic predictors. The main conclusion is that an intensification (weakening) of the South Pacific anticyclone and the sub-polar low pressure systems and lower (higher) contribution moisture from the north, were observed in dry years (wet). This result let us select some variables and calculate the correlation field between them and RAS in order to define the best predictors. These predictors were used in a multiple regression model. A crossvalidation technique was applied to determine the forecast snow values. Some measures of efficiency were calculated to prove the agreement between predict and observed snow values.