Summer seasonal predictability of warm days in Argentina: statistical model approach

Abstract

Predicting extreme temperature events can be very useful for different sectors that are strongly affected by their variability. The goal of this study is to analyze the influence of the main atmospheric, oceanic, and soil moisture forcing on the occurrence of summer warm days and to predict extreme temperatures in Argentina northern of 40°S by fitting a statistical model. In a preliminary analysis, we studied trends and periodicities. Significant positive trends, fundamentally in western Argentina, and two main periodicities of summer warm days were detected: 2–4 years and approximately 8 years. Lagged correlations allowed us to identify the key predictors: El Nino-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Standardized Precipitation Indices (SPI). We also noticed that the frequency of warm days in spring acts as a good predictor of summer warm days. Due to the collinearity among many predictors, principal component regression was used to simulate summer warm days. We obtained negative biases (i.e., the model tends to underestimate the frequency of summer warm days), but the observed and simulated values of summer warm days were significantly correlated, except in northwest Argentina. Finally, we analyzed the predictability of the summer warm days under ENSO neutral conditions, and we found new predictors: the geopotential height gradient in 850 hPa (between the Atlantic Anticyclone and the Chaco Low) and the Atlantic Multidecadal Oscillation (AMO), while the PDO and SPI lost some relevance.