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

  • Soledad CollazoEmail author
  • Mariana Barrucand
  • Matilde Rusticucci
Original Paper

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 Niño-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.

Notes

Acknowledgments

This research was supported by CONICET PIP 0137-Res 4248/16 and UBACyT 2018 20020170100357BA. We want to thank the National Weather Service of Argentina and National Institute of Agricultural Technology for providing the data for this study. The authors want to especially thank Dr. Mariela Sued and Dr. Ana Bianco for her collaboration.

Supplementary material

704_2019_2933_MOESM1_ESM.docx (369 kb)
ESM 1 (DOCX 368 kb)
704_2019_2933_MOESM2_ESM.docx (32 kb)
ESM 2 (DOCX 32 kb)

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© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Atmospheric and Ocean Sciences, Faculty of Exact and Natural SciencesUniversity of Buenos Aires (DCAO-FCEN-UBA)Buenos AiresArgentina
  2. 2.National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina

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