Evaluación de tendencias de extremos climáticos y su relación con factores climáticos en la superficie continental del Ecuador

Abstract

This research identified correlations between extreme weather events and indices that characterize the ocean-atmosphere processes using the wavelet coherence method. The climatic daily data of precipitation and maximum and minimum temperature for the period 1985 to 2015 were provided by the National Institute of Meteorology and Hydrology (INAMHI). A total of 177 precipitation stations and 53 temperature stations were subjected to quality control, data homogenization and filling of missing data using the Climatol computational package. After data processing, the values of the study variables were interpolated using two methods: Inverse Distance Weighting (IDW) for precipitation and co-kriging for temperature. The results obtained from this process were used to calculate ten climate extreme indices (five related to precipitation and five related to temperature). Subsequently, using the TREND software, which contains the Mann-Kendall statistical test, the trends of the extremes were calculated according to the nine types of climate that coexist in Ecuador. As a result, stationary trends were obtained for the CDD, PRCTOT and R99pTOT indices, i.e., they show oscillatory behavior; for the CWD and R95pTOT indices, increasing trends were obtained for certain types of climate. In the case of temperature, the trends obtained were statistically significant, showing an increase or decrease according to the climatic extreme and type of climate analyzed. Finally, cross-correlations between climate extremes and large-scale indices were performed using the WaveletComp package (within R) to generate Morlet wavelet power spectra and bivariate analysis with crossed wavelet. The results show the phase relationship between the analyzed time series and the observed predominant cycles showing the interaction between extremes and climatic indices. The highest correlations were observed between the indices related to the ENSO phenomenon (BEST, SOI, Niño 1+2, Niño 4, Niño 3.4, PDO and ONI) with correlations higher than 0.8 in different time lapses for return periods higher than 1.5 years and the climatic extremes related to precipitation and temperature. Regarding the results of the cross-correlations with temperature extremes, it is evident that the best correlations are between the large-scale indices related to ENSO and precipitation. Finally, the correlations obtained with the other analysis indices were low or negative.