Correlações entre os ciclos solares e o regime pluviométrico na Região Geográfica Intermediária de Caicó/RN

Abstract

In this study, time series of rainfall were used in the municipalities of Caicó, Florânia and Cruzeta, as well as the historical series of sunspots, to analyze and understand if there is any relationship between these two variables and their repercussions in the semi-arid region of the state of Rio Grande do Norte , specifically in the intermediate region of Caicó. The analyzed period was from 1913 to 2019. Precipitation data were obtained from the Technical Assistance and Rural Extension Company of Rio Grande do Norte (EMATER), the Seridó Climatological Station (CP) and the Meteorological Database of the National Institute of Meteorology (BDMEP). The solar activity data were obtained from the Observatoire Royal de Belgique, (ORB). For data analysis, descriptive statistics, probability, turnaround time, accumulated deviation techniques were used, in addition to the Mann-Kendall non-parametric statistical test and Pearson's correlation analysis. The results showed high amplitude for both precipitation and solar activity. The result of the correlation of solar activity and precipitation found for the historical series (1913-2019) was r -0.2 (t test 0.05), showing a weak correlation for the complete series. However, some cycles showed a strong correlation with precipitation values. As the analysis between solar activity x precipitation presented a weak correlation, we moved on to a second stage of data analysis, this time, to verify possible correlation between teleconnections (ENOS and GRADM) and rainfall in the RGIC. The results of Pearson's correlation for GRADM x Rain in RGIC was 0.47 and for ENOS x Rain in RGIC it was 0.28, with a 99% confidence level for both. Therefore, the Dipole of the Atlantic when compared to the annual rainfall values in the RGIC, showed a greater correlation with these than the El Nino and La Nina phenomena and sunspots. However, we cannot continue to leave extraterrestrial variables out of climate studies, since this work shed light on the understanding of the complexity of factors responsible for the high variability of precipitation in the study area (RGIC).