Modeling extreme minimum air temperature series under climate change conditions

Abstract

Considering the presence of non-stationary components, such as trends, in the extreme minimum air temperature series available from three locations of the State of São Paulo-Brazil, the aim of this research was to describe the probabilistic structure of this variable by using a non-stationary model (based on the general extreme value distribution; GEV model) in which the parameters are estimated as a function of time covariate. The Mann-Kendall test has proven the presence of significant increasing trends in all analyzed series. Furthermore, according to the Pettitt (changing-point) test, 1991 is the initial year of these trends (in the three locations). The applied selection criteria indicated that a GEV model in which the location parameter is estimated as a function of time is recommended to describe the probability structure of the variable under evaluation. The others two parameters of this model remained time-independent. According to this non-stationary model, the detected trends in the climate conditions of these locations have shown the same rate of change (0.04°C per year).

Climatic trends of temperatures and precipitation in Brazilian localities

“…Sansigolo and Kayano (2010), observed an increase of 0.5°C per decade in the minimum temperature in southern Brazil in the period from 1913 to 2006. Positive trends for the minimum temperature in the state of São Paulo were also observed by Blain (2011) and Blain and Lulu (2011), respectively in the period of 1951 to 2010 and 1890 to 2010.…”

“…In order to verify the presence of possible changes in local weather patterns, many authors have used the statistical nonparametric test of Mann-Kendall (Back, 2001;Yue, Pilon, Phinney, & Cavadias, 2002;Blain, 2011). According to Yue et al (2002), this test has been widely used in many parts of the world for the detection of meteorological and hydrological trends.…”

Precipitation Extremes Analysis over the Brazilian Northeast via Logistic Regression

“…The increase of extreme events in a short period of time became in the society more vulnerable at weather and climate extremes variability, resulting in great socioeconomic losses [1]. These extremes are related to several environmental factors that favor the increase on their frequency and intensity: 1) ocean-atmospheric variables relationships, such as: air temperature [2], precipitation [3], wind speed [4] and sea surface temperature (SST) [5]; 2) regional micro-climate changes due to rapid urbanization of the cities without proper urban planning [6]; and 3) orographic effects [7]. These factors when combined at atmospheric circulation or meteorological systems in several spatiotemporal scales [8] can favor the extremes occurrence; the aim of this paper is to diagnose the precipitation extremes.…”