Assessment of Local Climate Change: Historical Trends and RCM Multi-Model Projections Over the Salento Area (Italy)

Abstract

This study provides an up-to-date analysis of climate change over the Salento area (southeast Italy) using both historical data and multi-model projections of Regional Climate Models (RCMs). The accumulated anomalies of monthly precipitation and temperature records were analyzed and the trends in the climate variables were identified and quantified for two historical periods. The precipitation trends are in almost all cases not significant while the temperature shows statistically significant increasing tendencies especially in summer. A clear changing point around the 80s and at the end of the 90s was identified by the accumulated anomalies of the minimum and maximum temperature, respectively. The gradual increase of the temperature over the area is confirmed by the climate model projections, at short—(2016–2035), medium—(2046–2065) and long-term (2081–2100), provided by an ensemble of 13 RCMs, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). All the models agree that the mean temperature will rise over this century, with the highest increases in the warm season. The total annual rainfall is not expected to significantly vary in the future although systematic changes are present in some months: a decrease in April and July and an increase in November. The daily temperature projections of the RCMs were used to identify potential variations in the characteristics of the heat waves; an increase of their frequency is expected over this century.

Drought Index as Indicator of Salinization of the Salento Aquifer (Southern Italy)

“…On the contrary, compared to the Mann-Kendall test on a yearly basis there does not seem to be a statistically significant trend at 5%. This is in agreement with the results found by D'Oria et al [46]. It should be remarked that these usual applications of trend tests are mainly carried out with the evaluation of only type I error of the test (rejecting the null hypothesis when it is true), neglecting the type II error (non-rejecting the null hypothesis when it is false).…”

“…As stated in the previous paragraph, only monthly rainfall data in 1949-2011 were available for this study. Moving from some of the most recent climate studies on the Salento peninsula [46,47], we 3), Monitoring Wells (Table 2), and wells of the Acquedotto Pugliese (AqP) potable net considered in the study. Wells belong today to different public organizations: however, in the period 1973-1995 measures were carried out by operators from a same institution with similar technical means and with reference to a constant well-head having an accurate value of the elevation.…”

“…As stated in the previous paragraph, only monthly rainfall data in 1949-2011 were available for this study. Moving from some of the most recent climate studies on the Salento peninsula [46,47], we decided to investigate these time series not only in the entire interval, but also in two sub-periods, namely 1949-1979 and 1980-2011. This choice was also motivated by a visual analysis of time series and the opportunity of analysing two time series of comparable length.…”

Future changes in five extreme precipitation indices in the lowlands of Romania

“…There are at least two reasons to apply this approach: (a) after interpolation of the RCM data to the meteorological station locations, direct verification of the RCM results is possible (common approach for model verification against direct observations), and (b) observation data can be directly used for model result bias corrections. There are many studies using the same or a similar approach (Engen-Skaugen, 2007;Gudmundsson et al, 2012;Ruml et al, 2012;Wang et al, 2014;Fang et al, 2015;D'Oria et al, 2018).…”

Evaluation of Regional Climate Models (RCMs) Performance in Simulating Seasonal Precipitation over Mountainous Central Pindus (Greece)

“…The main advantages of a statistical downscaling approach are low computational requirements, whereas dynamic downscaling is appreciated by researchers for its superiority of embracing more systematic characteristics in relation to topography and climatic dynamical processes. In recent years, several studies used RCMs in order to assess climate change effect on hydrology [31][32][33][34][35]. The effectiveness of these models is mostly dependent on their inputs, especially the past climate data [36].…”