A new homogenized daily data set for temperature variability assessment in Italy

Abstract

We present a new data set of quality‐controlled and homogenized daily maximum (Tmax) and minimum (Tmin) temperature for Italy. The data set includes 144 Tmax and 139 Tmin long‐term series, covering the period 1961–2017. First, the paper provides a description of data sources and quality controls implemented for the detection of erroneous daily observations. Next, the temperature records used for this work are introduced. Following strict data continuity and completeness requirements, we identified more than 500 time series with at least 20 years of valid data (raw data set), which were spatially partitioned using a hierarchical clustering approach. For each cluster, the time series homogeneity was assessed using two different statistical automatic approaches: ACMANT and Climatol. The results of the homogenization process are illustrated only for the long‐term series subset. Both homogenization methods revealed the presence of non‐climatic discontinuities in most of the temperature series. Although Climatol detected a slightly lower number of breakpoints than ACMANT, the two methods are in good agreement with respect to the statistics which describe the number and timing of the breakpoints. Since no metadata are available, the plausibility of the homogenized time series was evaluated using different statistical measures: RMSE, Spearman correlation coefficient and trends estimation. Our results show that the homogenized data sets are more spatially coherent than the raw time series. In particular, the analysis of the annual temperature trends shows more realistic and reliable climatic patterns when the homogenized data sets are considered. For our data, the homogenization process only marginally changes the annual and seasonal warming trend values found for the area‐averaged anomaly raw series. The Tmax and Tmin homogenized data set will be regularly updated and is intended to be used for a variety of climate studies that require data at daily resolution, as the analysis of climate extremes.

A Homogeneous Dataset for Rainfall Trend Analysis in the Calabria Region (Southern Italy)

“…In particular, in this paper, the Climatol package provided under the R programming language was applied because this method, by performing an iterative procedure, allows us to automatically identify and remove inhomogeneities from the series. Moreover, although it is acknowledged that homogenization methods cannot provide the real adjustment for artificial discontinuities affecting a climate time series, the efficiency of Climatol has been documented in several papers, also when compared to several other methods [23,38]. Finally, as pointed out by Fioravanti et al [38] the choice of automatic methods is required when there is a high number of weather stations and a lack of metadata documenting potential change points.…”

“…Moreover, although it is acknowledged that homogenization methods cannot provide the real adjustment for artificial discontinuities affecting a climate time series, the efficiency of Climatol has been documented in several papers, also when compared to several other methods [23,38]. Finally, as pointed out by Fioravanti et al [38] the choice of automatic methods is required when there is a high number of weather stations and a lack of metadata documenting potential change points. The results of the homogenization procedures allowed us to detect that more than 50% of monthly rainfall series resulted as homogeneous.…”

Анализа Промјена Екстремних Климатских Индекса У Мостару

“…a region whose climate system will be particularly responsive to global change) (Giorgi, 2006). Significant increase in warm temperature indices and decrease in cold ones was already detected all over the Mediterranean region (Burić et al, 2015;Espirito Santo et al, 2014;Fernández-Montes & Rodrigo, 2012;Fioravanti et al, 2016;Fonseca et al, 2016;Kioutsioukis et al, 2010). In contrast to the consistent warming trends globally reported for the extreme temperature indices, most of the extreme precipitation indices (e.g.…”

“…регион у којем ће климатски систем посебно реаговати на глобалне промјене) (Giorgi, 2006). Значајно повећање топлих температурних индекса и смањење хладних већ је забиљежено широм региона Медитерана (Burić et al, 2015;Espirito Santo et al, 2014;Fernández-Montes & Rodrigo, 2012;Fioravanti et al, 2016;Fonseca et al, 2016;Kioutsioukis et al, 2010). За разлику од конзистентног тренда загријавања који је глобално установљен за екстремне температуре, већина утврђених трендова индекса екстремних падавина (нпр.…”

Pan-European homogenization of daily multi-decadaltemperature series from station-based observations

“…The reduction of standard deviation (due to the removal of step-like changes) and the increase of mutual correlation that is observed Mamara et al [2014] have confirmed the trustworthy of the homogenized dataset. The improved features of the networks have also been seen in the better spatial consistency of the trends [Syrakova and Stefanova, 2009;Hannart et al, 2014;Delvaux et al, 2018;Osadchyi et al, 2018;Yosef et al, 2018;Fioravanti et al, 2019] and in the removal of the anomalous ones [Vincent et al, 2002;Mamara et al, 2014;Pérez-Zanón et al, 2015; as can be seen in Figure 1.8. [Lawrimore et al, 2011], calculated with the method by [Menne and Williams Jr, 2009] Figure 1.8: Trends on TX90p over Austria after (left) and before (right) the homogenization.…”