An analysis of Italian seasonal temperatures from 1961 to 2006 was carried out, using homogenized data from 49 synoptic stations well distributed throughout Italy. The results show remarkable differences among seasons. Stationarity characterizes winter series, except for Northern Italy (where a warming trend from 1961 is identified); a positive trend over the entire period is recognized for spring series. Summer series are marked by a negative trend until 1981 and by a positive trend afterwards; finally, autumn series show a warming starting from 1970. The relationship between seasonal temperatures and four teleconnection patterns (North Atlantic Oscillation, East Atlantic Pattern, Scandinavian Pattern and Arctic Oscillation) influencing European climate was investigated through Spearman rank correlation and composites. Among the results, the strong linear correlation with the East Atlantic Pattern in all seasons but autumn is remarkable; moreover, the explained variance varies between 31.9% and 50.4% (leaving out autumn). Besides these four atmospheric patterns the role of other factors (e.g. soil moisture) is not dealt with, but their importance and the need for more investigation is pointed out.
Annual and seasonal precipitation series were derived from a set of 59 synoptic meteorological stations homogeneously distributed over Italy, in order to evaluate possible changes in precipitation behaviour and identifying areas of coherent variability. The time series were homogenized and standardized anomaly series were calculated for three areas: north, centre and south of Italy. Rotated principal component analysis (PCA) was applied to monthly data and the related loading maps were generated. The annual series do not show significant trends, while among the seasonal series only those of winter in northern and central Italy are non-stationary; they are characterized, respectively, by a decreasing trend for the entire period and by a positive trend since 1989. Seven common patterns were identified from clustered rotated principal components and linked with synoptical weather regimes.
The assessment of climate change impacts requires updated estimates of the tendencies in temperature extremes. With the objective of studying recent variations in frequency and intensity of temperature extremes over Italy, a collection of daily minimum and maximum temperature time series was selected for the calculation of a set of indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The trend of each index was investigated through a non-parametric statistical analysis over the last half-century , and its spatial variability was illustrated through trend maps. Mean national-scale trends were also assessed at annual and seasonal level. The results show that mean annual series exhibit a general warming tendency from 1961 to 2011, with significant trends for summer days, tropical nights, heat waves, and percentile-based indices at most stations, with warming trends more pronounced in summer and spring and weaker in winter and autumn. As a changepoint was identified in 1977 for the minimum (T min ) and maximum temperature (T max ) Italian annual series, the trend analysis was also performed for the two sub-periods 1961-1977 and 1978-2011. Non-significant "cooling" trends characterize the sub-period 1961-1977, while significant "warming" trends were identified over the period 1978-2011. This study updates previous research in the extent of time series, in the number of indices and in the approach followed for their analysis, providing useful information for evaluating the impacts of temperature extremes in the context of a changing climate in Italy.
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.
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