资源环境科技发展态势分析平台

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.