Temperature Trend Analysis and Investigation on a Case of Variability Climate

Climate change is now evident on a global scale. In some regions, the phenomenon is especially amplified, generating different consequences for man and the environment. Sicily is one of the Mediterranean regions, the biggest in terms of area, where climatic variations produce significant effects. In this study, temperature trends on monthly time scales are examined in the time frame 1925–2015. The cluster analysis technique (Ward’s method) was used to homogenize the temperature series. The results show four statistically significant clusters, confirming the presence of climatic variability in the region. The non-parametric Mann–Kendall test was used to determine temperature trends. The non-parametric estimator Sen’s slope was used to quantify the variation of trends. The results showed the presence of statistically significant trends. A worrying and unexpected increase in temperatures was found during the winter period. This scenario was presented in three clusters, highlighting a mutation in the winter season, attributable to the climatic changes in progress rather than to territorial factors. If the trends maintain an increasing monotone character, in the coming decades there will be, in many areas of Sicily, a constant loss of fertile soil for the agricultural sector and the advancement of phenomena such as drought and desertification, to which the island is already predisposed. All of this will have serious socio-economic repercussions. Considering that a large part of the region’s economy is based on the agricultural sector, these repercussions will be followed by serious environmental implications that will negatively affect the ecological sustainability of the region.

Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results

Time series homogenization for 299 of the available precipitation records for the island of Ireland (IENet) was performed. Four modern relative homogenization methods, that is, HOMER, ACMANT, CLIMATOL and AHOPS were applied to this network of station series where contiguous intact monthly records range from 30 to 70 years within the base period 1941-2010. Break detection results are compared between homogenization methods, and coincidences with available documentary information (metadata) were analysed. The lowest (highest) number of breaks were detected with HOMER (ACMANT). Large differences of break frequency were found, namely ACMANT and AHOPS detected 8 times as many breaks than HOMER, while the break frequency with CLIMATOL was intermediate. Also, the ratio of series classified to be homogeneous varies widely between the methods. It is 85% with HOMER, 60% with CLIMATOL, 31% with AHOPS, while only 22% with ACMANT. In a further experiment, all the available time series for Ireland and Northern Ireland, (910 series) were used with ACMANT and CLIMATOL to explore the stability of break frequency for the same 299 series examined in the base experiment. While overall break frequency slightly increased (by 6-13%), the break positions notably changed for individual time series. The number of breaks changed for 59% (23%) of the series with ACMANT (CLIMATOL). For the breaks detected coincidentally by at least three methods including ACMANT and CLIMATOL in the base experiment, the second experiment confirmed the break positions in 86-87% of the breaks. The consequences of these results in relation to the reliability of statistical homogenization are discussed. Sometimes markedly different step functions provide comparable good approaches. However, the accuracy of homogenized time series cannot be related directly to the instability of break detection results.