REFERENCES Begert M., Schlegel T., Kirchhofer W., Homogeneous temperature and precipitation series of Switzerland from 1864 to Int. J. Climatol., 25, Böhm R., Auer I., Brunetti M., Maugeri M., Nanni T., Schöner W., Regional Temperature Variability in the European Alps from homogenised instrumental time series. Int. J. Climatol., 21, Brunetti M., Buffoni L., Maugeri M., Nanni T., Trends of minimum and maximum daily temperatures in Italy from 1865 to Theor. Appl. Climatol., 66, Brunetti M., Maugeri M., Monti F., Nanni T., Changes in daily precipitation frequency and distribution in Italy over the last 120 years. J. Geophys. Res., 109, D05102, doi: /2003JD Brunetti M., Maugeri M., Monti F., Nanni T., Temperature and precipitation variability in Italy in the last two centuries from homogenized time series. Int. J. Climatol., 25, (in press). Buffoni L., Maugeri M., Nanni T., Precipitation in Italy from 1833 to Theor. Appl. Climatol., 63, Maugeri M., Nanni T., Surface air temperature variations in Italy: recent trends and an update to Theor. Appl. Climatol., 61, Congresso del Dipartimento di Fisica Highlights in Physics –14 October 2005, Dipartimento di Fisica, Università di Milano CLIMATE CHANGE AND VARIABILITY IN ITALY IN THE LAST TWO CENTURIES Brunetti M.*, Buffoni L.**, Lentini G.†, Mangianti F.††, Maugeri M.†, Monti F.†, Nanni T.*, Pastorelli R.† *ISAC-CNR – Via Gobetti 101, I Bologna, Italy **Osservatorio Astronomico di Brera – Via Brera 28, I Milan, Italy †Istituto di Fisica Generale Applicata - Via Brera 28, I Milan, Italy ††Ufficio Centrale di Meteorologia Agraria – Via del Caravita 7A, I Rome, Italy The awareness of the importance of data quality and homogeneity issues for a correct detection of climate change has increased rapidly in the last few years. Considering the results obtained within the EU-funded ALPCLIM project (see Böhm et al., 2001), in the year 2000 the authors joined the CLIMAGRI research programme with the aim of better investigating the impact of data quality and homogeneity issues on the detection of Italian temperature and precipitation trends over the last two centuries. The final goal was to extend, revise, improve and update the data-sets of Italian monthly secular temperature and precipitation series (Maugeri and Nanni, 1998; Buffoni et al., 1999; Brunetti et al., 2000) and to give more reliable long-term trend estimates. The poster displays a synthesis of the principal results of the research program. Full details are discussed in Brunetti et al., METHODS QUALITY CHECK AND MISSING DATA FILLING All daily records were quality checked by means of crossed control and individual analysis of all the values that markedly disagreed with the ones of surrounding stations. Then, some minor gaps in precipitation records were filled by a method discussed in Brunetti et al., 2004 and monthly series were calculated. HOMOGENEITY TESTING AND RECORD ADJUSTING Monthly records were tested for homogeneity by means of a procedure that rejects the a- priori-existence of a homogeneous reference series. The resulting break signals were then collected in a decision matrix and checked against metadata (Böhm et al., 2001). STATION CLUSTERING AND REGIONAL AVERAGE SERIES Temperature and precipitation records were clustered into homogeneous regions by means of a Principal Component Analysis (PCA). Station records of the same area were then averaged in order to obtain regional average series. Some results and examples are given in the figures. TREND ANALYSIS Trends slopes were calculated by means of least square linear fitting, and their significance levels were estimated by progressive non-parametric Mann–Kendall test (Sneyers, 1990). Some examples of the results are given in the figures. Full details on the methods and trend results are discussed in Brunetti et al., GRIDDING A gridded version of the new data-sets was produced. The grid has one-degree resolution, both in latitude and in longitude, and was realised with a Gaussian weighting function. RESULTS AND CONCLUSIONS RECORD ADJUSTMENTS Our research highlighted that most Italian original temperature series are systematically biased by non-climatic noise. So, using the original data in estimating long-term temperature evolution produces biased results. This result is in agreement with the findings of Böhm et al., 2001 and Begert et al., STATION CLUSTERING Principal Component Analysis allows some climatic regions to be identified. In particular for minimum, mean and maximum temperatures, 3 EOFs, representing Po Plain (PP), Alpine (AL) and Peninsular Italy (PI) regions were extracted. 6 EOFs were extracted for precipitation. The identified regions are: North-Western Italy (NW), the Northern part of North-Eastern Italy (NEN), the Southern part of North-Eastern Italy (NES), the Central Italy (CE), the South-Eastern Italy (SE) and the Southern Italy (SO). TREND ANALYSIS Quite a uniform temperature trend was observed in the different regions, with a trend of 1 K per century all over Italy on a yearly basis. The trend is generally higher for minimum temperature than for maximum temperature for all seasons and the year. Precipitation trend analysis showed a decreasing tendency, though such decreases are very low and rarely significant. Considering the average all over Italy there is a 5% decrease per century in the annual precipitation amount, mainly due to the spring season (-9% per century). A progressive trend analysis revealed that, both for temperature and precipitation, the significance and the slope of the trends strictly depend on the selected period. Temperature and precipitation trends obtained by analysing the new data-set show significant differences from the results presented in Maugeri and Nanni, 1998, in Buffoni et al., 1999 and in Brunetti et al., 2000, the old trend assessments being characterised by a lower temperature increase (around 0.5 K per century) and a greater precipitation decrease (around 10% per century). Tables show the estimated trends of minimum, mean, maximum temperature and precipitation in period according to the new data-set. Bold values indicate trends with significance level higher than 99%. For precipitation, the values are expressed in percentage per century relative to the mean of the standard period INTRODUCTION Around the mid 1990s the authors set up a wide research program with the aim of getting a better understanding of the evolution of Italian climate in the last 150/200 years. This program allowed to obtain a first set of thermometric and pluviometric records that was discussed in Maugeri and Nanni, 1998, in Buffoni et al., 1999 and in Brunetti et al., Thanks to the CLIMAGRI project and to other international projects, in the year 2000 a new research program was set up with the aim of constructing a new database with a great improvement of station density and metadata availability. Monthly precipitation and temperature series included in the new data-sets are shown and information on data availability are also given. Italian Temperature SeriesItalian Precipitation Series Development of the final homogenised temperature and precipitation networks. TEMPERATURE PCA/CLUSTERING PRECIPITATION PCA/CLUSTERING Maps of the six rotated EOFs for Precipitation Map of the Precipitation sub-regions based on PCA Maps of the three rotated EOFs for Mean Temperature Map of the Temperature sub-regions based on PCA Precipitation trends over the period. The values are expressed in percentage per century relative to the mean of the standard period Only values with significance level greater than 90% are indicated; for lower values of significance only the sign of the trend is indicated. Bold values indicate trends with significance level higher than 99%. Trends of mean, maximum and minimum temperatures over the period. The values are expressed in K per century. Bold values indicate trends with significance level higher than 99%. Average regional series for mean, maximum, and minimum yearly temperature series (expressed in anomalies respect to the mean of the standard period ). The series were filtered with an 11-year window 3-year Gaussian low-pass filter. Average regional yearly total precipitation series (expressed in ratios respect to the mean of the standard period ). The series were filtered with an 11-year window 3-year Gaussian low- pass filter. Progressive trend analysis for annual precipitation records. Each point of a curve represents the regression coefficient (expressed in percentage per century relative to the mean of the standard period ) calculated for the series beginning from the year in which it is located and ending with the last one (2003). Thick portions of the curves indicate trends with s.l.>95%. Progressive slopes (K/100 years) of annual temperature records. Curves refer to the series starting from i (i = 0, ….., 100) and ending in The slopes of the series are displayed in correspondence of their starting year. The upper limit for i has been selected in order to perform trend analysis over at least 40-year periods. Bold parts indicate slopes significant at a 95% level. REFERENCES Begert M., Schlegel T., Kirchhofer W., Homogeneous temperature and precipitation series of Switzerland from 1864 to Int. J. Climatol., 25, Böhm R., Auer I., Brunetti M., Maugeri M., Nanni T., Schöner W., Regional Temperature Variability in the European Alps from homogenised instrumental time series. Int. J. Climatol., 21, Brunetti M., Buffoni L., Maugeri M., Nanni T., Trends of minimum and maximum daily temperatures in Italy from 1865 to Theor. Appl. Climatol., 66, Brunetti M., Maugeri M., Monti F., Nanni T., Changes in daily precipitation frequency and distribution in Italy over the last 120 years. J. Geophys. Res., 109, D05102, doi: /2003JD Brunetti M., Maugeri M., Monti F., Nanni T., Temperature and precipitation variability in Italy in the last two centuries from homogenized time series. Int. J. Climatol., 25, (in press). Buffoni L., Maugeri M., Nanni T., Precipitation in Italy from 1833 to Theor. Appl. Climatol., 63, Maugeri M., Nanni T., Surface air temperature variations in Italy: recent trends and an update to Theor. Appl. Climatol., 61,