SUMMER SCHOOL: CLIMATE CHANGES IN THE MEDITERRANEAN AREA HISTORICAL CLIMATOLOGY Maurizio Maugeri Istituto di Fisica Generale Applicata – via Celoria 16 – Milano Istituto di Scienze dell'Atmosfera e del Clima – via Gobetti 101 - Bologna [email protected] Enna – September 6th, 2008 Climate System and complexity The atmosphere is strongly influenced by the other parts of the climate system atmo hydro crio lito bio Modelling the climate system is extremely difficult Climate System and complexity What can we do? Using the observations! Long-period and high-quality climatic instrumental time series Long-period and high-quality climatic instrumental time series are essential for the production of reliable assessments of the global climate system with a view to better understand, detect, predict and respond to global climate variability and change. Such key datasets are not only of immense scientific value, they also ultimately offer political, social and economic advantages, and they are required in order to: Place extreme events in a longer-term context allowing, for example, for more accurate assessments of their return periods. Enhance our knowledge about instrumentally measured climate variability and change, and the possible factors causing these changes. Contribute to the advancement of climate change detection and attribution studies. Develop climate change scenarios by combining observational climate measurements with projections from Regional Climate Model (RCM) simulations. Provide input to extended historical reanalysis (i.e. reanalyses prior to 1948) Calibrate natural/documentary proxies to extend the known climatic history of a country/region Calibrate satellite estimates of surface variables. Provide better observational data for the validation of climate model outputs (both RCMs and Global Climate Models [GCMs]) Perform more robust analyses in climate and applied climatological studies. Provide the best regional climate data sets for the use in environmental studies including the real and potential threats that various terrestrial, hydrological and marine ecosystems faces in the changing climate conditions. Improve adaptation to climate change impacts, by developing longer series for assessing impact sector models. Enhance the scientific contribution in the climate component of large field experiments/programmes. http://www.omm.urv.cat/MEDARE/rationale-background.html Availability of long-period and highquality climatic instrumental time series for the Mediterranean area The Greater Mediterranean Region (GMR) has a very long and rich history in monitoring the atmosphere, going back in time several centuries in some countries and at least to the 19th century across much of the GMR. However, despite the efforts undertaken by some National Meteorological and Hydrological Services and scholars in Data Rescue (DARE) activities aimed at transferring historical longterm climate records from fragile media (paper forms) to new electronic media, accessible digital climate data is still mostly restricted to the second half of the 20th century. Climate data heritage over the GMR is, then, largely underexploited. http://www.omm.urv.cat/MEDARE/rationale-background.html So it is very important to increase the availability of longperiod and high-quality climatic instrumental time series of the Mediterranean area How to proceed: let’s see the Italian case study: the UNIMI/CNR ISAC research programme and other contributions Recovering data and metadata. Homogeneity issues. Data analysis. Local vs larger scale. Italy has a very important role in the development of meteorological observations Invention of some of the most important meteorological instruments (thermometer, barometer). Establishment of the first network of observations (rete del Cimento, set up by Galileo’s scholars). The strong Italian presence in the development of meteorological observations is also testified by six stations that have been in operation since the eighteenth century (Bologna, Milan, Rome, Padua, Palermo and Turin) and other 15 stations where observations started in the first half of the nineteenth century (Aosta, Florence, Genoa, Ivrea, Locorotondo, Mantua, Naples, Parma, Pavia, Perugia, Trento, Trieste, Udine, Urbino and Venice). As a consequence, a heritage of data of enormous value has been accumulated in Italy over the last three centuries This heritage has been known for a long time and many attempts have been made to collect data into a meteorological archive….. Cantù V. and Narducci P. (1967) Lunghe serie di osservazioni meteorologiche. Rivista di Meteorologia Aeronautica, Anno XXVII, n. 2, 71-79. Eredia F. (1908) Le precipitazioni atmosferiche in Italia dal 1880 al 1905. In: Annali dell'Ufficio Centrale di Meteorologia. Serie II, Vol. XXVII, anno 1905, Rome. Eredia F. (1919) Osservazioni pluviometriche raccolate a tutto l'anno 1915 dal R. Ufficio Centrale di Meteorologia e Geodinamica. Ministero dei Lavori Pubblici, Rome. Eredia F. (1925) Osservazioni pluviometriche raccolate nel quinquennio 1916-1920 dal R. Ufficio Centrale di Meteorologia e Geodinamica. Ministero dei Lavori Pubblici, Rome. Mennella C. 1967. Il Clima d'Italia. Napoli: Fratelli Conti Editori, 724 pp. Millosevich (1882) Sulla distribuzione della pioggia in Italia. In: Annali dell'Ufficio Centrale di Meteorologia. Serie II, Vol. III, anno 1881, Rome. Millosevich (1885) Appendice alla memoria sulla pioggia in Italia. In: Annali dell'Ufficio Centrale di Meteorologia. Serie II, Vol. V, anno 1883, Rome. Narducci, P., 1991: Bibliografia Climatologica Italiana, Consiglio Nazionale dei Geometri, Roma. … however, in spite of the huge heritage of data and even if most records were subjected to some sort of analysis, until a few years ago only a small fraction of Italian data was available in computer readable form Precipitazioni Stazione Reggio Calabria Palermo Perugia Torino Padova Alessandria Arezzo Belluno Rovigo L’Aquila Reggio Emilia Napoli Cagliari Mantova Sassari Siracusa Roma Pesaro Bologna Ferrara Parma Piacenza Pavia Cuneo Messina Livorno Temperature Lunghezza serie Dati mancanti (%) Lunghezza serie Dati mancanti (%) 1878-1972 1874-1973 1874-1973 1866-1969 1877-1968 1875-1970 1879-1972 1879-1986 1879-1976 1879-1973 1879-1970 1865-1969 1879-1971 1880-1973 1876-1971 1874-1973 1862-1999 1876-1992 1879-1988 1879-1974 1878-1994 1875-1999 1883-1991 1879-1993 1881-1974 1876-1994 19.8 0.7 21.2 10.2 5.0 16.8 17.7 7.7 12.4 8.5 10.8 2.7 4.0 11.5 12.1 12.5 6.2 11.6 10.3 17.9 6.3 6.0 23.8 12.5 12.2 10.3 1878-1972 1876-1973 1876-1973 1870-1969 1877-1968 1878-1970 1879-1972 1879-1966 1879-1966 1879-1973 1879-1970 1870-1969 1879-1971 1880-1973 1876-1971 1878-1973 1870-1999 1876-1992 1879-1988 1879-1974 1878-1994 1878-1999 1870-1979 1879-1993 1881-1974 1870-1994 19.2 0.1 1.3 17.9 4.7 14.7 19.0 6.9 13.0 15.2 23.8 9.3 2.5 11.4 8.8 11.4 0.4 7.8 14.8 16.6 2.5 9.4 26.1 12.4 10.7 11.8 Archivio delle serie secolari UCEA - Anzaldi C., Mirri L. and Trevisan V., 1980: Archivio Storico delle osservazioni meteorologiche, Pubblicazione CNR AQ/5/27, Roma. Within this context, a number of projects where set up in Italy in the last 5 to 10 years to recovery as much as possible secular meteorological records The activities can be clustered in two general classes Projects concerning single stations High temporal resolution, complete metadata documentation, etc… Projects concerning national/regional networks Lower temporal resolution, less metadata, etc… Projects concerning single stations are particularly important for the records beginnig in the 18th century Milan: a 10-year project developed Padova: as for Milan but activities by Osservatorio Astronomico di performed by Istituto di Scienze Milano-Brera and Milan University dell'Atmosfera e del Clima – section allowed to recovery metadata and of Padova daily T, P, R records Palermo: recovery started later on; Torino: as for Milan and Padova The activities are performed by Os. but activities performed by Società Astronomico. Available: metadata Meteorologica Italiana and daily R and T records. Bologna: as for Milan, Padova and Roma: as for Milan, Padova and Torino for the data after 1813. Still Torino for the data after 1862. Only in progress for the 18th century data monthly data for the 18th century …there is a lot of still unexploited information… Cloudiness, sunshine, vapour pressure, wind, etc… Projects concerning national/regional networks Second part of the 1990s: the CNR project “Reconstruction of the past climate in the Mediterranean area” allowed the UCEA secular series data set to be updated, completed, and revised. In spite of significant improvements, the new data set had the fundamental limitation of very poor metadata availability. Moreover, the number of stations was still too low. So homogenisation could not be performed. Around 2000 a new research programme was established. It was initially developed within a national project (CLIMAGRI), then an extension of the activities was performed within some other projects. Thanks to the availability of resources from more projects and to additional results from other projects, the initial goal of homogenising the existing records was extended and the construction of a completely new and larger set of data and metadata was also planned. The new dataset of Italian secular records Meteorological variables • Air Temperature (minimum, mean, maximum) • Precipitation • Air Pressure • Cloud Cover • Other data Temporal resolution Daily/Monthly The new Italian dataset: air temperature STATION CODE LON (º) LAT (º) z (m) ALESSANDRIA AOSTA L'AQUILA AREZZO BELLUNO BOLOGNA BOLZANO BRÁ BRIXEN CAGLIARI CASTROVILLARI CATANIA CHIAVARI COSENZA CATANZARO CUNEO DOMODOSSOLA FERRARA FIRENZE FOGGIA FOSSANO GENOVA IMPERIA LIVORNO LOCARNO LUGANO MANTOVA MESSINA MILANO MONTE MARIA NAPOLI NIZZA PADOVA PALERMO PARMA PAVIA PERUGIA PESARO PIACENZA POTENZA POLA REGGIO CALABRIA REGGIO EMILIA RIVA TORBOLE ROVERETO ROMA ROVIGO ROSSANO SASSARI SAN BERNARDO SIRACUSA TARANTO TORINO MONCALIERI TORINO TORTONA TRENTO TRIESTE TROPEA UDINE VALLOMBROSA VALSINNI VENEZIA ALE AOS AQU ARE BEL BOL BOZ BRA BRI CAG CAR CAT CHA COS CTA CUN DOM FER FIR FOG FOS GEN IMP LIV LOC LUG MAN MES MIL MMA NAP NIZ PAD PAL PAR PAV PER PES PIA POT PUL RCA REM RIV ROE ROM ROV RSS SAS SBE SIR TAR TOM TOR TOT TRE TRI TRO UDI VAL VAS VEN 8.63 7.30 13.40 12.00 12.25 11.25 11.33 7.87 11.65 9.15 16.20 15.11 9.30 16.25 16.58 7.50 8.27 11.50 11.30 15.52 8.38 9.00 8.02 10.25 8.79 8.97 10.75 15.50 9.00 10.49 14.25 7.20 11.75 13.35 10.25 9.25 12.50 13.00 9.75 15.82 13.87 15.65 10.75 10.83 11.05 12.47 11.75 16.62 8.60 7.18 15.28 17.30 7.70 7.75 8.87 11.12 13.75 15.88 13.20 11.00 16.42 12.25 44.92 45.73 42.35 43.45 46.12 44.48 46.50 44.70 46.72 39.20 39.80 37.50 40.30 39.28 38.90 44.40 46.10 44.82 43.80 41.45 44.57 44.40 43.87 43.55 46.17 46.00 45.15 38.20 45.47 46.74 40.88 43.65 45.40 38.10 44.80 45.17 43.10 43.87 45.02 40.63 44.86 38.10 44.70 45.88 45.87 41.90 45.05 39.55 40.72 45.87 37.05 40.45 45.00 45.05 44.88 46.07 45.65 38.67 46.00 43.72 40.15 45.43 98 544 753 274 404 60 272 290 569 55 353 75 5 250 343 536 300 15 51 80 351 21 54 3 379 276 51 54 64 1323 149 4 14 71 57 75 520 11 50 826 30 15 62 70 206 56 9 300 224 2472 23 22 238 275 199 199 11 51 51 955 250 21 Min/Max (daily) 1854-1985 1879-2003 1879-2003 1879-2003 1814-2003 1879-2003 1925-2002 1901-2003 1925-2002 1924-2002 1879-2003 1879-2003 1889-2003 1901-2003 1833-2003 1870-2001 1828-2003 1881-2003 1763-2003 1870-2003 1774-2003 1876-2003 1878-2003 1870-2002 1876-2003 1871-2003 1878-2003 1924-2002 1878-2002 1879-2003 1862-2003 1879-2003 1925-1997 1876-2003 1878-2003 1901-2003 1753-2003 1924-2002 1872-2003 1924-2002 1900-2002 Min/Max (monthly) 1854-1985 1891-2003 1869-2003 1876-2003 1875-2003 1814-2003 1879-2003 1925-2002 1901-2003 1925-2002 1924-2002 1879-2003 1865-2003 1878-2003 1901-2003 1833-2003 1875-2003 1865-2001 1935-1997 1901-1997 1828-2003 1881-2003 1763-2003 1865-2003 1774-2003 1876-2003 1872-2003 1865-2002 1865-2003 1871-2003 1871-2003 1924-2002 1878-2002 1866-2003 1862-2003 1879-2003 1925-1997 1874-2003 1878-2003 1901-2003 1865-2003 1753-2003 1883-2003 1924-2002 1803-2003 1872-2003 1924-2002 1900-2002 Mean (monthly) 1854-1985 1840-2003 1869-2003 1876-2003 1875-2003 1814-2003 1850-2003 1862-1970 1865-2003 1879-2003 1925-2002 1901-2003 1883-2002 1925-2002 1924-2002 1879-2003 1872-1997 1865-2003 1878-2003 1901-2003 1874-1973 1833-2003 1875-2003 1865-2001 1864-1997 1864-1997 1828-2003 1881-2003 1763-2003 1857-2003 1865-2003 1806-2003 1774-2003 1876-2003 1872-2003 1861-2002 1865-2003 1871-2003 1871-2003 1924-2002 1864-2003 1878-2002 1866-2003 1869-2003 1862-2003 1862-2003 1879-2003 1925-1997 1874-2003 1818-1998 1878-2003 1901-2003 1864-2003 1753-2003 1892-1965 1816-2003 1841-2003 1924-2002 1803-2003 1872-2003 1924-2002 1900-2002 The new Italian dataset: air temperature The new Italian dataset: precipitation STATION CODE LON (º) LAT (º) z (m) ALESSANDRIA ANDRIA AOSTA L'AQUILA AREZZO ASTI BARLETTA BALMÈ BARDONECCHIA BELLUNO BENEVENTO BOLOGNA BORGOMANERO BOLZANO BRÁ BRIXEN BRINDISI CASTELLANETA CAGLIARI CANOSA CASALE MONFERRATO CATANIA CAVOUR CENTALLO CERIGNOLA CHIVASSO COSENZA CRISPIANO CROTONE CATANZARO CUNEO DOMODOSSOLA FENESTRELLE FERRARA FIRENZE FOGGIA FOSSANO GALATINA GALLIPOLI GENOVA GINOSA GINOSA SCALO IMPERIA IVREA LATIANO LECCE LESINA LIVORNO LIZZANO LOCARNO LOMBRIASCO LOCOROTONDO LUGANO MANFREDONIA MAGLIE MANTOVA ALE AND AOS AQU ARE AST BAE BAL BAR BEL BEN BOL BOR BOZ BRA BRI BRN CAE CAG CAO CAS CAT CAV CEN CER CHI COS CRI CRO CTA CUN DOM FEN FER FIR FOG FOS GAL GAP GEN GIN GIS IMP IVR LAT LEC LES LIV LIZ LOC LOM LOR LUG MAF MAG MAN 8.63 16.28 7.30 13.40 12.00 8.20 16.27 7.22 6.70 12.25 14.80 11.25 8.45 11.33 7.87 11.65 17.93 16.93 9.15 15.90 8.50 15.11 7.37 7.60 15.88 7.85 16.25 17.23 17.12 16.58 7.50 8.27 7.06 11.50 11.30 15.52 8.38 18.15 17.98 9.00 16.75 16.75 8.02 7.91 17.72 18.17 15.35 10.25 17.45 8.79 7.65 17.33 8.97 15.92 18.30 10.75 44.92 41.23 45.73 42.35 43.45 44.90 41.33 45.32 45.08 46.12 41.12 44.48 45.70 46.50 44.70 46.72 40.65 40.63 39.20 41.13 45.13 37.50 44.73 44.50 41.27 45.17 39.28 40.60 39.08 38.90 44.40 46.10 45.04 44.82 43.80 41.45 44.57 40.17 40.05 44.40 40.58 40.58 43.87 45.46 40.55 40.35 41.87 43.55 40.38 46.17 44.84 40.75 46.00 41.62 40.12 45.15 98 151 544 753 274 158 20 1432 1340 404 177 60 317 272 290 569 28 245 55 154 113 75 290 417 124 221 250 265 6 343 536 300 1200 15 51 80 351 73 31 21 257 5 54 267 98 78 5 3 67 379 239 420 276 2 77 51 Precipitation (daily) 1857-1986 1879-2003 1879-2003 1879-2003 1813-2003 1921-2003 1862-2003 1921-2003 1879-2003 1921-2003 1916-2002 1916-2002 1916-2002 1879-2003 1872-1998 1879-2003 1860-2003 1901-2003 1833-2003 1876-2002 1901-2002 1901-2002 1840-2003 Precipitation (monthly) 1857-1986 1921-1996 1841-2003 1874-2003 1876-2003 1881-1993 1921-1996 1913-2003 1913-2002 1875-2003 1870-1996 1813-2003 1881-1996 1856-2003 1862-2003 1878-2003 1877-2000 1877-1996 1853-2003 1922-1996 1870-2003 1892-2003 1879-1993 1883-1988 1922-1996 1892-1988 1873-2002 1916-1996 1916-2002 1868-2002 1877-2003 1872-1998 1912-1997 1865-2003 1860-2003 1873-2003 1875-1997 1923-1996 1877-1996 1833-2003 1887-1996 1928-1996 1876-2003 1837-2002 1925-1996 1875-2000 1928-1998 1857-2002 1916-1996 1886-2002 1913-1999 1829-1996 1861-2002 1921-1996 1908-1996 1840-2003 STATION CODE LON (º) LAT (º) z (m) MASSAFRA MATERA MESSINA METAPONTO MILANO MINERVINO LECCESE MONTE MARIA MONCALVO MONDOVI NAPOLI NARDÒ NIZZA NOVI LIGURE NOVOLI NOVARA OTRANTO OVADA PADOVA PALERMO PARMA PAVIA PERUGIA PESARO PIACENZA POTENZA PRESICCE POLA REGGIO CALABRIA REGGIO EMILIA RIVA TORBOLE ROVERETO ROMA ROVIGO SASSARI SAN BERNARDO SILANDRO SIRACUSA SAN MARCO SAN PIETRO STROPPO TARANTO TAVIANO TORINO MONCALIERI TORINO TORTONA TRENTO TRIESTE TROPEA UDINE URBINO VALLOMBROSA VARALLO VENEZIA VICO GARGANICO VIESTE MAS MAT MES MET MIL MIN MMA MOC MOD NAP NAR NIZ NOL NOO NOV OTR OVA PAD PAL PAR PAV PER PES PIA POT PRE PUL RCA REM RIV ROE ROM ROV SAS SBE SIL SIR SMA SPI STR TAR TAV TOM TOR TOT TRE TRI TRO UDI URB VAL VAR VEN VIC VIE 17.12 16.62 15.50 16.82 9.00 18.42 10.49 8.25 7.82 14.25 18.02 7.20 8.78 18.05 8.62 18.50 8.65 11.75 13.35 10.25 9.25 12.50 13.00 9.75 15.82 18.27 13.87 15.65 10.75 10.83 11.05 12.47 11.75 8.60 7.18 10.77 15.28 15.62 18.13 7.12 17.30 18.08 7.70 7.75 8.87 11.12 13.75 15.88 13.20 12.62 11.00 8.25 12.25 15.95 16.17 40.58 40.68 38.20 40.37 45.47 40.08 46.74 45.05 44.04 40.88 40.18 43.65 44.78 40.38 45.45 40.13 44.62 45.40 38.10 44.80 45.17 43.10 43.87 45.02 40.63 39.90 44.86 38.10 44.70 45.88 45.87 41.90 45.05 40.72 45.87 46.63 37.05 41.72 40.30 44.50 40.45 39.98 45.00 45.05 44.88 46.07 45.65 38.67 46.00 43.72 43.72 45.82 45.43 41.90 41.88 116 401 54 3 64 98 1323 297 44 149 43 4 186 37 181 52 187 14 71 57 75 520 11 50 826 114 30 15 62 70 206 56 9 224 2472 706 23 560 160 1087 22 61 238 275 199 199 11 51 51 451 955 454 21 450 25 Precipitation (daily) 1916-2002 1881-2003 1918-2000 1858-2003 1923-2003 1866-2003 1877-2002 1797-2003 1878-2003 1873-2002 1874-2003 1871-2003 1875-2003 1916-2002 1878-2002 1879-2003 1921-2003 1921-2003 1862-2003 1879-2003 1876-2003 1921-1999 1874-2003 1901-2003 1802-2003 1921-2003 1916-2002 1916-2000 1872-2003 1900-2003 - Precipitation (monthly) 1881-1997 1916-2002 1866-2003 1918-2000 1764-2003 1926-1996 1858-2003 1889-1988 1866-1995 1821-2003 1923-1996 1865-2002 1880-1979 1924-1996 1875-1996 1879-1996 1913-1996 1750-2002 1797-2003 1833-2003 1812-2002 1811-2003 1866-2003 1872-2003 1879-2002 1877-1996 1864-2002 1877-2002 1867-2003 1869-2003 1864-2003 1782-2003 1878-2003 1876-2003 1864-1997 1921-1999 1869-2003 1921-1998 1923-1996 1913-1996 1877-2003 1885-1996 1864-2003 1802-2003 1873-1998 1864-2003 1841-2003 1916-2002 1803-2003 1850-2000 1872-2003 1871-1995 1836-2003 1922-1998 1921-1998 The new Italian dataset: precipitation The new Italian dataset: other variables … the activities are still in progress (e.g. EU project ALP-IMP). They concern air pressure, cloud cover, humidity and snow… AIR PRESSURE (secular records) TRE LUG TRE GRE TRI MIL TOR TOM CLOUD COVER (secular records) MIL PAD VEN MAN PIA TOR MAN PIA FER PAR BOL GEN FIR BOL PES PES LIV LIV TER TER PEC ROM ROM MOV FOG FOG BAI NAP SAS NAP SAS LEC CAR CAG LEC CAR CAG TRO PAL TRO MES RCA TRA CAT HUMIDITY (i.e. dry / wet temperatures) daily data 2 records PAL MES RCA CAT SNOW (HS: snow at ground; HN: fresh snow) daily / monthly data About 15 records of northern Italy 1951-2004 PERIOD: All variables available in digital format Italian Air Force data-set. SECULAR RECORDS DOM ODOSSOLA TORINO LUGANO M ILANO PADOVA VENEZIA TRENTO TRIESTE PARM A PIACENZA M ANTOVA BOLOGNA GENOVA PESARO FIRENZE LIVORNO TERAM O - ANCONA ROM A CAGLIARI - CARLOFORTE SASSARI BARI LECCE FOGGIA NAPOLI M ONTEVERGINE TROPEA M ESSINA - REGGIO CALABRIA PALERM O - TRAPANI CATANIA 1725 1750 1775 1800 1825 year 1850 1875 1900 1925 1950 The new Italian dataset: metadata Metadata collection was performed with two main objectives: i) to understand the evolution of the Italian meteorological network ii) to reconstruct the “history” of all the stations of the data-set. The research on the history of the single stations was performed both by analysing a large amount of grey literature and by means of the UCEA archive. All information was summarized in a card for each data series. Each card is divided into three parts. In the first part all the information obtained from the literature is reported. In the second part there are abstracts from the epistolary correspondence between the stations and the Central Office. In the third part the sources of the data used to construct the record are summarized. For full details; see CLIMAGRI project WEB site (www.climagri.it) Metadata: for each station • Abstracts of all published papers (grey litterature) • Abstracts of the correspondence between the observatories and the Central Office • Position • Data sources • Data availability • Other notes For more details; see CLIMAGRI project WEB site 1765 1770 1775 1780 1785 1790 1795 1800 1805 1810 1815 1820 1825 1830 1835 1840 1845 1850 1855 1860 1865 1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 I 1 I I 2 I I 3 4 I 5 I I I I 6 7 I I I I I I 8 9 I I I II I I I 10 1: Carlini's 1835 reorganisation; 2: Other displacements of the instruments; 3: Changes in the meteorological screen and in its management; 4: Improvements introduced by Cesaris; 5: Substitutions of the barometer and changes in instrumental correction; 6: Effects of standardisation due to development of national and international meteorological networks; 7: Changes in the observer; they are included only until 1880 as later on standardisation causes the measures to be less dependent from the observer; 8: Interruption of Brera observatory series; 9: Changes in observation hours; 10: Urban heat island development. Data and metadata: integration with other data-set The HISTALP data-set The new Italian dataset: quality and homogeneity issues The problem: the real climate signal, that we try to reconstruct studying long (secular) records of meteorological data, is generally hidden behind nonclimatic noise caused by station relocation, changes in instruments, changes in observing times, observers, and observing regulations, algorithms for the calculation of means and so on. climatic time series should not be used for climate research without a clear knowledge about the state of the data in terms of quality and homogeneity. Quality Classification of the institutions (Observatory, high school, etc…) Data sources (hand-written original observations; year books; pre existing data sets, etc…) Time resolution (yearly, monthly, daily, etc…) Comparison with other records Homogeneity Climate variations Measuring problems “Signals” in the records of meteorological data Measuring problems Relocations Instrumental errors (changes of the instruments and/or recalibrations) Observation methods Screenings Changes in the environment around the station The problem is not easy to manage Meteorological series can be tested for homogeneity and homogenised both by direct and indirect methodologies. The first approach is based on objective information that can be extracted from the station history or from some other sources, the latter uses statistical methods, generally based on comparison with other series. Indirect Methods Basic idea: climate change and variability has low spatial gradients, at least for geographically homogeneous areas The homogeneity of a climatic record can be checked by means of the records of the neighbouring stations Testing the homogeneity of Milan yearly average temperature record against Turin STEP 2: calculating differences 15.0 14.0 13.0 2003 2001 1999 1997 1995 1993 1991 1989 1987 MIL + const 17.0 16.0 15.0 14.0 13.0 5.0 4.0 4.0 3.0 3.0 2.0 2.0 1.0 1.0 2003 2001 1999 1997 1995 1993 1991 1989 1987 1985 1983 1981 1979 1977 1975 1973 1971 1969 1967 12.0 sum of diff sum of diff 0.0 1985 1983 TOR 5.0 -2.0 -2.0 -3.0 -3.0 -4.0 -4.0 -5.0 -5.0 2003 2001 1999 1997 1995 1993 1991 1989 1987 1985 1983 1981 1979 1977 1975 1973 1971 1969 1967 1965 1963 -1.0 1961 2003 2001 1999 1997 1995 1993 1991 1989 1987 1985 1983 1981 1979 1977 1975 1973 1971 1969 1967 1965 1963 0.0 1961 -1.0 1981 18.0 diff diff 1979 12.0 1965 0.0 16.0 1977 0.0 MIL 1975 13.5 TOR 17.0 1963 13.5 sum of diff -0.4 -1.0 -1.0 -1.4 -1.4 -1.6 -0.2 0.6 1.2 1.1 0.6 0.3 0.2 0.3 0.4 0.2 0.6 1.2 1.1 0.6 0.1 -1.0 -1.6 -1.4 -1.2 -1.7 -2.3 -2.7 -2.0 -1.5 -0.9 -0.6 -0.2 0.3 1.4 2.1 2.3 2.0 1.9 2.0 2.1 0.9 0.0 1973 14.5 12.9 12.4 14.2 13.0 13.8 12.6 12.5 12.7 12.7 13.1 12.5 13.5 13.6 13.5 13.4 13.1 12.4 13.2 13.2 13.7 13.7 13.6 12.4 13.0 13.7 14.2 14.3 13.5 14.0 13.4 13.5 13.3 14.1 13.2 12.6 13.9 13.7 13.7 13.9 14.4 15.5 16.5 diff -0.4 -0.6 0.0 -0.4 0.1 -0.2 1.4 0.8 0.6 -0.1 -0.5 -0.4 -0.1 0.1 0.0 -0.1 0.4 0.6 -0.1 -0.5 -0.6 -1.1 -0.6 0.2 0.1 -0.5 -0.6 -0.4 0.7 0.5 0.6 0.4 0.4 0.5 1.1 0.7 0.2 -0.3 -0.1 0.0 0.1 -1.2 -0.9 1961 14.1 12.3 12.4 13.8 13.1 13.6 13.9 13.3 13.3 12.6 12.6 12.2 13.5 13.8 13.5 13.3 13.5 13.0 13.0 12.8 13.1 12.7 13.1 12.6 13.1 13.3 13.6 13.9 14.3 14.4 14.0 13.9 13.7 14.7 14.2 13.4 14.1 13.4 13.6 13.9 14.5 14.3 15.7 year 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1971 MIL + const 1969 14.1 18.0 and integrating TOR 1967 13.5 year 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1965 const = ave(MIL) - ave(TOR) MIL 15.1 13.5 13.0 14.8 13.6 14.4 13.2 13.1 13.3 13.3 13.7 13.1 14.2 14.2 14.1 14.0 13.7 13.0 13.8 13.9 14.3 14.3 14.3 13.1 13.6 14.4 14.8 14.9 14.2 14.6 14.0 14.2 13.9 14.7 13.8 13.2 14.5 14.3 14.3 14.5 15.0 16.1 17.1 1963 STEP 1: adding a constant value to MIL TOR 14.1 12.3 12.4 13.8 13.1 13.6 13.9 13.3 13.3 12.6 12.6 12.2 13.5 13.8 13.5 13.3 13.5 13.0 13.0 12.8 13.1 12.7 13.1 12.6 13.1 13.3 13.6 13.9 14.3 14.4 14.0 13.9 13.7 14.7 14.2 13.4 14.1 13.4 13.6 13.9 14.5 14.3 15.7 1961 STEP 0: the data year 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Craddock test – Bologna precipitazioni record News about a damage to the pluviometer. In corrispondence with repairing the damage, the cause of the underestimation of precipitation has been removed for the period 1900-1928 “All’inizio del 1857 a questo pluviometro, 6000 ridotto in cattivo stato pel lungo uso, ne venne sostituito un altro di migliore costruzione, e lavorato con 5000molta precisione...” 4000 3000 2000 1000 0 Change in data origin: from “Osservatorio Astronomico” to “Istituto Idrografico” -1000 -2000 Introduction of a new pluviometer (Fuess recorder): “... fu collocato a cura del prof Bernardo Dessau nel periodo 1900-1903 ...” -3000 -4000 -5000 CRADD-FER CRADD-PAD CRADD-VAL CRADD-PAR CRADD-FIR CRADD-REM CRADD-ARE CRADD-MAN CRADD-PIA CRADD-ROV 2000 1995 1990 1985 1980 1975 1970 1965 1960 1955 1950 1945 1940 1935 1930 1925 1920 1915 1910 1905 1900 1895 1890 1885 1880 1875 1870 1865 1860 1855 1850 1845 1840 1835 1830 1825 1820 1815 1810 -6000 Both direct and indirect methodologies have severe limits Direct methodologies are not easy to use as: 1) it is generally very difficult to recover complete information on the history of the observations (metadata); 2) even if available, metadata hardly give quantitative estimates of the inhomogeneities in the measures. Also indirect methodologies have important deficits: 1) they require some hypothesis about the data (e.g. homogeneous signals over the same region); 2) inhomogeneities and errors are present in all meteorological series, and so it is often difficult to decide where to apply corrections and, when the results are not clear there is a high risk of applying subjective corrections. How to overcome the intrinsic limit of indirect homogenisation methods is, at present, still an open question. The possibilities range from homogenising all suspect periods, to correcting the series only if the results of the statistical methods are very clear and also supported by metadata. So, at present, an universal approach to manage the problem is lacking. Our approach: 1) Collecting as much metadata as possible; 2) Performing a first homogenisation by means of direct methologies; 3) Performing final homogenisation by means of indirect methologies Important open question: trends critically depend on the methods used to homogenise the data North Italy long-term temperature evolution (filtered curves) in the 18761996 period according to Brunetti et al. (2000) and Boehm et al. (2001). 1.0 Po Valley (Boehm et al., 2001) NITA (Brunetti et al., 1999) 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 -0.6 -0.8 1984 1972 1960 1948 1936 1924 1912 1900 1888 1876 -1.0 Adapted from: Brunetti, M., Buffoni, L., Maugeri, M., Nanni, T., 2000: Trends of minimum and maximum daily temperatures in Italy from 1865 to 1996. Theor. Appl. Climatol., 66, 49-60 and Böhm, R., Auer, I., Brunetti, M., Maugeri, M., Nanni, T., Schöner W., 2001: Regional Temperature Variability in the European Alps 1760-1998 from homogenised instrumental time series. Int. J. Climatol., 21, 1779-1801. Important open question: trends critically depend on the methods used to homogenise the data Long-term evolution of summer temperatures in the 1775-2003 period according to Auer et al. (2007) and Brunetti et al. (2006). 3.0 2.0 1.0 0.0 -1.0 -2.0 Differences between ALPIMP (South) and NITA temperature anomalies - Summer 1999 1991 1983 1975 1967 1959 1951 1943 1935 1927 1919 1911 1903 1895 1887 1879 1871 1863 1855 1847 1839 1831 1823 1815 1807 1799 1791 1783 1775 -3.0 The homogenisation of the Italian records Brunetti M, Maugeri M, Monti F, Nanni T. 2006. Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series. Int. J. Climatol. Mean T Maximum T Minimum T Precipitation N. of years (excluding filled gaps) 8292 5848 5848 13355 N. of breaks 766 347 398 170 N. of break per series 11.43 7.23 8.29 1.53 N. of break per year per series 0.092 0.059 0.068 0.013 Mean homogeneous sub-period (years) 10.8 16.9 14.7 78.6 More infos….. Action ES0601: Advances in homogenisation methods of climate series: an integrated approach (HOME) Long instrumental climate records are the basis of climate research. However, these series are usually Notice board affected by inhomogeneities (artificial shifts), due to changes in the measurement conditions The Action (relocations, instrumentation and others). As the number is not artificial shifts often have the same magnitude as the climate signal, such as long-term variations, trends or cycles, a direct analysis of the raw data series can lead to wrong conclusions about climate change. In order to deal with this crucial problem many statistical homogenisation procedures have been developed for detection and correction of these inhomogeneities. At present only a limited number of publications intercompare some common methods and their impact on the climate record. The large number of different methods could be seen as a weakness in the science and is a challenge for the climatological community to address. There is therefore a need for a coordinated European initiative in order to produce standard methods designed to facilitate such comparisons and promote the most efficient methods of homogenisation. The Action's main objective is to achieve a general method for homogenising climate and environmental datasets. The method will be derived from the most adapted statistical procedures for detection and correction of varying parameters at different space and time scales. Keywords: Homogenisation, Climate Change, Statistics disclaimer | copyrights valid > Parties > Management Committee > Non COST Institutions > Download MoU > Download Progress Report > Action ES0601 Fact Sheet Data analysis Anomalie records - Station clustering Principal Component Analysis (PCA) Anomalie records - Gridding Anomalie records - Spatial patterns From the anomalie records to the absolute value ones Some results: temperature Year and seasons +1.7: 2003 W S Sp A -2.2: 1816 Brunetti M, Maugeri M, Monti F, Nanni T. 2006. Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series. Int. J. Climatol. 26, 345-381 Some results: temperature TREND (˚C/100y) Tmed Tmax Tmin AL PP PI ITA AL PP PI ITA AL PP PI ITA Y 1.0±0.1 1.0±0.1 1.0±0.1 1.0±0.1 0.8±0.1 1.1±0.1 0.7±0.1 0.9±0.1 1.2±0.1 0.9±0.1 1.3±0.1 1.1±0.1 W 1.2±0.2 1.0±0.3 1.0±0.2 1.1±0.2 1.2±0.2 1.2±0.3 0.8±0.2 1.0±0.2 1.4±0.2 1.1±0.3 1.2±0.2 1.2±0.2 Sp 1.0±0.2 1.0±0.2 1.0±0.2 1.0±0.2 0.9±0.2 1.2±0.2 0.7±0.2 0.9±0.2 1.2±0.1 0.9±0.2 1.2±0.1 1.0±0.1 S 1.0±0.2 1.1±0.2 1.2±0.2 1.1±0.2 0.4±0.2 1.1±0.2 0.7±0.2 0.9±0.2 1.2±0.2 0.9±0.2 1.6±0.2 1.2±0.2 A 0.8±0.2 0.8±0.2 0.9±0.2 0.8±0.2 0.6±0.2 0.9±0.2 0.6±0.2 0.8±0.2 1.0±0.2 0.8±0.2 1.1±0.2 0.9±0.2 Brunetti M, Maugeri M, Monti F, Nanni T. 2006. Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series. Int. J. Climatol. 26, 345-381 Some results: precipitation Year and seasons Brunetti M, Maugeri M, Monti F, Nanni T. 2006. Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series. Int. J. Climatol. 26, 345-381 W S Sp A Some results: precipitation TREND (%/100y) NW NEN PP CE SE SO ITA Y - - - -(10±3) -(8±5) + -(5±3) W - + + - - + - Sp - - - -(20±5) - - -(9±5) S - - + -(13±8) - - - A - - - - - + - Brunetti M, Maugeri M, Monti F, Nanni T. 2006. Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series. Int. J. Climatol. 26, 345-381 GAR Precipitation series and running trend analysis. The y axis in running trend figures represents the window width, and the x axis the central years of the windows over which the trend is calculated. Only trends having a significance greater than 90% are plotted. 200 y 120 y 1840 40y 40y 30y 30y 1820 1970 1940 1960 And what about other Mediterranean countries Begert M, Schlegel T, Kirchhofer W. 2005. Homogeneous temperature and precipitation series of Switzerland from 1864–2000. International Journal of Climatology 25: 65–80. Boehm R, Auer I, Brunetti M, Maugeri M, Nanni T, Sch¨oner W. 2001. Regional temperature variability in the European Alps: 1760–1998 from homogenised instrumental time series. International Journal of Climatology 21: 1779–1801. Brunet, M., Jones, P.D., Sigro, J., Saladie, O., Aguilar, E., Moberg, A., Della-Marta, P.M., Lister, D., Walther, A., and Lopez, D., 2007 “Temporal and spatial temperature variability and change over Spain during 1850-2005” Journal of Geophysical Research, 112, D12117, doi:10.1029/2006JD008249 Trend of the mean, of the variance and extreme values Daily Precipitation 1 2 3 4 5 6 7 8 9 10 Bo 0.0 0.0 0.0 34.1 5.4 37.5 41.3 7.0 0.0 0.0 Fe 0.0 0.0 15.1 5.1 9.8 7.6 0.0 4.5 0.0 0.0 Ge 0.0 0.0 0.0 4.3 11.1 35.4 13.5 55.6 9.7 7.5 Mn 0.0 0.0 0.0 40.3 11.3 6.4 3.4 38.8 0.7 0.5 Mi 0.0 0.0 0.5 15.4 30.7 22.2 1.8 42.4 0.0 0.0 0.0 – 2.5 2.5-12.5 12.5-25.0 Series to analyse: ratios precipitazioni of each class and total precipitation Selection of the classes….. 25.0-50.0 >50.0 i pij i p i 1 Distribuzione Gamma x 1 e ( ) f ( x) 0 x f(x) 0.1 0.01 0.001 0.0001 0 10 20 30 40 50 60 x0 α Shape parameter β Scale parameter [mm-1] Influenza la forma della curva: È indicativo dell’intensità: α piccolo la media è piccola rispetto alla deviazione standard β piccolo alta intensità di precipitazioni α grande la curva tende ad una gaussiana (per α>50 la differenza da una gaussiana è trascurabile) β grande bassa intensità di precipitazioni mm 1 x0 0.8 F(x) 0.6 0.4 Distribuzione cumulativa 0.2 0 0 10 20 30 40 mm 50 60 x 1 1 t F ( x) t e dt ( ) 0 NORD SUD 50 50 40 40 30 30 20 20 % % 10 10 0 0 -10 -10 -20 -20 1 3 5 categorie 7 9 1 3 5 7 9 categorie Brunetti, M., Buffoni, L., Maugeri, M., Nanni, T., 2000: Precipitation intensity trends in Northern Italy. Int. J. Climatol., 20, 1017-1031. Brunetti, M., Colacino, M., Maugeri, M., Nanni, T., 2001: Trends in the daily intensity of precipitation in Italy from 1951 to 1996, Int. J. Climatol., 21, 299-316. Brunetti, M., Maugeri, M., Nanni, T., 2001: Changes in total precipitation, rainy days and extreme events in northeastern Italy, Int. J. Climatol., 21, 861-871. Trend delle Classi di Precipitazioni (%/100y) NW % Brunetti M, Maugeri M, Monti F, Nanni T. 2004. Changes in daily precipitation frequency and distribution in Italy over the last 120 years. Journal of Geophysical Research Atmosphere, 109, D05, doi:10.1029/2003JD004296, 2004. W NEN 50 50 30 30 30 30 30 10 10 10 10 % -10 % -10 -30 -30 -30 -50 -50 -50 2 3 4 5 6 1 2 6 1 2 3 4 5 6 -30 -50 1 2 Category 3 4 5 6 1 50 50 50 30 30 30 30 10 10 10 10 % % -10 % -10 -30 -30 -30 -50 -50 -50 -50 2 3 4 5 6 1 2 3 4 5 6 1 2 Category 3 4 5 % -10 -30 6 2 3 4 5 6 1 50 50 50 30 30 30 10 10 10 10 % -30 -30 -30 -50 -50 -50 -50 3 4 5 6 1 2 3 4 5 6 1 Category 50 30 10 % -10 -30 -50 50 30 10 % -10 -30 -50 2 3 4 5 6 4 6 5 6 10 -10 -30 -50 1 Category 50 30 10 % -10 -30 -50 % -10 -30 3 Category 30 2 2 Category -10 5 -50 1 50 % 6 -30 Category -10 5 10 30 % 4 -10 50 -10 3 Category 50 -10 2 Category 30 2 3 4 5 6 1 Category 50 30 10 % -10 -30 -50 2 3 4 Category 50 30 10 % -10 -30 -50 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 Category Category Category Category Category 50 50 30 10 % -10 -30 -50 30 Y 5 Category Category % 4 10 -10 50 1 A 3 % -10 -50 Category S % -10 -30 1 % SO 50 Category Sp CE 50 1 % NES 50 10 -10 -30 -50 1 2 3 4 5 6 Category Significance level > 99% 50 30 10 % -10 -30 -50 50 30 10 % -10 -30 -50 50 30 10 % -10 -30 -50 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 Category Category Category Category Significance level > 95% Significance level > 90% NORD SUD 100 100 1981-2000 80 60 % 60 1951-1980 1951-1980 % 40 40 20 20 0 0 1950 1981-2000 80 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000 Brunetti, M., Maugeri, M., Nanni, T. Navarra A., 2002, Droughts and extreme events in regional daily Italian precipitation series, Int. J. Climatol., 22, 543-558 Il ruolo del progetto FIRB Cloudiness Nord 066 076 080 098 094 099 090 059 110 6 084 140 158 Sud 040 020 6 Inverno 6 Primavera 6 Inverno Primavera 146 149 172 206 216 5 5 5 5 4 4 4 4 3 3 3 3 230 234 242239 232 244 280 258 261 252 289 520 300 270 312 320 332 310 550 360 560 350 400 420 450 470 460 1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000 480 5 5 Estate 5 Estate 4 4 3 4 3 3 2 3 2 2 1 2 1950 1960 1970 1980 1990 2000 Maugeri M, Bagnati Z, Brunetti M. 2001. Trends in Italian total clouds amount, 1951-1996. 4 Autunno 6 1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000 5 Anno 5 4 4 3 Autunno 1950 1960 1970 1980 1990 2000 Anno Geophysical Research Letters - 28, 24, 4551-4554, 2001. 3 1950 2 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000 Links with atmospheric circulation 1951-2000 period: coherent picture: Positiv trend: T (Tmax > Tmin), DTR, Precipitation Intensity Negativo trend: P, Rainy days, Cloudiness Central and Northern Europe: completely different pattern Atmospheric circulation Colacino e Conte (early ’90): increase of the frequency of subtropical anticyclones on the central/western Mediterranean Our contribution SLP existing records (UK Met Office – NCAR/NCEP) New records (progetti UE ALPIMP e COFIN 2001) 80 Brunetti, M., Maugeri, M., Nanni, T., 2002: Atmospheric circulation and precipitation in Italy for the last 50 years, Int. J. Climatol., 22, 1455-1471. 60 40 Positiv trend: winter 20 -60 -40 -20 0 20 40 SLP - WINTER AVERAGES 10 8 1951-1980 1981-2000 6 4 Maugeri, M., Brunetti, M., Monti, F., Nanni, T., 2003, Trends in Italian sea level pressure. Il Nuovo Cimento 2 hPa 0 -2 -4 -6 -8 -10 1950 1960 1970 1980 YEAR 1990 2000 60 …. But up to now… more ideas than results… 3. Relations Between Variability in the Mediterranean Region and Mid-Latitude Variability. 1. 2. 3. 4. 5. 6. 7. 8. 9. Introduction. Modes of Atmospheric Circulation and their Impact. Temperature Variability. Precipitation Variability. Trends. Other Important Forcing Factors. Future Outlook. Acknowledgments. References. Extension of the spazial scale (Data-set HISTALP + …) Integration with data with higher resolution spazial-temporal lagrangian vs eulerian approach Trend per century (estimated over the period 1887-2002) SL>90% SL>95% SL>99% SUNSHINE DURATION AND CLOUD COVER TEMPERATURE AND VAPOR PRESSURE CLOUD COVER AND PRECIPITATION 1887 ARE THESE RELATIONSHIPS STABLE THROUGHT THE WHOLE PERIOD SPANNED BY THE DATA?