SUMMER SCHOOL: CLIMATE CHANGES IN THE MEDITERRANEAN AREA

1. 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 maurizio.maugeri@unimi.it Enna – September 6th, 2008

2. atmo hydro crio lito bio Climate System and complexity The atmosphere is strongly influenced by the other parts of the climate system  Modelling the climate system is extremely difficult

3. Climate System and complexity What can we do? Using the observations!

4. 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:

5.  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

6. Availability of long-period and high-quality 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 long-term 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

7. So it is very important to increase the availability of long-period and high-quality climatic instrumental time series of the Mediterranean area

8. 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.

9. 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).

10. As a consequence, a heritage of data of enormous value has been accumulated in Italy over the last three centuries

11. 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.

12. … 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 Archivio delle serie secolari UCEA - Anzaldi C., Mirri L. and Trevisan V., 1980: Archivio Storico delle osservazioni meteorologiche, Pubblicazione CNR AQ/5/27, Roma.

13. 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…

14. Projects concerning single stations are particularly important for the records beginnig in the 18th century Milan: a 10-year project developed by Osservatorio Astronomico di Milano-Brera and Milan University allowed to recovery metadata and daily T, P, R records Padova: as for Milan but activities performed by Istituto di Scienze dell'Atmosfera e del Clima – section of Padova Torino: as for Milan and Padova but activities performed by Società Meteorologica Italiana Palermo: recovery started later on; The activities are performed by Os. Astronomico. Available: metadata and daily R and T records. Bologna: as for Milan, Padova and Torino for the data after 1813. Still in progress for the 18th century data Roma: as for Milan, Padova and Torino for the data after 1862. Only monthly data for the 18th century …there is a lot of still unexploited information… Cloudiness, sunshine, vapour pressure, wind, etc…

15. 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.

16. Meteorological variables • Air Temperature (minimum, mean, maximum) • Precipitation • Air Pressure • Cloud Cover • Other data The new dataset of Italian secular records Temporal resolution Daily/Monthly

17. The new Italian dataset: air temperature

18. The new Italian dataset: air temperature

19. The new Italian dataset: precipitation

20. The new Italian dataset: precipitation

21. 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) CLOUD COVER (secular records) HUMIDITY (i.e. dry / wet temperatures) daily data 2 records 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.

23. The new Italian dataset: metadata • Metadata collection was performed with two main objectives: • to understand the evolution of the Italian meteorological network • 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)

24. 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

26. Data and metadata: integration with other data-set The HISTALP data-set

27. 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 non-climatic 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.

28. 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

29. 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

30. 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.

31. 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

33. 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, ridotto in cattivo stato pel lungo uso, ne venne sostituito un altro di migliore costruzione, e lavorato con molta precisione...” Change in data origin: from “Osservatorio Astronomico” to “Istituto Idrografico” Introduction of a new pluviometer (Fuess recorder): “... fu collocato a cura del prof Bernardo Dessau nel periodo 1900-1903 ...”

34. 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. Both direct and indirect methodologies have severe limits

35. 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.

36. Collecting as much metadata as possible; • Performing a first homogenisation by means of direct methologies; • 3) Performing final homogenisation by means of • indirect methologies So, at present, an universal approach to manage the problem is lacking. Our approach:

37. Important open question: trends critically depend on the methods used to homogenise the data North Italy long-term temperature evolution (filtered curves) in the 1876-1996 period according to Brunetti et al. (2000) and Boehm et al. (2001). 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.

38. 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).

39. 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.

40. More infos….. Notice board

41. Data analysis Anomalie records - Station clustering Principal Component Analysis (PCA)

42. Anomalie records - Gridding

43. Anomalie records - Spatial patterns

44. From the anomalie records to the absolute value ones

45. Some results: temperature Year and seasons +1.7: 2003 W S -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 Sp A

46. Some results: temperature TREND (˚C/100y) 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

48. Some results: precipitation Year and seasons W S 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 Sp A

49. Some results: precipitation TREND (%/100y) 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