maurizio maugeri istituto di fisica generale applicata milan university

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
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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
x0
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
x0
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?