CPClimate of the PastCPClim. Past1814-9332Copernicus PublicationsGöttingen, Germany10.5194/cp-13-1451-2017Climate signals in a multispecies tree-ring network from central and
southern Italy and reconstruction of the late summer temperatures since the
early 1700sLeonelliGiovannigiovanni.leonelli@unimib.itCoppolaAnnaSalvatoreMaria CristinaBaroniCarloBattipagliaGiovannahttps://orcid.org/0000-0003-1741-3509GentilescaTizianaRipulloneFrancescohttps://orcid.org/0000-0003-4851-3422BorghettiMarcoConteEmanueleTognettiRobertoMarchettiMarcoLombardiFabioBrunettiMichelehttps://orcid.org/0000-0003-3487-2221MaugeriMaurizioPelfiniManuelahttps://orcid.org/0000-0002-3258-1511CherubiniPaoloProvenzaleAntonellohttps://orcid.org/0000-0003-0882-5261MaggiValterhttps://orcid.org/0000-0001-6287-1213Dept. of Earth and
Environmental Science, Università degli Studi di Milano–Bicocca, Milan, ItalyDept. of Earth Sciences, Università degli Studi di Pisa, Pisa, ItalyIstituto di Geoscienze e Georisorse, Consiglio Nazionale delle
Ricerche, Pisa, ItalyDept. DiSTABiF, Università degli Studi della Campania “L. Vanvitelli”, Caserta, ItalyPALECO EPHE, University of Montpellier 2, Montpellier, FranceSchool of
Agricultural, Forestry, Food and Environmental Sciences, Università degli Studi della Basilicata, Potenza, ItalyDept. of Biosciences and
Territory, Università degli Studi del Molise, Campobasso, ItalyDept. of Agricultural, Environmental and Food Sciences, Università degli Studi del Molise, Campobasso, ItalyDept. of Agronomy, Università Mediterranea di Reggio Calabria, Reggio Calabria, ItalyIstituto di Scienze dell'Atmosfera e del Clima, Consiglio Nazionale
delle Ricerche, Bologna, ItalyDept. of Environmental Science and Policy, Università degli Studi di Milano, Milan, ItalyDept. of Earth Sciences, Università degli Studi di Milano, Milan, ItalySwiss Federal Institute for Forest, Snow and Landscape Research
WSL, Birmensdorf, SwitzerlandGiovanni Leonelli (giovanni.leonelli@unimib.it)2November201713111451147114March201717March201725August201716September2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://cp.copernicus.org/articles/13/1451/2017/cp-13-1451-2017.htmlThe full text article is available as a PDF file from https://cp.copernicus.org/articles/13/1451/2017/cp-13-1451-2017.pdf
A first assessment of the main climatic drivers that modulate the
tree-ring width (RW) and maximum latewood density (MXD) along the Italian
Peninsula and northeastern Sicily was performed using 27 forest sites, which
include conifers (RW and MXD) and broadleaves (only RW). Tree-ring data were
compared using the correlation analysis of the monthly and seasonal variables
of temperature, precipitation and standardized precipitation index (SPI, used
to characterize meteorological droughts) against each species-specific site
chronology and against the highly sensitive to climate (HSTC) chronologies
(based on selected indexed individual series). We find that climate signals
in conifer MXD are stronger and more stable over time than those in conifer
and broadleaf RW. In particular, conifer MXD variability is directly
influenced by the late summer (August, September) temperature and is
inversely influenced by the summer precipitation and droughts (SPI at a
timescale of 3 months). The MXD sensitivity to August–September (AS) temperature and to summer
drought is mainly driven by the latitudinal gradient of summer precipitation
amounts, with sites in the northern Apennines showing stronger climate
signals than sites in the south. Conifer RW is influenced by the temperature
and drought of the previous summer, whereas broadleaf RW is more influenced
by summer precipitation and drought of the current growing season. The
reconstruction of the late summer temperatures for the Italian Peninsula for
the past 300 years, based on the HSTC chronology of conifer MXD, shows a stable
model performance that underlines periods of climatic cooling (and likely
also wetter conditions) in 1699, 1740, 1814, 1914 and 1938, and follows well the
variability of the instrumental record and of other tree-ring-based
reconstructions in the region. Considering a 20-year low-pass-filtered series,
the reconstructed temperature record consistently deviates < 1 ∘C
from the instrumental record. This divergence may also be due
to the precipitation patterns and drought stresses that influence the
tree-ring MXD at our study sites. The reconstructed late summer temperature
variability is also linked to summer drought conditions and it is valid for
the west–east oriented region including Sardinia, Sicily, the Italian
Peninsula and the western Balkan area along the Adriatic coast.
Introduction
Climate reconstructions for periods before instrumental records rely on
proxy data from natural archives and on the ability to date them. Among the
available proxies, tree rings are one of the most used archives for
reconstructing past climates with annual resolution in continental areas and
they often come from the temperature-limited environments with high
latitudes and altitudes (e.g., Briffa et al., 2004; Rutherford et al.,
2005). Tree-ring data can be used at regional to global scales (IPCC, 2013)
and long chronologies covering millennia, going back as far as the early
Holocene, are available (for Europe: Becker, 1993; Friedrich et al., 2004;
Nicolussi et al., 2009).
The reconstruction of past climate variability and the analysis of its
effects on forest ecosystems are crucial elements for understanding climatic
processes and for predicting what responses should be expected in ecosystems
under the ongoing climatic and global changes. In particular, the
Mediterranean region is a prominent climate change hot spot (Giorgi, 2006;
Turco et al., 2015), and by the end of this century, it will likely
experience a regional warming higher than the global mean (up to +5 ∘C in summer)
and a reduction of the average summer precipitation
(up to -30 %; Somot et al., 2007; IPCC, 2013). As a consequence of the
poleward expansion of the subtropical dry zones (e.g., Fu et al., 2006),
subtropical environments under climate change are already facing strong
hydroclimatic changes due to lower precipitation and human exploitation
(e.g., in southwestern North America; Seager et al., 2007; Seager and
Vecchi, 2010). Moreover, in these environments (including the
Mediterranean region), soil moisture will likely drop, resulting in a
contraction of temperate drylands by approximately a third (converting into
subtropical drylands), and longer periods of drought in deep soil layers are
expected (Schlaepfer et al., 2017). The increase in drought conditions
during the growing season is already negatively impacting tree growth,
especially at xeric sites in the southwestern and eastern Mediterranean
(e.g., Galván et al., 2014). At the ecosystem level, in the near future,
the responses to climate changes will impact the various forest species in a
different way, depending on their physiological ability to acclimate and
adapt to the new environmental conditions (e.g., Battipaglia et al., 2009;
Ripullone et al., 2009), and on their capacity to grow, accumulate biomass
and contribute as sinks in the terrestrial carbon cycle. Natural summer
fires in the Mediterranean area are also expected to increase in frequency
over the coming decades as a response to increasingly frequent drought
conditions, assuming a lack of additional fire management and prevention
measures (Turco et al., 2017).
Tree-ring response to climate
Climate–growth relationships have been studied for several species in the
Mediterranean region, with different objectives: forest productivity (e.g.,
Biondi, 1999; Boisvenue and Running, 2006; Nicault et al., 2008; Piovesan et
al., 2008; Babst et al., 2013), tree ecophysiology, wood formation and
related dating issues (Cherubini et al., 2003; Battipaglia et al., 2014),
sustainability of forest management (e.g., Boydak and Dogru, 1997; Barbati
et al., 2007; Marchetti et al., 2010; Castagneri et al., 2014), provision of
ecosystem services (e.g., Schröter et al., 2005) such as carbon
sequestration (e.g., Scarascia-Mugnozza and Matteucci, 2014; Calfapietra et
al., 2015; Borghetti et al., 2017), effective biodiversity conservation
(e.g., Todaro et al., 2007; Battipaglia et al., 2009) and climate
reconstruction (see next section), which have led to a variety of
associations between climate variables and growth responses in conifers and
broadleaves from different environments and ecosystems. Mainly considering
the species of this study, we report the main findings on the climate–growth
responses found in this region.
Conifers. Studies on silver fir (Abies alba Mill.) growth in the Italian Peninsula reveal high
sensitivity to the climate of the previous summer, August-1 in
particular, and show positive correlations with precipitation and negative
correlations with temperature (Carrer et al., 2010; Rita et al., 2014).
Moreover, tree growth in this region is moderately negatively correlated
with the temperature of the current summer (unlike that in stands located in
the European Alps; Carrer et al., 2010), namely, high temperatures in July
and August negatively affect tree growth. A dendroclimatic network of pines
(Pinus nigra J. F. Arnold and P. sylvestris L.) in east-central Spain shows that drought (namely, the
standardized precipitation–evapotranspiration index – SPEI; Vicente-Serrano
et al., 2010) is the main climatic driver of tree-ring growth (Martin-Benito
et al., 2013). In a P. uncinata network from the Pyrenees, an increasing influence of
summer droughts (SPEI) on tree-ring widths (RW) during the 20th century as
well as the control of May temperatures on maximum latewood density (MXD) is found
(Galván et al., 2015). However, in the abovementioned analyses, the
possible influences of the summer climate variables from the year prior to
the growth were not considered. Elevation, and particularly the related
moisture regime, in the eastern Mediterranean region is the main driver of
tree-ring growth patterns in a multispecies conifer network comprised of P. nigra,
P. sylvestris and P. pinea L. specimens (Touchan et al., 2016). A dipole pattern in tree-ring
growth variability is reported for Mediterranean pines ranging from Spain to
Turkey, with higher sensitivity to summer drought in the east than in the
west, and with higher sensitivity to early summer temperature in the west
(Seim et al., 2015). Strong correlations between autumn-to-summer
precipitation and tree-ring growth and between summer drought and tree-ring growth have been reported
for sites (mainly of conifers) in northern Africa–western Mediterranean,
with trees in Morocco also responding to the North Atlantic Oscillation
index (Touchan et al., 2017).
Broadleaves. In the western Mediterranean (northern Morocco, Algeria, Tunisia, Italy
and southern France), deciduous oaks, including Quercus robur L., reveal a direct
response of tree-ring growth to summer precipitation and an inverse response
to summer temperature (Tessier et al., 1994). Beech (Fagus sylvatica L.) is particularly
sensitive to soil moisture and air humidity; in past decades, long-term
drought conditions have been shown to be the main factor causing a growth
decline in the old-growth stands in the Apennines (Piovesan et al., 2008).
Beech shows different responses to climate at high- vs. low-altitude sites
(Piovesan et al., 2005), with the latter being positively affected by high
May temperatures. Despite an expected higher drought sensitivity stress
close to the southern limit of the distribution area, a late 20th
century tree-ring growth increase in beech has been reported in Albania
(Tegel et al., 2014), thus underlining the different climate–growth
responses in the Mediterranean region. Beech, indeed, presents complex
climate growth-responses and also appears to be a less responsive species in
the Mediterranean area when compared to conifers such as P. sylvestris, P. nigra, P. uncinata or A. alba (as found in
southeastern France; Lebourgeois et al., 2012).
Tree-ring-based climate reconstructions
One of the most powerful tools in terrestrial paleoclimatology is obtaining
date information about the past climate and past environmental conditions
in a region by analyzing the tree rings. However, in the Mediterranean
region, the low temporal stability of the recorded climatic signals (e.g.,
Lebourgeois et al., 2012; Castagneri et al., 2014), the scarcity of long
chronologies and the high variability of climatic and ecological conditions
(Cherubini et al., 2003) often make this analysis difficult. Ring widths are
among the most used variables for climate reconstruction but they usually
show higher temporal instability in their relationship with climate than
that of maximum latewood density (for the Pyrenees, see Büntgen et al.,
2010).
The potential to analyze relatively long chronologies in the Mediterranean
region has allowed for the reconstruction of the past climate (mainly
precipitation and droughts). Several reconstructions of May–June
precipitation have been performed, mainly over the last 300–400 years, in a
region including northern Greece, Turkey and Georgia: in northern
Aegean–northern Anatolia a tree-ring network of oaks was used for
reconstructing precipitation variability from 1089 (Griggs et al.,
2007); in the Anatolian Peninsula a mixed conifer–broadleaf tree-ring
network (mainly P. nigra, P. sylvestris and oaks;
Akkemik et al., 2008), a P. nigra network (Köse et
al., 2011) and a multi-species conifer network (mainly P. nigra,
P. sylvestris and Abies nordmanniana (Steven) Spach;
Köse et al., 2013) were used. In the western Mediterranean, in central
Spain, higher frequency of exceptionally dry summers has been detected to
exist since the beginning of the 20th century using a mixed tree-ring network of
Pinus sylvestris and P. nigra ssp. salzmannii covering the past four centuries (Ruiz-Labourdette et al.,
2014), whereas a 800-year temperature reconstruction from southeastern Spain
using a site of P. nigra underlined predominantly higher summer temperatures during
the transition between the Medieval Climate Anomaly and the Little Ice
Age (Dorado Liñán et al., 2015). A recent reconstruction of
spring–late summer temperature from the Pyrenees by means of a P. uncinata MXD network
dating back to 1186 (Büntgen et al., 2017) underlines warm conditions
around 1200 and 1400 and after 1850.
Reconstructions of past droughts and wet periods over the Mediterranean
region have been created using climatic indices such as the standardized
precipitation index (SPI; McKee et al., 1995) in Spain (modeling 12-month
July SPI using several species of the Pinus genus; Tejedor et al., 2016), and in
Romania (modeling 3-month August standardized SPI using P. nigra; Levanič et al.,
2013), which allows for the identification of common large-scale synoptic
patterns. Droughts have been reconstructed using the Palmer drought severity
index (PDSI; Palmer, 1965). Using actual and estimated multispecies tree-ring
data, Nicalut et al. (2008) found that the drought episodes at the end of
the 20th century are similar to those in the 16th and 17th centuries for the
western Mediterranean, whereas in the eastern parts of the region, the
droughts seem to be the strongest recorded in the past 500 years.
Early summer temperature has been reconstructed for 400 years in Albania, from
a P. nigra tree-ring network, finding stable climate–growth relationships over time
and a spatial extent of the reconstruction spanning over the Balkans and
southern Italy (Levanič et al., 2015). Currently, two summer temperature
reconstructions close to the study area and based on maximum latewood
density (MXD) chronologies are available: (1) a reconstruction of AS
temperatures published by Trouet (2014) covering the period 1675–1980 and
centered on the northeastern Mediterranean–Balkan region includes sites from
the Italian Peninsula (used in this paper), the Balkan area, Greece and
sites from the central and eastern European Alps to central Romania and
Bulgaria, the latter areas being characterized by continental climates, and (2) a reconstruction of JAS
temperatures published by Klesse et al. (2015),
covering the period 1521–2010 and based on a chronology from Mt. Olympus
(Greece). As separate climate (temperature) reconstructions for the northeastern
Mediterranean–Balkan region including Italy have been published to
date, the goal of this study was to collect dendrochronological data from
Italian research groups and screen the ITRDB for suitable data for climate
reconstructions. We therefore investigate RW and MXD climate signals across
Italy. After carefully testing the climatic signals recorded in the
tree-ring RW and MXD from different sites and different species, the
reconstruction that is proposed in this study is the first one including
only forest sites from the Italian Peninsula.
Overall, the main objectives of this paper are as follows:
To identify the most important climatic drivers modulating tree-ring
width (RW) and tree-ring maximum latewood density (MXD) variability in
forest sites from central and southern Italy. To our knowledge, this is the
first attempt performed in Italy with the clear objective to find common
response patterns in conifer and broadleaf species using a multispecies
tree-ring network and site-specific historical climatic records.
To estimate the temporal stability of the climate–growth and
climate–density relationships.
To perform a climatic reconstruction based only on trees highly sensitive to climate (HSTC).
To estimate the spatial coherence of the obtained reconstruction in the
region.
Data and methodsStudy area and study sites
The study region includes the whole Italian Peninsula and eastern Sicily and
covers a latitudinal range from 37 to 44∘43′ N
(Fig. 1). The peninsula is roughly oriented NW–SE and its longitudinal axis
is characterized by the Apennines that reach their maximum altitude at their
center (Corno Grande, 2912 m a.s.l., Gran Sasso Massif); a higher
altitude is reached in eastern Sicily by the Etna Volcano (3350 m a.s.l.).
The study region is surrounded by the Tyrrhenian and Adriatic Seas and is
characterized by a typical Mediterranean climate, with high temperatures and
low precipitation during the summer (from June to September), and by a
Mediterranean-temperate regime at the higher altitudes of the Apennines
(Fig. 2). Considering the climatic means at all the study sites (at a mean
elevation of 1225 ± 520 m a.s.l.) over the period 1880–2014, the
temperatures over the study region range from 0.2 ∘C (January) to
17.6 ∘C (in July and in August) and only 11 % of the total
annual precipitation falls during the summer (from June to August: 155 mm),
whereas 34 % falls during winter (from December-1 to February: 493 mm).
Autumn is the second wettest season (31 % of total annual
precipitation) and spring is the third wettest (24 % of total annual
precipitation; Fig. 2).
Distribution of the tree-ring sites from central and
southern Italy available to the NEXTDATA project and used in this study.
Sites were subdivided by the type of tree (conifer or broadleaf), parameter (RW or MXD) and data used (site chronology or only
tree-ring series).
Monthly mean temperatures and precipitation over the
period of 1880–2014 for all sites considered in this study. For both
temperature and precipitation, the error bars indicate 1 standard
deviation; for precipitation, the seasonal percentages of precipitation with
respect to the mean annual value (i.e., 1433 mm) are reported.
The total forest cover in Italy, excluding the regions of the European Alps,
is approximately 5.8 million hectares (Corpo Forestale dello Stato, 2005),
28 % of the considered surface. Forests characterize the landscape of the
inner portion of the Apennine range, at middle to high elevations, and an
additional 1.4 million hectares are covered by woodlands and shrublands, which are
the Mediterranean “macchia” that border the forests at low
elevations and in areas relatively close to the sea. Overall, broadleaf
species are much more abundant in the study region than conifer species,
accounting for approximately three-fourths of the forest cover (Dafis,
1997).
References for all the dendrochronological data used in this
research, information on site locations, types of parameter used at each site
and the tree species. Sites are ordered along a decreasing latitudinal
gradient, after differentiating between conifers and broadleaves (horizontal
line).
Database information and site location Type of tree-ring parameter Data set nameDatabaseOriginal contributorBibliographic referenceLocation nameLatitude NLongitude EElevationRW chr.RW seriesMXD chr.Speciessource(m a.s.l.)ITRDBITAL017ITRDBOri (2015)https://www.ncdc.noaa.gov/paleo/study/4079Monte Cantiere44∘16′48′′10∘48′00′′800xPinus sp.ITRDBITAL009ITRDBSchweingruber (2015a)https://www.ncdc.noaa.gov/paleo/study/4301Abetone44∘07′12′′10∘42′00′′1400xxAbies albaITRDBITAL004ITRDBBiondi (2015b)https://www.ncdc.noaa.gov/paleo/study/2753Campolino44∘06′45′′10∘39′44′′1650xPicea abiesITRDBITAL008ITRDBSchweingruber (2015f)https://www.ncdc.noaa.gov/paleo/study/4540Mount Falterona43∘52′12′′11∘40′12′′1450xxAbies albaITRDBITAL003ITRDBBiondi (2015d)https://www.ncdc.noaa.gov/paleo/study/2760Pineta San Rossore43∘43′12′′10∘18′00′′5xPinus pineaITRDBITAL022ITRDBBecker (2015)https://www.ncdc.noaa.gov/paleo/study/2706Pratomagno Bibbiena – Appennini43∘40′12′′11∘46′12′′1050xAbies sp.ITRDBITAL012ITRDBSchweingruber (2015c)https://www.ncdc.noaa.gov/paleo/study/4374Ceppo Bosque di Martense42∘40′48′′13∘25′48′′1700xxAbies albaAbies-Abeti-SopraniUNIMOLColle Canalicchio-Abeti Soprani41∘51′40′′14∘17′51′′1350xAbies albaITRDBITAL016ITRDBSchweingruber (2015e)https://www.ncdc.noaa.gov/paleo/study/4536Monte Mattone41∘46′48′′14∘01′48′′1550xxPinus nigraITRDBITAL001ITRDBBiondi (2015a)https://www.ncdc.noaa.gov/paleo/study/2752Camosciara Mt. Amaro41∘46′12′′13∘49′12′′1550xPinus nigraITRDBITAL002ITRDBBiondi (2015c)https://www.ncdc.noaa.gov/paleo/study/2759Parco del Circeo41∘19′48′′13∘03′02′′5xPinus pineaAAIBAUNIBASRuoti (PZ)40∘42′04′′15∘43′43′′925xAbies albaITRDBITAL011ITRDBSchweingruber (2015g)https://www.ncdc.noaa.gov/paleo/study/4541Mount Pollino39∘54′00′′16∘12′00′′1720xxAbies albaITRDBITAL015ITRDBSchweingruber (2015h)https://www.ncdc.noaa.gov/paleo/study/4644Sierra de Crispo39∘54′00′′16∘13′48′′2000xxPinus leucodermisITRDBITAL010ITRDBSchweingruber (2015d)https://www.ncdc.noaa.gov/paleo/study/4420Gambarie Aspromonte38∘10′12′′15∘55′12′′1850xxAbies albaITRDBITAL013ITRDBSchweingruber (2015b)https://www.ncdc.noaa.gov/paleo/study/4304Etna Linguaglossa37∘46′48′′15∘03′00′′1800xxPinus nigraITRDBITAL019ITRDBNola (2015)https://www.ncdc.noaa.gov/paleo/study/4042Corte Brugnatella44∘43′12′′09∘19′12′′900xQuercus roburFagus-Parco-AbruzzoUNIMOLVal Cervara41∘49′00′′13∘43′00′′1780xFagus sylvaticaFagus-GarganoUNIMOLParco Nazionale del Gargano Riserva Pavari41∘49′00′′16∘00′00′′775xFagus sylvaticaFagus-MontedimezzoUNIMOLRiseva MaB Unesco Collemeluccio-Montedimezzo41∘45′00′′14∘12′00′′1100xFagus sylvaticaCervialto-FASYUNINA2Monti Picentini40∘50′23′′15∘10′03′′800xFagus sylvaticaFagus-CilentoUNIMOLParco Nazionale del Cilento Ottati40∘28′00′′15∘24′00′′1130xFagus sylvaticaQCIBGUNIBASGorgoglione (MT)40∘23′09′′16∘10′04′′820xQuercus cerrisQFIMP1UNIBASSan Paolo Albanese (PZ)40∘01′20′′16∘20′26′′1050xQuercus frainettoQFIMP2UNIBASOriolo (CS)40∘00′10′′16∘23′29′′960xQuercus frainettoFagus-SilaUNIMOLParco Sila39∘08′00′′16∘40′00′′1680xFagus sylvaticaFagus-Parco-AspromonteUNIMOLAspromonte38∘11′00′′15∘52′00′′1560xFagus sylvatica1235 meanelevation15 sites12 sites8 sites
The study sites are distributed along the whole latitudinal range of the
Italian Peninsula and tree-ring proxies include both RW and MXD series
collected within the NEXTDATA project, from Italian universities, and from
the ITRDB (www.ncdc.noaa.gov site consulted on September 2015;
see Table 1 for full bibliographic references). The data set is based on 27 forest
sites composed of several species (conifers at 16 sites, and
broadleaves at 11 sites), from which tree-ring series of conifers (RW and
MXD) and of broadleaves (RW) were prepared (Fig. 1, Table 1).
Climate variables
The availability of long and reliable time series of meteorological
variables, possibly from stations located very close to forest sites, is
crucial for estimating the climate–growth relationships. However, global or
regional climatological data sets frequently lack local resolution,
especially in remote sites. We, therefore, reconstructed synthetic records
of monthly temperature and precipitation series to be representative of the
sampled sites using the anomaly method (New et al., 2000; Mitchell and
Jones, 2005), as described in Brunetti et al. (2012). Specifically, we
reconstructed independently climatological normals following the procedure
described in Brunetti et al. (2014) and Crespi et al. (2017) by estimating
a local temperature (precipitation)–elevation relationship, and exploiting
a very high density data set from time series that are at least 30 years
long. We also estimated the deviations from the normals by means of a
weighted average of neighboring series, by exploiting the great amount of
very long and high quality temperature and precipitation series available
for Italy over the past 200–250 years (obtained from an improved version of
Brunetti et al., 2006). Finally, by the superposition of the two fields, we
obtained temporal series in absolute values for each sampling site. The
climate series start in different years due to data availability; however,
most of the series start around the mid-19th century. Finally, in order to
characterize meteorological drought conditions, we calculated the monthly
SPI at timescales of 1, 2, 3, 6, 9 and 12 months
for all sites, based on the monthly values of precipitation, using
the SPI_SL_6 code of the National Drought
Mitigation Center at the University of Nebraska (http://drought.unl.edu).
Chronology construction, climate sensitivity and climate
reconstructions
Raw data. We examined all individual series of RW and MXD for correct dating using
visual and statistical cross-dating. In particular, we used statistical
techniques to remove potential dating errors by comparing each individual
series from one site against the mean site chronology, which was constructed
excluding the analyzed individual series. Using the COFECHA software
(www.ldeo.columbia.edu), the individual series were moved
forward and backward 10 years from their initial positions, and similarity
indices were calculated over a 50-year time window, thus highlighting the
potential dating errors.
Site chronologies. We used the regional curve standardization approach (RCS; Briffa et al.,
1992; Briffa and Melvin, 2011; Esper et al., 2003) both with the RW and MXD
series to preserve the low-frequency variability in the site chronologies.
We used the ARSTAN software (ver. 44 h3, www.ldeo.columbia.edu)
and did not consider the pith offset estimates between the first measured
ring and the actual first year of growth (Esper et al., 2009; Leonelli et
al., 2016). The regional curve (RC) for the mean chronology, which was
obtained after the series alignment to the first measured ring, was smoothed
using a cubic spline with a width of 10 % of the chronology length
(Büntgen et al., 2006). We computed ratios of raw measurements vs. the
values of growth predicted by the RC for all years of the individual series,
and the resulting indexed series were averaged by a biweight robust mean to
obtain the site chronologies of RW and of MXD. We constructed the RW and MXD
site chronologies only for sites with at least 10 individual series
fulfilling the following conditions: (i) the individual series length was
> 100 years; (ii) the individual series correlation with the
respective site chronology had r > 0.3; (iii) the mean inter-series
correlation (MIC) had r > 0.3; and (iv) the expressed population
signal (EPS; Wigley et al., 1984; Briffa and Jones, 1990) was > 0.7.
We used only the individual series fulfilling these conditions to
construct the site chronologies. However, we accepted some exceptions in
order to maximize the number of sites and chronologies available for
analysis (see exceptions in Table 1).
Climate sensitivity. We assessed species-specific climate
sensitivity for the constructed RW
and MXD site chronologies over the common period of 1880–1980 using
correlation analysis and the site-specific monthly variables of temperature,
precipitation and standardized precipitation index, from March of the year
prior to growth to September of the year of growth. We computed correlations
using the DENDROCLIM software (Biondi and Waikul, 2004), applying a
bootstrap with 1000 iterations, and the obtained results were analyzed by
grouping together conifer and broadleaf species.
Testing for climate–growth relationships at the site level.
To assess the influence of environmental settings on climate–growth
relationships, for the conifer MXD site chronologies (i.e., the chronologies
holding the strongest climatic signal; see Results), we performed a
redundancy analysis (RDA) selecting the bootstrapped
correlation coefficients of climate–growth relationships (Fig. 3) as response variables and the environmental variables as
explanatory variables (geographical
characteristics and climatic averages over the period 1880–1980). In order
to attenuate co-variation within the environmental variables, we ran a
principal component analysis (PCA)
before the RDA and the following variables were finally chosen: elevation
(co-varying with longitude: our sites are placed at higher elevation at
increasing longitude; Table 1); average AS temperature; average JJA
precipitation (co-varying with latitude: higher latitude means higher
precipitation amounts); average JJAS SPI_3 (at timescale of 3 months, i.e., the timescale
resulting most significant; see Results).
Moreover, for each of the MXD site chronologies, we calculated the site
fitness (SF; Leonelli et al., 2016), representative of the percentage of
selected HSTC series of conifer MXD with respect to the total of series
available at each site.
Bootstrapped correlation analysis performed over the
common period of 1880–1980, considering chronologies of conifer MXD (left
column; a, d, g), of conifer RW (center; b, e, h) and of
broadleaf RW (right; c, f, i) vs. monthly
temperature (a, b, c), precipitation (d, e, f)
and SPI_3 (g, h, i) from March of the year prior to growth to September of the year of
growth. In (a), (b), (c) the percentages of the species composing the pool
for each site used for the analysis is reported.
Means of statistically significant (p < 0.05) correlation coefficient
values (r) are depicted with squares, whereas maximum and minimum
significant r values are indicated with grey lines; the blue lines depict
the total number of sites in each comparison and the green lines indicate
the total number of sites with statistically significant r values.
Black-filled squares are given for those variables that show significant
correlation values for at least 50 % of the total sites and have |r‾| >0.25; where both conditions occur, a
circled number in the plot is given and the comparisons are selected for the
following moving correlation analysis (Fig. 5). In each plot the climate
variables with the highest number of sites with significant r values and
nearby variables showing up to one-half of this number are depicted
with a black area.
We used the results of the climate sensitivity analysis to detect the
driving climate variables (DCVs; of temperature, precipitation and SPI)
for each of the three groups of
chronologies: MXD conifer, RW conifer and RW broadleaf. Specifically, for
each group of chronologies and for each climate variable, we first
identified the months with significant correlations at most sites
(> 50 %) and with mean correlation values of |r‾| > 0.25 (black-filled squares in Fig. 3).
Starting from the monthly climatic averages of the sites presenting
significant correlations with these selected months, we constructed regional
climate series by z scoring the monthly series of each site and calculating
regional mean departures; the series of each site were then completed over
the maximum period covered by data and reconverted in original units (based
on regional mean departures and their specific means and standard
deviations), and finally averaged between sites. We calculated the DCVs as
means of 2–4 consecutive months of the regional series, except for
August-1 temperature vs. conifer RW (according to what was obtained in
the site-level analysis of Fig. 3).
HSTC chronologies. Based on the available RW and MXD indexed individual series from all of
the sites, we constructed six HSTC chronologies, as in Leonelli et al. (2016).
However, given the smaller number of data sets available in this
study and the shortness of the time series, a modified version of the method
was applied. Specifically, we tested all of the RW (conifer and broadleaf)
and MXD (only conifer) indexed individual series against each of the
above-defined six DCVs, and we used only the individual tree-ring indexed
series with correlation values of |r‾| > 0.25 in both
of the 100-year subperiods of the climatic data set
(1781–1880 and 1881–1980) for building each of the six HSTC chronologies
(which was done by simply averaging together the selected indexed series).
We constructed the six HSTC chronologies starting from all of the indexed
individual series of conifer MXD (148 series), of conifer RW (245) and of
broadleaf RW (140), which were previously obtained, while constructing the
site chronologies (indexed individual series from sites not
meeting the fixed quality standards for a site chronology were included at
the beginning of the selection).
Climate sensitivity through time. To test the stability of the climate signals recorded in the HSTC
chronologies, we conducted a moving correlation analysis between the six
HSTC chronologies and their respective DCV, computing bootstrapped
correlation coefficients with DENDROCLIM over 60-year time windows that were
moved 1 year per iteration over the longest available periods.
Climate reconstruction. We used only the HSTC chronology showing the highest absolute values of
correlation and the most stable signal over time (i.e., the conifer MXD for
late summer temperature; see Results) for the climate reconstruction. To
extend this HSTC chronology as far back in time as possible, we also added
the oldest available individual MXD indexed series with correlations of
|r‾| > 0.25 with this chronology and
which had a minimum length of 100 years. For constructing the chronology for
climate reconstruction, we applied an arithmetic mean to the indexed series,
after having normalized all individual series over the common period
1879–1962. Moreover, to account for the changing sample size through time, a
variance stabilization of the resulting chronology was performed using
Briffa's RBAR-weighted method (Osborn et al., 1997). In order to improve
the HSTC chronology over the early period showing an EPS < ∼ 0.8
(i.e., before 1713 in the first version of the HSTC
chronology), we considered the yearly difference of the indexed normalized
series from the mean and discarded the early portion of the series exceeding
the threshold of 2.5 standard deviations in a given year (one series was
truncated at 1713, whereas the other nine fell within a common
variability). Finally, we re-normalized all series and recalculated the
final version of the HSTC chronology used for the temperature reconstruction
as described above. We calibrated and verified linear regression and scaling
models (Esper et al., 2005) over the 100-year periods 1781–1880 and 1881–1980,
respectively, and then the same was done over the inverted periods, in order
to estimate model performances and stability. We computed reduction of error
(RE; Fritts, 1976) and coefficient of efficiency (CE; Briffa et al., 1988)
statistics to assess the quality of the reconstructions. We then used the
reconstructed series of late summer temperatures over the period 1901–1980
to build a spatial correlation map with the KNMI Climate Explorer
(https://climexp.knmi.nl/; Trouet and van Oldenborgh, 2013), using the
0.5∘ grid of August–September average temperature and of AS
average precipitation (CRU TS 4.0, Climatic Research Unit, University of
East Anglia Harris et al., 2014). We used this independent data set instead
of the Italian one, as our primary goal was to analyze how far from the
Italian Peninsula the reconstructed climatology is still representative.
Main characteristics of the chronologies used in this
research, separating RW (comprised of both broadleaf and conifer species)
and MXD (only conifer species). For each site and parameter, the total
number of series available and the number of series showing a correlation
value 0.2 < r < 0.3 with the respective master chronology is
reported. Values in bold are those that do not exceed the fixed
thresholds of MIC > 0.3, EPS > 0.7 and a number of
series > 10, determining the exclusion of the chronology from
further analyses. Sites ordered as in Table 1.
RW series characteristics MXD series characteristics on the maximum period available Data set nameStartEndTimeMICaEPSbno. seriesno. series 0.2 < rStartEndTimeMIC1EPS2no. seriesno. series 0.2 < rdatedatespan< 0.3 vs. masterdatedatespanITRDBITAL017185619891340.430.76140–––––––ITRDBITAL009[1846][1980][135][0.73][0.66]130184619801350.760.86210ITRDBITAL004[1836][1988][153][0.51][0.49]110–––––––ITRDBITAL008182719801540.620.70120182719801540.660.87120ITRDBITAL0031; 2186119881280.510.7290–––––––ITRDBITAL0223[1539][1972][434][0.45][0.67]61–––––––ITRDBITAL012165419803270.570.85260165419803270.590.91250Abies-Abeti-Soprani1[1838][2005][168][0.53][0.50]110–––––––ITRDBITAL016184419801370.540.84170184419801370.430.75150ITRDBITAL001175019872380.520.77160–––––––ITRDBITAL0021187819881110.510.72160–––––––AAIBA1[1866][2007][142][0.51][0.55]130–––––––ITRDBITAL011180019801810.580.85200180019801810.540.84180ITRDBITAL015141519805660.580.95220144119805400.500.76210ITRDBITAL010179019801910.530.76190179019801910.500.85180ITRDBITAL013177319802080.570.88200179519801860.440.78180ITRDBITAL019177919892110.540.82160–––––––Fagus-Parco-Abruzzo[1716][2008][293][0.36][0.73]30–––––––Fagus-Gargano[1821][2009][189][0.23][0.42]33–––––––Fagus-Montedimezzo184420051620.670.85150–––––––Cervialto-FASY[1828][2003][176][0.39][0.52]100–––––––Fagus-Cilento[1837][2007][171][0.41][0.26]71–––––––QCIBG1; 3[1897][2013][117][0.60][0.66]90–––––––QFIMP1185120131630.500.78340–––––––QFIMP2185420131600.550.79340–––––––Fagus-Sila[1854][2009][156][0.30][0.21]43–––––––Fagus-Parco-Aspromonte[1874][2009][136][0.27][-0.42]52–––––––TOTAL178541989420540.5540.80438510175019802310.550.831480meanmeanmeanmean rmean EPSsum (all sites)sum (all sites)meanmeanmeanmean rmean EPSsumsum (all sites)
a Mean inter-series correlation of raw series, calculated using the
maximum period available at each site.
b Expressed population signal of indexed series in the common period of
1880–1980.
1 Series up to 80 years included. 2 Chronology built with fewer than 10 series (good EPS).
3 Common period with later start date or earlier end date.
4 Sites without chronology [....] are not included in the computation.
Results
Site chronologies. We obtained 15 RW site chronologies (11 from conifers and 4 from
broadleaves) and 8 MXD site chronologies (from conifers) and we used
them to estimate climate sensitivity at the site level and to detect the
most important climatic drivers over the study region (for species
percentages, see boxes in Fig. 3a, b and c). We performed the
construction of the HSTC chronologies (for the analysis of the temporal
stability of climate signals and for climate reconstruction) using also the
individual series from the 12 sites (5 from conifers and 7 from
broadleaves; see Table 1, bold values in Table 2 and Sect. 2) for
which the site chronologies did not meet the quality standards. The maximum
time span of tree-ring data covers the period from 1415 (ITRDBITAL015) to
2013 (QFIMP1 and QFIMP2). However, the mean chronology length is 215 ± 130 years for conifers
and 175 ± 25 years for broadleaves (values rounded to
the nearest 5 years; Table 2). Over the common period considered (1880–1980 for
all MXD and RW chronologies), the mean series intercorrelation and expressed
population signal are approximately 0.5 and 0.8, respectively.
Tree-ring sensitivity to climate. The site-specific sensitivity
analysis performed over the common period of
1880–1980 revealed that MXD in conifers records stronger climatic signals
than RW in either conifers or broadleaves, in terms of the average
correlation coefficient, the number of months showing statistically
significant values (p < 0.05) and the fraction of chronologies (over
the maximum number available) responding to the same climatic variable (Fig. 3).
In particular, all conifer MXD chronologies were found to be positively
influenced by late summer temperatures (August and September), whereas
precipitation from June to August is negatively correlated with most of them
(Fig. 3a and b). In terms of SPI, the highest correlations (for both MXD
and RW) were obtained for the indices calculated at the timescales of 2 and 3 months (only the SPI at 3 months, SPI_3, is reported in the
Results), while longer timescales showed fewer significant correlation
values. Most conifer MXD were found to be negatively correlated with
SPI_3 from June to September, highlighting that low index
values, i.e., drought periods, are associated with high MXD in the tree
rings, and vice versa (Fig. 3c).
For conifer RW, significant correlation coefficients, i.e., those exceeding
the mean value of |r‾| > 0.25 for more
than 50 % of the available chronologies, were obtained only for the
August temperatures of the year prior to growth (a negative correlation;
Fig. 3b). In the other months, correlations are generally low and sometimes
show opposite signs for the same climatic variable. However, a slightly
stronger influence from the climatic variables for the summer months prior
to growth can be noted (black areas in Fig. 3a, d and g).
Broadleaf RW were found to be positively influenced by high precipitation
and low drought occurrences (high SPI_3 values) during the
summer months (June and July precipitation and June to August
SPI_3; Fig. 3f and i), whereas the temperature did not
show a significant influence (Fig. 3c).
Influence of environmental settings on climate–growth relationships and site fitness. We
found that the strength of the AS signal correlated positively with
latitude (mean precipitation) and negatively with elevation (longitude; Fig. 4a). Summer precipitation amounts and elevation correlated negatively
in our data set of MXD, revealing the dominance of the latitudinal gradient
of larger precipitation in northern areas over the expected altitudinal
gradient of higher precipitation at higher altitudes: sites in northern
areas, even if at lower altitudes, receive more summer precipitation than
sites in southern regions at higher altitude. The RDA analysis revealed that
both parameters were on opposing sides of the first two axes explaining
89.55 % of the variance of the data set: the F1 axis alone explains up to
72 % of the variance in response variables, and especially in AS
temperature and JJAS SPI_3 signals. Concerning site fitness,
especially sites located at higher latitudes, in particular north of
42∘ N (all of Abies alba) presented values of SF > 80 %, and up
to 86 % (Fig. 4b). South of 42∘ N, all sites (including two
sites of Abies alba) presented an SF of approximately 10 %, with the Pinus leucodermis site showing
the highest SF value (52 %) and a P. nigra site the lowest (0 %).
Ordination biplot (RDA analysis) of climate–growth
relationships (response variables, Y) and environmental settings
(explanatory variables X: elevation and climatic averages over the period
1880–1980) (a). Site fitness evaluated on single indexed series included in
the MXD HSTC chronology (SF; Leonelli et al., 2016) and total series per
site (grey line) (b). Sites are ordered with decreasing latitude along the
x axis. Mean SF values for each species are also reported. ABAL stands for Abies alba;
PILE stands for Pinus leucodermis; PINI stands for Pinus nigra.
Bootstrapped moving correlation analysis with a 60-year time window, performed over the maximum period available for the HSTC
chronologies and their respective climate variables (temperature,
precipitation and SPI_3) selected in the previous analysis
(circled numbers as in Fig. 3). The statistically significant values
(p < 0.05) of r are depicted by bold lines.
Stability of the climatic signal over time. The six comparisons performed
between the HSTC chronologies and the DCVs were
deemed important to understand the influence of temporal climatic
variability on conifers MXD and RW and on broadleaf RW (Fig. 5). The
moving-window correlation analysis revealed that the HSTC conifer MXD
chronology held the strongest and most stable climatic signal of late summer
temperature over time, with values of correlation coefficient ranging from
approximately 0.4 to nearly 0.8 in the more recent periods analyzed (curve 1
in Fig. 5). In the other two HSTC chronologies based on conifer MXD (curve 2
and 3 in Fig. 5), starting from the time window 1881–1940 up to recent
periods, we always found higher absolute values for SPI_3
than for precipitation, with values of correlation reaching approximately
-0.7 and -0.6, respectively, (curve 3 and 2 in Fig. 5). For the conifer
RW, a strong change in the temperature signal of August prior to growth was
found (curve 4 in Fig. 5), with correlation values shifting from positive (and
statistically non-significant) in the early period of analysis to negative
(approximately -0.5) in the middle to late analysis period. The two HSTC
chronologies of broadleaf RW showed nearly the same correlation values and
similar patterns with both the June and July precipitation and the June to
August SPI_3, with values at approximately +0.5 (curve 5 and
6 in Fig. 5).
Reconstruction of late summer (August and September)
temperature using the conifer MXD chronology with the scaling approach for
the period 1650–1980 (a). The bold black line indicates the total number of
series (composed by a number of Abies alba (thin black line), Pinus leucodermis (dashed line) and P. nigra
(dotted line) specimens). The low-pass-filtered series with a 20-year Gaussian
smoother for both the reconstructions based on scaling and regression are
also depicted (b). The reconstructions were truncated when there were fewer
than 5 trees, and the grey areas in the graphs depict the period where the
conifer MXD chronology shows an EPS < 0.79 (prior to 1714, fewer than
10 trees. EPS > 0.85 from 1734). A comparison of the
reconstructed late summer temperature (this paper) with the ones of Trouet (2014) and Klesse et al. (2015)
using z scores series (calculated over the
common period 1714–1980 with EPS > 0.8 in all the original
chronologies), filtered with a 20-year Gaussian low-pass filter (c). At the
bottom the annual mean of stratospheric aerosol optical depth (AOD) at 550 nm for the Northern
Hemisphere is reported (d); data set available at https://data.giss.nasa.gov/modelforce/strataer/;
site accessed 30 May 2017;
the red triangles mark major volcanic eruptions (volcanic explosivity index
≥ 6): in chronological order Kolumbo-Santorini, Grímsvötn,
source unknown, Mount Tambora, Krakatau, Santa María and Novarupta.
Reconstruction statistics computed for both regressions
and scaling over the inverted subperiods of calibration and verification. RE stands for reduction of error; CE stands for coefficient of efficiency.
Regression Scaling R2RECERECEFull period R2Calib.1781–18800.383Verif1881–19800.4840.3050.5330.371Calib.1881–19800.5060.435Verif1781–18800.4090.2230.2780.060
Climate reconstruction. The reconstruction of the late summer temperature for the Italian Peninsula
was, therefore, based on the HSTC chronology of conifer MXD, while the
conifer RW chronology was disregarded due to its low signal stability over
time. The reconstructed series based on the scaling approach starts in 1657
and has a minimum sample replication of ten trees from 1713 (Fig. 6a); it
reproduces well the variability of the instrumental record and underlines
the periods of climatic cooling (and likely also wetter conditions) in the
years 1699, 1740, 1814, 1914 and 1938. The low-pass-filtered series emphasize
the mid-length fluctuations and show evidence of periods of temperature
underestimations (centered around 1799, 1925 and 1952) and of
overestimations (around 1846; Fig. 6b); however, the differences from the
instrumental record were always found to be within 1 ∘C for both
scaling and regression approaches. The two models tended to have higher
values when they were calibrated over the period 1781–1880 and lower values
when they were calibrated over the period 1881–1980 (Table 3). The
CE statistics showed similar patterns of RE and its values were always positive
for both the regression and the scaling model.
Spatial correlation pattern of the reconstructed late
summer temperature (using the MXD chronology from the Italian Peninsula)
versus the 0.5∘ grid CRU TS 4.0 August–September mean
temperature (a, b) and mean precipitation (c, d), over the period of 1901–1980. Left
side (a, c) Pearson's correlation coefficients; right side (b, d) the
associated p values.
Spatial coherence of the reconstruction. The spatial coherence
of the late summer temperature reconstruction of the
Italian Peninsula performed over the Mediterranean region indicated that,
for the period of 1901–1980 (defined by the beginning of the CRU TS 4.0
climate series and the end of the MXD series), the reconstructed temperature
series matched very well the temperature variability in Italy south of the
Po Plane, Sardinia and Sicily and the western Balkan area
(r > 0.6). Correlations above 0.4 were still found throughout the Alpine arc, the
central Balkans, western Anatolia, as well as in northwestern Maghreb (Fig. 7a, b).
In detail, the reconstructed temperature highly correlated westward
up to Sicily and Sardinia, and eastward to the western Balkan area along the
Adriatic Sea up to northern Greece, whereas r values were already lower than
0.5 in a wide arc including northern Tunisia, southern France, the inner
range of the European Alps, Turkey and southern Anatolia. The reconstructed
AS temperature series significantly correlated also with mean AS
precipitation, especially in a wide belt between 35∘ and
50∘ N centered over Croatia (negative correlations,
below -0.6) and the Balkan region up to the Black Sea. For Italy,
correlations above 0.4 were found in the southern portion of the peninsula,
whereas weaker correlations were found westward up to the eastern Pyrenees
and northern Maghreb. Positive correlations, above 0.3, were found in a belt
in northern Europe at approximately 55∘ N, centered
over Ireland, Scotland and Wales, and up to Denmark and the southern
Scandinavian Peninsula (Fig. 7c, d).
Discussion
The climate signals recorded in the multispecies and multiproxy tree-ring
network from the Italian Peninsula revealed a general coherence with other
climate–growth analyses performed in Mediterranean environments. As found in
the Pyrenees for a conifer tree-ring network (Büntgen et al., 2010), we
found generally strong and coherent signals in MXD, independent of species.
In particular, in our record, the late summer temperature was well recorded
in MXD chronologies, and the correlations with climate were stable over
time. The MXD chronologies were mainly related to temperature; however, we
found clear signals of the influence of summer precipitation and drought.
In the Mediterranean area, especially during summer, high temperature is
often associated with low precipitation and drought; therefore, when
interpreting the temperature reconstructions based on tree-ring MXD in the
Mediterranean area, the associated influence of precipitation and
droughts on MXD should also be taken into account. The SPI, which was used here to
represent drought conditions, was found to have higher correlations with
both MXD and RW for the index calculated at the timescales of 2 and
of 3 months, whereas lower correlations were found at lower (1-month) and
higher (6-, 9- and 12-month) timescales. Thus, trees respond to the drought
signal at this timescale, which reflects soil moisture droughts in the root
zone (the SPI_3 is also the index used for modeling
agricultural droughts; see, e.g., WMO, 2012). Conversely, trees
apparently do not respond strongly to the signal of hydrological droughts at
the catchment level (SPI at timescales of above 6 months).
Intercorrelation between reconstructed temperature series
of late summer (AS; Trouet, 2014; Leonelli et al., this study) and of summer
(JAS; Klesse et al., 2015) based on tree-ring MXD in the study region. The
correlation coefficients were calculated over the common period 1714–1980,
for both z scores and 20-year filtered series.
AS Temp – TROUET_MXD AS Temp – LEONELLI_MXD_scaling z scores20-year Gaussianz scores20-year GaussianAS Temp – LEONELLI_MXD_scaling0.850.74––JAS Temp – KLESSE_MXD0.750.690.580.65
The reconstructed series of the late summer temperatures for the Italian
Peninsula were shown to have a strong coherence with the instrumental record
and with both the reconstruction of AS temperature proposed by Trouet (2014)
for the northeastern Mediterranean–Balkan region, and of JAS temperature
proposed by Klesse et al. (2015; Fig. 6c and Table 4). The three
reconstructions are highly consistent, and the reconstruction of Trouet (2014)
also includes the sites used in this paper. However, there are some
differences between the Trouet (2014) reconstruction and the one presented
here: our reconstructed AS temperature in the Italian Peninsula tends to
generally show less marked negative fluctuations over time than the reconstruction
from the Balkan area. While common periods of climatic cooling were recorded
in both areas in 1741 and 1814, similar events were seen in 1913 and in 1977
only in the Balkan area. Interestingly, the periods of the larger
differences between the reconstructed AS temperature and the instrumental
record (around 1799, 1846, 1925 and 1952) are also those with strong
coherence between the two reconstructions, suggesting a regional consistency
in the responses to climate, possibly facilitated by similar precipitation
patterns in the two regions during late summer. We also compared all these
tree-ring-based temperature reconstructions (of AS and JAS) with the summer
(JJA) temperature gridded Luterbacher et al. (2004) data set (based on
proxy, documentary and instrumental data), for the grid points containing
our MXD sites and over the common period covered by instrumental data from
Italy used in the present work, i.e., from 1763 (Supplement S1). Both
the instrumental data for Italy and the proxy-based reconstructions showed
good coherence with Luterbacher et al. (2004) at the decadal scale, however
in the 1790–1810 period they showed opposite trends (with generally lower
temperatures than in Luterbacher et al., 2004) and more marked negative
fluctuations in the 1810s.
Contrary to what was found in our reconstruction and in the northeastern
Mediterranean, another late summer temperature reconstruction from Corsica,
based on tree-ring stable carbon isotopes (Szymczak et al., 2012), shows
periods of high temperatures at the end of 1600 and beginning of 1700 and a
very slight cooling during the 1810s, probably owing to the effect of the
surrounding seas.
An important factor influencing the tree-ring MXD is volcanism, especially
with regards to highly explosive eruptions that can change the
intensity of the incoming solar radiation and that are able to change
circulation patterns and cool the climate at the hemispheric to global scale
(e.g., Briffa et al., 1998). The largest explosive eruptions (volcanic
explosivity index ≥ 6; Siebert et al., 2011) correspond to local
minimum densities in the tree rings (Fig. 6c and d), and some of them are
well known to be associated with years of famine and low crop yields. The
year 1699 and the proceeding decades are known for being years of recurrent
explosive eruptions in Iceland and Indonesia (Le Roy Ladurie, 2004),
inducing great famines around Europe and North America (Mitchison, 2002).
The 1809 eruption of unknown source (Guevara-Murua et al., 2014) and the
1815 eruption of Mount Tambora induced a decade of very low summer
temperature and high precipitation (Luterbacher and Pfister, 2015). This was
the coldest decade of the Little Ice Age (Lamb, 1995),
corresponding also to glacier advance phases in the Alps that reached their
first maximum extent of the Holocene (the second and last, was around 1850;
e.g., Matthews and Briffa, 2005). Eruptions of Mount Krakatoa in 1883 and of
Novarupta (Aleutian Range) in 1912 correspond to local minima in the MXD.
But a straightforward relationship between minimum values of MXD densities
and large eruption is lacking: some differences of the regional scale with
respect to the global scale may occur owing to local circulation patterns and/or
the presence of seas, as it is the case of the 1783 Grímsvötn
Volcano eruption (Iceland), which correlates with unexpected high MXD
densities in tree rings from the Mediterranean area (Fig. 6) but not at the
global scale (see Fig. 1 in Briffa et al., 1998), or the local minimums of
MXD density of 1740 and 1938 found in this paper that are not linked to any
particular large eruption.
The Apennines and the European Alps often show similar annual changes in
precipitation amounts. However, in some periods, they show opposite decadal
trends, such as after 1830, when precipitation was increasing in northern
Italy but decreasing in the south, and after 2000, when the opposite
behavior was observed (Brunetti et al., 2006). In the Italian Peninsula, the
summer (JJA) and the autumn (SON) precipitation in 1835–1845 showed local
minimum values in the instrumental record, likely inducing higher densities
in the tree-ring latewood and, therefore, overestimations in model
temperature values (Fig. 6b). Moreover, uncertainties between the
instrumental records and MXD may rise given that trees do not respond
linearly to high temperatures, resulting in a divergence between
climatological and MXD records (e.g., for the Alps and Europe; Battipaglia
et al., 2010). As found in this study, MXD is influenced by both late summer
temperature and summer precipitation and drought. In the Mediterranean,
these variables are usually negatively correlated. Therefore, in some
periods, a given value of MXD could have been caused either by temperature
and less by drought or vice versa. Of the considered explanatory
environmental variables, it is especially the latitudinal regime of summer
precipitation that modulates the MXD sensitivity to AS temperature and to
summer drought (Fig. 4a): sites in northern Italy (more mesic and at lower
elevation) show stronger climate signals than sites in the southern areas
(more xeric and at higher elevation). In addition to the stronger AS
temperature influence on MXD in the northern chronologies, the effect of
summer precipitation/drought becomes equally stronger at the southern sites.
MXD sites from southern Italy present a markedly lower SF than sites from
central-northern Apennines. Considering the responses related to the type of
species that in our data set the influence of AS temperature on MXD in A. alba is
more affected by summer precipitation amounts than in P. leucodermis and P. nigra. On the other
hand, the influence of summer drought on MXD in pines is more affected by
elevation.
Climatic signals recorded in RW tree-ring chronologies of conifers and
broadleaves showed fewer clear common patterns in their correlations with
climate variables than conifer MXD, although some climatic signals, which
were valuable for climate reconstructions and for understanding climate
impacts on tree-ring growth, were detected. In our records, the summer
drought signal was clearly recorded at all broadleaf sites (Fig. 3i), with
moist periods (low recurrence of drought, i.e., high SPI_3
values) positively affecting tree-ring growth. The drought signal (as well
as the precipitation signal) was fairly stable over time (curve 6 and 5 in
Fig. 5), suggesting the possibility for climate drought (and precipitation)
reconstructions in the Italian Peninsula with the availability of longer
dendrochronological series. Differently from Levanič et al. (2015), we
did not find a stable signal in conifer RW associated with the temperature
signal, even though our correlations are related only to August-1
temperatures (curve 4 in Fig. 5). The signal of previous August temperatures
recorded in conifer chronologies (Fig. 3b) is too variable through
time to allow for a reconstruction (Fig. 5). Here, the change in sensitivity
is probably related to the negative effect of droughts in summer and autumn
(June to October) prior to growth (see SPI_3 correlations;
Fig. 3h). The question of the temporal stability of climate–growth
relationships is sometimes underestimated in climate reconstructions, even
though changes of climate signals over time have been identified in the
Mediterranean region (Lebourgeois et al., 2012; Castagneri et al., 2014) and
in the European Alps (Leonelli et al., 2009; Coppola et al., 2012).
Tree-ring growth may be affected also by large-scale climate variability,
such as the North Atlantic Oscillation (NAO), the prominent mode of
atmospheric circulation in the North Atlantic that affects temperature and
precipitation patterns in Europe (D'Arrigo et al., 1993; Cook et al., 2002).
In the eastern Mediterranean region centered over Bulgaria, Trouet et al. (2012)
found a teleconnection, driven by summer NAO, between summer climate
conditions in the British Isles and a summer temperature reconstruction
based on MXD of Pinus heldreichii. For Greece and the region eastward, a prominent dipole
pattern of summer NAO over Europe was also found (Klesse et al., 2015). With
our MXD chronology we find a comparable dipole pattern in late summer
precipitation, but not in temperature. In Italy a major effect on tree-ring
growth was found for winter NAO, which correlates negatively with winter
precipitation, which in turn determines soil moisture during the growing
season (Piovesan and Schirone, 2000). Temporal instabilities of tree growth
with climatic variables may be linked to several environmental and
physiological factors that may influence tree growth processes and tree-ring
sensitivities to climate, such as the still-debated fertilization effect due
to increasing CO2 concentration in the atmosphere (e.g., Brienen et
al., 2012). On the other hand, biomass production and tree growth in
Mediterranean forests seem to be linked to nutrient availability and
environmental constraints rather than to the availability of CO2 (e.g.,
Jacoby and D'Arrigo, 1997; Körner, 2003; Palacio et al., 2013). Local
low-energy geomorphological processes such as sheetfloods (e.g., Pelfini et
al., 2006) may impact tree-ring growth as well as the presence of an active
volcano and its direct influence on local climate and atmospheric conditions
(such as the Vesuvius Volcano, Battipaglia et al., 2007; or the Etna Volcano,
Seiler et al., 2017), or air/soil pollution linked to SO2, NO2 or
O3 depositions and dust depositions from industrial plants or mines (in
central Europe; Elling et al., 2009, Kern et al., 2009; Sensula et al.,
2015): all these environmental factors may lower the tree-ring sensitivity
to climate. Emissions from car traffic may also alter the tree-ring stable
isotope signals and the related climatic signals (Saurer et al., 2004;
Leonelli et al., 2012). The species-specific physiological responses of tree
growth to climate variability may be nonlinear when high summer temperatures
and low soil moisture exceed specific physiological thresholds, and can
interrupt tree-ring growth during the growing season in Mediterranean
climates (Cherubini et al., 2003). In terms of ecological factors, the
recurrent attacks of defoliator insects (e.g., the pine processionary moth;
Hódar et al., 2003), the occurrence of forest fires (e.g.,
San-Miguel-Ayanz et al., 2013) or herbivory grazing and land abandonment
(Herrero et al., 2011; Camarero and Gutiérrez, 2004) may influence
vegetation dynamics and tree growth in Mediterranean forests, thus
potentially introducing non-climatic effects into the chronologies.
Our reconstruction of the late summer temperature based on conifer MXD shows
a clear stable climatic signal over time, and we could define the spatial
coherence of the temperature reconstruction, thus allowing for the
determination of the regions that could be included to extend the
reconstruction further back in time. The late summer temperature
reconstruction of Trouet (2014) is more appropriate for the region around
the southern and inner Balkans; our reconstruction is the first fully
coherent late summer temperature reconstruction for Mediterranean Italy,
extending in a west–east direction from Sardinia and Sicily to the western
Balkan area. As evidenced by the site-level analysis, MXD depends also
on precipitation and drought (Fig. 3b and c), especially in southern sites:
our late summer temperature reconstruction also negatively correlates with
late summer precipitation, more in southern than in the central and northern
Italian Peninsula, in the whole Balkan region up to the Black Sea and
especially in a region centered over Croatia. By contrast, it is positively
correlated with precipitation in Ireland, Scotland and southern
Scandinavia. This spatial approach allows for the definition of areas
responding to climatic forcing in homogenous ways, which may also help
predict the forest response to future climate change in the Mediterranean
region.
Conclusion
The climate sensitivity analysis of a multispecies RW and MXD tree-ring
network from the Italian Peninsula reveals that conifer MXD chronologies
record a strong and stable signal of late summer temperatures and, to a
lesser extent, of summer precipitation and drought. In contrast, the signals
recorded by both conifer and broadleaf RW chronologies are less stable over
time but are still linked to the summer climates of the year prior to growth
(conifer) and the year of growth (broadleaves). The MXD sensitivity to AS
temperature and to summer drought is mainly driven by the latitudinal
gradient of summer precipitation amounts, with sites in northern areas
(above 42∘ N, all silver fir sites, at lower altitudes) showing
stronger climate signals than sites in the south (below 42∘ N,
mainly P. leucodermis and silver fir sites at higher altitudes).
The reconstruction of the late summer temperatures over the 300 years up
to 1980, based on the conifer MXD chronologies, shows a strong coherence
with the reconstruction performed by Trouet (2014) for the northeastern
Mediterranean–Balkan region and by Klesse et al. (2015) for Greece and region to the east. With respect to the former reconstruction, however, the
temperatures reconstructed in our study show less marked negative fluctuations
during the last century, likely because all of our sites are located along
the Italian Peninsula and are relatively close to the sea. According to our
reconstruction, 1699, 1740, 1814, 1914 and 1938 were years of particularly
low late summer temperatures over the study region (with some of them linked
to large volcanic eruptions affecting climate at the global scale), whereas
the highest temperature was found in 1945. The late summer temperature
reconstruction proposed here is representative of a wide area covering the
Italian Peninsula, Sardinia, Sicily and the Balkan area close to the
Adriatic Sea. These areas could be considered to further enhance the
regional reconstruction discussed here. Moreover, this reconstruction is
correlated also with late summer precipitation in the central Mediterranean
and the Balkan region, thus further helping to better assess climate
change impacts on forests in homogenous areas within the Mediterranean
climate change hot spot.
Data used in this study are available in
Supplement S2.
The Supplement related to this article is available online at https://doi.org/10.5194/cp-13-1451-2017-supplement.
The authors declare that they have no conflict of interest.
Acknowledgements
This study was funded by the project of strategic
interest NEXTDATA (PNR National Research Programme 2011–2013; project
coordinator Antonello Provenzale CNR-IGG, WP leader Valter Maggi UNIMIB and CNR-IGG),
and by the following PRIN 2010–2011 projects (MIUR – Italian Ministry of
Education, Universities and Research): grant nos. 2010AYKTAB_006 (national leader C. Baroni)
and B21J12000560001 “CARBOTREES”.
This study is also linked to activities conducted within the following COST
Actions (European Cooperation in Science and Technology), financially
supported by the EU Framework Programme for Research and Innovation HORIZON
2020: FP1106 “STReESS” (Studying Tree Responses to extreme Events: a
SynthesiS), and CA15226 CLIMO (Climate-Smart Forestry in Mountain Regions).
We thank several researchers who uploaded their raw data onto the ITRDB as
well as the two anonymous reviewers and Jürg Luterbacher for their useful comments
and suggestions.
Edited by: Jürg Luterbacher
Reviewed by: two anonymous referees
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