Tree-ring based June – July mean temperature variations since the Little Ice Age in the Adamello-Presanella Group ( Italian Central Alps )

Introduction Conclusions References

The reconstruction spans from 1596 to 2004 and accounts for about 35 % of the temperature variance.The statistical verification of the reconstruction demonstrates the positive skill of the tree-ring data set in tracking temperature variability, but a divergence is visible starting from about 1980 between actual and reconstructed temperature, which slightly underestimate instrumental data.An analysis of moving mean sensitivity over a time window of thirty years evidences a decrement of this parameter in recent times, which is likely related to the noticed divergence and indicates a recent more complacent response to climate of larch at the tree-line.

Introduction
In the large framework of the climate change detection and of the assessment of climate related impacts, mountain environments represent valuable study areas.The detection of climate changes in mountain environments, and in particular in the European Alps, involves detailed reconstructions of climate parameters for pre-instrumental periods, because the identification of long-term climate variation and past natural variability is a key issue for the understanding of recent climate dynamics within the natural climate variability (Houghton et al., 1990(Houghton et al., , 2001;;Bradley and Jones, 1992;Bradley et al., Introduction Conclusions References Tables Figures

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Full 2003).In the Great Alpine Region (GAR), and in particular in high-elevation areas, instrumental climate-parameters records are scarce and extend back in time not more than few centuries.The analysis of proxy climate records becomes therefore essential for studying such climatically-impacted environments and their evolution (Beniston et al., 1997).The widely proven correlation between annual tree-ring growth and climate (e.g.Fritts, 1976) makes them an excellent source of palaeoclimatic information.

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Full include and average a large number of site chronologies reflecting peculiar and local climate characteristics.It is widely recognized that mountain climate, and also climate over central-western Alpine region, is strongly conditioned by the specific topographic characteristics and the peculiar environmental features of the different mountain areas (Beniston et al., 1994;Marinucci et al., 1995;Beniston, 2003Beniston, , 2005;;Leonelli et al., 2009).Therefore, the development of detailed climate reconstructions can help in understanding, at high resolution, how climate change have influenced the environmental dynamics in these climate-sensitive areas.In the Alpine region the dendroclimatic reconstructions have been mainly performed on the northern sector of the Alps and, at present, reconstructions that bring into focus the climate reconstruction for single mountain groups are not available yet.
We present here the first tree-ring based reconstruction of early summer (June-July) temperature for the Adamello-Presanella area, in the Italian Central Alps.Larch treering growth at the treeline in the Adamello-Presanella Group is mainly driven by June temperature, although the strength and the stability of this relationship have varied over time (Coppola et al., 2012); the same paper identifies a more stable signal of seasonal parameters with respect to monthly parameters, with tree-ring width presenting a rather constant response over time to summer temperatures.In this work we intend to verify if the mentioned variation in sensitivity persists when it is examined in a seasonal transfer function mode (Hughes et al., 2011), performing a June-July (JJ) temperature reconstruction for the Adamello-Presanella area.The study area is characterized by one of the largest glacial system of the Italian Alps (CGI-CNR, 1959-1962;Ranzi et al., 2010) and, in the view of a better comprehension of glacial mechanism and dynamics, the reconstruction of climate parameters in pre-instrumental period in this area is a strategic issue.Introduction

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Full 2 Materials and methods

Study area
The Adamello-Presanella Group belongs to the central-southern sector of the Italian Central Alps (namely Rhetian Alps, 45  [1959][1960][1961][1962], primarily in the northern and central sectors of the Group, among which the Adamello Glacier (Ranzi et al., 2010), the widest of the whole Italian Alps, and the Lobbia Glacier, both high plateau glaciers which active fronts stand at the head of the valleys originating from the summit area (Baroni andCarton 1990, 1996;Baroni et al., 2004).The other glaciers are mainly located in small cirques and at the head of topo- ) (Andreis et al., 2004;Baroni et al., 2007).Introduction

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Tree-ring data and chronology development
This study is based on four European larch (Larix decidua Mill.) chronologies.As the main part of the alpine mountain environment, the investigated area is characterized by high geomorphological dynamics involving tree vegetation, that is subjected to diverse growing disturbance factors such as avalanches and mass wasting movements (Baroni et al., 2007;Gentili et al., 2010).Therefore, sampling procedures were conducted below the treeline selecting dominant and undisturbed trees without stem or crown anomalies potentially fading the climatic signal (e.g.Leonelli et al., 2011).The four treering chronologies were obtained from core samples extracted by means of an increment borer and replicated within and between trees, following standard dendrochronological sampling procedures (Stokes and Smiley, 1968;Swetnam, 1985).Samples with evidences of mechanical disturbances or with time series shorter than 100 yr were discarded.Therefore, among about 70 sample trees, 57 trees have been used for the development of the four mean site chronologies.Tree-ring widths were measured to the nearest 0.01 mm by means of a LINTAB increment measuring table (RinnTech) and then visually and statistically (Fritts, 1976) cross-dated with the TSAPwin software (version 0.53; Rinn, 2005).The correct cross-dating was then checked using the program COFECHA (Holmes, 1983;Grissino-Mayer, 2001).The residual chronologies were calculated using the program ARSTANwin (Cook and Holmes, 1984;Cook, 1985) by applying a double detrending method.The wave-length of the spline was fixed at 67 % of the mean length of the series, with a 50 % frequency cut-off (Cook and Briffa, 1990) and the autocorrelation was removed from each series using an autoregressive model (Cook and Briffa, 1990).Individual indexed series were then computed into the mean site residual chronology by means of a bi-weight robust estimate of the mean (Cook, 1985).To assess the adequacy of the replication in the early years of the four chronologies we used the subsample signal strength (SSS) limiting our analysis to the period with SSS > 0.85 (Wigley et al., 1984).The four residual site chronologies Introduction

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Full (named PRS, AVI, FUM and PRL) have been then averaged to form a mean chronology (named ALL).

Data processing
Climate-tree-ring-growth relationships of the four chronologies were evaluated by means of the standard correlation function (CF) and response function (RF) analysis (Fritts, 1976; see Coppola et al., 2012 for details).
To facilitate the comparison between the time series, tree-ring standardized residual indices and June-July (JJ) mean temperature values were scaled computing z-scores by subtraction of the mean and division by the standard deviation over their entire length, according to the formula: where v i is the ring-width index or the temperature anomaly value, m is the mean and std the standard deviation of the entire series length.The resulting time series have zero mean and unit variance.The four ring-width chronologies were directly compared with the JJ mean temperature series.
To further delineate the response of our tree-ring dataset to climate we have checked the stability of mean sensitivity over time.Mean sensitivity (MS) is a measure of the year-to-year variation of tree-ring indexes, it is correlated to the series intercorrelation Introduction

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Full and depends on the species, on the climatic and site conditions, and on the number of samples (Fritts, 1976).It is a measure of the relative differences in width from one tree-ring to the next therefore it can be considered as an index of the limiting effects of climate on tree-rings and as a functional parameter in the assessment of the climatic severity of a site (Fritts, 1976).MS values usually range between 0.1 and 0.6 (Fritts and Schatz, 1975).Low MS values give evidence to a complacent response of trees to the climatic variability.MS was calculated here according to the formula: x t+1 + x t (Fritts, 1976) (2) To evaluate the stability of this statistic over time in the ALL chronology, we calculated MS over a time window of 30 yr, progressively shifting the window of one year forward and reporting the mean value for each period.
Both climate (JJ temperature) and tree-ring (the ALL residual chronology) series were checked for normality (Kolmogorov-Smirnof test) and the direct relationship between the two series was verified by means of a linear regression and some related statistics.
As reported in Coppola et al. (2012), the CF and RF analysis results show that larch growth at the treeline is primarily driven by early summer (June-July) temperature.Moving Response Function (MRF) analyses allow detecting a weakening relationship between tree-ring width and June mean temperature in last decades, but an overall stability over time in the response of larch to summer (June to August) temperature.
We have then chosen June-July mean temperature as the optimum climate predictand for the reconstruction.
The reconstruction model is a linear regression of June-July temperature on the ALL residual chronology (Fritts, 1976).Within the instrumental data time span (1760-2004) we chose a 120 yr time period to apply a transfer function analysis for obtaining the prediction equation.The chosen calibration/verification period is 1840-1959, and, as done by Jacoby et al. (2000), we decided to exclude the more recent time period from Introduction

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Full calibration/verification procedures, to avoid the inclusion of data from a period when a loss of thermal response with June mean temperature is visible (Coppola et al., 2012).Calibration and verification models were then stopped in 1960.The earliest period of the climate data series (1760-1839), was excluded as calibration/verification period because that data is based only on few meteorological stations.
Regression assumptions (Ostrom, 1990) were verified by the analysis of the residuals.Residuals were visually (histogram) and statistically (Anderson-Darling test) checked for normality and the lack of autocorrelation in the residuals was verified by means of Durbin-Watson statistic.The non-dependence of the residuals on the fitted values was also checked by means of a scatterplot (not shown).The existence of a long record of climate data allows to verify the stability of the transfer function over sufficiently long non-overlapping calibration and validation periods.To verify the stability of the regression model we have compared the estimated values against independent data not used in the calibration, using a split-sample procedure (Snee, 1977;Meko and Graybill, 1995;Touchan et al., 2003), dividing the full period  in two subsets (1840-1859 and 1900-1959), fitting the model to one part of the data and testing on the other part and replying the operation inverting the two calibration and verification periods.Several statistics were conducted to test the reliability of our splitsample calibration and verification procedure and the long-term reconstruction skills.Pearson's correlation between instrumental and reconstructed values was calculated in each phase of the calibration/verification procedure.The reduction of error statistic (RE) has been performed to measure the association between the series of actual values of monthly mean temperature and their estimates.The theoretical limits for RE values range from a maximum of +1 to minus infinity, but an RE value grater than 0 has to be considered as a positive skill (Fritts, 1976).Moreover, another statistical test was applied, the Coefficient of Efficiency (CE).The use of this statistic in Dendroclimatology was introduced by Briffa et al. (1988) and, as for RE, its theoretical limits range from +1 to minus infinity, any positive values indicating positive skill, a minus value indicates no agreement.It is a very rigorous test to pass if there are important differences Introduction

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Full The full calibration period  was then used for the final reconstruction model and the verified transfer function was applied to the total extension of the ALL residual chronology  to produce the time series of reconstructed values.The June-July mean temperature reconstruction for the Adamello-Presanella Group for the 1596-2004 period was finally obtained.

Results and discussion
On comparing z-scores of the four ring-width chronologies and the ALL chronology with the JJ mean temperature anomalies series (Fig. 2), an increasing divergence between the two time series is visible for all the four chronologies in the most recent part of the entire time period.However, all the single chronologies show an increasing growth trend that is likely caused by the strong trend in the JJ instrumental temperature record.The analysis of mean sensitivity over a 30 yr time window performed on the ALL residual chronology, evidences a slight decreasing trend of this statistic starting from the first decades of the twentieth century and persisting until the most recent times (Fig. 3).This trend can be considered as a consequence of an increasing complacent response of the ALL residual chronology to the climatic variability and we think that it could be involved in the reduced capacity of the four tree-ring width chronologies in following the strong recent increasing temperature trend.Although these concerns, the verified normality and the checked direct relationship between the two time series (Figs. 4  and 5) and the statistical verification of the regression functions computed during the calibration/verification procedure (which however excludes from the analysis the most recent period) indicate positive skill of the reconstruction and the stability of the relationship over the halves of the chosen period .The correspondence between Introduction

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Full instrumental and reconstructed data is satisfying (r = 0.60 and r = 0.62 in the two calibration and verification periods: Fig. 6), and the JJ mean temperature reconstruction (Figs.7-8) tracks the observed instrumental temperatures quite well (r = 0.53 over the entire overlapping period, 244 yr).The trend of the reconstructed JJ temperature over its entire extension goes clearly towards an increase of early summer mean temperatures.Looking in detail to the reconstructed series in the pre-instrumental period (1596-1760) (Fig. 8), relatively cool conditions are recorded during much of the 17th and early 18th centuries, with some decadal and multi decadal fluctuations.Relatively low temperatures are visible in the first part of the time series until about 1650, when an increase in JJ mean temperature follows until about 1680.Another reduction follows until about 1700 when a regular increment starts and reaches the mid 1700s, when a progressive decrease occurs, with an early summer cooling trend that continues until 1821.In this phase, a little fluctuation centred on 1750 is visible in our reconstruction.These findings are overall consistent with the results of other studies reporting reconstructed summer temperatures for the Alpine region (e.g.B üntgen et al., 2005, 2006;Corona, 2011), while the noticed slight reduction of temperatures in mid-1750s seems to be peculiar of our reconstructed series.The lowest recorded temperatures of the entire reconstruction are found in the 1813-1821 period.Actually, this decade is known as one of the coldest phases of the entire Little Ice Age (Bradley and Jones, 1993;Grove, 1988), characterized by the maximum Holocenic extension of Alpine glaciers (Oerlemans, 2001;Nicolussi and Patzelt, 2000;Holzhauser et al., 2005) that is also verified in the Adamello Group (Baroni andCarton, 1990, 1996).The 1813-1821 period corresponds to the Dalton minimum in solar activity (Wagner and Zorita, 2005;Wilson, 1998) and follows extensive volcanic activity, including the 1815 eruption of the Tambora, on the Indonesian Sumbawa Island (D'Arrigo and Jacoby, 1999;Sadler and Grattam, 1999;Crowley, 2000;Oppenheimer, 2003), resulting in a marked recrudescence of summer temperatures over the Northern Hemisphere (Briffa et al., 1998).
The 1816 year, known as the "year without a summer" on global climate for the consequences of the Tambora eruption, does not reaches the absolute lower value JJ Introduction

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Full temperature in our reconstruction but is anyway among the lower values.In fact, the minimum value of the reconstructed temperature in this cold period corresponds to the year 1813 that also represents the lowest value of our entire reconstruction.The period after 1821 is characterized by an overall tendency to an increase in temperature, particularly evident after the 1850 that is in this sector of the Alps recognized as the end of the LIA (Baroni andCarton, 1990, 1996).Following the registered temperature, the JJ tree-ring based reconstructed temperature shows a post-LIA warming trend, with some minor fluctuations.Actually, the phase that follows the end of the LIA is clearly marked by a sharp rising of JJ temperatures, but the positive trend goes through a break in the early 1870s when a short relatively cool period centred on 1885-1890 takes place.
After that, the rising trend starts again ceasing in correspondence of a relatively cool phase centred on about 1970s, which brings the reconstructed JJ mean temperatures to relative lower values.This recent reduction of early summer mean temperatures is well tracked and has been found in diverse reconstruction of summer temperatures reported for other sectors of the Alps (B üntgen et al., 2005, 2006;Corona et al., 2011), while is less apparent in the large scale tree-ring based summer temperature reconstructions (e.g.Briffa et al., 2001;D'arrigo et al., 2006).From this last relatively cool phase onwards, the JJ mean temperature rising trend appears constant, and goes on up to the end of the time series following the recent and persisting warming trend.In the most recent part of the time series, starting about from 1980, our reconstruction slightly underestimates actual JJ-temperatures values, showing some evidences of the well-known "divergence problem" (D'Arrigo et al., 2008), that has been largely reported in numerous studies (B üntgen et al., 2006;D'Arrigo et al., 2006;Jacoby et al., 2000;Wilmking et al., 2004;Wilson et al., 2007).As demonstrated in B üntgen et al. ( 2008), divergence is not a systematic issue at temperature-sensitive conifer sites and is likely to be addressed at a local level.Introduction

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Conclusions
The European larch tree-ring growth at the treeline in the glacial valleys surrounding the Adamello-Presanella Group is limited by early summer temperature.We have here performed the first attempt of a tree-ring based JJ mean temperature reconstruction for the Adamello-Presanella area, potentially useful also for glaciological models and reconstructions, being based in an Alpine region characterized by the presence of one of the largest alpine glacier of the Italian Alps.The tree-ring based reconstruction of temperature record fits very well the JJ temperature reported in the HISTALP database since 1760 AD.The reconstructed data are therefore a tool also for climate reconstruction of pre-instrumental period.The actual temperature variations characterizing the LIA are well evident also in the reconstructed data, particularly the coldest years of the 19th Century (e.g.1813, 1816 and 1821 AD).
The reconstructed temperature dataset records the recent warming trend of summer conditions starting from about 1970 and mostly, follows the measured instrumental trends.The proposed model slightly underestimates the actual early summer temperature in the recent period, but the correlation between the two datasets is good and the reconstruction statistically sound.The good potential showed by our tree-ring data set as a proxy climate of early summer temperature encourages an extension of the record further back in time improving sample replication in the earliest part of the chronologies.Introduction

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Full C.Baroni   • 53 E), one of the most glaciarized mountain Group of the Central Italian Alps.The reconstruction has been based on four larch tree-ring width chronologies derived from living trees sampled in four valleys surrounding the Group.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | graphically protected valleys.The Adamello-Presanella Group shows a well-developed alpine glacial topography characterized by U-shaped valleys, sharp crests, cirques and horns.Valley floors show a longitudinal profile with overdeeppened hollows and steps of glacial shoulder (Riegel) representing inherited Pleistocene morphology, since the Holocene and presently affected by fluvial processes.Furthermore the steep slopes show evidence of widespread active mass wasting processes, underlined by debris flow channels and avalanche tracks, dissecting the upper portion of the slopes, and scree slope, talus, debris flow cones and rock fall at their foot.The sampling forest sites involved in this study are located at the top of four different glacial valleys surrounding the Group (Fig.1).The vegetation characteristics in the four sampling sites are quite similar, and mainly consists in Norway spruce (Picea abies L.) woodlands that dominate the forest cover and that, above about 1800 m a.s.l., give way to open Larix decidua Mill.-Picea abies L. Karst mixed stands.In Val d'Avio and in Val di Fumo at approximately 2000 m a.s.l., Norway spruce is replaced by an open mixed association of larch and stone pine (Pinus cembra L.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | between calibration and verification periods means, similar RE and CE values can be considered indicative of high stability of the calibration and verification periods data sets.
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Fig. 1 .Fig. 5 .
Fig. 1.Sketch map of the study area.Black asterisks indicate the location of the study sites.
://www.zamg.ac.at/histalp) was used in this study.We used gridded monthly and seasonal mean temperature anomaly records referring to the grid point 10 • N, 46 • E. Temperature anomalies span from 1760 to 2007(247 yr; 2004-2011 release)and are referred to the 20th Century mean(1901- 2000)