Reconstruction of summer temperatures in Eastern Carpathian Mountains (Rodna Mts, Romania) back to AD 1460 from tree-rings

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: (2014) Published online 13 June 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.3730 Reconstruction of summer temperatures in Eastern Carpathian Mountains (Rodna Mts, Romania) back to AD 1460 from tree-rings Ionel Popa* and Olivier Bouriaud Forest Research and Management Institute, Research Station for Norway Spruce Silviculture, Calea Bucovinei 73 bis, , Câmpulung Moldovenesc, Romania ABSTRACT: Tree-ring series from a single site and a single species were used as proxies to reconstruct past summer temperatures over 550 years in the Eastern Carpathian Mountains. The chronology was built using standard procedures in order to provide comparable information about this under-sampled region while preserving the low-frequency signals. The studied site offered abundant samples of both living trees and dead wood, which were carefully selected in order to minimize heterogeneity sources owing to a reduced availability of suitable material as e.g. in the Alps. The ring-width chronology spanned over 550 years with satisfying replication and showed an even segment length. The chronology correlated to temperature over quite a narrow window, temperatures of June and July only being significant. The reconstruction showed that the last 180 years was the warmest period with only three short episodes of anomalies. We present evidence that the summer temperatures in the Eastern Carpathian Mountains showed divergences as compared to the Alps and a clear regionality, the coolest period over the last 600 years occurring during KEY WORDS Carpathians; paleoclimate; dendroclimatology; Pinus cembra Received 2 February 2011; Revised 27 March 2013; Accepted 3 May Introduction Tree-ring chronologies have been acknowledged as a useful annually resolved climate proxy. As such they have been widely used to produce temperature reconstruction over the past millennium (Esper et al., 2002; Mann et al., 2003). Their interest does not lie in their ability to record external climate forcing over long periods (Hegerl et al., 2006; Mann et al., 1998) but also in their reduced spatial representativity evidenced by regional patterns of temperature variability (Briffa et al., 2004; Luterbacher et al., 2004; Mann et al., 2003). Understanding and predicting regional climate change has been a challenge for long. According to the last IPCC report, climate changes that currently occur differ greatly according from one region to another (IPCC, 2007). Improving both climate models and climate scenarios at detailed regional scale is a key issue stressing the need for more local paleoclimatic information (Jones et al., 2009). Previous studies pointed out the advantage of high spatial resolution paleoclimatic networks with minimized gaps (Hughes et al., 1982). Observational data are a key element to reduce the uncertainty in predictions through their contribution to a refined initialization (Smith * Correspondence to: I. Popa, Forest Research and Management Institute, Research Station for Norway Spruce Silviculture, Calea Bucovinei 73 bis, , Câmpulung Moldovenesc, Romania. popaicas@gmail.com et al., 2007). However, the tendency is to have densely sampled regions while Carpathian Mountains still remain poorly investigated despite their great interest. In spite of rather sparse and short meteorological records, the peculiarities of this region s climate have been reported. Indeed, existing studies on the long-term temperature reconstructions showed some noticeable features of the climate in the Carpathian Mountains that is worth being accounted for in large-scale reconstructions (Popa and Kern, 2009; Büntgen et al., 2008). Paleoclimatic information about the Romanian territory has been provided mainly on the basis of pollen analysis, making it possible to reconstruct the vegetation dynamic during the Holocene (Wohlfarth et al., 2001; Björkman et al., 2002; Bodnariuc et al., 2002; Tantau et al., 2006). The time resolution of this climatic information derived from pollen analysis is low and rare over the last millennium. But there are still quite few tree-ring based studies in spite of the great forest cover, the diversity of species and the presence of old trees, which favour the development of long homogeneous chronologies (Schweingruber, 1985; Popa, 2004; Popa and Sidor, 2010). Long climate reconstructions based on tree-ring series require a great sampling effort to homogeneously cover the oldest parts of the chronology. Collecting the so-called relict or subfossil wood is indeed one of the challenges for tree-ring based studies (Büntgen et al., 2012) since wood is decomposed, burned or used. In some previous studies of the Alpine Arc, dead wood was 2013 Royal Meteorological Society

2 872 I. POPA AND O. BOURIAUD partially sampled from old constructions (Büntgen et al., 2006) raising the problem of its provenience. Carpathian Mountains, however, host many famous old-growth and undisturbed forests that offer an opportunity to build up paleoclimatic data collections. The amount of dead wood is larger due to the absence of human influence and the wet conditions that prevent fires. The demand remains high for long chronologies and more local reconstructions created from sites that enable a sampling of both living trees and dead wood (subfossil data) in undersampled zones (Jones et al., 2009). According to Rutherford et al. (2005), focusing on a single proxy of appropriate seasonal temperature indicator is essential to ensure the quality of large-scale temperature reconstructions. Many examples prove that the construction of a tree-ring based chronology from a selected site can offer such highresolution seasonal temperature proxy (Büntgen et al., 2007; Jones et al., 2009). Our study aims at compensating the current lack of paleoclimatic studies in the Carpathian Mountains which remain a largely undersampled region, based on dendrochronological series. We present a multicentury chronology derived from the ring-width series of a single species, namely stone pine (Pinus cembra L.), collected in Eastern Carpathian Mountains (Rodna Mountains National Park), which, using conservatively traditional methods to retain low and higher frequency variations, allow the reconstruction of long-term temperature trends for the mountain, and its comparison to Alpine series. 2. Data and methods 2.1. Site The study area is located in the Eastern Carpathian Mountains, in Rodna Mountain National Park (47 32 N, E), in a mixed timberline forest of stone pine and Norway spruce (Picea abies (L.) Karst.), with a compact area of mountain pine (Pinus mugo) in the upper part. Norway spruce is the dominant species covering 80% of the stem number. The stone pine can be found in close mixture with spruce, but also as isolated individuals in mountain pine areas at high altitude. The altitude ranges from 1700 to 1800 m a.s.l., with slopes of 30 to 35 (Figure 1). The substrate is composed of crystalline shale and the soil is shallow and organic. The sampling area is limited to about 100 ha on the north slope of Lala Valley. The sampling area was carefully selected to avoid obvious stand disturbances (snow damage, large scale insect attacks, etc.) and any traces of wood removal Tree-ring data To reconstruct the temperature fluctuations during the last six centuries, we compiled a tree-ring chronology based on dead and living stone pines. According to standard dendrochronological methods, two cores were sampled at opposite sides from living trees at a height of 1.30 m (Fritts, 1976; Cook and Kairiukstis, 1990; Popa, 2004), at right angle to the slope, in order to avoid the presence of compression wood in the increment cores. Besides, a disk of dead wood was taken as close to the base as possible. All samples were dried out and sanded in agreement with classical dendrochronological procedures (Stokes and Smiley, 1968). The samples with a large inner part missing due to inside decay (over 10 rings) were excluded from analysis (less than 2% of the total number of samples). The LINTAB equipment and TSAP software were used to measure the annual rings width with a precision of 0.01 mm and to crossdate the series by graphical comparison in a logarithmic scale (Rinntech, 2005). Ringwidth series were measured along two right angle radii on the disks. The results were checked for missing rings and dating errors using the COFECHA software through the analysis of the correlation on successive subperiods (Holmes, 1983; Grissino-Mayer, 1997). The final dataset comprised 212 individual series from 129 trees (107 series from dead trees and 105 series from living trees). The growth series were standardized in order to eliminate the age- and size-related trends from the individual series. To preserve the low frequencies in the tree-ring series, the Regional Curve Standardization method (RCS) was used (Briffa et al., 1992; Esper et al., 2003). All the individual series were therefore aligned by cambial age to produce the mean chronology referred to as Regional growth Curve (RC). A cubic spline with a 50% frequency response cut-off at 10% of the length was subsequently applied to the RC. The individual tree-ring index series were calculated as the ratio between individual growth series and the RC. All detrended series were averaged to form the chronology using biweight robust mean (Cook and Kairiukstis, 1990). Bootstrapped confidence intervals were computed for both the RC and the chronology (Efron, 1987). The standardization was done using the ARSTAN software (Cook and Krusic, 2006). We tested the opportunity to split the dead and living trees in two populations and to build one chronology for each (Esper et al., 2003). Two RCs were therefore separately computed for tests. The two resulting RCs were very similar with the exception of the initial segment (0 40 years) which in fact proved to have no consequence over the mean chronologies. Furthermore, we preferred to merge the population, which ensures a better replication. For the dendroclimatic analyses, the early portion of the chronology with a low replication was truncated to keep a minimum of 14 series. The expressed population signals (EPS) and inter-series running correlations (Rbar) (Frank and Esper, 2005) were used to assess the theoretical number of individual series needed to build a robust mean chronology that represents the population climatic signal (Briffa and Jones, 1990). EPS is calculated from between-tree correlation and number of trees included in the calculation. It can have a value between 0 and 1 and a threshold of 0.85 is considered to be adequate to ensure that a chronology is suitable for climate reconstruction. Rbar is the mean correlation of all tree-ring series within a population. These statistics were calculated for 50-year interval with an overlap of 25 years.

3 RECONSTRUCTION OF SUMMER TEMPERATURES IN EASTERN CARPATHIANS 873 Figure 1. Map of the study areas showing the location for study site (rectangle) and weather stations (black triangle) Climatic data Instrumental climatic data for the study area are available only for the period from Iezer Pietrosu weather station (47 36 N, E 1785 m a.s.l.). The mean temperature is C with the warmest period July August (+9.8 C) and coldest January February ( 6.8 C). Precipitation reaches 1241 mm cumulated over 1 year, with a maximum seasonal precipitation in June July. To extend the instrumental data, we used the temperature data available over from the resolution CRU3.0 grid data-basis (Mitchell and Jones, 2005). Both grid and instrumental monthly temperature data were normalized to the reference period (WMO, 1989). The correlation between normalized grid and instrumental temperatures is over 0.97 for May to September and range from 0.78 to 0.89 for October to April, computed over the period. The reliability of the CRU 3.0 grid data set before 1961 was tested using three nearest long instrumental temperature chronologies (Cluj, Baia Mare and Bistrita) which go back to 1853 (Bistrita). The correlation exceeded 0.9. Spatial correlation between tree-ring width index and climatic data were computed using the KNMI climate explorer (Oldenborgh and Burgers, 2005; knmi/nl) Temperature reconstruction The analysis of ring width response to temperature was conducted over the period We first established which climate variables had significant relationships with the standardized chronology and therefore used a 17-month window from May previous year to September of the current growth year. Multiple-month temperature means of current year of growth were also included: June July, June August. The strength of the tree-ring index and temperature relationship was assessed using bootstrap correlation with 1000 replications (Efron, 1987). The temperature reconstruction was based on the scaling method of the mean chronology to the grid temperature anomalies according to which the mean and the variance of the standard chronology were set equal to those of the calibration temperature data set over the periods. We preferred this method because it is known to minimize the loss of reconstructed temperature magnitude, which is caused by the reduction of variance due to the linear regression (Esper et al., 2005). To assess the temporal stability of the model used for temperature reconstruction, the temperature data were split into two periods, one of calibration ( ), one of verification ( ). Pearson s correlation coefficient (r) was used as statistic (Fritts, 1976) in addition to the computation of reduction of error (RE) and coefficient of efficiency (CE) for which values

4 874 I. POPA AND O. BOURIAUD Figure 2. RCS detrended chronology and signal strength statistics. (a) Swiss stone pine chronology covering the period (b) Smoothed chronology using a 20-year low-pass filter. Shaded envelopes indicate the bootstrap 95% confidence interval. (c) Average cross-chronology correlation r-bar calculated for 50-year periods (offset is 25 years). (d) EPS statistics of the Swiss stone pine chronology calculated for 50-year periods (offset is 25 years). Horizontal grey line is the 0.85 threshold. (e) Mean segment length calculated for 50-year periods (offset is 25 years). (f) Sample distribution of the chronology with each bar representing a single series. In grey are the samples from dead wood and in black the sample from living trees. greater than zero indicate a satisfying reconstruction performance of the model (Briffa et al., 1988; Jacoby and D Arrigo, 1989; Cook et al., 1994). 3. Results 3.1. Multi-centennial Rodna tree-ring width chronology The stone pine chronology constituted for Rodna Mountains spanned over the period, but was limited to for further analyses where sample depth exceeds 14 samples. Ring width series presented a strong within-site common signal with an overall mean interseries correlation of r = The mean sensitivity of raw series was 0.18 and the first-order autocorrelation was 0.83, indicating quite a high influence of the growth from the prior years. The distribution through time of living and dead trees series ensured a good sample replication over the entire period studied (Figure 2). Thanks to the relatively

5 RECONSTRUCTION OF SUMMER TEMPERATURES IN EASTERN CARPATHIANS RC all Spline Radial growth (mm) Cambial age Figure 3. RC chronology used for detrening tree-ring data Grid data Instrumental data Bootstrap correlation coeff pm pj pj pa ps po pn pd J F M A M J J A S JJ JJA Month Figure 4. Correlations of the RCS detrended Swiss stone pine with monthly mean temperatures of the previous and current year calculated over the (grey) and (black). large amount of dead wood available in the field, we obtained a very even distribution of ring ages over several centuries. As a consequence, the mean segment length also remained rather constant (Figure 2). The comparison of the split RCS chronologies for living and dead tree during the common periods (with more than 14 samples of each) shows a good agreement (r = 0.63). These characteristics of the sampling are important for the RCS method and ensure here the absence of bias related to smaller replications or trends in the mean age. The mean growth rate of dead RC was 0.91 mm/year, against 1.08 mm year 1 for the living wood and 0.98 mm year 1 for pooled series RC (Figure 3). The mean EPS over the analysis period ( ) was 0.92 and the mean Rbar was Growth response to temperature The response of stone pine growth to temperatures fluctuations was studied by computation of bootstrapped Pearson correlations between RCS tree-rings indices and monthly mean temperatures over (Figure 4). The bootstrapped correlations with instrumental data available for the period, were very comparable. The pattern of bootstrapped correlations indicated a significant response of stone pine ring width to early summer temperature of current year: June (0.30) and July (0.37), and August (0.18) to a much lesser extent. In addition, a positive reaction of tree growth is observed to prior late autumn temperature (October November). Correlations with winter temperatures were never significant. The bootstrapped correlation between tree-ring width

6 876 I. POPA AND O. BOURIAUD index and mean air temperature of June July reached 0.43, being highly significant. The growth climate relationship proves that the stone pine TRW chronology is a suitable proxy for the (JJ) summer temperature Temperature reconstruction Using (P1) as calibration period the Pearson s correlation coefficient between grid data and reconstruction was 0.51 and for verification (P2) was The RE values were positive for both periods: 0.26 (P1) and 0.43 (P2). Similarly, the CE values were 0.20 (P1) and 0.47 (P2). These statistics prove that the model skill for temperature reconstruction is satisfying. The stability of the model being confirmed, the reconstruction of summer temperature for the last millennia was done based on the entire updated grid dataset (Figure 5). The temperature reconstruction showed inter-decadal fluctuations embedded in low-frequency sinusoids. The last 180 years represent the warmest segment of the reconstruction with only three short episodes of negative anomalies, the longest of which occurred during the 20th century. This segment succeeded to a cool episode during the early 19th century during which anomalies reached their minimum over the span of the chronology (Figure 6(a)). A succession of extreme years, defined as years during which the departure from the mean exceeds two standard deviations (std) computed over the period, were identified in the reconstruction. Extreme negative years were observed in three major periods, 1733 to 1736, 1818 to 1821 and 1868 to The coldest reconstructed summers were first 1818 (anomaly = 3.7) followed by The distribution along the reconstruction of negative and positive years was very different. Indeed, five anomalies occurred in the second-half of the 19th century and already three in the 21st. All anomalies above +3 std occurred in the last 70 years. In addition, we observed several rings with typical frost-rings anatomic characteristics, among which 1876 showed the strongest replication (over 80% of the samples). In that year, the reconstructed summer temperature was quite low but that year should not be accounted for since the low growth was due to frost damages and so no inference can actually be done. During the common period , the correlation between our reconstruction and that for the Alps (Büntgen et al., 2007) is only 0.48, with 0.46, respectively for the North Hemispheric (Briffa, 2000) and more with the Calimani chronology (0.50) (Popa and Kern, 2009) (Figure 6(b)). The pattern of spatial correlation between the treering index chronology and the June July temperature data (Figure 7) shows the limited extent of the high correlations (>0.5) zone located in the North-Eastern Carpathians. This proves the regional character of the proxy established. Correlation with the Alpine Arc gridded temperatures is quite low, being below Discussion The relatively high abundance of dead wood combined with careful sampling enabled us to produce a chronology based on a highly homogeneous local material of over 550 years and avoiding trends in sample ages. The stone pines sampled in the Eastern Carpathian Mountains were found to be most responsive to June July temperatures, and the chronology enabled a reconstruction of the temperature for this summer period over 550 years. The lack of sensitivity to winter or spring climate and the homogeneity of the material used to build up the chronology offered favourable conditions for summer temperature reconstruction. Most temperature reconstructions for the Northern Hemisphere indicate low temperatures in the earlier part of the 19th century followed by a steep increase. Our reconstruction showed that the last decades also are the warmest of the chronology. But unlike the Alps, the rest of the last century was not unusually warm as high anomalies of equivalent magnitude were observed during and periods (Figure 6(b)). Several elements distinguish the chronology from other long tree-ring chronologies constituted in the Alps for example. First, the last 150 years did not witness a markedly cool period, as anomalies barely reached 1, so they can be best described as a period without cool decades. Second, unlike in the Alps, the coolest period over the last 600 years occurred between 1720 and 1850 while the periods remained fairly warm. In contrast, the summers of 1639, 1627 and 1632 are considered the three coldest ones during the last millennium in the Alps with anomalies of up to 2.2 C (Büntgen et al., 2006). However, according to Casty et al. (2005), the absolute coldest Alpine summer was 1816, which is closer to the minimum observed in our study (1818). That coldest year was also observed in other Carpathians temperature reconstructions (Büntgen et al., 2007; Popa and Kern, 2009). A high concordance in other extreme years such as: 1582, 1637, 1751, 1729, and 1892, etc, was observed between our reconstruction and that for the Călimani Mts. (Popa and Kern, 2009). While the decadal variation is quite comparable with the Tatra temperature reconstruction, there is a period of noticeable divergence between 1760 and 1790 or of different magnitude ( ). Available historical documents for the surrounding regions provide confirmation of several extreme years detected by our reconstruction. For example in a severe drought and a locust invasion were reported in North-Eastern Romania (Cernovodeanu and Binder, 1993), which corresponds to the peak of temperature in the 18th century. Moreover, the cold period was documented as an excessively wet period. Hence, our study points out marked divergences between the Alps and the Eastern Carpathians in summer temperature fluctuations. One divergence between our chronology and previously published studies (Briffa, 2000; Büntgen et al., 2006; D Arrigo et al., 2006; Frank

7 RECONSTRUCTION OF SUMMER TEMPERATURES IN EASTERN CARPATHIANS 877 JJ temperature anomalies (C wrt ) Grid data Reconstructed Year A.D. Figure 5. Comparison of grid data (solid line) and reconstructed (dotted line) mean summer temperature anomalies. A JJ temperature anomalies C (wrt ) B Z -scores Actual study D'Arrigo et al. (2006) Briffa (2000) Frank et al. (2007) Buntgen et al. (2006) Popa&Kern (2009) Buntgen et al. (2007) Year A.D. Figure 6. Mean summer temperature anomalies reconstructed for the period (a) and comparison with Northern Hemisphere temperature reconstructions smoothed using a 20-year low pass filter (b). et al., 2007) can originate from the discrepancy in the spatial scale used in the study: while most studies rely on a large-scale network, our study was focused on a restricted zone. It is widely acknowledged that, beyond an overall trend that remains true for large areas, regional divergences can be pointed out and alters the global picture (Jones et al., 2009). The use of a single species and the careful selection of the relic wood could have contributed to such divergences since tree-ring based reconstructions in the Alps are faced with the difficulty of sampling relic wood from a given species and with uncontrolled geographic origins. In contrast, the relict dead wood sampled in our study was taken in-situ, which eliminates the risk of agglomerating samples from different altitudes, with therefore potentially different climate signals. Another asset is the very low level of human disturbance reflected by the abundance of dead wood, and that hence substantially eased its collection. This tree-ring width chronology represents a consistent proxy for June July temperature for Eastern Europe (Figure 6). High spatial correlation between treering index and temperature from the Carpathian basin (over 0.5) show the importance of this reconstruction in the knowledge of past climate dynamics in this part of Europe. The variability in maximum mean air temperature is directly connected to changes in the large scale air circulation (Tomozeiu et al., 2002). The reduced spatial extent of the high correlation zone further proves the regionality of the Carpathian

8 878 I. POPA AND O. BOURIAUD Figure 7. Spatial correlation between tree-ring index and June July temperature computed during the period climate, particularly in its differentiation from the Alps June July temperatures regime. The north of the Eastern Carpathians is more influenced during the summer period by continental cold air masses from north Europe than Western oceanic air circulation (ANM, 2008). Given a mean sample length of 154 years, with a minimum of 77 years and a maximum of 427 years, it was likely that traditional detrending methods would not preserve information at greater frequencies than the mean segment length (Cook et al., 1995). Thus using the RCS method to process the tree-ring series in order to reconstruct the multi-centennial temperature dynamics is sustainable. The low reconstruction value observed in 1876 did not result only from peculiar summer temperatures, but rather from a severe frost in early summer (Popa et al., 2006). The two short episodes ( and ) during which the reconstructed temperature series showed a divergence from the instrumental series did not correspond to the temperature or precipitation anomalies in autumn. Indeed, the discrepancy between growth and summer temperature fluctuations could have been induced by peculiar autumn climatic conditions not reflected by the June July climate, as correlations between September and October temperatures previous to growth were significant. But the climate series did not show any specific feature. Nevertheless, it appears that the deviation observed in the growth series did not affect the agreement at decadal or longer frequency. For Alpine temperature reconstructions high correlation and sensitivity to October and November temperature of stone pine tree-ring width could be the reason for some divergences in the last decades (Rolland et al., 1998; Carrer et al., 2007). High temperatures in the spring induce a negative response of pine in current growth year. The negative, but not significant, reaction of trees to current spring temperature may result from a drought stress that can occur when photosynthetic activity is started early in spring while the soil is still frozen. Another negative effect of early summer cambial activity start could be clearly observed in the year 1876 when over 90% of the samples displayed a frost ring in that year. In that case, the low ring width is more associated with an extreme climatic event than with summer temperatures. Stone pine is known as a temperature sensitive species (Oberhuber, 2004; Carrer et al., 2007). It has already been used for temperature reconstructions in different regions in the Alps (Büntgen et al., 2005, 2009; Corona et al., 2009). Pine growth was found to be related to a rather narrow climatic window: current-year June and July, and to a lesser extent, to autumn temperatures previous to growth. One reason for a limited duration of the climate influence may come from the very short growing period on this high elevation site. Snowmelt is occurring very late (e.g. up to June in extreme years) on this site. Moreover, the high precipitation level easily compensates the losses by drainage/lateral runoff due to the topography. The distribution of the precipitation during the year is also very favourable as over 317 mm falls in June July (mean computed from the CRU data over ). All these factors result in a sustained water supply contributing to decoupling tree growth from precipitation fluctuations, which in turn, favours the presence of temperature signal in tree-rings by reducing

9 RECONSTRUCTION OF SUMMER TEMPERATURES IN EASTERN CARPATHIANS 879 undesired factors influence. Furthermore, the absence of any influence of winter temperatures on pine growth on the sampling site sharpens the seasonal temperature signal of the reconstruction and reduces the risk of bias. Indeed, Jones et al. (2003) have shown a higher increase in winter temperatures as compared to summer temperatures during the last two centuries which could translate into a potentially greater but indirect impact on summer month temperature reconstructions. 5. Conclusions A new pine tree-ring chronology is presented that primarily aims at covering an undersampled part of Europe, the Eastern Carpathian Mountains. The standard dendrochronological sampling and data processing techniques were used to produce a chronology that preserves both low- and high-frequency climatic signals. Using CRU climate data the chronology was used to produce a 550 years temperature reconstruction for the Northern Carpathians summer temperature. Efforts were made to minimize the traditional variance sources that blur the signal (Jones et al., 2009), such as un-controlled relic wood provenience, disparity in the species used, multiple sites and climate series. The reconstructed summer temperature showed both decadal and multi-decadal fluctuations had a pattern similar to that of the Alps but also pointed out noticeable regional divergences. Acknowledgements Ionel Popa and Olivier Bouriaud were supported by the IDEII program, project ID65 and partially by CNCS- UEFISCDI project number PN-II-RU-TE References Administraţia Naţională de meteorologie (ANM) Clima României, 365 p. Björkman L, Feurdean A, Cinthio K, Wolfhart B, Possnert G Lateglacial and early Holocene vegetation development in the Gutaiului Mountains, northwestern Romania. Quaternary Science Reviews 21: Bodnariuc A, Bouchette A, Dedoubat JJ, Otto T, Fontugne M, Jalut G Holocene vegetational history of the Apuseni mountains, central Romania. Quaternary Science Reviews 21: Briffa K Annual climate variability in the Holocene: interpreting the message from ancient trees. Quaternary Science Reviews 19: Briffa KR, Jones PD Basic chronology statistics and assessment. In Methods of Dendrochronology: Applications in the Environmental Sciences, Cook E, Kairiukstis L (eds). Kluwer Academic Publishers: Dordrecht; Briffa KR, Jones PD, Pilcher JR, Hughes MK Reconstructing summer temperatures in northern Fennoscandinavia back to A.D using tree-ring data from Scots pine. Arctic and Alpine Research 20: Briffa KR, Jones PD, Bartholin TS, Eckstein D, Schweingruber FH, Karlén W, Zetterberg P, Eronen M Fennoscandinavian summers from AD 500: temperature changes on short and long timescales. Climate Dynamics 7: Briffa KR, Osborn TJ, Schweingruber FH Large-scale temperature inferences from tree rings: a review. Global and Planetary Change 40: Büntgen U, Esper J, Frank DC, Nicolussi K, Schmidhalter M A 1052-year tree-ring proxy for Alpine summer temperatures. Climate Dynamics 25: Büntgen U, Frank D, Carrer M, Urbinati C, Esper J Improving Alpine summer temperature reconstructions by increasing sample size. Trace 7: Büntgen U, Frank DC, Nievergelt D, Esper J Summer temperature variations in the European Alps, AD Journal of Climate 19: Büntgen U, Frank D, Kaczka R, Verstege A, Zwijacz-Kozica T, Esper J Growth response to climate in a multi-species tree-ring network in the Western Carpathian Tatra Mountains, Poland and Slovakia. Tree Physiology 27: Büntgen U, Frank DC, Grudd H, Esper J Eight centuries of Pyrenees summer temperatures from tree-ring density. Climate Dynamics 31: Büntgen U, Tegel W, Heussner K-U, Hofmann J, Kontic R, Kyncl T, Cook ER Effects of sample size in dendroclimatology. Climate Research 53: Carrer M, Nola P, Eduard JL, Motta R, Urbinati C Regional variability of climate-growth relationships in Pinus cembra high elevation forests in the Alps. Journal of Ecology 95: Casty C, Wanner H, Luterbacher J, Esper J, Böhm R Temperature and precipitation variability in the European Alps since International Journal of Climatology 25: Cernovodeanu P, Binder P Cavalerii apocalipsului. Calamităţile natural din trecutul României (până la 1800). 255 p. Cook ER, Kairiukstis L (eds) Methods of Dendrochronology: Applications in the Environmental Sciences. Kluwer Academic Publishers: Dordrecht; 408. Cook ER, Briffa KR, Jones PD Spatial regression methods in dendroclimatology: a review and comparison of two techniques. International Journal of Climatology 14: Cook ER, Briffa KR, Meko DM, Graybill DA, Funkhouser G The segment length curse in long tree-ring chronology development for palaeoclimatic studies. The Holocene 5: Cook ER, Krusic PJ ARSTAN4.1b_XP. columbia.edu. Corona C, Guiot J, Edouard JL, Chalié F,Büntgen U, Nola P, Urbinati C Millennium-long summer temperature variations in the European Alps as reconstructed from tree rings. Climate of the Past Discussion 4: D Arrigo R, Wilson R, Jacoby G On the long-term context for late twentieth century warming. Journal of Geophysical Research 111: D DOI: /2005JD Efron B Better bootstrap confidence intervals. Journal of the American Statistical Association 82: Esper J, Cook ER, Schweingruber FH Low-frequency signals in long tree-ring chronologies and the reconstruction of past temperature variability. Science 295: Esper J, Cook ER, Krusic PJ, Peters K, Schweingruber FH Tests of the RCS method for preserving low-frequency variability in long tree-ring chronologies. Tree-Ring Research 59: Esper J, Frank DC, Wilson RJS, Briffa KR Effect of scaling and regression on reconstructed temperature amplitude for the past millennium. Geophysical Research Letters 31: 1 5. DOI: /2004GLO Frank DC, Esper J Temperature reconstructions and comparisons with instrumental data from a tree-ring network for the European Alps. International Journal of Climatology 25: Frank D, Esper J, Cook ER Adjustment for proxy number and coherence in a large-scale temperature reconstruction. Geophysical Research Letters 34: 1 5. DOI: /2007GL Fritts H Tree Rings and Climate. Academic Press: New York; 567. Grissino-Mayer HD Computer assisted independent observer verification of tree-ring measurements. Tree-Ring Bulletin 54: Hegerl GC, Karl TR, Allen M, Bindoff NL, Gillett N, Karoly D, Zhang X, Zwiers F Climate change detection and attribution: beyond mean temperature signals. Journal of Climate 19: Holmes RL Computer-assisted quality control in tree-ring dating and measurements. Tree-Ring Bulletin 43: Hughes MK, Kelly PM, Pilcher JR (eds) Climate from Tree Rings. Cambridge University Press: Cambridge; 223. IPCC Climate Change The Physical Science Basis. p 996. Jacoby GC, D Arrigo R Reconstructed northern hemisphere annual temperature since 1671 based on high-latitude tree-ring data from North America. Climatic Change 14:

10 880 I. POPA AND O. BOURIAUD Jones PD, Briffa KR, Osborn TJ, Lough JM, van Ommen TD, Vinther BM, Luterbacher J, Wahl ER, Zwiers FW, Mann ME, Schmidt GA, Ammann CM, Buckley BM, Cobb KM, Esper J, Goosse H, Graham N, Jansen E, Kiefer T, Kull C, Küttel M, Mosley- Thompson E, Overpeck JT, Riedwyl N, Schulz M, Tudhope AW, Villalba R, Wanner H, Wolff E, Xoplaki E High-resolution palaeoclimatology of the last millennium: a review of current status and future prospects. The Holocene 19: Jones PD, Briffa KR, Osborn TJ Changes in the Northern Hemisphere annual cycle: implications for paleoclimatology. Journal of Geophysical Research 108: Luterbacher J, Dietrich D, Xoplaki E, Grosjan M, Wanner H European seasonal and annual temperature variability, trends, and extremes since Science 303: Mann ME, Bradley RS, Hughes MK Global-scale temperature patterns and climate forcing over the past six centuries. Nature 392: Mann ME, Ammann CM, Bradley RS, Briffa KR, Crowley TJ, Hughes MK, Jones PD, Oppenheimer M, Osborn TJ, Overpeck JT, Rutherford S, Trenberth KE, Wigley TML On past temperatures and anomalous late-20th century warmth. EOS 84: Mitchell TD, Jones PD An improved method of constructing a database of monthly climate observations and associated high-resolution grids. International Journal of Climatology 25(6): DOI: /joc Oldenborgh GJ, Burgers GJ Searching for decadal variations in ENSO precipitation teleconnections. Geophysical Research Letters 32: L DOI: /2005GL Oberhuber W Influence of climate on radial growth of Pinus cembra within the alpine timberline ecotone. Tree Physiology 24: Popa I Fundamente metodologice şi aplicaţii de dendrocronologie. Editura Tehnică Silvică. 200 p. Popa I, Kern Z Millennial summer temperature reconstruction inferred from tree ring records for Calimani Mts. (Eastern Carpathians, Romania). Climate Dynamics 32: Popa I, Sidor C Reţeaua naţională de serii dendrocronologice RODENDRONET. 1. Conifere. Editura Silvică. 369 p. Popa I, Kern Z, Nagy B Frost ring: a biological indicator of widespread freezing days, and 1876 AD as a case study from the Eastern Carpathians. Proceeding of the Romanian Academy Series B 8: Rinntech TSAP User reference. 110 p. Rolland C, Petitcolas V, Michalet R Changes in radial tree growth for Picea abies, Larix decidua, Pinus cembra and Pinus uncinata near the alpine timberline since Trees: Structure and Function 13: Rutherford S, Mann ME, Osborn TJ, Bradley RS, Briffa KR, Hughes MK, Jones PD Proxy-based Northern Hemisphere surface temperature reconstructions: sensitivity to method, predictor network, target season, and target domain. Journal of Climate 18: Schweingruber FH Dendroecological zones in the coniferous forests of Europe. Dendrochronologia 3: Smith DM, Cusack S, Colman AW, Folland CK, Harris GR, Murphy JM Improved surface temperature prediction for the coming decade from a global climate model. Science 317(5839): Stokes MA, Smiley TL An introduction to tree ring dating. University of Chicago Press: Chicago; 73. Tantau I, Reille M, de Beaulieu JL, Farcas S Late Glacial and Holocene vegetation history in the southern part of Transylvania (Romania): pollen analysis of two sequences from Avrig. Journal of Quaternary Science 21: Tomozeiu R, Busuioc A, Stefan S Changes in seasonal mean maximum air temperature in Romania and their connection with large-scale circulation. International Journal of Climatology 22: WMO Calculation of monthly and annual 30-year standard normals. WCDP No. 10. WMO-TD/No. 341, Geneva. Wohlfarth B, Hannon G, Feurdean A, Ghergari L, Onac BP, Possnert G Reconstruction of climatic and environmental changes in NW Romania during the early part of the last deglaciation ( 15,000-13,600cal years BP). Quaternary Science Review 20: