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http://hol.sagepub.comThe Holocene

DOI: 10.1177/0959683607077012 2007; 17; 435 The Holocene

Mary Gagen, Danny McCarroll, Neil J. Loader, Iain Robertson, Risto Jalkanen and Kevin J. Anchukaitis non-detrended stable carbon isotope ratios from pine trees in northern Finland

Exorcising the `segment length curse': summer temperature reconstruction since AD 1640 using

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The Holocene 17,4 (2007) pp. 435–446

© 2007 SAGE Publications 10.1177/0959683607077012

Introduction

Ring width and wood density measurements from the annualrings of trees provide some of the most powerful high resolu-tion reconstructions of past temperatures in the Northern

Hemisphere, with both regional (Wilson and Luckman, 2003;Frank and Esper, 2005) and hemispheric (Briffa et al., 2001,2002a, b, 2004) networks of data from high elevation or highlatitude sites being used to produce long summer temperaturereconstructions. However, although large-scale temperaturereconstructions from tree rings show clear similarities atdecadal and shorter timescales (Briffa and Osborne, 2002;

Exorcising the ‘segment length curse’:summer temperature reconstructionsince AD 1640 using non-detrendedstable carbon isotope ratios frompine trees in northern FinlandMary Gagen,1* Danny McCarroll,1 Neil J. Loader,1Iain Robertson,1 Risto Jalkanen2 and Kevin J. Anchukaitis3

(1Department of Geography, School of the Environment and Society, University of Wales Swansea,Singleton Park, Swansea SA2 8PP, UK; 2METLA, Rovaniemen toimintayksikkö, Eteläranta 55, 96300,Rovaniemi, Finland; 3Laboratory of Tree Ring Research, University of Arizona, Tucson AZ, USA)

Received 24 August 2006; revised manuscript accepted 4 December 2006

Abstract: Stable carbon isotope ratios from the latewood cellulose of 12 trees from two sites in northernFinland are used to construct an isotope chronology covering AD 1640 to 2002. By measuring isotopicratios of every sample independently it is possible to identify and remove the juvenile portion of each �13Cseries, correct the individual series for anthropogenic changes in atmospheric carbon dioxide isotopic ratiosand concentrations, and to quantify changes in signal strength through time. Most importantly, it is pos-sible to demonstrate that there are no long-term trends in the carbon isotope series that are related to treeage. This means that it is not necessary to detrend the series and so they have the potential to retain cli-mate information at all temporal frequencies. The correlation between the non-detrended carbon isotopeseries and July/August mean temperature is high (r �0.72) and comparison with meteorological recordssuggests that the dominant control over tree ring �13C at these high latitude, moist sites is photosyntheticrate rather than stomatal conductance. Summer temperature reconstructions based on three different cal-ibrations are presented, with verification based on a mixture of jacknife and split period designs, provid-ing robust and near identical results. Reconstructed late summer temperatures in the early 1900s are verylow but the years centred around AD 1660 and 1760 appear to have experienced warmer summers than thelate twentieth century, thus our late summer reconstruction does not show a recent warming trend. Ourresults are in agreement with other palaeoclimate reconstructions for northern Fennoscandia, which showlate twentieth-century warming occurring predominantly in the winter. Our results suggest that, wherereplication and common signal strength are sufficiently high, stable carbon isotope dendroclimatology mayprovide high resolution proxy time series that also record climate information at lower temporal frequen-cies, thus avoiding the ‘segment length curse’ that can apply to palaeoclimate reconstructions based onother tree ring parameters such as ring widths and density.

Key words: Tree rings, carbon isotopes, palaeoclimate, dendroclimatology, segment length curse, Pinussylvestris, Finland, Arctic, late Holocene.

*Author for correspondence (e-mail: [email protected])



Esper et al., 2004), there are strong discrepancies in the lowestfrequency domain leading to a wide range of reconstructedtemperature amplitudes (Esper et al., 2004) and difficulty inestablishing the true range of temperature change during thelast millennium. A significant problem with regard to ringwidth- and wood density-based reconstructions is that, whenstandard individual-series detrending methods are utilized toremove age trends (eg, Lindholm and Eronen, 2000; Gruddet al., 2000), it is only possible to retain climate informationover periods equivalent to the mean segment length. This hasbeen described as the ‘segment length curse’ (Cook et al., 1995).Although the use of techniques such as Regional CurveStandardisation (eg, Briffa et al., 2001; Esper et al., 2003;D’Arrigo et al., 2006; Grudd, 2007) offer significant improve-ments upon traditional detrending methods, such techniquesare restricted to data sets with both a high sample replicationand a heterogeneous age structure. If statistical detrendingcould be avoided, it would be a great advantage in attemptingto reconstruct low-frequency, long-term changes in the climateof the past from tree rings.

A possible solution to the problem of retaining low-frequency climate information using tree rings lies in the mea-surement of stable carbon isotope ratios. These record changesin the isotopic composition and internal concentration of CO2,and thus in the balance between stomatal conductance and pho-tosynthetic assimilation rate (McCarroll and Loader, 2004). Thecontrols on the ratio of 13C to 12C (expressed relative to a stan-dard as �13C; McCarroll and Loader, 2004) are fewer than themyriad factors that control tree growth, and thus ring width anddensity. At cool, moist high latitude sites, the �13C signal in treerings tends to be dominated by those variables that controlassimilation rate; principally summer sunshine and temperature(McCarroll and Pawellek, 2001), whereas at xeric sites the cli-matic signal tends to be dominated by stomatal conductance,thus �13C records variations in air humidity and those variablesthat control soil moisture status (Gagen et al., 2004). Stable car-bon isotope ratios also have the advantage that the correlationbetween trees and between sites is often very strong, so that thesignal to noise ratio is high and relatively few trees are requiredto provide a representative average (Robertson et al., 1997a, b;McCarroll and Pawellek, 1998; Treydte et al., 2001). Perhaps thegreatest potential advantage of using tree ring stable carbon iso-tope ratios to reconstruct past climate, however, is that they maynot contain any long-term age-related trends and thus requireno statistical detrending (McCarroll and Loader, 2004). Herewe test this hypothesis and use a chronology that has not beenstatistically detrended to reconstruct summer temperatures innorthern Finland since AD 1640.

Sample collection and preparation

Pinus sylvestris L. trees were felled at two sites close to thenorthern limit for the species in northern Finnish Laplandand compiled to three chronologies. Site and sampling detailsare provided in Table 1 and Figure 1. Stable carbon isotopeseries from the two locations are known to contain very simi-lar climate signals and are considered in the same meanchronology (McCarroll and Pawellek, 2001; McCarroll et al.,2003). The correlation between the mean Lanilla and Utsjoki�13C series is r �0.6 (p �0.01). No attempt was made to sam-ple trees from sensitive microsites, for example where tree ringgrowth might be expected to be most powerfully controlled byclimate. Our sampling strategy was designed to select trees atrandom in the manner in which a subfossil assemblage mightbe preserved in a lake at this latitude. The mean regional

chronology used comprises 12 trees ranging in age from 120to over 400 years and covers the period AD 1612–2002. Breast-height wood disks were cut in the field and air dried in the lab-oratory. The latewood (or ‘summerwood’) of each ring wasseparated and processed to cellulose using a modified batchprocessing technique (Loader et al., 1997). Latewood onlywas processed to cellulose. The thicker walled latewood cellsare formed in summer and, unlike earlywood cells, are madesolely using photosynthate assimilated in the concurrentgrowth season. The analysis of latewood samples, rather thanwhole ring, limits the chance of a lagged climate signal and isrequired to achieve true annual resolution (Switsur et al.,1995). Cellulose carbon isotopic ratios were measured eitheroffline by sealed-tube combustion followed by cryogenic dis-tillation and analysis on a VG Micromass 602C dual inlet sta-ble isotope ratio mass spectrometer (SIRMS) or online via anANCA Elemental Analyser coupled to a PDZ Europa 20/20SIRMS. Sample precision was �0.1‰ on the online systemand �0.05‰ offline as measured via in-house sigma andIAEA cellulose standards. The raw �13C series are presentedin Figure 2A, values are expressed relative to VPDB (ViennaPee Dee Belemnite).

Although it is standard practice to ‘pool’ the wood from sev-eral trees prior to isolation of cellulose and isotopic analysis (eg,Treydte et al., 2001), in this study the latewood cellulose fromevery ring was analysed independently. Although time consum-ing and costly, this approach allows us here to monitor changesin the signal to noise ratio through time, recognize and correctfor changes caused by physiological responses to the increasedCO2 content of the atmosphere and, crucially, to test the hypoth-esis that there are no long-term age-related trends in tree ring�13C at this site. Correct identification of the length of the juve-nile period and subsequent removal of this section is essential.

Chronology construction

Because trees take in CO2 from the atmosphere, any changes inthe isotopic composition or amount of atmospheric CO2 havethe potential to affect tree ring �13C. Before the �13C chronol-ogy can be constructed, two corrections must be applied toaccount for these influences. The correction for changes in theisotopic composition of atmospheric CO2 is a simple addition.The correction for changes in the atmospheric concentration ofCO2 (or ca) uses a new methodology based on calculatingchanges in the internal partial pressure of CO2 (ci). The aim ofthe corrections is to estimate the tree ring �13C values thatwould have been obtained under pre-industrial conditions. Wehave used the suffix ‘raw’ to denote isotope series that have notbeen corrected at all, ‘cor’ for series that have been corrected forchanges in the isotopic composition of atmospheric CO2, and‘pin’ for those where the full ‘pre-industrial’ correction has beenapplied.

Atmospheric �13C correctionAnthropogenic increases in the concentration of atmosphericCO2 since the start of the industrial era (approximately AD

1850) have been accompanied by a change in atmospheric �13C,because fossil-fuel derived CO2 is depleted in 13C. Since frac-tionation is an additive process (Farquhar et al., 1982), anychange in the isotopic ratio of the source gas, in this case theCO2 in air, must be reflected in the isotopic ratios of the prod-ucts, in this case leaf sugars and, eventually, wood cellulose. Alltree ring �13C series, therefore, whether or not they display anytrend over the industrial period, must be corrected for changesin the isotopic ratio of atmospheric CO2. Since annual values

436 The Holocene 17 (2007)



for �13C of atmospheric CO2 are available from several sources(eg, McCarroll and Loader, 2004), it is a simple procedure tocorrect tree ring �13C values to a pre-industrial standardatmospheric �13C value. In this study the raw �13C values werecorrected to a pre-industrial atmospheric �13C value of �6.4‰using the correction factors tabulated by McCarroll andLoader (2004). The effect of the �13C correction is to raise themean tree �13C values by approximately 1.5‰ since AD 1850(Figure 2).

Correction for increased CO2Even when the effects of changes in the isotopic ratio of atmos-pheric CO2 have been removed, tree ring �13C series often dis-play a falling trend over the last few decades that does notcorrespond with any changes in climate. The likely explanationis a physiological response to changes in the amount of CO2 inthe air, seen as shifts in water use efficiency (the ratio of carbonfixed to water lost; Leavitt et al., 2003). Empirically derived‘correction factors’, to be added per unit (ppmv) increase in CO2have been proposed (ranging from �0.02‰ to �0.007‰; seeTreydte et al., 2001 and references therein), but they make theimplicit assumption that the physiological response of trees hasbeen constant and uniform, whereas the available evidence sug-gests that it has been non-linear and that different trees haveresponded in different ways (Waterhouse et al., 2004).

Here we have applied a new correction procedure based on thereasonable range of physiological response of trees to increasedCO2. The aim is to correct tree ring �13C values to those thatwould have been obtained had atmospheric concentrations ofCO2 remained close to their pre-industrial level of approximately

285 ppm. Only changes that can be logically explained via syn-chronous changes in CO2 are corrected for and the pin correctedis applied to each individual tree ring series. Where the tree dis-plays no CO2 synchronous changes in water use efficiency, no cor-rection is applied.

The correction first converts isotopic ratios into values forthe internal concentration of CO2 (ci), and then estimates thevalues of ci that would have been obtained under pre-industrialconditions using the difference between the measured valuesand a non-linear (loess) regression describing the relationshipbetween ci and CO2 (ca). Temporal detrending is restricted bytwo logical constraints based on the physiological response oftrees: first that a unit increase in ca cannot result in morethan the same unit increase in ci (a so-called ‘passive’ response bythe tree); second, that increases in water use efficiency, as aresult of an increase in ca, are limited to those required tomaintain a constant ratio ci/ca. The first constraint allows afalling trend in �13C, which exceeds that which could logicallybe attributed to a passive response to rising ca, to be retained.The second constraint ensures that any increase in �13C, result-ing from warming or a reduction in air relative humidity forexample, is not removed.

Rather than applying the same correction factor to all trees,or statistically removing all trends in the postindustrial period,the ‘pin’ correction removes only that proportion of any declinein �13C that could be, given the logical constraints, produced bya physiological response to rising CO2. Thus any decline in�13C that exceeds the logical constraints is not removed, andany increase, in response to warming for example, similarlyremains.

Mary Gagen et al.: Exorcising the ‘segment length curse’ in dendroclimatology 437

Table 1 Site details and chronology information for three tree ring �13C series, sampled from Pinus sylvestris growing in Finnish Lapland.Series information for the three climate data sets used is also given. Ivalo is a short-lived local station, Sodankylä has a longer regional tem-perature series

Chronology

Laanila M400 Laanila M200 Utsjoki 1995

Latitude 68° 30� N 68° 30� N 69° 40� NLongitude 27° 30� E 27° 30� E 27° 05� EAltitude (m) 220 220 110N 5 5 2Individual �13C time series lengths AD1619–1836 AD1777–2002 AD1891–1995

AD1612–2002 AD1780–2002 AD1869–1995AD1647–1870 AD1779–2002AD1612–1829 AD1774–2002AD1731–2002 AD1774–2002

Subsite chronology covers 1612–2002 1774–2002 1869–1995Mean �13C (VPDB) �23.79 �24.41 �24.37Min �13C (VPDB) �25.19 �27.45 �26.48Max �13C (VPDB) �22.59 �21.94 �21.22Range (‰) 2.60 2.33 2.41EPS 0.88 0.92 0.93Mean between tree r 0.59 0.69 0.66

Meteorological stations

Ivalo Sodankylä

Location 68° 36� N; 27° 25� E 67° 22� N; 26° 37� EType Instrumental InstrumentalSource Finish Met. Finish Met.Temperature (AD) 1958–2002 1908–2002Precipitation (AD) 1958–2002 1959–2002Sunshine hours (AD) 1961–1995 1959–2002Relative humidity (AD) 1961–2002 1959–2002



With regard to the trees used here, it appears that water useefficiency increased in response to rising CO2, so that the ratioci/ca remained near constant until the last few decades. Almostall of the decline in the raw isotope ratios is therefore due to thedecline in the isotopic ratio of atmospheric CO2 and is removedby the first correction (Figure 2). After AD 1970, however, wateruse efficiency appears to have stabilized, with the effect that theisotopic ratios fall steeply. The ‘pin’ correction, therefore, has anegligible effect before AD 1970 and strong effect thereafter.

StandardizationIt has been demonstrated elsewhere that even where differenttrees display near parallel variations in �13C, and are thus verystrongly correlated, the absolute �13C values may be offset(McCarroll and Pawellek, 1998). The offset, even between treesgrowing in close proximity, can be greater than the variationresulting from climate within any one tree. The ideal solution tothis problem is to use very large samples of trees, so that the vari-ation between trees can be averaged, but whilst time and costconstraints prevent this, a reasonable alternative is to standard-ize the values for each tree using differences from the mean overa common period (McCarroll and Pawellek, 1998). This eliminatesthe offset between trees whilst retaining all of the high frequencyvariability and any long-term trends. The common period usedwas AD 1941 to AD 1995. Three of the oldest trees have no datafor this period, because the rings were too thin to cut with guar-anteed accuracy. However, since the offset between trees tends toremain stable through time, the mean value that would beexpected for the three old trees between AD 1941 and AD 1995

can be estimated by calculating the difference between each treeand the average of the two complete long series for another com-mon period, in this case AD 1827 to AD 1777. All values in thefinal chronology are thus expressed relative to the overall meanfor the common period AD 1941 to AD 1995.

Age-trendsTo examine any potential age-related trends in the 12 �13Cseries, the pre-industrial corrected and standardized series werealigned by cambial age (ring number rather than calendar year:Figure 3). The only expected trend is a ‘juvenile effect’ mani-fested as depleted but rising values in the early years of growth(Duquesnay et al., 1998; McCarroll and Pawellek, 2001;Arneth et al., 2002; Gagen et al., 2004; Raffalli-Delerce et al.,2004; Anderson et al., 2005). This has been variously attributedto incorporation of respired CO2 and to changes in hydraulicconductivity as trees gain height (Schleser and Jayasekera,1985; McDowell et al., 2002; McCarroll and Loader, 2004).Few studies have been able to characterize the juvenile effectbecause of the pooling of samples prior to analysis or becauseit has become standard practice to simply remove the juvenileyears prior to analysis (eg, Tans and Mook, 1980).

In this sample of trees the juvenile period is remarkably con-sistent, with a mean change in �13C of 0.03‰ per year lastingfor the first 50 years of growth (see Figure 3). The juvenileeffect can be most simply and objectively dealt with by remov-ing the first few decades from each tree ring series (eg, Arneth et al., 2002). In this case the juvenile period was cut by remov-ing the first 50 years of data from each series. The resultant


Figure 1 Location of sampling sites at Laanila and Utsjoki and meteorological stations at Sodankylä and Ivalo are shown



mean chronology is shown in Figure 4A with chronology sam-ple depth and running Expressed Population Signal (Figure4B). The EPS, a correlation-based alternative to analysis ofvariance, is often used to define the number of trees required toprovide a mean value that is representative of the theoreticalpopulation, with an EPS of 0.85 generally regarded as accept-able (Wigley et al., 1984).

After the juvenile years have been removed, there is noremaining long-term age-related trend in the mean �13C series

(Figure 3B). The correlation between mean �13C and ring num-ber is just r �0.1. The same procedure has been applied to amuch larger data set, comprising trees covering a larger range ofages, with the same result; the 50-yr juvenile period is remark-ably consistent over the region (M. Gagen,D. McCarroll, N.J.Loader and R. Robertson, unpublished data, 2006). In contrastto tree ring widths and densities, therefore, it is not necessary tostatistically detrend tree ring �13C time series. The ‘segmentlength curse’, therefore, does not apply and stable carbon


Figure 2 Raw �13C time series for each tree (A), following correction for changes in atmospheric �13C CO2 (B) and pin correction, juvenileperiod removal and standardization to a common period mean (D). A single tree (tree Laanila 51) is highlighted (black) throughout to illus-trate the effect of each stage of chronology construction. Highlighted in grey, a younger tree shows the juvenile ‘tail’ (panels A and B) that isremoved prior to final chronology construction (D). An example of the changes over time resulting from rising CO2 is also given (C). Tree 51responds to increased CO2 with an increase in water use efficiency (stable ci/ca) until around 1940. Between 1940 and 2002 the responsebecomes more passive, with the rate of decline in �13C following that which would indicate stable water use efficiency (illustrated as ca-ci)



isotopes from tree rings, at these sites, have the potential toretain climate information at all temporal frequencies.

The final ‘pre-industrial’ corrected and standardized chronol-ogy (after removal of the juvenile period) comprises 12 treescovering the period AD 1612 to 2002, with sample depth varyingfrom seven to only three trees. Despite the small number oftrees, relative to the sample sizes used in ring width and densitystudies, the correlation between the trees is so strong that thesignal to noise ratio remains high throughout. Throughout thechronology the EPS remains above 0.85 (Figure 3B). Despitethe high EPS, even in the early years of the chronology, we haverestricted the climatic reconstruction to the period covered by atleast four trees, extending back to AD 1640.

Comparisons with climate data

For the calculation of correlations (Pearson) between the cor-rected �13C chronology and mean monthly climate variables thenearest meteorological station data were used (Ivalo: 68° 36� N27° 25� E). Correlations were run using the ‘growth year’including months from the previous autumn (starting at

November ‘t �1’) to the following October (year ‘t’) (Figure 5).Local meteorological data cover the period AD 1961–2002 fortemperature, precipitation and relative humidity and AD

1961–1995 for sunshine hours.The climate correlations reveal a strong relationship between

�13C and, in descending order: summer sunshine hours, temper-ature, relative humidity and precipitation. In all cases, thestrongest correlations are in July and August, which, at this lat-itude cover the latter part of a relatively short growing season.Temperature and sunshine are significantly correlated (r �0.65),as are precipitation and sunshine (r ��0.7), but temperatureand precipitation are only weakly correlated (r ��0.32). In astep-wise multiple regression including all four instrumental pa-rameters, July/August sunshine hours explain 50% of the vari-ability in �13C with mean July/August temperature adding afurther 17% variability and neither precipitation or relativehumidity entering the regression as significant variables (Table2).

Tree ring �13C is a record of how internal concentrations ofCO2 have varied, as a response to changes in the dominance ofstomatal control and photosynthetic rate. The strong positivecorrelations between �13C and temperature and sunshine hours


Figure 3 Cambial age aligned �13C after atmospheric and ‘pin’ corrections were applied and the series were standardized to a commonmean. Each tree is shown for the first 250 years of growth (A). The mean of all series is shown (B) with a linear trendline showing the lackof any long-term trend after the trees reach ~50 years in cambial age




Figure 4 Mean pre-industrial corrected and standardized �13C chronology after truncation to remove the juvenile growth period (A). Onlythe post AD 1640 portion of the chronology was used for the purposes of climate reconstruction in order to retain sample depth �4 (B).Running Expressed Population Signal (30-yr window, 1-yr overlap run in ARSTAN; Cook, 1985) is also given (B, solid black line) andremains �0.85 (B, dashed black line)

suggest that the dominant control on internal concentrations ofCO2 at these high-latitude cool-moist sites is photosyntheticrate rather than stomatal conductance. Rate of photosynthesisis normally limited by photon flux, which determines the rateof activity of the photosynthetic enzymes, rather than by tem-perature, which controls the enzyme production rate (Beerling,1994). Records of sunshine hours provide a proxy for photonflux, measurements of which are not available locally.

Although antecedent precipitation and air relative humidityappear unimportant in the multiple regression results, and aretherefore unlikely to be important controls in most years, there isgood evidence that during dry years stomatal conductance doesimpart a strong influence over internal CO2 concentrations andtherefore �13C, even at sites where the trees show no signs ofmoisture stress (McCarroll and Pawellek, 1998). In the regressionof �13C on summer sunshine, for example, the highest positiveresidual is the driest summer (year AD 1973, Figure 6). Long-termrecords of sunshine hours are unusual; had such records beenunavailable at this site, a step-wise multiple regression includingthe remaining three parameters would have indicated summertemperature as the dominant control (Table 2, 53%), with onlyprecipitation additionally entering the model to explain a further

9% variability. The strong correlation with summer temperaturewas confirmed using a much longer temperature series(1908–1997) available from Sodankylä approximately 150 kmfrom the Laanila research forest. The correlation between treering �13C and July/August mean temperature at Sodankylä isr �0.67.

The results of the correlation and multiple regression analy-ses suggest that there is no single climatic factor that controlstree ring �13C at all times. In most years the dominant controlis the amount of energy that is available during the summer,mainly in the form of sunlight, but in dry years those factorsthat control stomatal conductance, principally soil moisturestatus and air humidity, will also be important. Switchingbetween different primary controlling climate variables hasbeen detected elsewhere in �13C/climate relationships (Ackroydet al., 2001). In terms of palaeoclimate reconstruction it is arecord of summer temperature that is likely to be most valuableat these high latitudes. In using the �13C series to reconstructsummer temperature, however, we must stress that the recon-struction is made on the basis of empirical correlation results,rather than a clear causative mechanistic link, so that thereconstructed temperatures need to be interpreted with care,



and with reference to other possible climatic controls. Thiscaveat should of course be applied to all palaeoclimate recon-structions based on proxy records of any kind.

Climate reconstruction

A model based on simple least squares linear regression was usedto develop three reconstructions of July/August temperature. Acombination of split sample and a jacknife (‘leave-1-out’) wereused for robust model validation (Meko, 1997; Woodhouse et al.,2006). Both Durbin-Watson and Portmanteau statistics (Ljungand Box, 1978; Draper and Smith, 1998) confirmed that residualsare not autocorrelated, and are approximately normally distrib-

uted. Each reconstruction revealed high reduction of error (RE)and coefficient of efficiency (CE) statistics (Cook et al., 1994;Table 3). Error in the reconstructions was estimated as �2 stan-dard errors of the prediction (Wilks, 1995). Reconstructions oneand three (Table 3) are shown in Figure 7.

The temperature reconstruction was initially developed usingthe Ivalo series (calibration/verification using a jacknife on theperiod AD 1958–2002). However, a further two reconstructionswere produced to test model accuracy using the longer temper-ature series from Sodankylä (Table 3). Reconstruction two wasdeveloped using a jacknife for calibration and verification in themodern period (AD 1953–1997) in the same manner in which theshort Ivalo station data were used.


Figure 5 Simple linear correlations between mean �13C and annual monthly climate variables from November of the previous year to Octoberof the current year. Correlations with various combinations of summer months are also highlighted where this yielded the highest correlations.Correlations with: relative humidity (a) average monthly temperature (b), total monthly precipitation (c) and total monthly sunshine hours (d)are shown. The two strongest correlating climate variables are indicated in black (July–August sunshine hours and temperature)

Table 2 Results from stepwise multiple linear regression of �13C using various climate data variables. Calibration and verification were bothcarried out using the 1961–1995 period (using a ‘leave-out-one’ procedure for verification)

Met. Climate Input R2 cali- R2 veri- Standard Standard Reduction First Second Non-station data variables bration fication error of the error of the of error significant significant significant

series prediction prediction parameter parameter parameterslength (calibration) (verification)

Ivalo 1961– Temperature 0.67 0.67 0.28 0.34 0.46 Sun 50% Temp. 17% Relative 1995 (July/Aug), humidity

sunshine hours Precip.(July/Aug)relative humidity(July/Aug),precipitation(June/July/Aug)Temperature, 0.62 0.62 0.30 0.32 0.52 Temp. 53% Precip. 9% Precip.relative humidity,precipitation(months as above)



found a significant warm period in northern Fennoscandiansummer temperatures reconstructed from Scots pine tree ringwidth centred on the AD 1650s, and this period was also anom-alously warm in the Central England Temperature series as wellas in several other European records (see Bradley et al., 2003).Briffa et al. (1988) detailed anomalously high temperaturesacross Scandinavia centred on AD 1760. Our reconstructionshows a slightly higher temperature anomaly than the Lindholmand Eronen (2000) reconstruction, at between 1 to 1.5°Ccompared with about 0.64°C.

Comparison with the Tornedalen recordThere are no very long instrumental temperature records fromnorthern Finland with which to compare our reconstructions,but there is a high quality composite series developed for the‘Tornedalen’ region of northern Sweden, east of the Scandes,covering the period AD 1802–2002 (Klingbjer and Moberg,2003). The correlation between the corrected isotope series andthe Tornedalen summer temperature series is high over themodern period (AD 1953–1997; r �0.67) although this result isnot surprising given the high correlation between the two sum-mer temperature series (r �0.92, AD 1961–2002). However, thereconstruction does not compare as well with the Tornadalentemperature series in the early half of the record (Figure 8).


In order to test whether any bias was imparted into the treering �13C series by the new ‘pin’ correction procedure, a thirdreconstruction was developed using the older portion of theSodankylä series (AD 1908–1949, Table 3), when the ‘pin’ cor-rection has a negligible effect. The regression statistics for allthree reconstructions are given in Table 3 with the first andthird shown in Figure 7 revealing almost identical patterns ofwarming and cooling over the last 360 years. Were there to havebeen any bias introduced into the �13C series by the ‘pin’ cor-rection, these two reconstructions would differ.

The most notable features of the reconstruction are the pat-tern of multidecadal warm/cool periods (Figure 7). The coldestperiod over the last 350 years is centred on AD 1910, which iswidely recognized as a cold phase in northern Fennoscandia andis notable in other proxy records from the area (Grudd, 2007).The period AD 1902–1917 is a period of considerably reducedtree growth in the whole of northern Fennoscandia. Extremetemperatures in early autumn 1902 are known to have causedwidespread destruction of pine shoots and needles (Andersson,1905). Significant warm periods are centred on AD 1760 and AD

1660. It is notable that in the reconstructions both of these peri-ods appear warmer than the late twentieth century; a feature inagreement with other reconstructions from the area (Matthewsand Briffa, 2005; Grudd, 2007). Lindholm and Eronen (2000)

Figure 6 Simple linear regression of �13C and summer sunshine hours (monthly). The high (square) outlier corresponds to the driest sum-mer in the Ivalo series, revealing a reduction in stomatal conductance during unusually dry years

Table 3 Regression statistics for three reconstructions. A model based on simple least squares linear regression was used to develop recon-structions of July/August temperature. A combination of split sample tests and a modified jacknife (leave-out-one) were used for verification

Met station, climate R2 calibration R2 verification Standard error Standard error Reduction of Coefficient ofparameter, time of the estimate of the estimate error efficiencyperiod used (calibration) (verification)

1 Ivalo July/Aug 0.52 0.52 0.92 0.94 0.48 –mean temp.1958–2002.Modified jacknife forverification

2 Sodankylä 0.52 0.45 0.86 1.09 0.55 0.42July/Aug mean temp.Calib. 1953–2002Verif. 1908–1952

3 Sodankylä 0.46 0.46 0.95 0.98 0.4 –July/Aug mean temp.1908–1952 Modifiedjacknife for verification



Between AD 1802 and 1900 the �13C reconstructions giveslightly warmer temperatures (~0.75°) than the Tornedalenseries (Figure 8). There are three possible explanations for thisdisparity:

1. inaccuracies in the early part of the Tornedalen compositerecord, particularly in the summer months;

2. overestimation of summer temperatures based on tree ring�13C because other factors influenced the degree of frac-tionation; or

3. a genuine difference in temperatures between Tornedalen,close to the Scandes, and our sites further to the east.

The possibility that the earliest part of the Tornedalenrecord may be inaccurate has been raised by the authors whocreated it (Klingbjer and Moberg, 2003). They note that ‘thereconstructed Tornedalen temperature record is reliable backto November 1832. Before this, the summer (June–August)temperatures in particular are less reliable’. However, this doesnot explain the decay in the correlation between AD 1830 and1900. It is notable that the Tornedalen summer temperaturevalues also do not correlate strongly with the tree ring densityseries from Northern Sweden presented by Grudd et al. (2002).

The second possibility, that the isotope-based reconstructionsover-estimate past summer temperatures because summer tem-perature is not the only control over fractionation, is a serious


Figure 8 The �13C based reconstruction of summer temperature (reconstruction 1: Table 3) is shown compared with the compositetemperature series from Tornedalen (Klingbjer and Moberg, 2003). Both series are plotted with centralized five-year moving averages

18(a)

(b)

16

14

12

10

81640 1660 1680 1700 1720 1740 1760 1780 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000

1640 1660 1680 1700 1720 1740 1760 1780 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000

18

16

14

12

10

8

Tem

pera

ture

(¼C

)Te

mpe

ratu

re (¼

C)

Figure 7 The reconstructions of summer (mean July–August) temperature were created using a linear regression model. Ivalo (a) andSodankylä (b) station data were used with a combination of split-sample tests and a modified jacknife (leave-out-1) for verification. Error inthe reconstructions is shown by the grey dashed line (�2 standard errors of the prediction)



concern. However, the correlation between summer temperatureand �13C at these sites is strong (r �0.72), and much strongerthan the correlations used in most other palaeoclimate recon-structions. Stable isotope dendroclimatology, like every otherproxy-based approach to palaeoclimate reconstruction, mustrely on the uniformitarian principle that the relationships thatwe see during our calibration and verification periods alsoexisted in the past. If it was substantially drier in the past, forexample, then �13C would have been higher, but this would onlylead to an overestimation of temperature if the correlationbetween these parameters and temperature also changed. Thatis not impossible, but it is difficult to test.

Our interpretation, in the absence of strong evidence to thecontrary, is that the isotope-based temperature reconstructionis reasonable and that disparity with the Tornedalen recon-struction probably reflects a real difference in summer temper-ature between these two areas during the nineteenth century.

Summary and conclusions

A stable carbon isotope chronology comprising 12 trees fromnorthern Finland is presented. The latewood cellulose of eachring from each tree was analysed separately, allowing the juve-nile increase to be identified and removed and signal strengthto be quantified. The �13C results for each tree were mathemat-ically corrected for changes in the isotopic ratio of atmosphericCO2 and for physiological responses to increased concentra-tions of atmospheric CO2 during the industrial period. The off-set in �13C values between trees was eliminated by taking thedifferences from the mean during a common period. By plot-ting the series by cambial age rather than calendar age, it wasdemonstrated that there are no long-term trends in the stablecarbon isotope series that are related to tree age, at these sites.This means that statistical detrending is not required and suchchronologies have the potential to retain palaeoclimate infor-mation at all temporal frequencies. The final ‘pre-industrialcorrected’ chronology, with a minimum sample depth of fourtrees and an Expressed Population Signal of �0.85 throughoutextends from AD 1640 to AD 2002.

Comparison with meteorological measurements suggeststhat the dominant control on the internal concentration ofCO2, and therefore stable carbon isotopic ratios at these sites,is photosynthetic rate rather than stomatal conductance. Thestrongest correlations are with the amount of energy receivedduring the summer, with sunshine exerting more control thantemperature. The correlation with mean July/August tempera-ture is, however, very strong (r �0.72) allowing this parameterto be reconstructed.

Temperature reconstructions are presented based on calibra-tion using the nearest meteorological station (Ivalo), with jack-nife verification, and using a longer series from northernFinland (Sodankylä), allowing independent calibration andverification periods. A third reconstruction was constructedavoiding the period post-AD 1950, to test whether the correc-tions for anthropogenic changes in atmospheric CO2 concen-trations imparted any bias. All three reconstructions are robustand virtually identical.

The summer temperature reconstruction captures a wellknown cold period at the turn of the twentieth century in theregion and suggests that summer temperatures around AD 1660and AD 1760 were at least as high as those experienced during thelate twentieth century. The results are in agreement with a recentreconstruction of summer temperatures in northern Swedenbased on changes in tree ring maximum latewood density (Grudd,2007).

The results presented here suggest that tree ring stable car-bon isotopes can be used to produce chronologies that are verystrongly correlated with climate and which do not containlong-term trends related to tree age. In contrast with otherpalaeoclimate proxies such as ring width and density, �13Cseries, at least at these sites, do not need to be statisticallydetrended and so the ‘segment length curse’ does not apply.Tree ring stable carbon isotope series, therefore, may provide anefficient way to reconstruct past climate at very high temporalresolution whilst retaining climate information at all temporalfrequencies.

Acknowledgements

This work was conducted as part of the EU-funded projectsFOREST (ENV4-CT95–0063), PINE (EVK2-CT-2002–00136)and MILLENNIUM (017008–2). We would like to thankPaula Santillo and Jonathan Woodman-Ralph at Swansea fortheir tireless precision in the lab. NJL thanks the UK NERC(NE/B501504/1 & NE/C511805/1) for research support. Thismanuscript was prepared whilst MHG was a visiting scholar atthe Laboratory of Tree Ring Research at the University ofArizona, thanks to all at the lab for a kind welcome and muchuseful input.

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