Clim. Past, 13, 1515–1526, 2017https://doi.org/10.5194/cp-13-1515-2017© Author(s) 2017. This work is distributed underthe Creative Commons Attribution 4.0 License.

Modelling tree ring cellulose δ18O variations in twotemperature-sensitive tree species from Northand South AmericaAliénor Lavergne1,b, Fabio Gennaretti1,a, Camille Risi2, Valérie Daux3, Etienne Boucher4, Martine M. Savard5,Maud Naulier6, Ricardo Villalba7, Christian Bégin5, and Joël Guiot1

1Aix Marseille Université, CNRS, IRD, Collège de France, CEREGE, ECCOREV, Aix-en-Provence, France2Laboratoirede Météorologie Dynamique, IPSL, UPMC, CNRS, Paris, France3Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France4Department of Geography and GEOTOP, Université du Québec à Montréal, Montréal, Canada5Geological Survey of Canada, Natural Resources Canada, 490 rue de la Couronne, QC, G1K9A9, Canada6Institut de Radioprotection et de Sureté Nucléaire (IRSN), PRP-ENV, SERIS/LRTE, Saint-Paul-lez-Durance, France7Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales, IANIGLA-CONICET, Mendoza, Argentinaanow at: INRA Centre Grand Est – Nancy, UMR1137 Ecologie et Ecophysiologie Forestières, Champenoux, 54280, Francebnow at: Imperial College London, Department of Life Sciences, Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK

Correspondence to: Aliénor Lavergne (a.lavergne@imperial.ac.uk) and Fabio Gennaretti (fabio.gennaretti@inra.fr)

Received: 13 July 2017 – Discussion started: 24 July 2017Revised: 4 October 2017 – Accepted: 9 October 2017 – Published: 10 November 2017

Abstract. Oxygen isotopes in tree rings (δ18OTR) are widelyused to reconstruct past climates. However, the complex-ity of climatic and biological processes controlling isotopicfractionation is not yet fully understood. Here, we use theMAIDENiso model to decipher the variability in δ18OTR oftwo temperature-sensitive species of relevant palaeoclimato-logical interest (Picea mariana and Nothofagus pumilio) andgrowing at cold high latitudes in North and South America.In this first modelling study on δ18OTR values in both north-eastern Canada (53.86◦ N) and western Argentina (41.10◦ S),we specifically aim at (1) evaluating the predictive skill ofMAIDENiso to simulate δ18OTR values, (2) identifying thephysical processes controlling δ18OTR by mechanistic mod-elling and (3) defining the origin of the temperature sig-nal recorded in the two species. Although the linear regres-sion models used here to predict daily δ18O of precipitation(δ18OP) may need to be improved in the future, the result-ing daily δ18OP values adequately reproduce observed (fromweather stations) and simulated (by global circulation model)δ18OP series. The δ18OTR values of the two species are cor-rectly simulated using the δ18OP estimation as MAIDENisoinput, although some offset in mean δ18OTR levels is ob-

served for the South American site. For both species, thevariability in δ18OTR series is primarily linked to the effectof temperature on isotopic enrichment of the leaf water. Weshow that MAIDENiso is a powerful tool for investigatingisotopic fractionation processes but that the lack of a denserisotope-enabled monitoring network recording oxygen frac-tionation in the soil–vegetation–atmosphere compartmentslimits our capacity to decipher the processes at play. Thisstudy proves that the eco-physiological modelling of δ18OTRvalues is necessary to interpret the recorded climate signalmore reliably.

1 Introduction

Oxygen isotopes in tree rings (δ18OTR) are increasinglyused as indicators of past climatic changes in temperate ar-eas (Cernusak and English, 2015; Hartl-Meier et al., 2014;Saurer et al., 2008). They have been widely used to recon-struct past atmospheric conditions such as air temperature(Naulier et al., 2015), drought (Labuhn et al., 2016), precip-itation amount (Rinne et al., 2013), isotopic composition of

Published by Copernicus Publications on behalf of the European Geosciences Union.

brought to you by COREView metadata, citation and similar papers at core.ac.uk

provided by Depositum

1516 A. Lavergne et al.: Modelling tree ring cellulose δ18O variations

precipitation (Danis et al., 2006), relative air humidity (Wer-nicke et al., 2015), cloud cover (Shi et al., 2012), and evenatmospheric circulation patterns (Brienen et al., 2012). Thisdiversity of climatic targets possibly reconstructed based onoxygen isotopes hints at the challenge of understanding thecomplexity of the climatic and biological processes that con-trol isotopic fractionation of oxygen in trees (Treydte et al.,2014). Uncertainties arise because different poorly measuredfactors influence δ18OTR values. Isotopic signals in tree ringscellulose are strongly influenced by isotopic signature of soilwater taken up by the roots and by evaporative and phys-iological processes occurring at the leaf level and duringdownstream metabolism (Barbour et al., 2005; Gessler et al.,2014). Thus, a comprehensive approach that embraces exist-ing mechanistic understanding of the fractionation processesinvolved is required.

A few isotopic process-based models have been developedto investigate the mechanistic rules governing the δ18OTRvariations (Guiot et al., 2014): the Péclet-modified Craig–Gordon model (Kahmen et al., 2011) and the Roden’s model(Roden et al., 2000) are able to estimate, at a daily timestep, the δ18O values of soil and xylem waters as well asthe isotopic fractionation occurring in the leaves due to evap-otranspiration. Versions of these models are integrated inmore complete forest ecophysiological models simulatingthe ensemble of forest water and carbon fluxes: (1) MAIDEN(Modeling and Analysis In DENdroecology) (Gea-Izquierdoet al., 2015; Misson, 2004), which contains the isotopicmodule MAIDENiso (Danis et al., 2012), and (2) MUSICA(Ogée et al., 2003, 2009). Both account for important post-photosynthetic factors and are able to link photosynthesis andcarbohydrate allocation to stem growth.

In this paper, we use the MAIDENiso model to decipherthe δ18OTR variability in American temperature-sensitivespecies (Picea mariana in northeastern Canada and Nothofa-gus pumilio in western Argentina). The selected sites are ofspecial interest for palaeoclimatology given that their δ18OTRchronologies carry strong temperature signals. A summertemperature reconstruction was already developed at theNorth American site (Gennaretti et al., 2017b; Naulier et al.,2015) and a calibration study conducted at the South Amer-ican one highlighted the strong potential of δ18OTR valuesto reflect variations in summer–autumn temperatures over alarge region south of 38◦ S (Lavergne et al., 2016). However,up to now, the climate-δ18OTR relationships were analysedusing a black box approach based on linear models. Here,we specifically aim at (1) evaluating the predictive skill ofMAIDENiso to simulate δ18OTR values, (2) identifying thephysical processes controlling δ18OTR by mechanistic mod-elling and (3) defining the origin of the temperature signalrecorded in the two species.

2 Data and methods

2.1 Sampling sites and tree ring data

Two high-latitude American native species were studiedhere: (1) Picea mariana (Mill. B.S.P.; black spruce), which isa conifer widely distributed over the American boreal forest(Viereck and Johnston, 1990), and (2) Nothofagus pumilio(Poepp. et Endl. Krasser; lenga), which is an angiosperm de-ciduous species dominating the high-elevation forests alongthe Patagonian Andes from 35 to 55◦ S (Donoso, 1981;Schlatter, 1994). We selected two sites of P. mariana inthe centre of the Québec–Labrador Peninsula in northeast-ern Canada (L01 and L20; from 53◦51′ N–72◦24′W to54◦33′ N–71◦14′W;∼ 480 m elevation; see Gennaretti et al.,2014, and Naulier et al., 2014, for details) and three sites ofN. pumilio in northern Patagonia, western Argentina (NUB,ALM and CHA; from 41◦09′ S–71◦48′W to 41◦15′ S–71◦

17′W; 1270–1610 m elevation; see Lavergne et al., 2016,2017, for details). Climate in northeastern Canada is mostlycontinental and subarctic with short, mild and wet summersand long, cold and dry winter. Total annual precipitation av-erages 825 mm, with up to 46 % falling during the growingseason in summer (June to September) (Naulier et al., 2014).In western Argentina, precipitation is largely concentratedfrom late autumn to early spring (May–November) followedby a drier and mild period during summer and early autumn(December–April) (López Bernal et al., 2012).

Four trees per site were collected for both species. The se-lection of the samples and analytical procedure for δ18OTRmeasurements were described in Lavergne et al. (2016) andNaulier et al. (2014). The developed δ18OTR chronologiescovered the 1950–2005 and 1952–2011 periods at the north-eastern Canadian and western Argentinian sites, respectively.The chronologies that were built for each species were sig-nificantly correlated between stands (Fig. 1). This supportedthe construction of a combined isotope chronology for boththe northeastern Canada and western Argentina sites.

2.2 Modelling oxygen isotopes in tree ring cellulose withMAIDENiso

MAIDENiso is a process-based model that can simulate inparallel phenological and meteorological controls on photo-synthetic activity and carbon allocation (Danis et al., 2012).It explicitly allocates carbohydrates to different carbon pools(leaves, stem, storage and roots) on a daily basis using phe-nological stage-dependent rules (see Gennaretti et al., 2017a,for details on the construction of the main MAIDEN model).It also simulates the fractionation of carbon and oxygen iso-topes during growth processes. In particular, it estimates, ata daily time step, δ18O values of soil water and xylem wa-ter, the isotopic fractionation occurring in the leaves due toevapotranspiration and the biochemical fractionation duringcellulose formation. As input it uses daily maximum and

Clim. Past, 13, 1515–1526, 2017 www.clim-past.net/13/1515/2017/

A. Lavergne et al.: Modelling tree ring cellulose δ18O variations 1517

1520

2530

35

1950 1960 1970 1980 1990 2000 2010

δ18O

TR

Nothofagus pumilio

Picea mariana

Figure 1. Tree ring δ18O time series (‰) at the three sites inArgentina (NUB, ALM and CHA in dark grey) and two sites inQuébec (L01 and L20 in dark grey; single trees in light grey). Thebold black lines are the averaged values. The mean inter-site corre-lation coefficients are r = 0.60, p<0.05 and r = 0.80, p<0.01 inthe South and North American sites, respectively.

minimum temperature (◦C), precipitation (cm day−1), atmo-spheric CO2 concentration (ppm) and δ18O values of precip-itation (δ18OP in ‰).

In this study, the calculation of the daily δ18OTR in treering cellulose (‰) is based on the Danis et al. (2012) formu-lation of the Craig–Gordon model (Craig and Gordon, 1965):

δ18OTR = (1− fo) · [ε∗+ εk · (1−hair)+hair · δ18OV

+ (1−hair) · δ18OXW] + fo · δ18OXW+ ε0. (1)

This equation summarizes how δ18OTR is determined bythe following:

i. The δ18O of the source (xylem) water (δ18OXW), whichis computed by averaging the δ18OSW values of the dif-ferent soil layers weighted by the volume of water takenup by the roots in each layer. The isotopic effects of wa-ter mixing and soil evaporation on the δ18OSW valuesof the different soil layers are computed by a mass andisotopic balance (Danis et al., 2012). It is worth notingthat fractionation occurs neither during water uptake byroots (Wershaw et al., 1966) nor during the transport ofwater from the roots to the leaves.

ii. The 18O enrichment of the leaf water due to transpira-tion is described by (ε∗+εk ·(1−hair)+hair·δ

18OV+(1−hair) · δ18OXW) after Craig and Gordon (1965), where

a. ε∗ is the equilibrium fractionation due to the changeof phase from liquid water to vapour at the leaf tem-perature (fixed at 21.4 ◦C, the temperature thresh-old for maximum carbon assimilation, ε∗ is 9.65 ‰,Helliker and Richter, 2008);

b. εk is the kinetic fractionation due to the diffusion ofvapour into unsaturated air through the stomata andthe leaf boundary layer;

c. hair is the relative humidity of the evaporating airmass estimated from daily air temperature (Tair; ◦C;mean of the maximum and minimum air tempera-tures), and the dew point temperature (Tr; ◦C) (Run-ning et al., 1987);

d. δ18OV is the atmospheric water vapour calculatedassuming a precipitation–vapour isotopic equilib-rium (see below).

iii. The biochemical fractionations (ε0) due to oxygen ex-change between carbonyl groups (C=O) in the organicmolecules and water (DeNiro and Epstein, 1979; Far-quhar et al., 1998).

iv. The dampening factor fo reflecting the exchange of theoxygen atoms between sucrose and xylem water duringcellulose synthesis in the xylem cells of tree rings.

As previously evoked (i), δ18OXW of Eq. (1) depends onδ18OSW and thus on δ18OP values. However, long continuoustime series of δ18OP are not available in the studied area.Here, we tested the impact of using two different methodsfor deriving δ18OP time series.

First, a linear model was used to estimate the daily val-ues of δ18OP and subsequently δ18OV based on the primarydrivers of their temporal variability (Dansgaard, 1964; Horitaand Wesolowski, 1994), which are air temperature (Tair; ◦C)and precipitation at the corresponding site (P ; mm):

δ18OP = a · Tair+ b ·P + c, (2)

δ18OV = δ18OP− ε

∗

Tair, (3)

with ε∗Tairthe fractionation due to the change of phase from

liquid water to vapour at the mean air temperature. The coef-ficients a and b were allowed to vary over a plausible range(or prior range) in the calibration process together with otherMAIDENiso parameters, while coefficient c was fixed to alikely value (see Table 1 and Sect. 2.4). This estimated setof data is referred to in the following as the estimated δ18OPdataset.

Second, we run the model with the series of the dailyδ18OP derived from two general circulation models (GCMs)with different spatial resolutions and enough available dataat our site locations: (1) the Melbourne University model(MUGCM; Noone and Simmonds, 2002) forced by varyingsea surface temperature (SST) from the HadISST dataset forthe 1950–2003 period (2◦× 2◦ resolution; hereafter referredto as the MUGCM δ18OP dataset), and (2) the Laboratoirede Météorologie Dynamique Zoom model (LMDZ5A; Hour-din et al., 2013; Risi et al., 2010) with the horizontal windsguided by those of the National Centers for EnvironmentalProtection 20th Century Reanalysis (NCEP20) for the 1950–2008 period (Compo et al., 2011) (2.5◦× 3.75◦ resolution;hereafter referred to as the LMDZ-NCEP20 δ18OP dataset).

The final δ18OTR time series are the annual average of theδ18OTR daily values (Eq. 1) weighted by the daily simulated

www.clim-past.net/13/1515/2017/ Clim. Past, 13, 1515–1526, 2017

1518 A. Lavergne et al.: Modelling tree ring cellulose δ18O variations

Table 1. Definition of sensitive parameters. The posterior medians and 90 % confidence intervals are also shown. Arg.: Argentina; Q.:Québec.

Parameter Definition Unit Parameter type(prior range)

Values with 90 % posteriorconfidence intervals

fo Dampening factor NA Calibrated (0.3 to 0.5) 0.36 [0.31; 0.46] (Arg.)0.41 [0.32; 0.48] (Q.)

ε0 Biochemical frac-tionation

‰ Calibrated (24 to 30) 29.99 [29.93; 30] (Arg.)26.81 [24.74; 28.04] (Q.)

εk Kinetic fractiona-tion

‰ Calibrated (10 to 30) 28.86 [18.25; 29.96] (Arg.)17.20 [11.16; 26.34] (Q.)

a Temperature de-pendence of δ18OP

NA Calibrated (0.2 to 0.5 forArg. and 0 to 0.38 for Q.)

0.50 [0.49; 0.50] (Arg.)0.31 [0.25; 0.37] (Q.)

b Precipitation de-pendence of δ18OP

NA Calibrated (−0.3 to 0 forArg. and −0.39 to 0 for Q.)

−0.009 [−0.15; 0] (Arg.)−0.22 [−0.35; −0.14] (Q.)

c Intercept of δ18OP ‰ Fixed −10.0 (Arg.)−11.9 (Q.)

stand gross primary production (GPP), assuming a propor-tional allocation of carbon to the trunk. For the northeasternCanadian sites, the GPP simulated by MAIDENiso was op-timized using observations from an eddy covariance station(see Gennaretti et al., 2017a). Unfortunately, such observa-tions were not available for N. pumilio, and therefore theparameterization obtained for the GPP of P. mariana wasalso used for the western Argentinian sites but constrain-ing the simulations with phenological observations extractedfrom the literature. For example, to respect the annual cycleof the leaf area index (LAI) for N. pumilio (Magnin et al.,2014; Rusch, 1993), in MAIDENiso we used a seasonal LAIannual cycle with a development of leaves (LAI increase)between October and November, a maximum LAI (set at 5leaf area/ground area) from November to April, a decreasingLAI (leaf fall) between April and May, and finally a leaf-less period (null LAI) from June to September (Magnin etal., 2014; Rusch, 1993). Furthermore, based on the findingthat δ18OTR annual time series were more correlated withclimate variables of specific months of the growing season(Lavergne et al., 2016), we also computed δ18OTR annualvalues by weighting the δ18OTR daily values (Eq. 1) withsynthetic GPP time series maximizing the correspondencebetween observations and simulations.

2.3 Meteorological and atmospheric CO2 data

At the western Argentinian sites, we did not have long dailyrecords of observed climate data. Therefore, daily minimum–maximum temperature and precipitation data were derivedfrom the 20th Century Reanalysis V2c (2◦× 2◦ resolution;Compo et al., 2011), which is one of the few reanalysis prod-ucts entirely covering the 20th century. The temperature dailytime series of the reanalysis were corrected in order to respect

the monthly mean values detected at Bariloche, the near-est meteorological station from our sampling sites (∼ 48 kmfrom the sites; 41◦12′ S–71◦12′W; 840 m a.s.l.; Servicio Me-teorológico Nacional, Argentina). The resulting maximumand minimum temperature series, covering the 1952–2011period, fit well with the daily local temperature data from LaAlmohadilla (ALM) site (41◦11′ S, 71◦47′W; 1410 m a.s.l.;data measured by data loggers and provided by IANIGLA)available over the 2002–2012 period (r = 0.74, p<0.001;Fig. S1 in the Supplement). For the northeastern Canadiansites, climate data were obtained from the gridded interpo-lated Canadian database of daily minimum–maximum tem-perature and precipitation covering the 1950–2005 studiedperiod (0.08◦× 0.08◦ resolution; Hutchinson et al., 2009). Inaddition to these data we also used modelled daily data fromthe GCMs described above for both the western Argentinianand northeastern Canadian sites (see Table 2 with the inputdata used for each tested configuration).

Data on the atmospheric CO2 concentration were derivedfrom the Mauna Loa station over the 1958–2012 period(Keeling et al., 1976). For the years 1950–1957, we extrap-olated atmospheric CO2 data using the trend and seasonalcycle as displayed in the observations over the subsequent10-year period (1958–1967).

2.4 Estimation of parameters influencing δ18OTR

We used a Bayesian method for the simultaneous calibra-tion of the various MAIDENiso parameters specific to thestudy species and site. A set of 50 plausible blocks of param-eters (posterior values) was selected according to the methoddescribed in Gennaretti et al. (2017a) using Markov chainMonte Carlo (MCMC) sampling (Table 1). The followingprior plausible ranges were considered:

Clim. Past, 13, 1515–1526, 2017 www.clim-past.net/13/1515/2017/

A. Lavergne et al.: Modelling tree ring cellulose δ18O variations 1519

Table 2. Climate input data for all tested simulations.

Daily Tmin and Tmax Daily P Daily δ18OP CO2

Configuration 1 Canadian database/NOAA-CIRES dataset Linear regression MaunaConfiguration 2 Canadian

database/NOAA-CIRES dataset

MUGCM data Loa

Configuration 3 LMDZ-NCEP20 data station

1. The prior ranges of the a and b coefficients in the equa-tion of the daily δ18OP (Eq. 2) were selected in order toget δ18OP values for each site consistent with the mea-sured monthly local values from the nearest stations ofthe Global Network of Isotopes in Precipitation (GNIP),and with the simulated daily values from the LMDZ-NCEP20 model and from the MUGCM model (see Ta-ble 1).

2. The range for the biochemical fractionation factor ε0was chosen between 24 and 30 ‰ (+27± 3 ‰ afterDeNiro and Epstein, 1981; Sternberg, 1989; Yakir andDeNiro, 1990).

3. The range for the kinetic fractionation εk , which hasbeen set to 26.5 ‰ in Farquhar et al. (1989) but that canvary over larger ranges (Buhay et al., 1996), was takenbetween 10 and 30 ‰ here.

4. The range for the dampening factor fo was allowed tovary between 0.3 and 0.5 following Saurer et al. (1997).

We tested the sensitivity of the MAIDENiso model to thecalibrated parameters by modifying them within their respec-tive prior calibration range. To control the robustness of thecalibrated parameters, we performed the calibration of theseparameters over two equal length intervals (1950–1977 and1978–2005 for P. mariana; 1952–1981 and 1982–2011 forN. pumilio) keeping the second half for independent valida-tion of the parameter estimates. Once the model was cali-brated for the two species, the MAIDENiso model’s perfor-mance to simulate P. mariana and N. pumilio δ18OTR inter-annual data was evaluated using the correlation coefficients(r) and the root mean square errors (RMSEs) between ob-served and simulated values. This is a standard approach toevaluate how well a mechanistic model is simulating δ18OTRvariations (e.g., Danis et al., 2012; Lorrey et al., 2016).

2.5 Disentangling leaf-level fractionation processes andsource water influences on δ18OTR signature

To define the relative contributions to the δ18OTR signature ofthe isotopic signal of the source water (xylem water) and ofthe fractionation processes due to transpiration taking placein the leaves, we designed two experimental simulations withMAIDENiso based on Eq. (1):

1. To quantify the influence of the variability in the iso-topic composition of the xylem water on δ18OTR, wecompared the reference simulations to those where therelative humidity (hair) and the isotopic composition ofatmospheric vapour (δ18OV) were assumed to be con-stant. The constant values for hair and δ18OV were de-fined as the averages of the respective MAIDENisooutputs (hair = 0.62 and 0.9, and δ18OV =−26.28 and−17.34 ‰, respectively, for northeastern Canada andwestern Argentina; the XW source experiment simula-tion hereafter),

2. To quantify the influence of the isotopic enrichmentof the leaf water due to transpiration on δ18OTR, wecompared the reference simulations to those where theδ18OXW series were assumed to be constant. The con-stant value for δ18OXW was estimated as the average ofthe δ18OXW MAIDENiso outputs (δ18OXW =−13.81and−7.03 ‰, respectively, for northeastern Canada andwestern Argentina; the leaf water enrichment-driven ex-periment simulation hereafter).

Comparison between the experimental and reference sim-ulations (i.e. using the optimal values of the parameters) wasachieved through the calculation of the coefficient of deter-mination (R2).

3 Results

3.1 Estimated versus modelled and observed δ18OPvalues

The modelled δ18OP series from the GCMs are similar to theGNIP datasets, with mean values ranging from−12 to−8 ‰over June–September in northeastern Canada (Fig. S2a) andfrom −7 to −3 ‰ over December–April at the western Ar-gentinian sites (Fig. S2b). In general, δ18OP series fromLMDZ-NCEP20 model in western Argentina are slightly dis-placed toward higher values (+1 ‰) in comparison with theGNIP and MUGCM data. The estimated δ18OP values basedon plausible values of coefficients a and b agree well withthose of the models and observations in northeastern Canada.For the western Argentinian sites, they are 2–3 ‰ lower fromApril to October, i.e. late spring–early autumn (Fig. S2).

www.clim-past.net/13/1515/2017/ Clim. Past, 13, 1515–1526, 2017

1520 A. Lavergne et al.: Modelling tree ring cellulose δ18O variations

(a)

0.30

0.50

0.0 0.2 0.4

R

(b)

1824

30

0.0 0.2 0.4

Mea

n le

vels

a

0.30

0.50

−0.4 −0.2 0.0

R

1824

30

−0.4 −0.2 0.0

Mea

n le

vels

b

0.30

0.50

0.30 0.40 0.50

R

1824

30

0.30 0.40 0.50

Mea

n le

vels

fo

0.30

0.50

24 26 28 30

R

1824

30

24 26 28 30M

ean

leve

ls

ε0

0.30

0.50

10 15 20 25 30

R

1824

30

10 15 20 25 30

Mea

n le

vels

εk

Figure 2. Dependence of the correlation coefficients between ob-served and simulated δ18OTR series (a) and of the mean simulatedδ18OTR levels (‰) (b) as a function of the range of calibrated pa-rameters a, b, fo, ε0 and εk for the 50 simulations performed. Thetests sites from Québec are in black and the Argentinean ones are inred. The vertical lines are the values of a plausible block of param-eters retained in the MCMC optimization. The horizontal dashedlines are their respective 90 % confidence interval calculated with50 simulations (see Table 1). The horizontal dot lines in (b) are themean values of the observed δ18OTR.

3.2 Sensitivity of the model to the calibrated parameters

Most of the calibrated parameters have an influence on thecorrelations between observed and simulated δ18OTR seriesand/or on the mean levels of the simulated series (Fig. 2). Thetemperature and precipitation dependences of δ18OP values(respectively a and b coefficients) have the strongest influ-ence on correlations. Increasing a and b values increase themean δ18OTR levels, more strongly in western Argentina thanin northeastern Canada (Fig. 2). Changes in the dampeningfactor (fo) and in the biochemical fractionation (ε0) have al-most no effect on correlation, but their increase induces sig-nificant decrease of the mean levels of δ18OTR series. Finally,increasing the kinetic fractionation (εk) leads to lower corre-lations and to higher mean levels of δ18OTR (Fig. 2).

1921

23

δ18O

TR r = 0.56 **

02

46

GP

P

1921

23

δ18O

TR r = 0.62 **

02

46

GP

P

2830

32

δ18O

TR

r = 0.48 **

02

46

GP

P

2830

32

1950 1960 1970 1980 1990 2000 2010

δ18O

TR

r = 0.52 **

Year

02

46

GP

P

0 200

DOY

(a)

(b)

(c)

(d)

Figure 3. Comparison between observed (red or green) and sim-ulated (grey) δ18OTR chronologies in Québec (a and b) and Ar-gentina (c and d), respectively, using GPP (in g C m−2 day−1) sim-ulated by MAIDENiso for each day of the year (DOY) (a and c) orsynthetized for maximizing correlations (b and d). The simulationsare based on estimated δ18OP series. The 50 different simulationsinferred from the Markov chain Monte Carlo (MCMC) chains arein dark grey. The±1 root mean square error (RMSE) range is repre-sented in light grey. The mean correlation coefficients are significantat 99 % level (∗∗).

3.3 MAIDENiso performance in reproducing observedδ18OTR series

Split-period verifications of the calibrated relationships for P.mariana and N. pumilio when using estimated δ18OP seriesfrom Eq. (2) indicate that the calibration over either the first-half or the second-half periods provides similar posteriordensities of the calibrated parameters than the ones obtainedwhen calibrating over the whole periods (Fig. S3). One ex-ception is observed in the calibration of coefficient a in north-eastern Canada over the two half periods, where the posteriordensities of a are different from the one obtained by calibrat-ing over the entire period. Over the entire periods, observedand simulated δ18OTR series are significantly correlated innortheastern Canada (r = 0.56, p<0.01 and RMSE= 0.67;Fig. 3a) and in western Argentina (r = 0.48, p<0.01 andRMSE= 0.63; Fig. 3c). The correlations between observedand simulated δ18OTR series are slightly improved when weused synthetic daily GPP (r = 0.62 and r = 0.52, p<0.01,respectively, for northeastern Canada and western Argentina;Fig. 3b and d). It is worth noting that the mean levels of thesimulated δ18OTR series for the Argentinian sites are lowerthan those of the observations (offset of around −2.5 ‰;Fig. S4). The series were therefore corrected to respect themean values detected in the observations (Fig. 3c and d). In

Clim. Past, 13, 1515–1526, 2017 www.clim-past.net/13/1515/2017/

A. L a v er g n e et al.: M o d elli n g tr e e ri n g c ell ul o s e δ 1 8 O v ari ati o n s 1 5 2 1

0 20

40

60

80

100

0. 0 0. 2 0. 4 0. 6 0. 8 1. 0

Ker

nel

dens

ity

esti

mat

es

R

E sti m at e d _ Q u e b e c

M U G C M _ Q u e b e c

LM D Z - N C E P 2 0 _ Q u e b e c

E sti m at e d _ Ar g e nti n a

M U G C M _ Ar g e nti n a

LM D Z - N C E P 2 0 _ Ar g e nti n a

Fi g ur e 4. D e nsit y distri b uti o ns of t h e c o ef fi ci e nt of c orr el ati o n ( R )b et w e e n o bs er v e d a n d si m ul at e d δ 1 8 O T R c hr o n ol o gi es i n Q u é b e ca n d Ar g e nti n a w h e n t h e si m ul ati o ns ar e b as e d o n δ 1 8 O P s eri es es-ti m at e d b y t h e r e gr essi o n m o d el or fr o m t h e M U G C M a n d L M D Z-N C E P 2 0 m o d els.

c o ntr ast, t h e c orr el ati o ns b et w e e n o bs er v ati o n a n d si m ul ati o nc o nsi d er a bl y d e cr e as e w h e n w e us e d m o d ell e d δ 1 8 O P fr o mM U G C M m o d els or L M D Z- N C E P 2 0 r e a n al ysis d at a. T h e yo nl y r e a c h r = 0. 1 3 ( p > 0 .0 5) t o 0. 2 3 ( p < 0 .0 5) i n n ort h-e ast er n C a n a d a a n d r = 0. 2 3 t o 0. 2 6 ( p < 0 .0 5) i n w est er nAr g e nti n a, r es p e cti v el y ( Fi g. 4).

3. 4 I n fl u e n c e of s o ur c e w at er a n d l e af w at er i s ot o pi c

e nri c h m e nt t o t h e δ 1 8 O T R si g n at ur e

T h e r el ati v e c o ntri b uti o ns t o t h e δ 1 8 O T R si g n at ur e of t h eis ot o pi c si g n al of t h e s o ur c e ( x yl e m) w at er a n d of t h e 1 8 Oe nri c h m e nt of t h e l e af w at er d u e t o tr a ns pir ati o n w er e i n-v esti g at e d. I n b ot h r e gi o ns, t h e l e af w at er e nri c h m e nt e x-p eri m e nt al si m ul ati o ns a n d t h e r ef er e n c e si m ul ati o ns h a v e ahi g h er c orr el ati o n c o ef fi ci e nt ( R 2 c e ntr e d o n 0. 9 a n d 0. 9 5, r e-s p e cti v el y, f or n ort h e ast er n C a n a d a a n d w est er n Ar g e nti n a;Fi g. 5) t h a n ar e t h e X W s o ur c e si m ul ati o ns wit h t h e r ef er-e n c e si m ul ati o ns ( R 2 c e ntr e d o n 0. 6 5 a n d 0. 8, r es p e cti v el y,f or n ort h e ast er n C a n a d a a n d w est er n Ar g e nti n a). T his s u g-g ests t h at, wit h t h e m o d el, t h e v ari a bilit y i n δ 1 8 O X W h a s aw e a k er i n fl u e n c e o n δ 1 8 O T R v ari ati o ns t h a n t h e c h a n g es oft h e l e af w at er is ot o pi c e nri c h m e nt d o. N ot a bl y, P. m ari a n ai n n ort h e ast er n C a n a d a a p p e ars t o b e m or e s e nsiti v e t o b ot hi n fl u e n c es t h a n N. p u mili o i n w est er n Ar g e nti n a ( Fi g. 5).

4 Di s c u s si o n

4. 1 Pr e ci pit ati o n δ 1 8 O P v ari ati o n s a n d e sti m ati o n

Alt h o u g h t h e r e gr essi o n m o d els us e d t o pr e di ct d ail y δ 1 8 O P

v al u es ar e li k el y t o o si m plisti c, t h e r es ult a nt m o nt hl y a v-er a g e d v al u es a d e q u at el y r e pr o d u c e t h e distri b uti o n of t h eo bs er v e d (fr o m G NI P st ati o ns) a n d m o d ell e d ( b y G C Ms)

05

10

15

0. 0 0. 2 0. 4 0. 6 0. 8 1. 0

X W _ s o ur c e

L e af _ w at er _ e nri c h m e nt _ dri v e n

Q u e b e c

Ar g e nti n a

R 2

Ker

nel

dens

ity

esti

mat

es

Fi g u r e 5. D e nsit y distri b uti o ns of t h e c o ef fi ci e nts of d et er mi n ati o n(R 2 ) b et w e e n t h e r ef er e n c e si m ul ati o ns a n d t h e ( 1) X W s o ur c e e x-p eri m e nt si m ul ati o n ( δ 1 8 O V a n d h air s et as c o nst a nt, bl a c k) a n d( 2) l e af w at er e nri c h m e nt- dri v e n e x p eri m e nt si m ul ati o n (δ 1 8 O X Ws et as c o nst a nt, gr e e n) i n Q u é b e c ( b ol d li n e) a n d Ar g e nti n a ( d as h e dli n e).

m o nt hl y δ 1 8 O P s eri es i n n ort h e ast er n C a n a d a. I n w est er n Ar-g e nti n a, t h e distri b uti o n of m o nt hl y δ 1 8 O P v al u es is als o w ellr e pr o d u c e d b ut t h e a m plit u d e of v ari ati o n i n t h e pr e di ct e dv al u es is t o o hi g h, l e a di n g t o si m ul at e d v al u es l o w er t h a nt h e m e as ur e d o n es d uri n g t h e c ol d er m o nt hs. T h e t e m p o-r al δ 1 8 O P v ari ati o ns ar e p ositi v el y r el at e d t o air t e m p er at ur egi v e n t h e p ositi v e c o ef fi ci e nt a . I n a gr e e m e nt wit h t h e si m pl eR a yl ei g h distill ati o n m o d el ( D a ns g a ar d, 1 9 6 4), as air t e m p er-at ur e d e cr e as es, t h e s p e ci fi c h u mi dit y at s at ur ati o n d e cr e as es,a n d w at er v a p o ur c o n d e ns es. H 1 8

2 O c o n d e ns es pr ef er e nti all y,a n d t h e r esi d u al w at er v a p o ur b e c o m es m or e a n d m or e d e-pl et e d as c o n d e ns ati o n pr o c e e ds. C o ns e q u e ntl y, i n t h e tr o p-i cs, t h e 1 8 O / 1 6 O r ati o i n t h e m et e ori c w at er h as b e e n o b-s er v e d t o d e cr e as e wit h i n cr e asi n g a m o u nt of pr e ci pit ati o na n d/ or r el ati v e h u mi dit y ( R o z a ns ki et al., 1 9 9 3). I n e xtr a-tr o pi c al r e gi o ns, δ 1 8 O P m a y als o c orr el at e wit h pr e ci pit ati o na m o u nt ( n e g ati v e c o ef fi ci e nt b ), si n c e b ot h v ari a bl es d e p e n do n t h e m et e or ol o gi c al c o n diti o ns.

T h e r es ults of t h e li n e ar r e gr essi o ns s h o w c o m p ar ati v el yl o w er i n fl u e n c e of pr e ci pit ati o n o n δ 1 8 O P i n w est er n Ar-g e nti n a t h a n i n n ort h e ast er n C a n a d a ( Ta bl e 1). T his s u g g estst h at t h e i m pri nt of t h e pr e ci pit ati o n a m o u nt o n δ 1 8 O P i n w est-er n Ar g e nti n a is l o w a n d t h at δ 1 8 O P v ari ati o ns ar e m ai nl yc o ntr oll e d b y s e as o n al c h a n g es i n t e m p er at ur e, w hi c h is i na gr e e m e nt wit h pr e vi o us w or k ( R o z a ns ki et al., 1 9 9 5). H o w-e v er, d u e t o t h e str o n g w est-t o- e ast pr e ci pit ati o n gr a di e nt i nt his r e gi o n ( or o gr a p hi c r ai n s h a d o w), l ar g e δ 1 8 O P v ari ati o nso c c ur o v er s h ort dist a n c es ( R o z a ns ki et al., 1 9 9 5; S mit h a n dE v a ns, 2 0 0 7; St er n a n d Blis ni u k, 2 0 0 2). T h er ef or e, t h e d ail ypr e ci pit ati o n d at as et e xtr a ct e d fr o m t h e gri d d e d r e a n al ysisd at a, w hi c h h as a l o w s p ati al r es ol uti o n ( > 2 0 0 k m), m a y n otr e pr es e nt t h e d ail y v ari ati o ns i n pr e ci pit ati o n at a l o c al s c al e

w w w. cli m- p a st. n et/ 1 3/ 1 5 1 5/ 2 0 1 7/ Cli m. P a st, 1 3, 1 5 1 5 – 1 5 2 6, 2 0 1 7

1522 A. Lavergne et al.: Modelling tree ring cellulose δ18O variations

faithfully. Therefore, the model may underestimate the con-tribution of precipitation on δ18OP variability in this particu-lar area.

In contrast, in northeast Canada, both temperature and pre-cipitation amount equally control the δ18OP variations. Thehigh amount of precipitation falling in summer (∼ 46 %)should have a strong effect and decrease the δ18OP valuesin the condensed water, while high temperatures counteractthis effect by increasing this ratio. Before reaching northeast-ern Canada, the air masses pushed by the dominant westerlywinds discharge most of their humidity over the land, lead-ing to a depleted δ18OP signal at our sites (for the same rea-son, δ18OTR values at L20, which is located 110 km north-east of L01, are ∼ 1 ‰ lower). Moreover, the δ18OP signalin the Canadian sites is comparatively more depleted thanin the Argentinian sites, because of their higher latitude. Itis worth noting that the resolution of the gridded meteoro-logical dataset used for the Canadian sites is relatively high(∼ 10 km), which means that the local processes are likelywell represented.

4.2 Relative performance in modelling δ18OTR values

The simulated δ18OTR series based on daily δ18OP estimationfrom the regression models reproduce the observations bet-ter than the ones based on δ18OP values derived from GCMs(Fig. 4). This is in part due to the greater number of param-eters to optimize, as the calibration process can more easilyfind a solution that fits the observations better. This may how-ever reflect error compensations especially in western Ar-gentina where the estimated annual variability in δ18OP is toolarge. Conversely, in northeastern Canada, the annual varia-tions in δ18OP that are estimated, simulated by GCMs andobserved are in good agreement (Fig. S2). Although isotope-enabled atmospheric global models can reproduce the meanannual precipitation isotopic values and seasonality for manyareas (Risi et al., 2010), results at specific sites, especially inmountainous regions such as at our western Argentinian site,can be less accurate (Fig. S2; see the offset between GNIPstations and LMDZ-NCEP20). Ideally, daily δ18OP long-term records from meteorological stations in the study re-gion should be used as an input of MAIDENiso. Simulationsfrom high-resolution regional circulation models, such asREMOiso, which has a 0.5◦× 0.5◦ (∼ 55 km) horizontal res-olution (Insel et al., 2013; Sturm et al., 2005, 2007), may pro-duce reliable local δ18OP values. Such a dataset has proven tobe quite helpful with MAIDENiso in the Fontainebleau forest(France) (Danis et al., 2012). However, up to now, measuredor REMOiso δ18OP datasets in our regions of study do notexist, which is the case for most regions of the world. More-over, early data (1970–1980s) from GNIP stations may havebeen compromised by pan evaporation and therefore isotopicenrichment. Therefore, we recommend that daily GNIP sta-tions are set up in various forested ecosystems, that an effortis accomplished to homogenize older GNIP time series, and

that high-resolution simulations of δ18OP are performed inwider regions.

The modelling of δ18OTR values based on the estimationof δ18OP is relatively more accurate for northeastern Canadathan for western Argentina (Fig. 3). As the mean levels ofthe measured δ18OTR values are high at the western Argen-tinian sites (mean value of about 30 ‰ ), the Bayesian op-timization tends to increase the biochemical (ε0) and kinetic(εk) fractionations as well as the coefficient a, while reducingthe dampening factor (fo) to reach more representative meanlevels of the δ18OTR simulation. But still, these levels are toolow in comparison with the observations (about 2.5 ‰ lower;Fig. S4). When the posterior value of a calibrated parameteris limited to the upper bound of the prior range of plausiblevalues, as it is the case at the western Argentinian sites for a,b and ε0 (Fig. S3), it means that either the prior range is toonarrow, the model is inadequate, or some important processis not considered in the model. Here, the estimation of theprior ranges of both coefficients a and b were based on ob-served (GNIP stations) and simulated (GCMs) δ18OP values.Therefore, we expect their respective ranges to be consistentwith local processes. When the prior range of a is extendedto higher values in the optimization process, observed andsimulated δ18OTR mean levels in western Argentina are bet-ter matching. However, in this case, the distribution of δ18OPvalues is shifted toward higher values, advocating for unreal-istic estimated δ18OP variations.

One other possibility is that the prior range of ε0 is too nar-row. In accordance with DeNiro and Epstein (1981), Stern-berg (1989) and Yakir and DeNiro (1990), the biochemi-cal fractionation ε0 is assumed here to be lower than 30 ‰.However, a recent study has demonstrated that this parame-ter, nearly constant between 20 to 30 ◦C, increases at lowertemperatures to values of 31 ‰ (Sternberg and Ellsworth,2011). During the growing season, maximum temperaturescan reach 20 ◦C in western Argentina and 30 ◦C in northeast-ern Canada, which suggests that the high mean δ18OTR levelsin N. pumilio may be due to biochemical fractionation higherthan 30 ‰ due to temperature generally lower than 20 ◦C.However, when the prior range of ε0 is extended to 31 ‰in the optimization process, the mean δ18OTR levels of N.pumilio are still too low in comparison with the observations.These results advocate for the existence of other processes,which can explain this offset in mean levels in Argentina.For example, higher soil water evaporation than modelledby MAIDENiso should lead to less negative δ18OSW (andtherefore δ18OXW), which could explain the high mean lev-els of δ18OTR in Argentina. Caution should be exercised withsuch an interpretation since other species living in similarconditions as N. pumilio in western Argentina show com-paratively lower mean δ18OTR levels than N. pumilio (i.e.,Fitzroya cupressoides; see Lavergne et al., 2016). The on-going monitoring and evaluation of isotopic processes basedon synchronous measurements of vapour, precipitation, soilwater and xylem water will certainly help understanding the

Clim. Past, 13, 1515–1526, 2017 www.clim-past.net/13/1515/2017/

A. Lavergne et al.: Modelling tree ring cellulose δ18O variations 1523

high mean levels observed in Argentina, as well as increasingthe representation of the involved processes in MAIDENiso.

The better fit between observed and simulated δ18OTR val-ues obtained with specific forms of synthetic distributions ofdaily GPP for northeastern Canada and western Argentina(Fig. 3) suggests differential limiting factors in the two re-gions. The synthetic bimodal distribution of daily GPP withmaxima in spring and autumn, as simulated in western Ar-gentina, is often observed in a diversity of ecosystems such asin the Mediterranean environments (Baldocchi et al., 2010;Gea-Izquierdo et al., 2015). After the activation of the pho-tosynthesis in early spring, increasing temperatures tend tobe optimal for tree growth. However, in a modelling study,Lavergne et al. (2015) have shown that the influence of tem-perature on N. pumilio’s growth becomes negative once atemperature threshold (soil moisture) is exceeded. Therefore,we assume that after reaching a threshold of temperature andsoil moisture summer conditions, tree growth is inhibited,leading to a decrease of primary productivity. However, whentemperature starts to decline and soil water supply tends toincrease with increasing precipitation events, tree growth in-creases again until the end of the growing season. In contrast,because precipitation is more abundant in summer (June toSeptember) in northeastern Canada (Naulier et al., 2014),high summer temperatures should be always beneficial totree growth if enough soil water is available. Therefore, inagreement with GPP-derived eddy covariance data from theFluxnet network (see Gennaretti et al., 2017a), a better fit be-tween observations and simulations is observed when usinga unimodal rather than a bimodal GPP distribution. Monitor-ing of tree physiology, environmental conditions and woodcell formation will provide a more detailed representation ofthe complex biological and ecological processes operating inPatagonia, allowing us to run the MAIDENiso model withbetter constraints.

4.3 What is the main origin of the temperature signalrecorded in δ18OTR?

The investigation of the relative contributions of the isotopiccomposition of the source (xylem) water and of the 18Oenrichment of the leaf water by transpiration on the simu-lated δ18OTR reveals that the variability in the former hasa weaker influence on δ18OTR variations than that of thelatter in North and South America. Therefore, the temper-ature signal recorded in δ18OTR series more likely reflectsthe effect of temperature on isotopic enrichment of the leafwater rather than on the isotopic composition of the sourcewater. At the leaf level, air temperature has a strong effecton the relative humidity and therefore on the vapour pres-sure deficit (VPD), i.e. the difference between the saturationvapour pressure and the actual vapour pressure, which mod-ulates the transpiration (Barbour, 2007). Thus, the imprint ofthe ambient air temperature on the fractionation processesoccurring during transpiration is preferentially recorded in

the tree rings of the two species. Furthermore, both the iso-topic signature of the xylem water and of the fractionationprocesses occurring at the evaporation sites of the leaves havecomparatively higher influence on δ18OTR in P. marianathan in N. pumilio. This is probably due to the lower ampli-tude of the day-by-day variations in the relative humidity inwestern Argentina (SD= 5 %) versus in northeastern Canada(SD= 16 %), which translates into a weaker influence of hairvariations and therefore of leaf-level isotopic fractionationprocesses on δ18OTR values in western Argentina than innortheastern Canada. These results highlight the potential ofMAIDENiso model to better refine the origin of the climaticsignal recorded in the oxygen isotopic signature in the treerings of different species.

5 Conclusions

Here, by using MAIDENiso model, we have provided amechanistic overview of the climatic and biological pro-cesses controlling oxygen isotopic fractionation in two Northand South American temperature-sensitive tree species. First,we have shown that using regression-based rather thanmodel-based δ18OP estimates as inputs increases the pre-dictive skills of our simulations, although this may be atthe price of error compensations. Second, our study revealsthat the variability in the isotopic composition of the source(xylem) water has a weaker influence on δ18OTR variationsthan that of the 18O enrichment of the leaf water by transpira-tion. Last, these findings suggest that the imprint of temper-ature recorded in δ18OTR of the two species is likely relatedto the effect of temperature on isotopic enrichment of theleaf water. The isotopic monitoring of water within the soil–vegetation–atmosphere compartments in future work willcertainly provide the input and control data necessary tobetter constrain MAIDENiso. Our study demonstrates thatthe eco-physiological modelling of δ18OTR values is neces-sary and likely the only approach to accurately interpret therecorded climate signal. Based on the calibrations of MAID-ENiso presented here, the next step involves inverse mod-elling approaches to perform palaeoclimatic reconstructionsin North and South America that are less biased by the com-plex and nonlinear interactions between climate, CO2 con-centrations and tree growth as recommended by Boucher etal. (2014).

Code and data availability. The code of the model can be foundat https://doi.org/10.6084/m9.figshare.5446435.v1 (recent versiondeveloped by Gennaretti et al., 2017). The daily δ18OP datafrom the MUGCM model were extracted via the SWING projectwebpage (SWING project, http://paos.colorado.edu/~dcn/SWING/database.php). The daily climatic data used for Québec were re-trieved from the webpage of the Natural Resources Canada prod-ucts (Natural Resources Canada, http://cfs.nrcan.gc.ca/projects/3/4), while those for Argentina were extracted from the Earth Sys-

www.clim-past.net/13/1515/2017/ Clim. Past, 13, 1515–1526, 2017

1524 A. Lavergne et al.: Modelling tree ring cellulose δ18O variations

tem Research Laboratory (ESRL) of the National Oceanic & Atmo-spheric Administration (ESRL-NOAA) webpage (ESRL-NOAA,20th Century Reanalysis V2c, https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2c.html). The atmospheric CO2 concen-tration data derived from the Mauna Loa station were extractedfrom the ESRL-NOAA webpage (ESRL-NOAA, Mauna Loa data,http://www.esrl.noaa.gov/gmd/ccgg/trends/).

The Supplement related to this article is available onlineat https://doi.org/10.5194/cp-13-1515-2017-supplement.

Competing interests. The authors declare that they have no con-flict of interest.

Acknowledgements. This study has been co-funded by theresearch federation ECCOREV. Aliénor Lavergne has been sup-ported by a research associate/lecturer position at the Aix-MarseilleUniversity (France). Fabio Gennaretti has received funding fromthe European Union’s Horizon 2020 research and innovationprogram under the Marie Sklodowska-Curie grant agreementno. 656896. We acknowledge all data providers: the InstitutoArgentino de Nivología, Glaciología y Ciencias Ambientales(IANIGLA, Argentina) for providing the daily temperature datafrom La Almohadilla site; the National Meteorological Servicefrom Argentina for providing the monthly temperature data fromBariloche meteorological station (Argentina); the Department ofNatural Resources Canada for providing the daily climatic dataused for Québec; the US Department of Energy, Office of ScienceBiological and Environmental Research (BER) and the NationalOceanic and Atmospheric Administration Climate Program Officefor providing the daily climatic data used for Argentina; and theSWING project for providing the daily δ18OP data from MUGCMmodel.

Edited by: Laurie MenvielReviewed by: two anonymous referees

References

Baldocchi, D. D., Ma, S., Rambal, S., Misson, L., Ourcival, J. M.,Limousin, J. M., Pereira, J., and Papale, D.: On the differentialadvantages of evergreenness and deciduousness in mediterraneanoak woodlands: A flux perspective, Ecol. Appl., 20, 1583–1597,https://doi.org/10.1890/08-2047.1, 2010.

Barbour, M. M.: Stable oxygen isotope composition ofplant tissue: a review, Funct. Plant Biol., 34, 83–94,https://doi.org/10.1071/FP06228, 2007.

Barbour, M. M., Cernusak, L. A., and Farquhar, G. D.: Factors af-fecting the oxygen isotope ratio of plant organic material, in: Sta-ble isotopes and biosphere-atmosphere interactions: Processesand Biological Controls, edited by: Flanagan, L. B., Ehleringer,J. R., and Pataki, D. E., Elsevier, Amsterdam, 9–28, 2005.

Boucher, É., Guiot, J., Hatté, C., Daux, V., Danis, P.-A.,and Dussouillez, P.: An inverse modeling approach fortree-ring-based climate reconstructions under changing atmo-spheric CO2 concentrations, Biogeosciences, 11, 3245–3258,https://doi.org/10.5194/bg-11-3245-2014, 2014.

Brienen, R. J. W., Helle, G., Pons, T. L., Guyot, J.-L., andGloor, M.: Oxygen isotopes in tree rings are a good proxyfor Amazon precipitation and El Niño-Southern Oscillationvariability, Proc. Natl. Acad. Sci. USA, 109, 16957–16962,https://doi.org/10.1073/pnas.1205977109, 2012.

Buhay, W. M., Edwards, T. W. D., and Aravena, R.: Evaluatingkinetic fractionation factors used for reconstructions from ox-gen and hydrogen isotope ratios in plant water and cellulose,Geochem. Geophy. Geosy., 60, 2209–2218, 1996.

Cernusak, L. A. and English, N. B.: Beyond tree-ringwidths: stable isotopes sharpen the focus on climate re-sponses of temperate forest trees, Tree Physiol., 35, 1–3,https://doi.org/10.1093/treephys/tpu115, 2015.

Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Al-lan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G.,Bessemoulin, P., BroNnimann, S., Brunet, M., Crouthamel, R. I.,Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger,A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, O., Ross,T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S.J.: The twentieth century reanalysis project, Q. J. Roy Meteor.Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011.

Craig, H. and Gordon, L. I.: Deuterium and oxygen 18 variations inthe ocean and the marine atmosphere, Spoleto, 1965.

Danis, P. A., Hatté, C., Misson, L. and Guiot, J.: MAIDENiso:a multiproxy biophysical model of tree-ring width and oxy-gen and carbon isotopes, Can. J. Forest Res., 42, 1697–1713,https://doi.org/10.1139/x2012-089, 2012.

Danis, P. A., Masson-Delmotte, V., Stievenard, M., Guillemin,M. T., Daux, V., Naveau, P., and von Grafenstein, U.:Reconstruction of past precipitation δ18O using tree-ringcellulose δ18O and δ13C: A calibration study near Lacd’Annecy, France, Earth Planet. Sc. Lett., 243, 439–448,https://doi.org/10.1016/j.epsl.2006.01.023, 2006.

Dansgaard, W.: Stable isotopes in precipitation, Tellus A, 16, 436–468, https://doi.org/10.3402/tellusa.v16i4.8993, 1964.

DeNiro, M. J. and Epstein, S.: Relationship between the oxygenisotope ratios of terrestrial plant cellulose, carbon dioside, andwater, Science, 204, 51–53, 1979.

DeNiro, M. J. and Epstein, S.: Isotopic composition of cellulosefrom aquatic organisms, Geochim. Cosmochim. Ac., 45, 1885–1894, https://doi.org/10.1016/0016-7037(81)90018-1, 1981.

Donoso, C.: Tipos forestales de los bosques nativos de Chile., Doc-umento de Trabajo Nu. 38, Investigación y Desarrollo Forestal(CONAF, PNUD-FAO), FAO Chile, 1981.

ESRL-NOAA: 20th Century Reanalysis V2c, https://www.esrl.noaa.gov/psd/data/gridded/data.20thC_ReanV2c.html, last ac-cess: March 2017.

ESRL-NOAA: Mauna Loa data, http://www.esrl.noaa.gov/gmd/ccgg/trends/, last access: March 2017.

Farquhar, G. D., Hubick, H. T., Condon, A. G., and Richards, R. A.:Carbon isotope fractionation and plant water-use efficiency, in:Stable isotopes in ecological research, 21–40, 1989.

Farquhar, G. D., Barbour, M. M., and Henry, B. K.: Interpretation ofoxygen isotope composition of leaf material, in Stable isotopes:

Clim. Past, 13, 1515–1526, 2017 www.clim-past.net/13/1515/2017/

A. Lavergne et al.: Modelling tree ring cellulose δ18O variations 1525

integration of biological, ecological and geochemical processes,BIOS Scientific Publishers, Oxford, 27–61, 1998.

Gea-Izquierdo, G., Guibal, F., Joffre, R., Ourcival, J. M., Simioni,G., and Guiot, J.: Modelling the climatic drivers determiningphotosynthesis and carbon allocation in evergreen Mediterraneanforests using multiproxy long time series, Biogeosciences, 12,3695–3712, https://doi.org/10.5194/bg-12-3695-2015, 2015.

Gennaretti, F., Arseneault, D., Nicault, A., Perreault, L.,and Bégin, Y.: Volcano-induced regime shifts in mil-lennial tree-ring chronologies from northeastern NorthAmerica, Proc. Natl. Acad. Sci. USA, 111, 10077–10082,https://doi.org/10.1073/pnas.1324220111, 2014.

Gennaretti, F., Gea-Izquierdo, G., Boucher, E., Berninger, F., Arse-neault, D., and Guiot, J.: Ecophysiological modeling of photo-synthesis and carbon allocation to the tree stem in the boreal for-est, Biogeosciences, 14, 4851–4866, https://doi.org/10.5194/bg-14-4851-2017, 2017a.

Gennaretti, F., Huard, D., Naulier, M., Savard, M., Bégin, C., Ar-seneault, D., and Guiot, J.: Bayesian multiproxy temperaturereconstruction with black spruce ring widths and stable iso-topes from the northern Quebec taiga, Clim. Dynam., 1–13,https://doi.org/10.1007/s00382-017-3565-5, 2017b.

Gessler, A., Ferrio, J. P., Hommel, R., Treydte, K., Werner, R. A.,and Monson, R. K.: Stable isotopes in tree rings: towards a mech-anistic understanding of isotope fractionation and mixing pro-cesses from the leaves to the wood, Tree Physiol., 1–23, 2014.

Guiot, J., Boucher, E., and Gea-Izquierdo, G.: Process models andmodel-data fusion in dendroecology, Front. Ecol. Evol., 2, 1–12,https://doi.org/10.3389/fevo.2014.00052, 2014.

Hartl-Meier, C., Zang, C., Büntgen, U. L. F., Esper, J. A. N., Rothe,A., Göttlein, A., Dirnböck, T., and Treydte, K.: Uniform climatesensitivity in tree-ring stable isotopes across species and sitesin a mid-latitude temperate forest, Tree Physiol., 2003, 4–15,https://doi.org/10.1093/treephys/tpu096, 2014.

Helliker, B. R. and Richter, S. L.: Subtropical to boreal con-vergence of tree-leaf temperatures, Nature, 454, 511–514,https://doi.org/10.1038/nature07031, 2008.

Horita, J. and Wesolowski, D. J.: Liquid-vapor fractionation of oxy-gen and hydrogen isotopes of water from the freezing to thecritical temperature, Geochim. Cosmochim. Ac., 58, 3425–3437,https://doi.org/10.1016/0016-7037(94)90096-5, 1994.

Hourdin, F., Grandpeix, J. Y., Rio, C., Bony, S., Jam, A., Cheruy,F., Rochetin, N., Fairhead, L., Idelkadi, A., Musat, I., Dufresne,J. L., Lahellec, A., Lefebvre, M. P., and Roehrig, R.: LMDZ5B:The atmospheric component of the IPSL climate model with re-visited parameterizations for clouds and convection, Clim. Dy-nam., 40, 2193–2222, https://doi.org/10.1007/s00382-012-1343-y, 2013.

Hutchinson, M. F., McKenney, D. W., Lawrence, K., Pedlar, J.H., Hopkinson, R. F., Milewska, E., and Papadopol, P.: Devel-opment and testing of Canada-wide interpolated spatial mod-els of daily minimum-maximum temperature and precipita-tion for 1961–2003, J. Appl. Meteorol. Clim., 48, 725–741,https://doi.org/10.1175/2008JAMC1979.1, 2009.

Insel, N., Poulsen, C. J., Sturm, C., and Ehlers, T. A.:Climate controls on Andean precipitation δ18O interan-nual variability, J. Geophys. Res.-Atmos., 118, 9721–9742,https://doi.org/10.1002/jgrd.50619, 2013.

Kahmen, A., Sachse, D., Arndt, S. K., Tu, K. P., Farrington,H., Vitousek, P. M., and Dawson, T. E.: Cellulose δ18O is anindex of leaf-to-air vapor pressure difference (VPD) in trop-ical plants., Proc. Natl. Acad. Sci. USA, 108, 1981–1986,https://doi.org/10.1073/pnas.1018906108, 2011.

Keeling, C. D., Bacastow, R. B., Bainbridge, A. E., Ek-dahl Jr., C. A., Guenther, P. R., Waterman, L. S., andChin, J. F. S.: Atmospheric carbon dioxide variations atMauna Loa Observatory, Hawaii, Tellus A, 28, 538–551,https://doi.org/10.3402/tellusa.v28i6.11322, 1976.

Labuhn, I., Daux, V., Girardclos, O., Stievenard, M., Pierre, M., andMasson-Delmotte, V.: French summer droughts since 1326 CE: areconstruction based on tree ring cellulose δ18O, Clim. Past, 12,1101–1117, https://doi.org/10.5194/cp-12-1101-2016, 2016.

Lavergne, A., Daux, V., Villalba, R., and Barichivich, J.: Tempo-ral changes in climatic limitation of tree-growth at upper tree-line forests: Contrasted responses along the west-to-east humid-ity gradient in Northern Patagonia, Dendrochronologia, 36, 49–59, 2015.

Lavergne, A., Daux, V., Villalba, R., Pierre, M., Stievenard, M.,Srur, A. M., and Vimeux, F.: Are the δ18O of F. cupressoidesand N. pumilio promising proxies for climate reconstructions innorthern Patagonia?, J. Geophys. Res.-Biogeo., 121, 767–776,https://doi.org/10.1002/2015JG003260, 2016.

Lavergne, A., Daux, V., Villalba, R., Pierre, M., Stievenard, M., andSrur, A. M.: Improvement of isotope-based climate reconstruc-tions in Patagonia through a better understanding of climate in-fluences on isotopic fractionation in tree rings, Earth Planet. Sc.Lett., 459, 372–380, https://doi.org/10.1016/j.epsl.2016.11.045,2017

López Bernal, P., Defossé, G. E., Quinteros, C. P., and Bava, J. O.:Sustainable management of lenga (Nothofagus pumilio) foreststhrough group selection system, in: Sustainable forest manage-ment – current research, edited by: Diez, J. J., 45–66, 2012.

Lorrey, A. M., Brookman, T. H., Evans, M. N., Fauchereau,N.C., Barbour, M., Macinnis-Ng, C. Criscitiello, A., Eischeid,G., Fowler, A. M., Horton, T. W., and Schrag, D. P.: Sta-ble oxygen isotope signatures of early season wood in NewZealand kauri (Agathis australis) tree rings: Prospects forpalaeoclimate reconstruction, Dendrochronologia, 40, 50–63,https://doi.org/10.1016/j.dendro.2016.03.012, 2016.

Magnin, A., Puntieri, J., and Villalba, R.: Interannual variations inprimary and secondary growth of Nothofagus pumilio and theirrelationships with climate, Trees, 28, 1463–1471, 2014.

Misson, L.: MAIDEN: a model for analyzing ecosystem processesin dendroecology, Can. J. For. Res., 34, 874–887, 2004.

Natural Resources Canada: http://cfs.nrcan.gc.ca/projects/3/4, lastaccess: February 2017.

Naulier, M., Savard, M. M., Bégin, C., Marion, J., Arse-neault, D., and Bégin, Y.: Carbon and oxygen isotopes oflakeshore black spruce trees in northeastern Canada as prox-ies for climatic reconstruction, Chem. Geol., 374/375, 37–43,https://doi.org/10.1016/j.chemgeo.2014.02.031, 2014.

Naulier, M., Savard, M. M., Bégin, C., Gennaretti, F., Arseneault,D., Marion, J., Nicault, A., and Bégin, Y.: A millennial sum-mer temperature reconstruction for northeastern Canada usingoxygen isotopes in subfossil trees, Clim. Past, 11, 1153–1164,https://doi.org/10.5194/cp-11-1153-2015, 2015.

www.clim-past.net/13/1515/2017/ Clim. Past, 13, 1515–1526, 2017

1526 A. Lavergne et al.: Modelling tree ring cellulose δ18O variations

Noone, D. and Simmonds, I.: Associations between δ18O of waterand climate parameters in a simulation of atmospheric circulationfor 1979–95, J. Clim., 15, 3150–3169, 2002.

Ogée, J., Brunet, Y., Loustau, D., Berbigier, P., and Delzon,S.: MuSICA, a CO2, water and energy multilayer, multi-leaf pine forest model: Evaluation from hourly to yearly timescales and sensitivity analysis, Glob. Change Biol., 9, 697–717,https://doi.org/10.1046/j.1365-2486.2003.00628.x, 2003.

Ogée, J., Barbour, M. M., Wingate, L., Bert, D., Bosc, A.,Stievenard, M., Lambrot, C., Pierre, M., Bariac, T., Loustau,D., and Dewar, R. C.: A single-substrate model to interpretintra-annual stable isotope signals in tree-ring cellulose, Plant,Cell Environ., 32, 1071–1090, https://doi.org/10.1111/j.1365-3040.2009.01989.x, 2009.

Rinne, K. T., Loader, N. J., Switsur, V. R., and Water-house, J. S.: 400-year May-August precipitation re-construction for Southern England using oxygen iso-topes in tree rings, Quaternary Sci. Rev., 60, 13–25,https://doi.org/10.1016/j.quascirev.2012.10.048, 2013.

Risi, C., Bony, S., Vimeux, F., and Jouzel, J.: Water-stable isotopesin the LMDZ4 general circulation model: Model evaluation forpresent-day and past climates and applications to climatic inter-pretations of tropical isotopic records, J. Geophys. Res.-Atmos.,115, 1–27, https://doi.org/10.1029/2009JD013255, 2010.

Roden, J. S., Lin, G., and Ehleringer, J. R.: A mechanisticmodel for interpretation of hydrogen and oxygen isotope ratiosin tree-ring cellulose, Geochim. Cosmochim. Ac., 64, 21–35,https://doi.org/10.1016/S0016-7037(99)00195-7, 2000.

Rozanski, K. and Araguás-Araguás, L.: Spatial and temporal vari-ability of stable isotope composition of precipitation over theSouth American continent, Bull. l’Institut Fr. d’eìtudes Andin.,24, 379–390, 1995.

Rozanski, K., Araguás-Araguás, L., and Gonfiantini, R.: Isotopicpatterns in modern global precipitation, in: Climate change incontinental isotopic records, edited by: Swart, P. K., Lohmann,K. C., McKenzie, J., and Savin, S., American GeophysicalUnion, 1993.

Running, S. W., Nemani, R. R., and Hungerford, R. D.: Ex-trapolation of synoptic meteorological data in mountain-ous terrain and its use for simulating forest evapotranspira-tion and photosynthesis, Can. J. Forest Res., 17, 472–483,https://doi.org/10.1139/x87-081, 1987.

Rusch, V. E.: Altitudinal variation in the phenology of Nothofaguspumilio in Argentina, Rev. Chil. Hist. Nat., 66, 131–141, 1993.

Saurer, M., Aellen, K., and Siegwolf, R. T. W.: Correlating δ13Cand δ18O in cellulose of trees, Plant Cell Environ., 20, 1543–1550, 1997.

Saurer, M., Cherubini, P., Reynolds-Henne, C. E., Treydte, K.S., Anderson, W. T., and Siegwolf, R. T. W.: An investiga-tion of the common signal in tree ring stable isotope chronolo-gies at temperate sites, J. Geophys. Res.-Biogeo., 113, 1–11,https://doi.org/10.1029/2008JG000689, 2008.

Schlatter, J.: Requerimientos de sitio para la lenga, Nothofaguspumilio (Poepp. et Endl.) Krasser, Bosque, 15, 3–10, 1994.

Shi, C., Daux, V., Zhang, Q. B., Risi, C., Hou, S. G., Stievenard, M.,Pierre, M., Li, Z., and Masson-Delmotte, V.: Reconstruction ofsoutheast Tibetan Plateau summer climate using tree ring δ18O:Moisture variability over the past two centuries, Clim. Past, 8,205–213, https://doi.org/10.5194/cp-8-205-2012, 2012.

Smith, R. B. and Evans, J. P.: Orographic precipitation and watervapor fractionation over the Southern Andes, J. Hydrometeorol.,8, 3–19, https://doi.org/10.1175/JHM555.1, 2007.

Stern, L. A. and Blisniuk, P. M.: Stable isotope com-position of precipitation across the southern Patago-nian Andes, J. Geophys. Res.-Atmos., 107, D234667,https://doi.org/10.1029/2002JD002509, 2002.

Sternberg, L. D. S. L.: Oxygen and hydrogen isotope ratios in plantcellulose: Mechanisms and applications, in: Stable isotopes inecological research, edited by: Rundel, P. W., Ehleringer J. R.,and Nagy K. A., 124–141, 1989.

Sternberg, L. D. S. L. and Ellsworth, P. F. V.: Divergent biochem-ical fractionation, not convergent temperature, explains cellu-lose oxygen isotope enrichment across latitudes, PLoS One, 6,e28040, https://doi.org/10.1371/journal.pone.0028040, 2011.

Sturm, C., Vimeux, F., and Krinner, G.: Intraseasonalvariability in South America recorded in stable wa-ter isotopes, J. Geophys. Res.-Atmos., 112, 2156–2202,https://doi.org/10.1029/2006JD008298, 2007.

Sturm, K., Hoffmann, G., Langmann, B., and Stichler, W.:Simulation of δ18O in precipitation by the regional circu-lation model REMOiso, Hydrol. Process., 19, 3425–3444,https://doi.org/10.1002/hyp.5979, 2005.

SWING project: http://paos.colorado.edu/~dcn/SWING/database.php, last access: June 2017.

Treydte, K., Boda, S., Graf Pannatier, E., Fonti, P., Frank, D.,Ullrich, B., Saurer, M., Siegwolf, R. T. W., Battipaglia, G.,Werner, W., and Gessler, A.: Seasonal transfer of oxygen iso-topes from precipitation and soil to the tree ring: Source wa-ter versus needle water enrichment, New Phytol., 202, 772–783,https://doi.org/10.1111/nph.12741, 2014.

Viereck, L. A. and Johnston, W. F.: Picea mariana (Mill.) B. S. P., in:Silvics of North America: 1. Conifers; 2. Hardwoods, edited by:Burns, R. M. and Honkala, B. H., US, Department of Agriculture,Forest Service, Washington, DC, 443–464, 1990.

Wernicke, J., Grießinger, J., Hochreuther, P., and Braüning, A.:Variability of summer humidity during the past 800 years onthe eastern Tibetan Plateau inferred from δ18O of tree-ring cel-lulose, Clim. Past, 11, 327–337, https://doi.org/10.5194/cp-11-327-2015, 2015.

Wershaw, R. L., Friedman, I., and Heller, S. J.: Hydrogen isotopefractionation in water passing through trees, in: Advances in Or-ganic Geochemistry, edited by: Hobson, F. and Speers, M., NewYork, Pergamon, 55–67, 1966.

Yakir, D. and DeNiro, M. J.: Oxygen and hydrogen isotope fraction-ation during cellulose metabolism in lemna gibba L., Plant Phys-iol., 93, 325–332, https://doi.org/10.1104/pp.93.1.325, 1990.

Clim. Past, 13, 1515–1526, 2017 www.clim-past.net/13/1515/2017/