Dan J Charman
email: d.j.charman@exeter.ac.uk

David W Beilman

Maarten Blaauw

Robert K Booth

Simon Brewer

Frank M Chambers

J Andrés Christen

Angela Gallego-Sala

Sandy P Harrison

Paul D M Hughes

Maarten Blaauw

Simon Chambers

Frank M Christen

J Andrés

Sandy P Hughes

Stephen T And Korhola

Fraser J G Prentice

I Colin

Van Der Linden

De Vleeschouwer

J Bauer

I E Corish

Y M C And Garneau

Michelle Hohl

Yongsheng V Huang

Le Roux

Jonathan E Nieminen

Tiina M And Macdonald

Glen M And Phadtare

S T Jackson

A Korhola

D Mauquoy

F J G Mitchell

I C Prentice

M Van Der Linden

F De Vleeschouwer

Z C Yu

J Alm

I E Bauer

Y M C Corish

M Garneau

V Hohl

Y Huang

E Karofeld

G Le Roux

J Loisel

R Moschen

J E Nichols

T M Nieminen

G M Macdonald

N R Phadtare

N Rausch

Ü Sillasoo

G T Swindles

E.-S Tuittila

L Ukonmaanaho

M Väliranta

S Van Bellen

B Van Geel

D H Vitt

Y Zhao

Climate-related changes in peatland carbon accumulation during the last millennium

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Peatlands contain around 600 gigatonnes of carbon (Gt C) that has accumulated since the last glacial maximum in northern mid-high latitudes, tropical regions and temperate areas of the Southern Hemisphere, and the steady accumulation of carbon has been a small but persistent sink for atmospheric CO 2 throughout the Holocene [START_REF] Yu | Holocene carbon flux histories of the world's peatlands: Global carbon-cycle implications[END_REF]. The relationship between climate change and the rate of carbon sequestration is important for understanding the past and future global carbon cycle, and it has generally been assumed that because temperature drives increasing decay [START_REF] Ise | High sensitivity of peat decomposition to climate change through watertable feedback[END_REF][START_REF] Dorrepaal | Carbon respiration from subsurface peat accelerated by climate warming in the sub-arctic[END_REF], peatlands could be part of the positive feedback from the global carbon cycle [START_REF] Friedlingstein | Climate-carbon cycle feedback analysis: results from the C 4 MIP model intercomparison[END_REF]. A key objective in improving understanding of the global carbon cycle in climate models is to be able to simulate past observed atmospheric CO 2 changes.

There is growing interest in the last millennium as a climate-modelling target, and especially in the assessment of the sensitivity of the global carbon cycle to climate warming (Abe-Ouchi and [START_REF] Abe-Ouchi | Constraining the carbon-cycle feedback using palaeodata: the PalaeoCarbon Modelling Intercomparison Project[END_REF][START_REF] Jungclaus | Climate and carbon-cycle variability over the last millennium[END_REF]. In the Northern Hemisphere, the transition from the generally warmer Medieval Climate Anomaly (MCA) to the cooler Little Ice Age (LIA) [START_REF] Mann | Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia[END_REF][START_REF] Mann | Global signatures and dynamical origins of the Little Ice Age and Medieval Climate Anomaly[END_REF][START_REF] Jansen | Palaeoclimate, in: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF] was associated with a ca. 7-10 ppmv decline in atmospheric CO 2 concentration [START_REF] Ahn | Atmospheric CO 2 over the last 1000 years: A high-resolution record from the West Antarctic Ice Sheet (WAIS) Divide ice core[END_REF]. This pattern supports the existence of a positive global climate-carbon cycle feedback, as suggested by coupled climate-carbon cycle models [START_REF] Friedlingstein | Climate-carbon cycle feedback analysis: results from the C 4 MIP model intercomparison[END_REF][START_REF] Denman | Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF]. However, estimates of the magnitude of the climate sensitivity of the global carbon cycle based on data from the last millennium vary from 1.7-21.4 ppm CO 2 K -1 [START_REF] Frank | Ensemble reconstruction constraints on the global carbon cycle sensitivity to climate[END_REF] to 40-60 ppm CO 2 K -1 [START_REF] Cox | Illuminating the modern dance of climate and CO 2[END_REF]. Carbon cycle models also vary greatly in their assessment of this feedback [START_REF] Friedlingstein | Climate-carbon cycle feedback analysis: results from the C 4 MIP model intercomparison[END_REF], although recent estimates [START_REF] Jungclaus | Climate and carbon-cycle variability over the last millennium[END_REF] suggest sensitivity within the lower end of this range (3.2-12 ppm CO 2 K -1 ). The causes of the reduction in CO 2 concentrations during the MCA to LIA transition are poorly known, but reduced soil heterotrophic respiration is assumed to be important [START_REF] Jungclaus | Climate and carbon-cycle variability over the last millennium[END_REF][START_REF] Pongratz | Effects of anthropogenic land cover change on the carbon cycle of the last millennium[END_REF]. However, the models do not specifically take into account possible climate-related variations in the rate of peatland carbon sequestration.

Peatlands have sequestered and exchanged atmospheric carbon over millennia [START_REF] Macdonald | Rapid early development of circumarctic peatlands and atmospheric CH 4 and CO 2 variations[END_REF][START_REF] Frolking | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emission[END_REF], with the largest store in northern extratropical peatlands, an estimated 545 Gt C [START_REF] Yu | Global peatland dynamics since the Last Glacial Maximum[END_REF]. The annual uptake of CO 2 by peatlands, previously estimated as 0.076 Gt C yr -1 [START_REF] Gorham | Northern peatlands, role in the carbon cycle and probable responses to climatic warming[END_REF] or 0.088 Gt C yr -1 [START_REF] Yu | Holocene carbon flux histories of the world's peatlands: Global carbon-cycle implications[END_REF], without considering long-term decay (see below), is a small but temporally persistent component of land carbon uptake. This is equivalent to 36 ppm atmospheric CO 2 over 1000 yr, based on a simple conversion from change in carbon pool to atmospheric CO 2 of 1 Gt C = 2.123 ppm. However, over millennial timescales, carbon uptake of this magnitude would be compensated by ocean outgassing processes as a result of reduced CO 2 in the atmosphere and reduction in air-sea CO 2 partial pressure, so that the actual effect on the atmosphere is only 20-35 % of this total over periods of 200-2000 yr [START_REF] Archer | Atmospheric lifetime of fossil fuel carbon dioxide[END_REF], or 7-12 ppm atmospheric CO 2 over a 1000 yr period. Variations in the size of the peatland sink could therefore have a significant cumulative effect on global atmospheric CO 2 concentrations over the last millennium, of the same order of magnitude as the observed changes.

In this study, we compiled peat core data from northern peatlands to estimate changes in carbon accumulation over the last millennium and to explore the spatial relationship between climate and the total size of the carbon sink accumulated over this period. Further analysis on a subset of well-dated cores allowed an analysis of temporal variation in carbon accumulation in relations to the MCA-LIA climate changes estimated from palaeoclimate records. We use these data to help understand the relationships between climate and peatland carbon accumulation and to assess the direction and strength of the peatland carbon cycle feedback.

Methods

Site selection and carbon measurement

A list of Northern Hemisphere, extratropical peatland profiles with published and unpublished carbon accumulation data was compiled (Tables 1 and2) for sites that met the following criteria: a. at least 3 evenly spaced dates (including 210 Pb, tephra, spheroidal carbonaceous particles, pollen markers or 14 C (pre-or postdating the period of nuclear bomb testing), and the uncut peat surface) and spanning the last ca. 1000 yr. Most of the sites had more than 5 dates (Table 2), but we also rejected some sites where a satisfactory age-depth model could not be produced, because of age reversals or other problems; and b. contiguous bulk density measurements at < 5 cm resolution.

Application of these criteria resulted in the selection of 24 sites that were used in subsequent analyses of the temporal changes in carbon accumulation through the last millennium (Table 2). A second tier of sites (Table 1) was used for the millennium carbon inventory analysis against climate indices. These sites did not meet criteria (a) and (b), but did meet the following criteria: c. a basic age-depth model for the last millennium; and d. contiguous bulk density measurements but not necessarily at high resolution.

A total of 90 sites met these less stringent criteria and were used in the climate-carbon inventory analyses (Tables 1 and 2). The sites in both data sets are widely distributed geographically and broadly representative of the climate space occupied by northern peatlands (Fig. 1). Bulk density was measured on carefully cut fresh or frozen material using freeze drying or oven drying of samples of known volume. Sample sizes varied depending on the sampling method and core size, and sample resolution varied from 0.5 to 5 cm 3 (Table 2). In all cases samples were large enough to accurately measure bulk density and were taken contiguously to enable reliable estimates of dry mass accumulation over time. Carbon density was derived from bulk density multiplied by the carbon content for each sample. Where carbon data were not available, we assumed that 50 % of the organic fraction (measured by standard losson-ignition analysis at 500 • C) was organic carbon. A carbon value of approximately 50 % is routinely used for peat [START_REF] Gorham | Northern peatlands, role in the carbon cycle and probable responses to climatic warming[END_REF][START_REF] Vitt | Spatial and temporal trends in carbon storage of peatlands of continental western Canada through the Holocene[END_REF] and is reasonable compared to the mean carbon content of the nine sites for which we have measured values in this study (46.6 ± 0.33 %), and other studies in western Canada (51.8 %; Yu et al., 2009) and West Siberia (50.7-56.3 %;[START_REF] Beilman | Carbon accumulation in peatlands of West Siberia over the last 2000 years[END_REF].

To provide an assessment of hydrological differences among the peatlands in our analyses, we classified sites as either bogs or fens. Although differences between these two peatland types are related to the relative influence of different water sources (i.e. groundwater, surface water, precipitation), thresholds used for distinction between the two types are regionally varied. For our site classification, we used a relatively conservative approach, including only Sphagnumdominated systems that lacked vegetative or morphological evidence of minerotrophic conditions in our "ombrotrophic" category. Sites characterised as ombrotrophic included raised bogs, blanket bogs, and the extensive bog systems of western Siberia [START_REF] Kremenetski | Peatlands of the West Siberian Lowlands: current knowledge on zonation, carbon content and late Quaternary History[END_REF].

Chronology and age modelling

All sites were 14 C dated using selected aboveground plant remains, except for site 68 where bulk peat was 14 C dated. We recalibrated all the dates from the original studies. For modern (post-AD 1950) 14 C dates, the NH1 postbomb calibration curve was used [START_REF] Hua | Review of tropospheric bomb 14C data for carbon cycle modeling and age calibration purposes[END_REF]. Remaining dates were calibrated using IntCal09 [START_REF] Reimer | IntCal09 and Marine09 radiocarbon age calibration curves, 0-50,000 years cal BP[END_REF]. Age models for the temporal analysis were based on the program "Bacon", a flexible Bayesian age-depth modelling approach that uses prior information on plausible accumulation rates and their variability and autocorrelation over time (Blaauw andChristen, 2005, 2011). Peat cores were divided into contiguous 2 cm segments, and linear accumulation rates were calculated for all individual segments sequentially down the core. Age models were developed based on several million iterations, followed by thinning to remove any autocorrelation between individual model runs, yielding ca. 5000-8000 iterations for each site (Fig. 2a).

Spatial analysis of carbon accumulation

Total carbon accumulation over the 1000 yr was estimated based on a data set of the 90 radiocarbon-dated peat profiles (Tables 1 and2). The post-1000 yr carbon pool is the difference between total carbon additions from photosynthesis and cumulative respirative carbon release over this interval, reflecting carbon sequestration at a site. We analysed the relationship between total carbon accumulation over the last 1000 yr and climate parameters using a 0.5 • grid, derived from the CLIMATE 2.2 data [START_REF] Kaplan | Climate change and Arctic ecosystems II: Modeling, palaeodata-model comparisons, and future projections[END_REF]. Climate parameters included growing degree days above 0 • C (GDD0), cumulative photosynthetically active radiation during the growing season (PAR0), PAR over the growing season, growing season length (days) and the moisture index P /Eq, where P is annual precipitation and Eq is annually integrated equilibrium evapotranspiration calculated from daily net radiation and temperature [START_REF] Prentice | A simulation model for the transient effects of climate change on forest landscapes[END_REF]. PAR was calculated from latitude and sunshine hours [START_REF] Prentice | A simulation model for the transient effects of climate change on forest landscapes[END_REF][START_REF] Harrison | Ecophysiological and bioclimatic foundations for a global plant functional classification[END_REF].

Temporal variation in carbon accumulation

A composite carbon accumulation curve was constructed based on the subset of 24 well-dated, high-resolution sites with continuous records for the past 1000 yr (Table 2). The Bayesian age-depth models allowed chronological uncertainty to be included in carbon accumulation curves (Fig. 2b). (Blaauw andChristen, 2005, 2011). (b) Carbon accumulation derived from age-depth models, and bulk density and C measurements. Curves are fitted to each of 10 000 possible age models based on Bayesian analysis. Points represent individual samples on different age models, and grey lines are fitted curves for individual models. Only curves fitted incorporating long-term decay and ecosystem maturity [START_REF] Yu | Understanding Holocene peat accumulation pattern of continental fens in western Canada[END_REF] are shown here.

All age depth models were converted to carbon accumulation using bulk density and carbon or LOI measurements.

We derived different estimates of variability in carbon accumulation rates based on different assumptions about autogenic processes of long-term decay [START_REF] Clymo | The limits to peat bog growth[END_REF] and ecosystem maturity [START_REF] Yu | Understanding Holocene peat accumulation pattern of continental fens in western Canada[END_REF]. Carbon accumulation rates calculated from our age-depth models and carbon density do not take account of autogenic peat accumulation processes, most importantly the effect of long-term decay. Dead plant material decays rapidly in the surface layers, as the most labile organic matter is broken down quickly by microbial activity. Decomposition rates are much slower (though not zero) in the permanently saturated zone, which contains more recalcitrant organic matter [START_REF] Clymo | The limits to peat bog growth[END_REF][START_REF] Belyea | Beyond "the limits to peat bog growth": Cross-scale feedback in peatland development[END_REF]. If productivity and decay are constant, measured apparent accumulation rates will be higher for more recent peat, and the long-term carbon storage will appear to increase. We accounted for this ecological process by fitting decay curves to each profile [START_REF] Clymo | The limits to peat bog growth[END_REF]. We also tested the effect of "ecosystem maturity", that is the slowing of peat growth under stable conditions because of autogenic limits on the height of the peat surface [START_REF] Yu | Understanding Holocene peat accumulation pattern of continental fens in western Canada[END_REF]. We excluded carbon accumulation changes in the uppermost peat (conservatively approximated here as peat formed after 1850) where relatively rapid aerobic decay is still taking place. We used AD 1850 for this because this is likely outside of the aerobic decay zone for all cores.

The changes in accumulation rates for each site were expressed as differences between observed accumulation and those derived from three models: (1) linear decay model (i.e. no autogenic processes); (2) the Clymo model, which includes long-term decay only [START_REF] Clymo | The limits to peat bog growth[END_REF]:

M = P c a c (1 -e -a c t ), (1)
where M is the accumulated carbon, P c is the peat added to the catotelm each year (g C cm -2 ), a c is the catotelm decay constant and t is time; and (3) the extended peat accumulation rate (ExtPAR) model [START_REF] Yu | Understanding Holocene peat accumulation pattern of continental fens in western Canada[END_REF], which includes long-term decay and ecosystem maturity:

M = P c a c -b c (e -b c t -e -a c t ), (2)
where the parameters are the same as those listed above, with the addition of b c , a coefficient that allows the accumulation rate to be modified. Each curve fitting exercise produces estimated values for P c , a c and b c . There are several other more complex models that could be applied to account for longterm decay, but it is often difficult or impossible to determine the most appropriate one, given the subtle variations in the carbon accumulation curves [START_REF] Belyea | Beyond "the limits to peat bog growth": Cross-scale feedback in peatland development[END_REF]. Our intention here is to test whether observed variations in the raw carbon accumulation data could be explained by longterm decay and ecosystem maturity. Decay models were fit individually to each carbon accumulation curve derived from the Bacon routine. For the linear model, the fitting was carried out using ordinary leastsquares. For the Clymo and ExtPAR models, optimization was carried out using an iterative orthogonal sampling technique that samples the entire parameter space, then uses a least squares fit to obtain a subset of the parameter space. This subset is then sampled in the next iteration to produce Steps involved in deriving a non-autogenic accumulation curve from a single age profile after fitting of the peat accumulation model with long-term decay and ecosystem maturity [START_REF] Yu | Understanding Holocene peat accumulation pattern of continental fens in western Canada[END_REF]. See text for details.

an increasingly well defined parameter space. All following analyses were applied to results from all three versions (Fig. 3).

The number of age models varied by over an order of magnitude between sites (from ca. 2300 to ca. 44 000). To avoid biases towards sites with a higher number of age models, a single age model was randomly sampled from each site. The accumulation rates were interpolated onto a regular time step by taking the median value in a moving window (halfwindow of 25 yr) with a step of 10 yr to avoid bias toward sites with higher sampling resolution. Finally, a time series of accumulation rates was calculated as the median of the 24 interpolated accumulation rates (one per site). This was repeated 10 000 times to provide a fair sampling of the available age models, and gave a matrix of median time series on a regular time step. Finally, this matrix was used to calculate the median and percentile values of the accumulation rates for each time.

The effect of this Monte Carlo resampling of the possible age models (and associated accumulation rate curves) is to give greater weight to the sites with the best-constrained chronologies. In each iteration of the resampling, we took one age model and set of accumulation rates from each site. Sites that are well-constrained will provide age models that are similar in each iteration, and poorly constrained sites will provide age models that are widely different. The end result of this is that well-constrained sites will effectively have a greater weight in the overall composite.

To avoid any bias toward sites with generally very high accumulation rates, a second composite was made, based on transformed values. This followed the methodology used previously for charcoal data [START_REF] Marlon | Climate and human influences on global biomass burning over the past two millennia[END_REF]: (1) minimax transformation of the original accumulation rate time series; (2) Box-Cox transformation to normalise the time series; and (3) z-score calculation. The composite z-scores were estimated using the same procedure as for compilation of the untransformed values described above. The final composite curves are shown in Fig. 4.

Results and discussion

Spatial relationships between carbon accumulation and climate

Warming would be expected to increase net primary productivity (NPP) in high-latitude ecosystems because of increased growing season length. The growing season for northern peatlands is appropriately defined as the period of the year with air temperatures above freezing, because bryophytes begin photosynthesis at this threshold, and are the dominant peat-former in most of our sites. PAR, determined by latitude and cloudiness, is the driver of photosynthetic carbon fixation and may also be an important control on NPP. However, higher temperatures could also increase peat decomposition rates through accelerated microbial activity [START_REF] Ise | High sensitivity of peat decomposition to climate change through watertable feedback[END_REF][START_REF] Dorrepaal | Carbon respiration from subsurface peat accelerated by climate warming in the sub-arctic[END_REF]. Linear regression of total carbon accumulated over the last 1000 yr (C) against PAR0 yielded the strongest relationship:

C = 0.0055 PAR0 -3.82, (3)
with an R 2 of 0.33 (Fig. 5a). In single-predictor regressions, C showed a weaker relationship with GDD0 (R 2 = 0.13, Fig. 5b) and no significant relationship with P /Eq (P = 0.19, Fig. 5c). Residuals from Eq. (3) showed no systematic relation to either GDD0 or P /Eq and inclusion of these additional predictors in a multiple linear regression yielded nonsignificant regression coefficients. The correlation between PAR0 and GDD0 is high (0.83), owing to the growing season length that is shared by both variables. We checked the influence of two apparent outliers with higher PAR0 values on our conclusions. These are the two southernmost sites from Dhakuri (India) and Pinhook (USA). Removing these two sites does not affect the significance of the relationship between PAR0 and 1 ka C (P < 0.0001) but changes the R 2 values from 0.33 to 0.24 and slightly changes the slope from 0.0055 to 0.0049. Thus, it still explains more of the variation than GDD0. The influence of these two sites is not insignificant, but removing them does not impact our main conclusions concerning PAR. Without the two "outliers" total C still shows a positive significant relationship (P < 0.001) with GDD0 but with a change in R 2 from 0.18 to 0.13 and a reduction in slope from 0.0086 to 0.0073. Again, this would not change our main conclusions.

Our analyses thus show that total carbon accumulation over the past 1000 yr is linearly related to contemporary PAR integrated over the growing season (PAR0) (Fig. 5a) and that this relationship is stronger than that with growing season warmth expressed as accumulated temperature (GDD0, growing degree days above zero) (Fig. 5b). The implied effect of a warmer climate is to increase NPP to a greater extent than decomposition, suggesting a negative climate feedback in peatlands. Because no relationship was found between total carbon accumulation and moisture, we infer that although an adequate moisture supply is necessary for the presence of peat, above a threshold of moisture availability the effect on carbon accumulation is secondary relative to growing season temperature and light conditions, at least for millennial averages over large spatial scales. Some of the unexplained variability in carbon accumulation probably reflects local hydrological factors, not captured by the large-scale moisture index, as well as other local controls. Peatlands do not occur, for example, in climatically suitable locations with steep slopes. Local topographic and drainage features, as well as internal dynamics, create heterogeneity in peat accumulation that is not represented by our data. Furthermore, our sampling is necessarily biased toward peatlands that exist today, and we therefore cannot establish the threshold for cessation of carbon accumulation resulting from a reduction in moisture balance. Despite these caveats, it is clear that the changes in moisture balance are unlikely to be an important control on peat accumulation during the recent past because of the complete lack of any relationship with macroclimate. If moisture does not play a role in determining peat accumulation rates, this implies that the net balance between NPP and decay is similar under varying hydrological conditions. In dry sites high decay rates must be offset by high NPP, and similarly, wetter sites must have low NPP together with low decay because of the anaerobic conditions.

The results suggest that spatial variability in peatland carbon accumulation over the last 1000 yr is primarily controlled by spatial variation in NPP, which in turn is driven by growing season length (related to temperature) and growing season PAR. There is a statistically significant correlation between large-scale spatial variability in Sphagnum moss productivity and PAR0 that supports this hypothesized mechanism [START_REF] Loisel | Global-scale pattern of peatland Sphagnum growth driven by photosynthetically active radiation and growing season length[END_REF]. PAR0 incorporates both a PAR and temperature effect through growing season length; we therefore tested the relationship between total carbon and mean PAR over the unfrozen season. The reduced R 2 (0.13, p < 0.0001) compared with PAR0 (R 2 = 0.33), suggests that PAR has an effect independent of temperature. A much weaker relationship with growing season length (R 2 = 0.08, p < 0.01) implies growing season length is of subsidiary importance. Taken together, these results support our hypothesis that peatland carbon accumulation is driven by PAR over the growing season.

Temporal changes in carbon accumulation

There is an overall downward trend in the composite carbon accumulation rates from AD 1000 to 1850 (slope -0.0026 g C m -2 yr -2 , p < 0.0001), implying reduced peat accumulation during the LIA (Fig. 6a). The decline appears to be greater in the latter (post AD 1400) period than the earlier part of the record. The downward trend is present in the raw data as well as in the curves that include long-term decay and ecosystem maturity at individual sites, showing that the direction of change is insensitive to any assumptions concerning long-term ecosystem processes (Fig. 4). The magnitude of this change differs depending on the time period chosen. Here we compare long-term median values for the periods 1000-1425 and 1425-1850 AD, broadly corresponding to the times used to define the MCA and LIA [START_REF] Mann | Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia[END_REF][START_REF] Mann | Global signatures and dynamical origins of the Little Ice Age and Medieval Climate Anomaly[END_REF][START_REF] Jansen | Palaeoclimate, in: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF]. We also compare the difference at the start (1000) and end (1850) of this period based on the slope of a regression line through the data. Using both approaches means that the analyses are robust to short-term fluctuations in the data. If long-term decay is taken into account, the difference between the mean of the median accumulation rates between these two periods is 2.43 (± 0.92) g C m -2 yr -1 . The estimate for this is smaller (1.01 ± 0.89 g C m -2 yr -1 ) if some of the decay is compensated for by ecosystem maturity at individual sites (Fig. 4). The difference between the start (1000) and end of the data (1850) is 6.05 ± 5.40 and 3.15 ± 5.08 g C m -2 yr -1 , respectively, for a decay only and decay plus ecosystem maturity model (Fig. 4). Shorter term changes in the carbon accumulation curve may be related to temperature or other climate variables, but the changes are not sufficiently robust to draw firm conclusions about changes in carbon accumulation on (sub)centennial timescales.

Climate controls on carbon accumulation

One explanation for the reduction in carbon accumulation between the MCA and LIA is that decreased temperatures reduced NPP through shorter growing seasons (reduced GDD0). Shorter growing seasons would also reduce accumulated PAR0 assuming light levels remained unchanged. We tested this hypothesis by using the spatial relationship between total 1000-yr carbon accumulation and climate variables shown in Fig. 5 to calculate the effect of MCA-LIA cooling from palaeoclimate records (Fig. 6b) on carbon accumulation and comparing it with the observed changes in carbon accumulation (Fig. 6a).

We used the IGBP-DIS soil carbon gridded data set (http://daac.ornl.gov/SOILS/guides/igbp-surfaces.html) to select all grid cells occupied by northern peatlands, and summed their carbon accumulation rates as predicted by PAR0 from Eq. (1) (multiplied by the grid cell areas) to estimate the total carbon sink in northern peatlands. "Peatlands" were defined as 0.5 × 0.5 • grids north of 40 • N that contain 10-min IGBP soil C grids that are all > 31 kg C m -2 [START_REF] Wania | Integrating peatlands and permafrost into a dynamic global vegetation model: 1. Evaluation and sensitivity of physical land surface processes[END_REF]. This is conservative, designed to focus on the biggest peatland areas that dominate the global peatland C cycle. PeatStash [START_REF] Gallego-Sala | Application of a global bioclimatic envelope model to assess the impact of climate change on the distribution of blanket peatlands in Great Britain[END_REF] was used to calculate the accumulated PAR0 by summing the daily PAR0 over the growing season (days above freezing) for each peat-land grid cell. The daily PAR0 is obtained by integrating the instantaneous PAR between sunrise and sunset [START_REF] Harrison | Ecophysiological and bioclimatic foundations for a global plant functional classification[END_REF]. The seasonal accumulated PAR0 depends on latitude and cloudiness, and indirectly on temperature, because the temperature determines the length of the growing season, i.e. which days are included in the seasonal accumulated PAR0 calculation. We calculated changes in GDD0 and PAR0 that would result from the change in temperature inferred from the palaeoclimate reconstructions over the last 1000 yr (Fig. 6b). The temperature difference in the palaeoclimate records was based on calculations similar to those made for carbon accumulation over the same time periods. The median temperature difference between the two periods 1000-1425 and 1425-1850 is 0.116 ± 0.02 • C. We calculated the influence of this change in temperature on the duration of the growing season (Fig. 7a), and applied this to the relationships between carbon accumulation, GDD0 and PAR0 derived from the spatial analysis (Fig. 5a,b). Changes in PAR0 and GDD0 for each peatland grid cell were calculated by adding 0.116 • C to the input climate data set used in PeatStash.

The results suggest that the sensitivity of GDD0 and PAR0 to temperature is too small for either of these to provide the sole explanation for the observed change in carbon accumulation rate over the last 1000 yr. Over the peatland areas, a 0.116 • C increase results in a mean change of +18.5 GDD0 (5-95 % range, 12.3-24.7 GDD0), which is predicted by the regression equation to result in a mean increase of 0.16 g C m -2 yr -1 (from 0.11 to 0.21 g C m -2 yr -1 ). PAR0 increases by 13.5 mol photons m -2 season -1 (range 0-37.4 mol photons m -2 season -1 ), which is predicted by the regression equation to result in an increase of 0.07 g C m -2 yr -1 (from 0 to 0.21 g C m -2 yr -1 ). If the magnitude of temperature change estimated from the Northern Hemisphere records is assumed to be correct, it is therefore unlikely that the observed changes in carbon accumulation of 1.0-2.4 g C m -2 yr -1 are a result of temperature and growing season changes alone. The conclusion is similar if the magnitude of temperature change over the period 1000-1850 is used for this calculation. In this case, a temperature change of -0.266 • C is associated with a reduction in carbon accumulation of 3.2-6.1 g C m -2 yr -1 . However, mean changes in GDD0 would be 43 GDD, and 31 mol photons m -2 season -1 in PAR0, resulting in predicted carbon accumulation reductions of only 0.37 and 0.17 g C m -2 yr -1 , respectively. It is possible that Northern Hemisphere averages underestimate temperature changes in peatland regions, as estimates of maximum MCA-LIA difference range from > 0.3 to as much as 1.8 • C in a few locations [START_REF] Mann | Global signatures and dynamical origins of the Little Ice Age and Medieval Climate Anomaly[END_REF]. Repeating the analysis with a larger 1 • C change suggests a response of 0.4 to 0.9 g C m -2 yr -1 from PAR0, which is closer to but still less than the 1.0-1.4 g C m -2 yr -1 reduction in carbon accumulation shown in the data over the MCA-LIA.

If temperature change alone is not the driver of carbon accumulation changes, an additional hypothesis is that reduced PAR from an increase in growing-season cloudiness significantly reduced NPP during the LIA, causing a fall in peatland carbon accumulation. We therefore also examined the sensitivity of PAR0 and carbon accumulation to changes in cloudiness (Fig. 7) by altering, in addition to 0.116 • C warming, the annual sunshine hours by a range of +/-a percent of the input climate data set. Sensitivity tests (Fig. 7a) show that a 4 % increase in sunshine hours on top of the influence of a +0.116 • C change (equivalent to 199 mol photons m -2 season -1 ) could result in an average change of +1.1 g C m -2 yr -1 over the peatland areas. These are averages; the change in PAR0 has a 5-95 % range of 137-255 mol photons m -2 season -1 and change in carbon accumulation between 0.8 and 1.4 g C m -2 yr -1 for this sensitivity analysis. A geographical pattern emerges in these simulations where greater sensitivity of PAR0 to temperature and cloudiness occurs in lower latitudes (Fig. 7b). The existence of a positive relationship between PAR0 and peat carbon accumulation is further supported by Fig. 7a, which shows that high soil carbon grid cells in the independent soil carbon density data are located in areas with higher PAR0.

Our finding that spatial patterns of carbon accumulation can be explained by spatial variability in climate, specifically PAR0, implies that the temporal variations in carbon accumulation over the last millennium may also be explained by the same climatic variables. The data are consistent with this space for time substitution argument in that the warmer MCA has higher rates of carbon accumulation than the cooler LIA, i.e. longer, warmer growing seasons in the MCA increased carbon accumulation in comparison to the LIA. That the MCA was warmer than the LIA is not contested [START_REF] Jansen | Palaeoclimate, in: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF], but we also suggest from our sensitivity analysis that temperature alone cannot explain the magnitude of the change in observed carbon accumulation over the last millennium. Changes in PAR received by the plants due to cloudiness or some other influence such as snow cover (increased depth/snow lie after T > 0 during the LIA) or diffusivity are also required to generate the observed changes, assuming that temporal sensitivity is the same as that derived from spatial relationships. We explored the potential reduction of PAR by late-lying snow cover by calculating the PAR for days without snow and with temperature > 0 • C. The correlation between snow-free PAR0 and total carbon accumulated over the last 1000 yr was not significant (p = 0.23), so cloudiness is the more likely cause of change in PAR and carbon accumulation. The idea that the Northern Hemisphere LIA was characterised by greater summer cloudiness is consistent with historical documentary data [START_REF] Grove | Little Ice Ages, Ancient and Modern[END_REF], although recent findings from δ 13 C in Fennoscandian tree ring studies suggest that there may have been regional differentiation of this tendency [START_REF] Young | A 500-year record of summer near-ground solar radiation from tree-ring stable carbon isotopes[END_REF][START_REF] Gagen | Cloud response to summer temperatures in Fennoscandia over the last thousand years[END_REF].

Changes in the strength of the peatland carbon sink

An average decrease of 1.01 to 2.43 g C m -2 yr -1 in carbon sequestration rates in peatlands between the MCA and the LIA represents a reduction in the strength of the sink of 0.0035 to 0.0085 Gt C yr -1 , assuming a total northern peatland area of 350 million ha [START_REF] Gorham | Northern peatlands, role in the carbon cycle and probable responses to climatic warming[END_REF][START_REF] Tarnocai | Soil organic carbon pools in the northern circumpolar permafrost region[END_REF]. This should be compared with our estimate of the median net accumulation rate between 1000 and 1850 of 26.1 (sd. 1.47) g C m -2 yr -1 or 0.091 (sd. 0.005) Gt C yr -1 . This latter figure is similar to the average figure of 0.096 Gt C yr -1 (29 g C m -2 yr -1 ) estimated for the whole of the Holocene [START_REF] Gorham | Northern peatlands, role in the carbon cycle and probable responses to climatic warming[END_REF]. Neither of these figures for long-term net accumulation take into account the very slow long-term decay of the deeper peat layers. Decay of this store might reduce the actual average net ecosystem carbon balance to around 0.042 Gt C yr -1 [START_REF] Yu | Holocene carbon flux histories of the world's peatlands: Global carbon-cycle implications[END_REF] for the last millennium, depending on how much peat had accumulated before AD 1000, its permafrost status, and its average age and long-term decay rate. Thus, a reduction in carbon sink of 0.0035 and 0.0085 Gt C yr -1 represents a decrease of about 8 % and 20 % in net ecosystem carbon balance of northern peatlands. This is an average figure for the two 425-yr periods chosen to represent the MCA and LIA here. The extremes in change are much greater; for example, the difference between the 25-yr mean at 1250 and 1850 is 5.9 ± 5.2 to 3.9 ± 5.1 g C m -2 yr -1 , or a 33 to 45 % reduction in the strength of the CO 2 sink.

The main decrease in atmospheric CO 2 concentrations of approximately 6 ppm occurred over the period 1150 to 1750 (using the average curve in Fig. 6c). For this time period the decline in peatland carbon sink is between 2.79 (± 4.88) and 0.77 (± 4.87) g C m -2 yr -1 , equivalent to 0.0027 to 0.0097 Gt C yr -1 if applied to all northern peatlands. Assuming a linear decline in strength of the peatland sink over this period, this would represent a change equivalent to about 0.38 (± 2.41) to 1.38 (± 2.41) ppm in the atmosphere. Although this is a small change, because the observed decline in atmospheric CO 2 is unequivocally attributed to increased land carbon storage by the concomitant increase [START_REF] Elsig | Stable isotope constraints on Holocene carbon cycle changes from an Antarctic ice core[END_REF] in the δ 13 C of atmospheric CO 2 , the reduced carbon sequestration in northern peatlands has to be added to the amount of CO 2 reduction to be explained by carbon uptake, presumably as a result of suppressed decomposition of soil organic matter or increased NPP, in other terrestrial ecosystems.

Conclusions and implications

We have shown that there has been a small negative feedback to climate from changes in Northern Hemisphere peat accumulation over the last 1000 yr. The direction of the peat accumulation-climate feedback is supported by large-scale spatial patterns of peat accumulation over the past 1000 yr in relation to modern climate gradients. Northern peatlands sequestered carbon at a higher rate during the MCA than during the LIA. Although the magnitude of change is small (approximately 1 ppm CO 2 over the MCA-LIA transition), this suggests that carbon accumulation in northern peatlands may also change in response to future anthropogenic climate warming. Our spatial analyses indicate that the strength of any feedback effect could depend on changes in cloud cover as well as changes in temperature and growing season length. Continued carbon accumulation depends on sustained adequate moisture availability to maintain peatland growth. The sites we sampled all have adequate moisture supply at present and probably also during most of the last 1000 yr, but this may not continue in the future. Climate model projections suggest that most of the high latitudes will experience higher summer temperature and higher precipitation, with decreases in soil moisture for some regions [START_REF] Meehl | Global climate projections[END_REF]. Midlatitude peatlands in locations such as western Europe are most vulnerable, especially as summer precipitation is projected to decrease. If the threshold between presence and absence of peatlands on the gradient of precipitation/equilibrium evapotranspiration ratio is crossed (Fig. 5c), some peatland areas (including some of the blanket bogs characteristic of extremely oceanic climates; [START_REF] Gallego-Sala | Application of a global bioclimatic envelope model to assess the impact of climate change on the distribution of blanket peatlands in Great Britain[END_REF][START_REF] Gallego-Sala | Blanket peat biome endangered by climate change[END_REF] may stop growing. Changes in peatland extent in response to climate change occurred in the past, including during the last millennium [START_REF] Finkelstein | Wetlands, temperature, and atmospheric CO 2 and CH 4 coupling over the past two millennia[END_REF], and will occur in the future (Gallego-Sala and Prentice, 2013). Our analysis suggests that a reduction in peatland area in the midlatitudes could be compensated by increased carbon accumulation across the very large areas of peatland in higher latitude regions, but only if the majority of these peatlands retain sufficient moisture and there is no significant increase in cloud cover. Current projections of cloud cover are highly uncertain for the northern peatland regions; some areas are projected to have less cloud and others more cloud [START_REF] Meehl | Global climate projections[END_REF]. A further compensating factor for peatland loss is that peatlands may increase in extent in high-latitude areas that are currently too cold and dry for peat formation. Other major uncertainties concerning feedbacks between peatlands and climate change still exist, including changes in fire regimes, nitrogen deposition, permafrost thaw and the role of methane emissions. Our analyses show that only about a third of the variability in C accumulation is explained by PAR and other factors must play a role. We might expect climate changes at some of the sites used in our analysis to move beyond the climate envelope explored here, and this could affect their contribution to the carbon budget. However, based on our analyses of carbon accumulation over the past millennium, and contrary to the conclusions from soil decay models [START_REF] Ise | High sensitivity of peat decomposition to climate change through watertable feedback[END_REF][START_REF] Dorrepaal | Carbon respiration from subsurface peat accelerated by climate warming in the sub-arctic[END_REF], we suggest that carbon sequestration may increase in many high-latitude peatlands in response to future climate warming over the next century.

Fig. 1 .

 1 Fig. 1. Distribution of sites in geographic and climate space, overlain on soil carbon storage. Points show high-resolution peat records (large blue circles) used for calculating temporal variation in carbon accumulation rates and low-resolution peat records (small white circles) used for estimates of total millennial carbon. (a) The soil carbon density in the top 1 m mapped from 10-min IGBP soil data; (b) climate space (defined by growing degree days and moisture balance) of soil carbon density in the same classes as in panel a, at 0.5 × 0.5 • grid cells for all land north of 40 • N. Peatlands generally occur in the areas of > 31 kg C m -2 (brown and black).

Fig. 2 .

 2 Fig. 2. Age-depth and carbon accumulation estimates for individual profiles. The example shown here is Misten Bog, Belgium. (a) An agedepth plot showing the calibrated ages (blue shapes) and age-depth model (grey-scale graph)(Blaauw and Christen, 2005, 2011). (b) Carbon accumulation derived from age-depth models, and bulk density and C measurements. Curves are fitted to each of 10 000 possible age models based on Bayesian analysis. Points represent individual samples on different age models, and grey lines are fitted curves for individual models. Only curves fitted incorporating long-term decay and ecosystem maturity[START_REF] Yu | Understanding Holocene peat accumulation pattern of continental fens in western Canada[END_REF] are shown here.

Fig

  Fig. 3.Steps involved in deriving a non-autogenic accumulation curve from a single age profile after fitting of the peat accumulation model with long-term decay and ecosystem maturity[START_REF] Yu | Understanding Holocene peat accumulation pattern of continental fens in western Canada[END_REF]. See text for details.

Fig. 4 .Fig. 5 .

 45 Fig. 4. Composite carbon accumulation curves for the last millennium based on different assumptions concerning autogenic processes. Left panels show untransformed data, right panels show z-scores. (a) Linear accumulation without considering autogenic processes, (b) with long-term decay rates, and (c) including long-term decay and ecosystem maturity (left panel also shown in Fig.6). The data after AD 1850 are shown only in outline because the apparent upturn in carbon accumulation is due to incomplete decay of recently accumulated organic materials.

Fig. 6 .

 6 Fig. 6. Carbon accumulation variability, climate and atmospheric CO 2 over the past millennium. (a) Reconstruction of residual variation in carbon accumulation from long-term average rates taking into account autogenic processes composited from 24 well-dated sites from northern peatlands over the period AD 1000 to 1850, with confidence intervals (shading) based on bootstrap resampling by site. (b) Reconstruction of Northern Hemisphere temperature expressed as temperature anomaly from 1961-1990 (from Fig. 6.10c of[START_REF] Jansen | Palaeoclimate, in: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF]. (c) The individual and composite atmospheric CO 2 concentration records based on ice cores from Antarctica as used by[START_REF] Frank | Ensemble reconstruction constraints on the global carbon cycle sensitivity to climate[END_REF]. The correlation between the 10-yr smoothed median values of carbon accumulation (a) and the reconstructed temperature (b) is 0.39 (p = 0.00006) for the period 1000-1850. The shading in the temperature plot (b) represents the overlap in the uncertainty ranges of 10 different reconstructions (maximum 100 % where temperatures are within ±1 SE of all 10 reconstructions). The vertical line at AD 1425 marks the division between the analysis periods used for comparison of carbon accumulation and temperature.

Fig. 7 .

 7 Fig. 7. Sensitivity of PAR0 to changes in temperature and sunshine hours for 0.5 × 0.5 • peatland grid cells north of 40 • N. (a) Sensitivity of all peatland cells (soil carbon > 31 kg C m -2 ) to +0.116 • C change and a 4 % increase and decrease in sunshine hours. Symbol shades show C storage in each cell as 31-50 (brown) or > 50 (black) kg C m -2 following shading in Fig. 1b. (b) Spatial variability of PAR0 response to a 0.116 • C temperature increase and 4 % increase in sunshine hours for the peatland grid cells. Growing season length for this scenario only increases by 0 to 3 days (median of 1 day) across the peatland grid.

Table 1 .

 1 Characteristics of the low-resolution sites used in the analyses.

	No. Site name	Latitude	Longitude Peatland type	Contributor Source reference
	E115	67.8095	75.4346 Ombrotrophic D. Beilman Beilman et al. (2009)
	E110	66.4698	76.9943 Ombrotrophic D. Beilman Beilman et al. (2009)
	E113	66.4497	79.3234 Ombrotrophic D. Beilman Beilman et al. (2009)
	D122	65.5831	73.0058 Ombrotrophic D. Beilman Beilman et al. (2009)
	E119	65.4998	75.5025 Ombrotrophic D. Beilman Beilman et al. (2009)
	D127	64.3068	70.2948 Ombrotrophic D. Beilman Beilman et al. (2009)
	G136	64.1476	75.3611 Ombrotrophic D. Beilman Beilman et al. (2009)
	G137	63.7504	75.7662 Ombrotrophic D. Beilman Beilman et al. (2009)
	N015	63.6501	74.2693 Ombrotrophic D. Beilman Beilman et al. (2009)
	N001	63.1611	74.8233 Ombrotrophic D. Beilman Beilman et al. (2009)
	S009	62.1229	73.8412 Ombrotrophic D. Beilman Beilman et al. (2009)
	V034	61.4675	79.4601 Ombrotrophic D. Beilman Beilman et al. (2009)
	V039	61.0895	79.3806 Ombrotrophic D. Beilman Beilman et al. (2009)
	SIB02	61.0553	70.0588 Ombrotrophic D. Beilman Beilman et al. (2009)
	V026	61.0286	76.4686 Ombrotrophic D. Beilman Beilman et al. (2009)
	S022	60.8401	71.2558 Ombrotrophic D. Beilman Beilman et al. (2009)
	V038	60.8039	74.5416 Ombrotrophic D. Beilman Beilman et al. (2009)
	SIB01	59.3601	68.9849 Ombrotrophic D. Beilman Beilman et al. (2009)
	SIB06	58.4358	83.4343 Ombrotrophic D. Beilman Beilman et al. (2009)
	SIB05	57.3541	81.1647 Ombrotrophic D. Beilman Beilman et al. (2009)
	SIB03	56.3552	79.0689 Ombrotrophic D. Beilman Beilman et al. (2009)
	C site 01	60.167	72.8330 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 02	60.167	72.8330 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 03	56.833	78.4170 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 10	54.15 -130.2500 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 13	55.017 -114.1500 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 17	55.85 -107.6830 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 20	59.883 -104.2000 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 21	45.684	-74.0470 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 23	47.933	-64.5000 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 24	45.2	-60.2670 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 25	57.522	-5.1600 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 26	57.56	-5.3770 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 27	57.687	-5.6870 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 28	68.4	23.5500 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	C site 30	60.817	26.9500 Ombrotrophic Z. C. Yu	Yu et al. (2009)
	Kohlhütten Moor 47.9269	8.1844 Ombrotrophic G. Le Roux Le Roux et al. (2005)
	P131	66.1664	73.9889 Minerotrophic D. Beilman Beilman et al. (2009)
	C site 04	56.333	84.5830 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 05	60.446 -151.2470 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 06	60.641 -151.0800 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 07	60.416 -150.9020 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 08	60.784 -150.8190 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 09	64.875 -147.7670 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 11	53.583 -118.0170 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 12	52.45 -116.2000 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 14	61.8 -121.4000 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 15	68.288 -133.2500 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 16	69.493 -132.6720 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 18	64.713 -105.5790 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 19	66.451 -104.8350 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 22	82.333	-68.2500 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 29	68.4	23.5500 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 31	65.65	27.3170 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 32	65.65	27.3170 Minerotrophic Z. C. Yu	Yu et al. (2009)
	C site 33	65.65	27.3170 Minerotrophic Z. C. Yu	Yu et al. (2009)

Table 1 .

 1 Continued.

	No. Site name	Latitude	Longitude Peatland type	Contributor	Source reference
	57 Old Black Spruce Fen	53.9983 -105.1153 Minerotrophic I. Bauer	Bauer et al. (2009)
	58 Sandhill Fen	53.8261 -104.6250 Minerotrophic I. Bauer	Bauer et al. (2009)
	59 Dhakuri	30.0500	79.9333 Minerotrophic N. R. Phadtare Unpublished data
	60 Aeroport 4	54.1041	-72.5167 Minerotrophic M. Garneau	Unpublished data
	61 Aeroport 5	54.1041	-72.5167 Minerotrophic M. Garneau	Unpublished data
	62 Lac Le Caron Lateral Core4 52.2945	-75.8408 Ombrotrophic S. van Bellen	van Bellen et al. (2011)
	63 M179	60.5875 -149.5347 Minerotrophic Z. C. Yu	Unpublished data
	64 ZB08-S4	33.0954	102.6650 Minerotrophic Y. Zhao	Unpublished data
	65 OURS4 Peatland	54.0597	-72.4602 Minerotrophic M. Garneau	Unpublished data
	66 LG1 Peatland	54.0597	-78.4602 Minerotrophic M. Garneau	Unpublished data

Table 2 .

 2 Characteristics of the high-resolution sites used in the analyses. BDs -bulk density sample size; BDi -bulk density increment depth; dates (other): Pb -lead 210, S -spheroidal carbonaceous particles, T -tephra, A -Ambrosia pollen rise; scaling for carbon estimates based on C (carbon analyses), LOI (loss on ignition), or 50 % of dry mass.

	No. Site	Lat.	Long.	Alt. Peatland	BDs	BDi	14 C Dates	C	Main	Site	Site
	name			(m) type	(cm 3 ) (cm) dates (other) scaling investigator	code	reference
	67 Ballyduff	53.0861	-7.9930	60 Ombrotrophic	45-117	1	12 S	C	F. J. G. Mitchell	ir ballyd	Unpublished data
	68 Upper Pinto Fen	53.5833 -118.0167 1310 Minerotrophic	0.8	1	4	LOI	Z. C. Yu	ca uppin	Yu et al. (2003)
	69 Männikjärve Bog	58.8753	26.2556	79 Ombrotrophic 200-250	5	10 Pb	50 %	E. Karofeld	es mannik Sillasoo et al. (2007)
	70 Misten	50.5631	6.1642	620 Ombrotrophic	7-10	1	11 Pb	LOI	F. De Vleeschouwer be misten	De Vleeschouwer et al.
											(2010, 2012)
	71 Słowińskie Błota	54.3646	16.4902	30 Ombrotrophic	7	1	7 Pb	LOI	F. De Vleeschouwer po slowbl	De Vleeschouwer et al. (2009)
	72 Walton Moss	54.9833	-2.6670	109 Ombrotrophic	2	1	30	C	D. Mauquoy	en walton	Mauquoy et al. (2002)
	73 Dürres Maar	50.1228	6.8722	455 Ombrotrophic	3-4	2	5	50 %	R. Moschen	de durrma Moschen et al. (2009)
	74 Irwin Smith Bog	45.0319	-83.6181	223 Ombrotrophic	3	1	3 A	50 %	R. Booth	us irwsmi Unpublished data
	75 Sidnaw Bog	46.5618	-88.7824	400 Minerotrophic	3	1	5 Pb, A	50 %	R. Booth	us sidnaw Unpublished data
	76 Pinhook Bog	41.6152	-86.8468	245 Minerotrophic	3	1	5 A	50 %	R. Booth	us pinhoo	Unpublished data
	77 Colesdalen	78.0925	14.9787	8 Minerotrophic	1.2	0.5	9	50 %	D. Beilman	no colesd	Unpublished data
	78 Fenvyestetö	47.6670	24.0330 1340 Ombrotrophic	45	1	16	50 %	D. Charman	ro fenvye	Unpublished data
	79 Saxnäs Mosse	56.8558	13.4610	161 Ombrotrophic	10.5	1	25	C	M. van der Linden	se saxnas	van der Linden (2006)
	80 Dead Island Bog	54.8875	-6.5491	41 Ombrotrophic	20	1	0 S, T(5) 50 %	G. Swindles	ni deasis	Swindles et al. (2010)
	81 Sib-04	56.8039	78.7369	131 Ombrotrophic	2-3	1	5	LOI	D. Beilman	SIB04	Beilman et al. (2009)
	82 The Great Heath	44.7013	-67.8092	75 Ombrotrophic	3.5	1	8	50 %	J. Nichols	us greath	Unpublished data
	83 Kontolanrahka	60.7830	22.7830	87 Ombrotrophic 125-320	5	18	C	M. Väliranta	fi kontol	Väliranta et al. (2007)
	84 Lappmyran	64.1647	19.5830	295 Ombrotrophic	10.5	1	20	C	M. van der Linden	se lappmy van der Linden et al. (2008)
	85 Outokumpu	62.6667	28.8500	108 Ombrotrophic	3	1	4 Pb	C	N. Rausch	fi outoku	Rausch et al. (2005),
											Ukonmaanaho et al. (2006)
	86 Lac Le Caron Central 52.2897	-75.4331	254 Ombrotrophic	1	1	3	LOI	S. van Bellen	ca llcen	van Bellen et al. (2011)
	87 Hietajärvi	63.1500	30.6667	168 Ombrotrophic	3	1	3 Pb	C	N. Rausch	fi hietaj	Rausch et al. (2005),
											Ukonmaanaho et al. (2006)
	88 Mariana Lake	56.0167 -111.9333	700 Ombrotrophic	0.8	1	6	LOI	Z. C. Yu	ca marian	Unpublished data
	89 Harjavalta	61.3500	22.1833	40 Ombrotrophic	3	1	4 Pb	C	N. Rausch	fi harjav	Rausch et al. (2005),
											Ukonmaanaho et al. (2006)
	90 Nordan's Pond bog	49.1500	-53.5830	60 Ombrotrophic	5	2	2 Pb	50 %	P. Hughes	ca nordan	Hughes et al. (2006)

Acknowledgements. This paper is a contribution to the continuing work of PArCH (Peatland Archives of Carbon and Hydrology) and INQUA Project 0804 (Peatlands as Holocene Palaeoenvironmental Archives). The paper was conceived and analyses were initiated at two workshops in 2009 (Vihula, Estonia and Dartington, England) funded by the US National Science Foundation through a grant to S. T. J. for a Workshop on Peatland Archives of Holocene Carbon and Climate Variability (EAR-0907815), the QUEST (Quantifying and Understanding the Earth System) programme of the UK National Environmental Research Council (NERC), INQUA Project 0804, the International Geosphere-Biosphere Programme (IGBP) Past Global Changes (PAGES) project and the UK Quaternary Research Association. D. J. C., A. G. S. and C. I. P. acknowledge NERC grant NE/I012915/1. The paper benefitted from the comments of three anonymous reviewers.

Edited by: E. J. Javaux


C Nouveau
email: nouveau@cluny.ensam.fr

B Tlili

H Aknouche
email: h.aknouche@yahoo.fr

Y Benlatreche
email: benlatrechey@yahoo.fr

B Patel

Comparison of CrAlN layers obtained with one (CrAl) or two targets (Cr and Al) by magnetron sputtering

Keywords:

The aim of this study is to compare the properties of CrAlN coatings obtained by magnetron sputtering with one (CrAl) or two targets (Cr and Al). The influence of parameters such as the target bias voltage, the working pressure, the deposition time and the bias voltage applied on the Cr or Al targets on the properties of the layers was studied. We characterized the films by X-ray Diffraction, Scanning Electron Microscopy, coupled with Energy Dispersive Spectroscopy, nanoindentation and their residual stresses were also determined. The optimal films obtained with both methods are well crystallized, well-adherent to the substrate, and contained similar amounts of Al (20-30 at.%). The optimal coatings synthesized with one target presented properties not as good as those realized with two targets. Nevertheless, films made with one target showed a lower frictional coefficient probably due to composition control. The lack of compositional control with the use of one target limits the optimization process. With two targets, we have greater control over the film composition. This leads to higher hardness, lower stresses, and improved Young's modulus over films produced with a single CrAl target. Additionally, the morphologies are different (columnar with CrAl and dense with Cr and Al). To conclude, it seems more justified to work if possible with two independent targets. .

Introduction

Ternary systems, such as MeAlN (with Me = Ti, Cr etc.), are attracting more and more attention from the industry. This is due to their high resistance to wear. Several experiments have led to the development of TiAlN or CrAlN hard coatings by varying the aluminum target bias voltage to improve the properties of the traditional TiN and CrN. Titanium-based coatings, especially (Ti,Al)N, are industrial state-of-the-art tool coatings for most machining operations. Indeed, it is known that Al permits higher working temperatures because of the alumina thermal barrier that protects the tool core during high speed machining. In addition, other ternary systems such as CrSiN, TiSiN, CrWN or TiCN have been studied during the last decade, but we will focus our discussion on CrAlN. Due to the effectiveness of Al additions in the TiN binary system, the same result was expected in the CrN system. CrN is increasingly used in the industry and may replace TiN in certain applications due to its better corrosion resistance and lower friction coefficient. It appeared to be a logical step to develop the CrAlN ternary system to work at temperatures higher than 700 °C (thermal stability of the binary CrN system). Nevertheless, the topic of our work is not to compare TiAlN and CrAlN layers but to compare CrAlN layers obtained by magnetron sputtering with a sintered target (Cr 75 Al 25 ) versus two elemental targets (Cr and Al). Actually, we found no literature dealing with such a study and it would be very interesting to compare the properties of the deposited films obtained according to the variable deposition conditions like working pressure, gas mixture, gas flow, bias voltage but also the kind and number of targets in the same deposition system. Since 2000, most of the experiments on CrAlN and TiAlN ternary systems have used a single sintered target [1][2][3][4][5][6][7][8][9][10][11][12][13] instead of two targets [14][15][16][17][18][19]. According to the deposition technique (one cathode or more available) and the applications involved, they employed targets with a fixed composition such as Cr 90 Al 10 [1],Cr 50 Al 50 [3],Cr 30 Al 70 [4],Cr 75 Al 25 [7] to deposit CrAlN films, or Ti 33 Al 67 [10,11] and Ti 50 Al 50 [5,8,9,11,12,13]. Recently, Bobzin et al. [2] showed that the composition of coating systems deposited by single component targets (e.g., titanium/chromium and aluminum) in a dual-cathode arrangement can easily be altered by first changing pulse sequence, e.g., T On ,T Off and second a change in DC (Direct Current) power rate, e.g., increase in pulse energy. The high mobility of ions and, in particular, electrons results from a high ionization of the pulsed plasma [20]. This gain in mobility can finally lead to higher adatom mobility, resulting in different coating structures. The aim of the present study is to compare the physicochemical and mechanical properties of CrAlN layers obtained by dual RF (Radio Frequency) magnetron sputtering using one sintered Cr 75 Al 25 target or two targets (pure Cr and Al) to establish what kind of solution is the best to obtain good protective coatings for mechanical applications.

Experimental details

The deposition of CrAlN coatings was carried out by a dual RF magnetron sputtering system (NORDIKO type 3500-13.56 MHz) (Fig. 1). First, CrAlN layers were obtained by using a sintered Cr 75 Al 25 target. The deposition time was fixed to 90 min. We studied the variation of parameters such as the nitrogen content in the plasma (10, 17.5, 25, 35, 50 and 100%), the working pressure (0.2, 0.4, 0.8 and 1.2 Pa) and the target voltage (-300, -500, -700 and -900 V). For comparison, pure Cr (99.995%) and Al (99.999%) targets were used. To vary the Al content in the CrAlN films, we first fixed the Cr applied voltage at -900 V and varied the Al voltage (-300, -500, -700 and -900 V); second, we fixed the Al applied voltage at -900 V and varied the Cr voltage (-500 and -700 V). The working pressure was about 0.4 Pa, the plasma discharge contained 80% of Ar and 20% of N 2 and the deposition time was 90 min. In both cases, the targets were 101.6 mm in diameter and 3 mm thick. The substrate/target distance was 80 mm.

To determine the physicochemical, mechanical and tribological properties of the films, we employed silicon (10 × 10 mm²) for internal stress and thickness measurements, SiO 2 (10 × 10 mm²) for hardness and Young's modulus determination, and 90CrMoV8 steel samples ("La Forézienne-MFLS", France trademark) frequently used to fabricate tools for wood machining [START_REF] Labidi | Proceedings of the Second International Symposium on Wood Machining[END_REF] (20 × 20 mm²) for pinon-disk tests. Nanoindentation tests have been made with an NHT from CSM Instruments equipped of a Berkovich indenter. The test parameters were a maximum load of 10 mN, a load and unload rate of 5 mN/min and we employed the sinus mode (1 Hz of frequency and 1 mN of amplitude). The tribological tests of pin-on-disk were made under an applied load of 5 N, with a 100Cr6 ball of 5 mm diameter, with an alternative friction for each 8 s and a test duration of 10 min. The steel samples were ground to a roughness R a of around 0.2 μm and R t of around 1 μm yielding roughness similar to industrial tools. The morphology, thickness and the composition of the CrAlN coatings were determined by Scanning Electron Microscopy (SEM-5 kV) observations and Energy-Dispersive X-ray Spectroscopy (EDS) microanalyses (Jeol JSM-5900 LV). A calibration was realized on silicon (light element). Then the calculations of the composition of layers were made thanks to the data we obtained from a specific calibration realized at 5 kV on pure metals or compounds. X-ray Diffraction (XRD) analyses (SIEMENS D500, Co-K α radiation, Bragg-Brentano configuration) permitted the determination of the crystalline orientations of the CrAlN layers. The residual stress was calculated using the Stoney's equation [START_REF] Stoney | Proceedings of the Royal Society A, Mathematical[END_REF] after measuring the radius curvature of the silicon coated samples with the Newton's ring method [START_REF] Wahl | [END_REF] or by 2D profilometry (VEECO-DEKTAT 3030).

Before deposition, all the substrates were ultrasonically cleaned in ethanol. The targets and the samples were then etched for 5 min in an Ar plasma by RF and DC (-1000 V) discharges, respectively.

Results and discussions

EDS composition of the CrAlN layers

According to the EDS microanalysis, in the case of CrAlN coatings deposited from a single target (CrAlN1), above 15% of nitrogen in the plasma, we obtain a constant atomic composition of 38% of N, 25% of Al, 25% of Cr and 10% of O (which is not negligible) as shown on Fig. 2. The other deposition parameters (working pressure and target voltage) had no influence on the coatings' composition.

Fig. 3 shows the chromium, aluminum and nitrogen atomic contents determined by EDS in the CrAlN films deposited from two targets (CrAlN2) as a function of the applied voltage on the Cr and Al targets. First, a negligible proportion of oxygen was detected (less than 2 at.%). We observe that the Al content increases from 0 to 30 at.%, with the Al target applied voltage. This result can be explained by the resulting increase in the Al target sputtering rate. However, between -700 and -900 V, there is only a weak increase in the Al content from 28 to 30 at.%. According to Ding et al. [24], for the same targets and applied voltages, chromium has a higher sputtering rate than aluminum, which can explain the increase of chromium content compared to the aluminium one for these specific deposition conditions at the highest target voltage. We also note a slow increase of the nitrogen content from 28 to 36 at.% with the increase of the Al target applied voltage, which indicates that the Al addition is beneficial for the enhancement of CrN stoichiometry. Once the maximum Al content was obtained, at around 30 at.%, we fixed the applied voltage on the Al target, and began varying the Cr one. It permitted us to obtain a maximum of 51 at.% of Al since the sputtering rate of Cr is higher than the Al, as a consequence, the Cr target voltage appears to be the decisive parameter. Applying a bias voltage of -900 V on both targets yields we obtained ternary layers of Cr 0.4 Al 0.3 N 0.3 .

The increase of the layers thickness can be explained by the increase of the applied voltage on a target or by the fact that Cr sputtering rate is higher than the Al one. Similar results have been obtained by Ding et al. [24].

As a first conclusion on the composition of these CrAlN layers, we can note that the maximum Al content is around 25 at.% after stabilization in the case of the CrAlN1 layers while the CrAlN2 have a maximum at 51 at.% of Al. It is also noteworthy that when we varied only the Al bias voltage we obtained 28-36 at.% of Al in the CrAlN2 coatings while the maximum of Al content in the CrAlN1 layers after varying the nitrogen content in the plasma was around 25-35 at.% before stabilization. These results are quite similar and confirm again the main possibility to increase the Al content by the use of two targets.

Al

XRD analyses of the CrAlN layers

The XRD analyses were made on ground steel samples as mentioned above in the case of the CrAlN1 layers. First, we studied the influence of the nitrogen content in the plasma: we observe a minor diffraction peak at 44°on the XRD patterns of the Fig. 4A except for the coatings obtained with 25 and 50% of N 2 . In the case of the layer obtained with 25% of N 2 , we have an intense and thin peak at 44°a nd a minor one at 98°not detected for the other nitrogen contents, which indicates a well crystallized phase. Unfortunately, XRD alone cannot answer whether this is a CrN in an AlN matrix, an AlN in a CrN matrix, or a ternary, quaternary system, such as pure CrAlN or CrAlNO because of the presence of 10% of oxygen. Some complementary analyses (Energy Backscattered Diffraction, X-ray Photoelectron Spectroscopy, and Secondary Ion Mass Spectrometry in progress) are necessary to define which phases are present. Above 25% of N 2 , the diffraction peaks are less intense, and broader. This could be explained by the fact that higher nitrogen content interferes in the sputtering process and is detrimental to good crystallinity. As a conclusion, to obtain well crystallized CrAlN coatings with a sintered Cr 75 Al 25 target, we have to work with nitrogen content at around 25%. In a second experiment, we verified the influence of the working pressure (Fig. 4B): only the intensity of the diffraction peaks at 44 and 98°varied. It seems that the optimal working pressure is 0.4 Pa. When the pressure increases, there are a lot of species in the plasma which interfere with the ones participating in the growth, decreasing their rate and energy. Below 0.4 Pa, there are not enough species and the sputtering rate is decreased as the species energy necessary to obtain a well crystallized coating. In these cases, or the grain size is too small or the layers are amorphous. Finally, the influence of the target voltage was studied (Fig. 5): the optimal voltage is the highest one. Indeed, we observe that when the target voltage increases, the intensity of the diffraction peaks at 44 and 98°also increases and the films are more crystallized. When we work at -300 V, the layers are amorphous or composed of very small grains not detectable by XRD. The optimal target voltage is -900 V in our case.

The XRD analyses of the CrAlN2 coatings were made on silicon substrates deposited with different Al contents and are presented in Fig. 6. A broad Cr 2 N (111) peak was observed on the XRD spectrum of the CrN film, which confirms the N/Cr ratio of 28/72 obtained by the EDS microanalysis (Fig. 3). This broad peak shows that the deposited film is not well crystallized. By adding 5, 13 and 28% of Al, CrN (200) and AlN (311) diffraction peaks appeared showing that the aluminum addition enhanced the CrN film crystallization. This result is in correlation with other works [24,25]. At 30 at.% of Al content, a broad peak probably coming from the presence of both CrN (200) and AlN (200) orientation appeared and the layer is well crystallized. This result was also obtained in several works [25][26][27]. Moreover, there is a shift of this peak to wider angles showing that CrAlN films contain compressive stresses, with the results already observed in a previous study [26]. This peak completely disappeared above 44 at.% of Al content where only the AlN (311) peak exists. This last peak disappears at 51 at.% of Al content, and is replaced by a broad AlN (002) peak indicating that the deposited film becomes amorphous. As a conclusion of these XRD analyses, the CrAlN1 films are always well crystallized on the contrary to the CrAlN2 ones which became amorphous for the highest Al content.

Internal stresses of the CrAlN layers

In the case of the CrAlN1 films, the stresses were determined by profilometry and the Newton's ring method using the Stoney's equation. It is noteworthy that only the method by profilometry permits to determine if the stress is in tension or compression. We applied the same sign to the stresses obtained by both methods. We can observe on Fig. 7 that both methods give a similar behavior: the same curve but with a different optimum (-4 GPa in the case of profilometry and -12 GPa for the Newton's ring method). Nevertheless, these results permit to determine an optimal deposition time for coatings with lower stresses. Below 40 min of deposition (layers 400 nm thick), the stresses are too high. Since we are looking for good adhesion of the coatings on cutting tools, we are looking for lower stresses. This occurs for coatings obtained with deposition times above 60 min (layers > 600 nm thick). Nouveau [27] obtained similar results for Physical Vapor Deposition (PVD) CrN layers compared with TiN or c-BN and explained this behavior in previous studies [27][28][29].

The residual stresses of the CrAlN2 coatings as a function of Cr and Al applied voltage are presented in Fig. 8. The CrAlN deposited films have compressive stresses which vary between -1 and -3.5 GPa. Similar results have been obtained by Choi [30]. It is noteworthy that the residual stresses reached their highest value for Al content until 30 at.%, and then decreased. The increase of the residual stresses with the Al content can be explained by the increase of micro structural defects causing by the substitution of Cr atoms by Al ones. Besides, when the Cr applied voltage decreased from -900 to -700 V (or -500 V), the Cr incident atoms lose energy which can explain the decrease of the residual stresses by the creation of defects, such as a columnar structure, and as a consequence, the layers are less dense (Fig. 10). So, the variation of the residual stresses can be explained by these two phenomena. As a conclusion, the CrAlN1 and CrAlN2 layers present compressive stresses. If we compare the stresses of both layers obtained in similar deposition conditions (90 min, 0.4 Pa, -700 / -900 V and around 20-25% of nitrogen in the plasma), they are around 0 GPa (CrAlN1) and -1 GPa (CrAlN2) which is quite similar. of Al are dense. This morphological change is correlated with the structural one in XRD (in the presence of different phases).

Morphology of the CrAlN layers

Hardness, Young's modulus and friction coefficient of the CrAlN films

The results about the hardness, Young's modulus and coefficient of friction of the CrAlN1 coatings are summarized in Table 1. We observe the influence of the working pressure, the nitrogen content, and the target voltage. Indeed, lowering the working pressure increases both the hardness and the Young's modulus of the layers. These results are in accordance with others: deposition at low working pressure appears to be optimal. Unfortunately, the instability of the RF discharge prevents us from working below 0.4 Pa. As a result, in our system 0.4 Pa seems to be the optimal working pressure to obtain hard coatings with a reasonable Young's modulus. The nitrogen content has a lower influence on the hardness of the coatings than the working pressure. Nonetheless, decreasing the content to 10-20% improves both the hardness and Young's modulus. The best improvement of the Young's modulus is obtained for a nitrogen content of 17.5% which means that the optimal nitrogen content for hardness and Young's modulus is about the same. These results tend to confirm, and to complete, the previous ones obtained by XRD where we determined the optimal nitrogen content (around 25%) to obtain well crystallized coatings and now an optimal nitrogen contents of 17.5% to reach good mechanical properties. The last parameter studied in the case of the CrAlN1 layers is the target voltage: whatever the deposition conditions, working at high target voltage (especially -900 V) yielded the highest hardness and Young's modulus. These results confirm the previous ones concerning the optimal target voltage to obtain coatings with good mechanical properties. The results of nanoindentation tests confirm and complete the XRD analyses: when the films are well crystallized, they are hard. The optimal deposition conditions permitting to have well crystallized and harder coatings are a working pressure of 0.4 Pa, a nitrogen content of 17.5 to 25% and a target voltage of -900 V. Additionally, one coating was tested in pin-on-disk to determine its friction coefficient; this one was very low, 0.31, in comparison to the untreated steel and so very promising for mechanical applications.

The hardness and Young's modulus of the CrAlN2 coatings as a function of Cr and Al applied voltage are summarized in Table 2. They varied between 15 and 36 GPa and 331 and 520 GPa respectively. They are higher than those of the conventional Cr-N system (18 GPa and 220 GPa respectively) [31]. Similar values have already been obtained in previous studies [31,32]. The maximum values of hardness and Young's modulus were obtained for 30 and 51 at.% of Al content, this can be explained by the formation of a solid solution due to the substitution of Cr atoms by Al ones [31]. The hardness and the Young's modulus decreased at 44 at.% of Al to 15 GPa and 331 GPa respectively. Indeed, this Al content has been obtained at -700 V on the Cr target and -900 V on the Al one. So, when we change the Cr applied voltage from -900 to -700 V the energy of the incident atoms on the substrate decreases, as the number of defects created during deposition. As the hardness depends on this defects density, it decreases consequently. So, two hardening effects occur: one is solid solution formation and the other is the number of created defects during the deposition process.

The friction coefficient of the CrAlN2 layers only varies from 0.6 to 0.7 (Table 2) with the Al applied voltage. Similar values of the friction coefficient have been obtained in previous studies [31]. According to Bobzin et al. [1] the AlN films have a higher friction coefficient than CrN and CrAlN ones, so we can say that Al addition is responsible for the AlN phase presence which explains the high values of friction coefficient of CrAlN coatings. Nevertheless, these values are lower than the substrate which should be close to 1.

Conclusions

The aim of this work was to compare the properties of CrAlN coatings obtained by RF magnetron sputtering with one sintered target (Cr 75 Al 25 ) versus two independent targets (Cr and Al). The above results support the conclusions that the composition of the CrAlN1 films is limited to a maximum Al content of around 25 at.% after stabilization while the CrAlN2 ones presented until 51 at.% of Al. These results prove the main possibility to increase the Al content significantly with two targets in comparison to only one sintered target. Besides, we observed that the CrAlN1 films are always well crystallized as opposed to the CrAlN2 ones which became amorphous for the highest Al content. Both CrAlN1 and CrAlN2 coatings present compressive stresses which are quite similar when the films are deposited in almost the same deposition conditions. Concerning the morphology of the layers, the CrAlN1 ones always present a columnar structure, typical of PVD coatings, while some CrAlN2 films, and especially the ones obtained with 5, 30 and 51 at.% of Al, are dense. This morphological difference was explained by a structural one in XRD (phases in presence). Finally, the CrAlN2 films present better tribological properties in comparison to the CrAlN1 coatings, but this last one presents the lowest friction coefficient (0.31 compared to ~0.6 for the CrAlN2 films) which could be explained by the high hardness and Young's modulus of these coatings. Indeed, the optimal deposition conditions permitted to get a maximum hardness and Young's modulus of 26 and 310 GPa respectively in the case of the CrAlN1 films while these maxima are 35-36 and 460-520 GPa respectively in the case of the CrAlN2 layers.

According to these results, it does not seem justified to work with a sintered target instead of two independent targets if the deposition system has two or more cathodes. Indeed, it has been shown that all the properties, except the friction coefficient of the films and their crystallinity, obtained with two targets are better than the ones obtained with a sintered target. Finally, the use of two targets is less expensive, needs no particular attention, and allows better control over more parameters (especially the Al content). It permits the development of coatings with very different properties and also allows obtaining films with good characteristics for mechanical applications. Nevertheless, as mentioned in the Introduction section, this choice will depend on the kind of deposition technique and the kind of applications to be developed (laboratory or industrial ones where the type and number of targets is generally imposed).

Fig. 1 .Fig. 2 .

 12 Fig. 1. Schematic inside view of the RF dual magnetron sputtering chamber.

Fig. 3 .

 3 Fig. 3. EDS results of the CrAlN2 films obtained by varying the applied voltage on the Al or the Cr target.

Fig. 4 .

 4 Fig. 4. XRD patterns of CrAlN1 coatings as a function of: (A) the nitrogen content in the plasma; (B) the working pressure.

Fig. 5 .

 5 Fig. 5. XRD patterns of CrAlN1 coatings as a function of the target voltage.

Fig. 6 .Fig. 7 .Fig. 8 .

 678 Fig. 6. XRD patterns of CrAlN2 coatings as a function of Al content.

Fig. 9 .

 9 Fig. 9. SEM cross-section observation of a CrAlN1 film.

Fig. 10 .

 10 Fig. 10. SEM cross-section observations of the CrAlN2 films.

Table 1

 1 CrAlN1 coatings properties (obtained with one CrAl target).

	Working	Target	N 2 content in	Hardness	Friction	Young's
	pressure	voltage	the plasma (%)	(GPa)	coefficient	modulus E
	(Pa)	(-V)				(GPa)
	0.4	700	35	19.7	-	270
			17.5	22.7	-	365
		900	10	25.8	-	315
			17.5	23.5	-	402
			25	20.3	0.31	283
			35	21.4	-	307
	0.8		25	8.2	-	153
	1.2			6.4	-	121

Table 2

 2 CrAlN2 coatings properties (obtained with two targets Cr and Al).

	Cr bias	Al bias	Al	Thickness	Hardness	Young's	Friction
	voltage	voltage	content	(μm)	(GPa)	modulus	coefficient
	(-V)	(-V)	(at.%)			(GPa)
	900	300	5	2.1	26	410	0.6
		500	13	2.5	26	410	0.65
		700	28	2.7	23	380	0.7
		900	30	3	35	460	0.65
	700		44	2	15	331	-
	500		51	2.1	36	520	-

Acknowledgments

The authors would like to thank the Regional Council of Burgundy, France for its funding and Michael Walock for his help in English revisions.