Laurent Kergoat
email: laurent.kergoat@cesbio.cnes.fr

Sébastien Lafont

Almut Arneth

Valérie Le Dantec

Bernard Saugier

Nitrogen controls plant canopy Light-Use-Efficiency in temperate and boreal ecosystems

Optimum daily Light-Use Efficiency (LUE) and normalized canopy photosynthesis (GEE*) rate, a proxy for LUE, have been derived from eddy covariance CO 2 flux measurements obtained at a range of sites located in the mid to high latitudes. These two variables were analyzed with respect to environmental conditions, plant functional types (PFT) and leaf nitrogen concentration, in an attempt to characterize their variability and their potential drivers. LUE averaged 0.0182 mol/mol with a coefficient of variation of 37% (42% for GEE*). Foliar nitrogen N of the dominant plant species was found to explain 71% of LUE (n=26) and 62% of GEE* (n=44) variance, across all PFTs and sites. Mean Annual Temperature, MAT, explained 27 % of LUE variance, and the two factors (MAT and N) combined in a simple linear model explain 80 % of LUE and 76% GEE* variance. These results showed that plant canopies in the temperate, boreal and arctic zones fit into a general scheme closely related to the one, which had been established for plant leaves worldwide. The N-MAT-LUE relationships offer perspectives for LUE-based models of terrestrial photosynthesis based on remote sensing. On a continental scale, the decrease of LUE from the temperate to the arctic zone found in the data derived from flux measurements is not in line with LUE resulting from inversion of atmospheric CO 2 .

Introduction

Canopy light-use efficiency (LUE), defined as either the ratio of gross (GPP) or net primary productivity (NPP) to absorbed light has received increasing attention over the last decades, primarily because the combination of remotely-sensed absorbed photosynthetically active radiation (APAR) and estimates of LUE allows investigations of GPP and NPP over large areas. Since the launch of the NOAA-TIROS satellites in 1981 and the consequent development of algorithms to derive APAR from space, LUE-based approaches have become a widely applied tool [START_REF] Prince | A model of regional primary production for use with coarse resolution satellite data[END_REF][START_REF] Potter | Terrestrial ecosystem production: A process model based on global satellite and surface data[END_REF][START_REF] Ruimy | Methodology for the estimation of terrestrial net primary productivity from remotely sensed data[END_REF][START_REF] Field | Global net primary production -Combining ecology and remote sensing[END_REF][START_REF] Running | A continuous satellite-derived measure of global terrestrial production[END_REF] and LUE-based productivity models have greatly contributed to the characterization of the temporal variability of global-scale terrestrial productivity [e.g., Nemani et al., 2003]. At a smaller scale, productivity models based on LUE, often called Production Efficiency Models (PEM), have been developed for a range of different ecosystems. Such models take advantage of field datasets of productivity and biomass when details of physiology or ecology are not known [e.g., [START_REF] Prince | A model of regional primary production for use with coarse resolution satellite data[END_REF][START_REF] Medlyn | Physiological basis of the light use efficiency model[END_REF][START_REF] Mäkelä | Developing an empirical model of stand GPP with LUE approach: analysis of eddy covariance data at five contrasting conifer sites in Europe[END_REF]. Both, global and ecosystem models depend on accurate estimates of LUE.

Because it is the ratio of two key physiological properties (light capture and photosynthesis), LUE subsumes a broad range of processes and has also been applied as an integrative diagnostic tool. As such it has been used, for instance, to analyze and intercompare output from ecosystem models that differ in their complexity, their parameterizations and/or their representation of processes. An example was provided by the intercomparison of global models of NPP [START_REF] Ruimy | Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light-use efficiency[END_REF], which demonstrated that grid-cell light-absorption and NPP were highly correlated for ten out of twelve global models, the two 'exceptions' being models that predicted NPP on the basis of the nitrogen cycle. [START_REF] Ruimy | Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light-use efficiency[END_REF] also drew attention to the large differences among average LUE values from the ten LUE-like global models, highlighting the need for accurate estimates of large-scale LUE. In addition, their results indicated that the relationship between LUE and the nitrogen cycle still has to be clarified at the global scale. The importance of accurate LUE values is also exemplified by atmospheric inversion studies. Such studies infer the surface sinks and sources of CO 2 from atmospheric measurements and transport models. LUE-based estimates of productivity are often used as a constraint in or as an end-product of the inversion process [START_REF] Randerson | Carbon isotope discrimination of arctic and boreal biomes inferred from remote atmospheric measurements and a biosphere-atmosphere model[END_REF][START_REF] Kaminski | Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle[END_REF][START_REF] Still | Large-scale plant light-use efficiency inferred from the seasonal cycle of atmospheric CO2[END_REF]Chevallier et al., 2005, among others].

Originally, field studies suggested LUE being rather invariable among different, well-watered crops [START_REF] Monteith | Climate and the efficiency of crop production in Britain[END_REF] but later reviews by [START_REF] Prince | A model of regional primary production for use with coarse resolution satellite data[END_REF], [START_REF] Ruimy | Methodology for the estimation of terrestrial net primary productivity from remotely sensed data[END_REF] and [START_REF] Medlyn | Physiological basis of the light use efficiency model[END_REF] have demonstrated significant variation among vegetation types at least for LUE derived from NPP (LUE NPP ). Part of this variation may be related to measurement aspects as neither NPP nor APAR are easy to capture precisely, especially across sites and across Plant Functional Types (PFT) [START_REF] Gower | Direct and indirect estimation of leaf area index, Fapar and net primary production of terrestrial ecosystems[END_REF], but there is little doubt overall that the assumption of a constant LUE NPP does not provide an accurate description of terrestrial ecosystems [START_REF] Binkley | Thinking about efficiency of resource use in forests[END_REF][START_REF] Bradford | The relative importance of light-use efficiency modifications from environmental conditions and cultivation for estimation of large-scale net primary productivity[END_REF].

From a physiological perspective some authors have argued that LUE derived from GPP (LUE GPP ) should be less variable than LUE NPP , mainly because differences in carbon allocation and respiration estimates are responsible for some of the variability in LUE NPP and should not affect LUE GPP [Ruimy et al., 1996a, Goetz and[START_REF] Goetz | Modelling terrestrial carbon exchange and storage: Evidence and implications of functional convergence in light-use efficiency[END_REF]. The first analyses of LUE GPP led however to a somewhat contradictory picture with the ratio either being more or less constant across ecosystems (ca. 0.02 mol CO 2 /mol APAR, [START_REF] Ruimy | CO2 fluxes over plant canopies and solar radiation: a review[END_REF]) or varying widely [Turner et al., 2003, Turner et al., 2005]. Intuitively a certain fluctuation of LUE GPP would be expected as GPP varies not only with APAR but also with other factors, e.g., soil water and nutrient availability, the ratio of direct to diffuse radiation, canopy age or site history [START_REF] Alton | The impact of diffuse sunlight on canopy light-use efficiency, gross photosynthetic product and net ecosystem exchange in three forest biomes[END_REF][START_REF] Delucia | Forest carbon use efficiency: is respiration a constant fraction of gross primary production?[END_REF]]. Yet, when investigating LUE over the course of one to several years, water and nutrient supply will be reflected to some extent in the canopy leaf area index, and therefore in the absorbed PAR. How LUE and the fraction of absorbed PAR (fPAR) are related to environmental constraints is largely unknown. Whether light capture and light-use efficiency show coordinated responses to environmental constraints has received theoretical interest [Field et al., 1995, Goetz and[START_REF] Goetz | Modelling terrestrial carbon exchange and storage: Evidence and implications of functional convergence in light-use efficiency[END_REF], but little observational support so far, and the opposite view of light-use efficiency increasing with decreasing light availability has also been considered [START_REF] Binkley | Thinking about efficiency of resource use in forests[END_REF].

In addition to the question on the range and variability of LUE there is also a debate on its global patterns. When plotted against latitude LUE increased towards the north for some global models, but decreased for others [START_REF] Ruimy | Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light-use efficiency[END_REF], illustrating the lack of consensus on the underlying processes. [START_REF] Kaminski | Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle[END_REF] and [START_REF] Still | Large-scale plant light-use efficiency inferred from the seasonal cycle of atmospheric CO2[END_REF] showed that largescale CO 2 inversion studies tend to impose a large increase of LUE from temperate to arctic ecosystems. In terms of modeling, [START_REF] Haxeltine | A general model for the light-use efficiency of primary production[END_REF] suggested that a pole-ward increasing trend in plant nitrogen content could support higher LUE at higher latitudes.

Conversely, based on CO 2 flux data over boreal sites, [START_REF] Lafont | Spatial and temporal variability of land CO2 fluxes estimated with remote sensing and analysis data over Western Eurasia[END_REF] found a correlation between mean annual temperature and LUE, which leads to a decrease in LUE towards high northern latitudes. This result was supported by the analysis of [START_REF] Schwalm | Photosynthetic light use efficiency of three biomes across an east-west continental-scale transect in Canada[END_REF] who, in addition, did not detect any significant correlation with foliar nitrogen based on 11 flux measurement sites.

Clearly, for increased confidence in satellite-derived productivity estimates as well as to offer better diagnostics to large-scale ecology, it is important to reduce the uncertainties affecting large-scale LUE patterns and to identify the relevant drivers for its variation. The large number of net CO 2 flux data that are by now becoming available give an unprecedented access to estimates of ecosystem GPP, owing to effort in collecting and processing data in networks like FLUXNET [START_REF] Baldocchi | FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities[END_REF][START_REF] Friend | FLUXNET and modelling the global carbon cycle[END_REF][START_REF] Owen | Linking flux network measurements to continental scale simulations: ecosystem carbon dioxide exchange capacity under non-water stressed conditions[END_REF]. For this study, we derive LUE from CO 2 flux time-series to estimate LUE GPP over a variety of sites spanning the temperate, boreal and arctic ecosystems. The questions we address are: i) Is optimum LUE GPP (when GPP achieves seasonal maxima) variable among these ecosystems?

ii) If yes, what are the large-scale and local-scale patterns behind LUE variability? iii) What are the major controls of spatial LUE variation, and how can it be parameterized? 2 Data and methods

Sites characteristics

CO 2 fluxes based on the eddy covariance technique have been compiled from the FLUXNET, EUROFLUX, AMERIFLUX, BOREAS and EUROSIBERIAN CARBONFLUX databases [START_REF] Baldocchi | FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities[END_REF][START_REF] Heimann | Foreword: The Eurosiberian Carbonflux project[END_REF][START_REF] Sellers | The Boreal Ecosystem-Atmosphere Study (BOREAS): an overview and early results from the 1994 field year[END_REF]Valentini et al., 2003] as well as from studies that had been conducted independently of these data sets. The emphasis has been put on mid to high latitude sites. Site descriptions and references are summarized in table 1.

As a result of the considerable efforts of the participants to these projects, there is a relatively high degree of homogeneity in the methods and algorithms used at different sites. To take advantage of this effort, we considered only fluxes measured with eddy covariance methods, and did not retain for example, fluxes measured and up-scaled from chamber techniques.

For each site, canopy leaf area index (LAI; projected leaf area basis and usually including understorey vegetation) or fPAR (the fraction of PAR absorbed), and mean annual temperature (MAT) data were compiled (Table 1). MAT was either provided by the database and reference articles or derived from climatology, using the gridpoint closest to the site [START_REF] Leemans | The IIASA climate database for mean monthly values of temperature, precipitation and cloudiness on a terrestrial grid[END_REF]Cramer, 1991, updated 1995]. The vegetation at the sites was classified into the following Plant Functional Types: Evergreen needleleaf, evergreen broadleaf, deciduous needleleaf, deciduous broadleaf, mixed forest, tundra and boreal wetlands, C4 grasses and crops, and C3 grasses and crops. Databases and literature were screened for site leaf or needle nitrogen content expressed on a mass basis (gN/g dry matter, hereafter N). For most sites, only the dominant species have been sampled for N, with a few exceptions in herbaceous canopies, which provide canopy-average nitrogen. For evergreen plants, most studies provided an average over the different needle or leaf age-classes. When seasonal course of nitrogen content was available, we retained the values closest to the date of maximum CO 2 flux. Leaf nitrogen was not corrected for sugar content.

Derivation of Light-Use-Efficiency and normalized photosynthetic rate.

From the CO 2 flux time series two variables were derived: Optimum daily photosynthetic light-use-efficiency (hereafter simply noted LUE), and a proxy for LUE which is the normalized maximum photosynthesis rate (or Gross Ecosystem Exchange) noted GEE*. The CO 2 flux data compiled in this study are of two types (noted I and II), for which different methods had to be used.

Type I dataset

For 42 datasets, typically one year or longer, for which flux data were available, time-series of half-hourly GEE were derived from NEE and an estimate of ecosystem respiration R eco . As used here, GEE is considered positive, whereas NEE and R eco follow the classical micrometeorological conventions, being positive when the CO 2 flux is upward.

GEE = -NEE + Reco

Eq. 1

For the long-term comprehensive data it was possible to estimate R eco using two different methods, including a simple one, which can also be applied for the less comprehensive type II data (see below). The first method estimates R eco from soil temperature using an Arrheniustype relationship with parameters that may vary seasonally. Nighttime flux data were selected above wind speed and/or friction velocity thresholds before fitting eq. ( 2) to soil temperature

(5 to 10 cm) for consecutive two-month periods of time [e.g. [START_REF] Falge | Gap filling strategies for defensible annual sums of net ecosystem exchange[END_REF]. This allows to account for seasonal variations in plant phenology, water budget and microbial processes.

e T T R Ea F Reco ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ - = 1 1 0 0 Eq. 2
where T0 is the reference soil temperature (283.16 K), F0 is the fitted respiration rate at reference temperature, Ea is the fitted activation energy and R is the gas constant.

To obtain daily totals, R eco was extrapolated during daytime periods based on soil temperature and GEE computed accordingly.

A second, simpler method estimates ecosystem respiration rate as the average of nighttime fluxes (i.e., period when global radiation Rg < 10 W/m 2 over a 24 h period of time, eq. 3)

Reco=<NEE> Rg<10
Eq. 3

where brackets indicate averaging. Half-hourly GEE were computed using a constant value for half-hourly R eco over a given day. This simple method assumes that the differences between nighttime-average and mid-day ecosystem respiration are small compared to seasonal and inter-sites differences, which is applicable in ecosystems where respiration does not respond strongly to rainfall events.

Once time-series of GEE had been derived, the maximum canopy photosynthesis, GEE max , was computed as the average of the upper 98.5-99.5 % bin of the half-hourly GEE histogram.

These 98.5 and 99.5 thresholds were defined to retain photosynthesis rates typical of optimal environmental conditions, while discarding outliers. All days with at least one half-hourly GEE value falling in the 98.5-99.5 % interval were considered 'optimal' in terms of environmental conditions. For these days, 24h totals (GEE 24h , PAR 24h ) were derived from half-hourly GEE and PAR. An optimum daily light-use-factor (LUF), based on incident PAR was derived from the slope of the linear relationship between GEE 24h and PAR 24h . To avoid circular analysis no gap filled data sets were used since light is used to fill gaps in NEE and GEE time series.

GEE 24h = LUF. PAR 24h

Eq. 4

To account for differences in PAR absorption due to canopy openness, daily LUE was computed as: LUE = LUF /fPAR Eq. 5

Depending on the site, fPAR was either obtained from literature and database sources or derived from projected LAI using:

⎟ ⎠ ⎞ ⎜ ⎝ ⎛ - × = Θ -

Type II data

For the additional 35 type II datasets, which either were short-term studies or not included in the above databases, maximum rates of canopy photosynthesis GEE max were derived by averaging 3 consecutive half-hourly CO 2 flux values during optimal environmental conditions. Averaging consecutive data prevents overestimation of GEE max created by statistical variation sometimes present in the half-hourly eddy flux data. A few studies directly provide GEE time series either from temperature driven R eco , or from the intercept of light/NEE curves, otherwise, R eco was estimated with equation 3.

For comparison among sites with different leaf area index, GEE max was normalized by fPAR, using a reference fPARc of 0.95 corresponding to a closed canopy. Additionally, GEE max was normalized by the cosine of Θs, to compensate for difference in incident PAR caused by latitude, assuming optimum conditions for CO 2 flux occurring near the solstice.

( ) ( )

s fPAR fPARc GEE GEE Θ = Θ = cos 0 cos * max Eq. 7
GEE* is expected to be a good proxy for LUE in type II data sets since both variables share the same normalization by absorbed PAR, although simplified in the case of GEE*, providing that daily integrated GPP and daily maximum GPP values are related.

Sensitivity tests

To evaluate the robustness of eq. 5 and 7 to fPAR (eq. 6), we tested the sensitivity of LUE and GEE* to three fPAR estimates. Firstly, we assumed that the period of maximum GEE departs from solstice depending on latitude, ranging from day of year 180 at 45° to 220 at 80° [START_REF] Falge | Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements[END_REF]. Θs is then replaced by the sun elevation at 12h (local solar time) for the corresponding day of year. Secondly, fPAR was assumed to be a linear mixture of fPAR for direct (eq. 6) and diffuse irradiance:

( )

e kLAI fPAR fPAR - - × × - + × = 1 95 . 0 ) 1 ( 2 λ λ
assuming a fraction of direct PAR, λ, of 0.65.

Eq. 8

Lastly, we tested the hypothesis that the extinction coefficient k is PFT dependent [e.g. [START_REF] Yuan | Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes[END_REF].

⎟ ⎠ ⎞ ⎜ ⎝ ⎛ - × = Θ - e s LAI k pft fPAR cos 3 1 95 . 0
Eq. 9 assuming k pft =0.45 for needleleaf, 0.55 for broadleaf, and 0.5 for other canopies instead of using k=0.5 for all sites.

Results and Discussion

The consistency of the GEE* estimation methods was first evaluated using the 42 type I dataset (Table 1). GEE* derived from either eq. 2 or 3 were similar (r 2 =0.93, slope 1.02, intercept -0.3 µmol s -1 m -2 ). A similar picture was found for LUE data based on eq. 2 and 3 (r 2 =0.87, slope 0.93, intercept -0.0007 mol/mol). Therefore, in the following, we choose to use eq. 3, which allowed us to merge type I and type II into a homogeneous GEE* dataset.

For consistency, we consequently used eq. 3 also for derivation of LUE.

GEE max computed with eq. 3 were well correlated (r 2 =0.81) with the maximum GEE derived

Analysis of LUE and GEE* variability

As expected LUE and GEE* from type I data were highly correlated (Figure 1) with a r 2 of 0.88 (n=42). Or in other words, GEE* can be used as a proxy for LUE for type II dataset.

Since the full dataset for GEE* spans a larger range of ecosystems, latitudes, soil and environmental conditions, especially in the harsh arctic environment, it provides more robust The histogram of LUE (Fig. 1a) reveals a significant variability in among-site LUE, with a mean of 0.0182 mol/mol, and a standard deviation of 0.0067 resulting in a coefficient of variation of 37 % (n=42, Table 2). Considering the range of ecosystems included, it is not surprising that GEE*, derived for the entire dataset in Table 1 was also highly variable, averaged at 26.2 µmolm -2 s -1 , with a standard deviation of 11 µmolm -2 s -1 and a coefficient of variation of 42 % (n=77).

The canopy LUE values derived here are naturally lower than commonly measured leaf-level quantum yields which may be of the order of 0.06 under normal environmental conditions [e.g. [START_REF] Ehleringer | Variation in quantum yield for CO2 uptake among C3 and C4 plants[END_REF]. Several reasons underlie the leaf vs. canopy difference:

Photosynthesis of leaves saturates in high irradiance, thereby decreasing canopy daily LUE, which is integrated over the course of a day and includes periods of high and low irradiance.

Moreover, although we select the optimum seasonal LUE value, environmental conditions still impose some stress on the plants. Highest canopy LUE were obtained for two crops in the data set, rice and corn (Table 1), which show almost no saturation of leaf level GEE / PAR curves at the half hourly time step, despite rice being a C3 plant. The average value of 0.018 is close to but lower than the value of 0.02 proposed by [START_REF] Ruimy | CO2 fluxes over plant canopies and solar radiation: a review[END_REF]Ruimy et al. [ , 1996b]].

For further illustration Figure 2 performed with the different formulations of fPAR and APAR (eq. 8, 9) did not affect these results (not shown). Since PAR irradiance tends to decrease from temperate to arctic latitudes, the trend of maximum GPP versus MAT is obviously stronger than GEE* (not shown).

LUE was not to be expected to be a simple function of MAT due to a range of additional factors. In continental high latitude ecosystems photosynthesis takes place over a short (2-3 months) and well defined period with sometimes quite warm temperatures [e.g., [START_REF] Lloyd | Seasonal and annual variations in the photosynthetic productivity and carbon balance of a central Siberian pine forest[END_REF][START_REF] Arneth | Comparative ecosystem-atmosphere exchange of energy and mass in a European Russian and a central Siberian bog II. Interseasonal and interannual variability of CO2 fluxes[END_REF]. However, the correlation almost disappears when MAT is replaced by the temperature corresponding to the period of CO 2 flux data retained to compute LUE and GEE* (r 2 = 0.12, P=0.06 and r 2 =0.19, P= 0.01 respectively). What is more, effects of physiology may override those of temperature with, for instance, LUE of a well-fertilized, C4 crop is expected to be higher than that of a 'natural' system at similar MAT. Nevertheless, LUE and GEE* tended to be organized along a MAT gradient (Fig. 2a,b), with a clear tendency to form clusters for some of the plant types like deciduous broadleaved forests, whereas for most of the other types a large variability was found (e.g., needleleaved forests, grasses and crops, and tundra/wetlands). The better correlation with GEE* is caused by a broader sampling of the possible range of MAT, including a number of sites with low annual temperatures (MAT < 0). The correlation of LUE and GEE* with MAT we find here is lower than the values reported by [START_REF] Lafont | Spatial and temporal variability of land CO2 fluxes estimated with remote sensing and analysis data over Western Eurasia[END_REF] for 18 boreal sites and closer to results reported by [START_REF] Schwalm | Photosynthetic light use efficiency of three biomes across an east-west continental-scale transect in Canada[END_REF] for yearly mean LUE at 17 Canadian forest and wetland sites. The broader range of PFT included here, especially inclusion of warmer broadleaved evergreen, grasses, crops on the one hand and cold climate fens and deciduous forests on the other hand explains the difference with [START_REF] Lafont | Spatial and temporal variability of land CO2 fluxes estimated with remote sensing and analysis data over Western Eurasia[END_REF]. These PFT add to the scattering of the GEE*/MAT relationship (Fig 2b andTable 2). Averaged per PFT, LUE ranges from 0.0116 for tundra and wetlands to 0.0270 for C3 grasses and crops (Table 2).

Role of leaf nitrogen content

At the leaf level, numerous studies have demonstrated a strong link between nitrogen content and photosynthesis [START_REF] Field | The photosynthesis-nitrogen relationship in wild plants[END_REF][START_REF] Wullschleger | Biochemical limitations to carbon assimilation in C3 plants -a retrospective analysis of the A/Ci curves from 109 species[END_REF]. A general framework for leaf structure and function, relating leaf assimilation rate, leaf nitrogen content and leaf mass per area (LMA) has been proposed by [START_REF] Reich | From tropics to tundra: Global convergence in plant functioning[END_REF] and extended worldwide by [START_REF] Wright | The worldwide leaf economics spectrum[END_REF]: Leaf photosynthesis (on a mass basis) correlates positively with leaf nitrogen (on a mass basis) and negatively with LMA. At the canopy level, several studies point towards a similarly strong incidence of nitrogen on photosynthesis, productivity and even net CO 2 flux [e.g. [START_REF] Choudhury | Estimating gross photosynthesis using satellite and ancillary data: approaches and preliminary results[END_REF]Williams et al., 2000;[START_REF] Smith | Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen[END_REF]. However, [START_REF] Schwalm | Photosynthetic light use efficiency of three biomes across an east-west continental-scale transect in Canada[END_REF] did not find any significant correlation between foliar nitrogen and canopy LUE across 9 forest sites. How leaf-level relations translate to the canopy level therefore remained elusive so far.

When plotted against leaf nitrogen content expressed on a mass basis, both LUE and GEE* significantly increased with N, Figure 3a andb. The variance explained by N, in a single variable 1:1 linear relationship, reaches values of r 2 = 0.71 (n=26, all type I sites with nitrogen data) and r 2 = 0.62 (n=44, all sites with nitrogen data) for LUE and GEE* respectively. It can be seen from Figure 3a that the increase of LUE with N becomes less well defined at leaf N levels > ca. 2% for deciduous broadleaf forest sites, suggesting a curvilinear relationship might exist for some biomes. The C4 crops and grasses achieved high LUE and GEE* at relatively low levels of leaf N.

Table 1 and Figures 3a and3b show the large differences in leaf N that exist among, and also within, plant types. Still, LUE and GEE* tended to group in well-defined PFT-clusters. In that respect, canopy-level data behave like the leaf-level data presented by [START_REF] Reich | From tropics to tundra: Global convergence in plant functioning[END_REF],

which also show such PFT clusters.

Combination of MAT and N

MAT and leaf N were not related in the data set analyzed here (Fig. 4), except for a weak tendency of evergreen tree N to increase with temperature and for deciduous trees and the two crop sites to have higher leaf N across the entire range of MAT when compared with other PFTs.

However, the residuals of the linear regression between LUE and N (Fig. 3a) were weakly correlated with MAT (not shown) which implies that the N:LUE relationships were not completely independent of climate. At same levels of leaf nitrogen, highest LUE were thus observed at warmest temperature. This was true also for the N vs. GEE* residuals.

Canopy index and function

To make use of the explanatory power of LMA, in addition to foliar nitrogen, it is tempting to scale the leaf level relationship of [START_REF] Reich | From tropics to tundra: Global convergence in plant functioning[END_REF] up to the canopy level. This was done by [START_REF] Green | Foliar morphology and canopy nitrogen as predictors of light-use-efficiency in terrestrial vegetation[END_REF], who proposed a 'Canopy Index', as a combination of canopy nitrogen, LMA and fPAR. Such a canopy index is theoretically related to canopy LUE (eq. 10, see Appendix). [START_REF] Green | Foliar morphology and canopy nitrogen as predictors of light-use-efficiency in terrestrial vegetation[END_REF] Eq. 10

When applied to our dataset, the canopy index (hereafter noted I G ) was found to explain a similar variance of LUE than foliar nitrogen alone (cf., [2003] such that a canopy index increases the linearity of LUE prediction compared to leaf nitrogen alone. However, the scatter also increased in our data and the overall predictive power did not improve. Particularly the C4 sites stand out as outliers in the I G index / GEE* relationship. Alternative formulations were developed and tested, that accounted for both, a non-linear relationship between leaf photosynthesis and leaf N, as well as for the differences in incoming PAR to refine [START_REF] Green | Foliar morphology and canopy nitrogen as predictors of light-use-efficiency in terrestrial vegetation[END_REF] formulation. This revised index, (N 0.77 LAI/fPAR.1/Ipar; see Appendix) still did not improve the overall r 2 of the relationship shown in Fig. 6, suggesting that the scattering of canopy index / LUE or GEE* may come from assumptions in scaling leaf level to canopy level, possibly in averaging LMA or leaf properties over the canopy. Given the limited number of sites and the approximations in fPAR derivation, it is at this stage difficult to further evaluate the appropriateness of the different mass-based canopy indices. Indeed, canopy structure, foliage clumping, non-leaf tissues can make the derivation of the factor LAI/fPAR particularly difficult, especially at the scale of a flux tower footprint.

Whether a unique nitrogen-based relationship can be applied to estimate LUE for different plant types is central to both application of remote sensing data and understanding and interpreting the observed LUE patterns. Leaf N concentration may be a better predictor of productivity than total N integrated over the canopy as soon as different plant types are considered. For instance, [START_REF] Smith | Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen[END_REF] were able to relate aboveground NPP to canopy averaged N concentration for deciduous and evergreen tree species using the same relationship. Conversely, different relationships for deciduous and evergreen trees emerged when canopy total N was used. Considering trees, grasses and crops, [START_REF] Green | Foliar morphology and canopy nitrogen as predictors of light-use-efficiency in terrestrial vegetation[END_REF] drew a similar conclusion, favoring N concentration rather than canopy total N. Similarly, expressing leaf nitrogen on a mass basis rather than on a surface basis may also seem counterintuitive [see also discussion by [START_REF] Smith | Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen[END_REF][START_REF] Hikosaka | Interspecific difference in the photosynthesis-nitrogen relationship: patterns, physiological causes, and ecological importance[END_REF]. From a practical point of view, foliar nitrogen content expressed on a mass basis is far less variable within the canopy than area basis nitrogen, and therefore easier to measure. As a consequence, mass basis N or indices based on mass basis N prove more convenient to evaluate in the field and can provide general framework for canopy LUE variability when addressing global or regional issues. So far our own, and other published evidence indicate that both canopy N concentration or mass-basis leaf N may be robust predictors of canopy functioning in terms of light use.

Idealized Canopy models

Considerations about the optimum functioning of canopies have suggested that the capture of different resources, like PAR, water, and nutrients could be regarded in an integrated fashion.

Such an integrated perspective would ensure coordinated resource acquisition resulting in a Balanced Canopy Functioning [START_REF] Field | Global net primary production -Combining ecology and remote sensing[END_REF], an appealing theoretical concept. As a result, plant canopies should not invest in PAR acquisition if PAR can not be transformed into NPP due to other resource limitations or environmental conditions. Leaf N content may reflect nutrient limitation, and therefore may co-vary with APAR. Other studies have suggested that resource-use could be either constant over a wide range of conditions [START_REF] Goetz | Modelling terrestrial carbon exchange and storage: Evidence and implications of functional convergence in light-use efficiency[END_REF] or related to resource-capture. Opposing views postulate either that resource use efficiency increases when resources availability decreases, or that resource use efficiency increases with the availability of a resource, as a result of improved overall functioning [START_REF] Binkley | Thinking about efficiency of resource use in forests[END_REF]. Development of such theoretical frameworks and their evaluation with observations are necessary to develop and test plant productivity models [see [START_REF] Field | Global net primary production -Combining ecology and remote sensing[END_REF].

Our compilation of data allows to test some aspects of these concepts. It becomes apparent (Figure 7), for instance, that LUE and GEE*, although highly variable, are not related to light resource capture (i.e., fPAR) in a simple way. Likewise, leaf nitrogen and fPAR are not related (Fig 8). Arguably, there are few high LUE associated with very low fPAR sites in our dataset, suggesting that high light-use efficiency is generally accompanied by high fPAR at least in the absence of opposing management practices, which is in line with [START_REF] Binkley | Thinking about efficiency of resource use in forests[END_REF]. The same is true for higher leaf N (e.g., >1.5%) being associated with relatively larger fPAR. However, other factors are required to explain the occurrence of LUE and N variability at similarly high fPAR level. For instance, the possible role of dense evergreen foliage as a nitrogen reservoir in nutrient poor environment (resulting in high mass of foliage with relatively low N) plays against a scaling of maximum LUE with fPAR because it increases fPAR and not LUE. It must be kept in mind though, that we investigate optimum LUE only.

Using time-integrated variables may result in a different picture [START_REF] Field | Global net primary production -Combining ecology and remote sensing[END_REF] The empirical evidence in our dataset either implies that there is a scaling between leaf N of the dominant plants and the whole canopy functioning, or implies that the variability within canopies is of second order compared to the explanatory power of leaf N of the dominant plants. The lack of relation between LUE or N and fPAR, as well as the relatively poor results of idealized canopy models, show that more pluri-specific studies are needed.

Concluding remarks: Latitudinal patterns of LUE and remote sensing based models.

A simple relationship of N with either, fPAR, MAT or latitude would facilitate the development and use of global LUE models greatly but the existence of such large-scale patterns is a matter of debate. Some studies point towards an increase of foliar N with latitude, MAT, and altitude, whereas others point towards an opposite pattern [Reich and Oleskyn, 2004, and references therein]. [START_REF] Reich | Global patterns of plant leaf N and P in relation to temperature and latitude[END_REF] found the highest foliar N values for mid-latitude and a weak decrease towards the coldest climate zones. In the data analyzed in our study the only significant trend in N versus latitude was for the evergreen needleleaf PFT.

It has been argued that LUE may increase with latitude because of a parallel increase in leaf nitrogen [START_REF] Haxeltine | A general model for the light-use efficiency of primary production[END_REF]. LUE derived from atmospheric inversions [e.g. Knorr and Heimann, 1995;[START_REF] Kaminski | Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle[END_REF][START_REF] Randerson | Carbon isotope discrimination of arctic and boreal biomes inferred from remote atmospheric measurements and a biosphere-atmosphere model[END_REF][START_REF] Still | Large-scale plant light-use efficiency inferred from the seasonal cycle of atmospheric CO2[END_REF] tend to indicate increasing LUE values from temperate to arctic latitudes, together with an increase for highly continental zones [START_REF] Kaminski | Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle[END_REF]. Interestingly, [START_REF] Still | Large-scale plant light-use efficiency inferred from the seasonal cycle of atmospheric CO2[END_REF] found higher LUE for continental Eurasia than for North America and suggested that the distribution of deciduous trees with higher needle N could explain this pattern, which is inline with our study.

Overall, our estimate of optimum LUE and GEE* along latitude gradients (e.g. Fig. 9a andb)

indicates a decreasing trend towards high latitude, supporting the statement of Kaminski et al.

[2002], who considered obtaining the highest LUE for tundra as unrealistic.

A variety of reasons may help to explain the discrepancy between our findings and LUE derived from inversion analyses: atmospheric inversion studies rely on an estimate of the heterotrophic respiration and other surface processes like snow-related processes (insulation effect, impact on water availability), whose errors can impair LUE estimates [START_REF] Randerson | Carbon isotope discrimination of arctic and boreal biomes inferred from remote atmospheric measurements and a biosphere-atmosphere model[END_REF]. However, inaccuracy of the atmospheric vertical mixing or meridional transport may also cause such a discrepancy. If this were to be the case, the consequences on the estimates and localization of the carbon sinks/sources might be significant, as it has been shown by [START_REF] Stephens | Weak Northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2[END_REF], and deserve further examination.

Overall, our results strongly support the view that LUE varies significantly both across and within biomes, and Plant Functional Types. Our data do not support the view that LUE GPP might be less variable that LUE NPP and might therefore span a small range of values [START_REF] Goetz | Modelling terrestrial carbon exchange and storage: Evidence and implications of functional convergence in light-use efficiency[END_REF]Ruimy et al., 1996a]. Our extensive use of the flux measurement network strengthens and extends the results of Turner et al. [2003] and [START_REF] Yuan | Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes[END_REF], who compared LUE GPP from 4 and 28 flux measurements sites respectively, as well as Choudhury [2001], who used data and canopy modeling and suggested a significant variability of LUE GPP . Convergent conclusions were drawn for aboveground LUE NPP in the past [Gower et al., 1999 and references therein]. Given the high diversity of measurement sites, which encompass managed and unmanaged stands, mono-or pluri-specific canopies, leaf nitrogen emerges as a strong organization factor of optimum canopy LUE and canopy photosynthesis rate.

As far as remote-sensing models are concerned LUE-based GPP models therefore have to account for variability in optimum LUE within an ecosystem and on biome scale. This has important consequences for the validation of global LUE-models with local data. Such models often combine an optimum LUE with different stress factors. Temporal variability in stress factors, incoming and absorbed PAR usually result in favorable model/data comparison, especially if site quality is accounted for [e.g. [START_REF] Yuan | Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes[END_REF][START_REF] Mäkelä | Developing an empirical model of stand GPP with LUE approach: analysis of eddy covariance data at five contrasting conifer sites in Europe[END_REF]. Remote sensing of the xanthophylls cycle (PRI), surface radiative temperature or fluorescence may help capturing some the short to seasonal variability of LUE [START_REF] Grace | Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?[END_REF] and may be included in the next generation of LUE based models. Improvements of optimum LUE estimates will be achieved if information on leaf nitrogen can be obtained in addition to fPAR, which may be done using ground based or airborne sensor (e.g. AVIRIS, CASI). For instance, [START_REF] Smith | Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen[END_REF] showed that forest productivity could be assessed through estimation of leaf nitrogen content mapped with AVIRIS. [START_REF] Boegh | Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthesis efficiency in agriculture[END_REF] were able to simulate canopy photosynthesis for different crops combining nitrogen-based modeling and hyperspectral data from CASI. The robustness of inversion methods for chlorophyll and nitrogen content, based on satellite-borne sensors like MERIS, CHRIS-PROBA or HYPERION, has to be investigated in the perspective of LUE-modeling.

Appendix

One way to derive the canopy index proposed by [START_REF] Green | Foliar morphology and canopy nitrogen as predictors of light-use-efficiency in terrestrial vegetation[END_REF]

Eq. 12

Using the leaf-level equation of [START_REF] Reich | From tropics to tundra: Global convergence in plant functioning[END_REF], (eq 13), and expressing leaf photosynthesis on a mass basis ( ), we have: [START_REF] Green | Foliar morphology and canopy nitrogen as predictors of light-use-efficiency in terrestrial vegetation[END_REF]. To avoid assumptions of eq 13 and 14, we also use the following Global Change Biol., 9, 383-395. Turner, D.P., et al. (2005), Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production, Global Change Biol., 11, 666-684 Valentini, R., G. Matteucci, A.J. Dolman, E.D. Schulze, and P.G. Jarvis (2003) Tree Physiol, 21, 969-976. Warren C.R., and M. A. Adams (2004) The controls on net ecosystem productivity along an Arctic transect: a model comparison with flux measurements, Global Change Biol., 6 (supp 1), 116-126. 1) as:

Figure captions

Evergreen needleleaf (▲), evergreen broadleaf (•), deciduous needleleaf (△), deciduous broadleaf (○), mixed forest (◆), tundra and boreal wetlands ( ), C4 grasses and crops (*), and C3 grasses and crops (+). PFT are indicated by the following symbols: ▲ evergreen needleleaf, △ deciduous needleleaf,

• evergreen broadleaf, ○ deciduous broadleaf, ◆ mixed forest, tundra or boreal wetland, * C4 grass or crop, + C3 grass or crop.

  Θs being the sun elevation at solstice Eq. 6

  from 25 FLUXNET sites by[START_REF] Falge | Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements[END_REF]. Our values were slightly higher since[START_REF] Falge | Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements[END_REF] used a smoothing over 15 days to compute the seasonal course of 'all weather' GEE whereas we are interested here in optimum half-hourly values. The general agreement between our GEE max and the one given in[START_REF] Falge | Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements[END_REF], based on a detailed examination of the data and methods supports our simplified data processing.

  statistics than an analysis restricted to long term comprehensive flux data. Therefore in the following, results are shown for both LUE and GEE*. For the purpose of clarity, plots showing GEE* have a second y-axis (on the right) that maps GEE* into LUE units based on the linear relationship of Figure 1b (LUE = 0.0006 GEE* + 0.0023).

  shows LUE and GEE* as functions of MAT at the site separated per PFT. MAT correlated weakly but significantly with LUE (Fig 2a, r 2 = 0.27, n=42, P=0.0004) and GEE* (Fig 2b, r 2 = 0.34, n=77, P<10e-7). The sensitivity tests

  Consequently the combination of N and MAT explained a larger part of LUE and GEE* variability and a simple linear model LUE = a MAT + b N + c was therefore fitted to the data (Fig 5a, same for GEE* Fig 5b). This simple model explained 80 % of LUE variance (LUE = 0.0063 N + 0.00036 MAT + 0.0064, n= 26) and 76 % of GEE* variance (GEE*=10.85 N + 0.66 MAT + 8.41, n=44). These regression coefficients are quite high, considering that the derivation of LUE and GEE* are affected by several approximations and uncertainties (fPAR estimates at low LAI for instance).Differences in leaf nitrogen reflect site quality combined with plant type. As opposed to foliar N, which is central to plant photosynthesis and physiology, MAT is a surrogate for a number of variables and processes in interaction, like the length of the growing season, the nutrient cycle, the water budget, to mention only three. Therefore, the relationship of LUE with MAT is best viewed as a large-scale pattern that subsumes these effects and that is locally supplanted by the role of PFT, leaf nitrogen, and other factors. A good example comes from the BOREAS data, where stands of aspen, Jack pines and black spruce co-exist at the landscape scale but show dramatically different LUE and leaf N, sometimes even within the same stand type[START_REF] O'connell | Comparison of Net Primary Production and light-use dynamics of two boreal black spruce forest communities[END_REF]. At the regional scale, averages of the LUE, weighted by the relative surface of the different PFT in BOREAS Northern and Southern Sites, tend to fall into the general large scale MAT/LUE gradient (not shown). Large scale patterns of LUE therefore depend on the relative surfaces occupied by different PFT or plant differing in N content, in line with the findings of[START_REF] Still | Large-scale plant light-use efficiency inferred from the seasonal cycle of atmospheric CO2[END_REF] of higher LUE in Eurasia than North America due to higher deciduous trees cover, combined to the overall environmental conditions correlated to MAT.

  Fig 6a, b compared to 3a, b). This was the case for both the percentage of variance explained as well as for the complementarities between I G and MAT. The comparison of Figures 6 and 3 confirms the analysis by Green et al.

  but based on our analysis there is little evidence for theoretical schemes relating of resource use to resource capture.At the leaf level, both empirical evidence and mechanistic analyses have established the strong relationship between N and photosynthesis. Leaf nitrogen is recognized as critical for the photosynthesis apparatus, but extensive discussions have addressed the observed departures from a single inter-specific relationship. Allocation of N to Rubisco, activity of Rubisco, C3 or C4 metabolisms, diffusion of CO2, respiration are some factors, which change the N-photosynthesis relationship[START_REF] Hikosaka | Interspecific difference in the photosynthesis-nitrogen relationship: patterns, physiological causes, and ecological importance[END_REF] and references therein}. Differences between evergreen and deciduous trees have been reviewed by Warren and Adams[2004], who pointed CO2 diffusion and overinvestment in Rubisco as important factors. At the canopy level however, mechanisms are still lacking and it is not possible to associate the relationship between LUE and nitrogen of the dominant plant to patterns of allocation of N to Rubisco, chlorophyll or other forms. The factors explaining inter-specific differences in leaflevel data potentially drive canopy-level differences. The question is complicated by the existence of gradients of N allocation within canopies. Different studies have found a tradeoff between N allocation to Rubisco and chlorophyll according to light availability, but such allocation pattern has often been shown to be sub-optimal.

  .P., S. Urbanski, D. Bremer, S.C. Wofsy, T. Meyers, S. Gower, and M. Gregory (2003) A cross-biome comparison of daily light use efficiency for gross primary production,

  , The carbon sink strength of forests in Europe: Results of the EUROFLUX Network. In Biospheric Exchanges of Carbon Water and Energy of European Forests (Ed R Valentini), Ecological Studies, 163, pp 225-232, Springer Verlag. Valentini, R., P. De Angelis, G. Matteucci, R. Monaco, S. Dore, and G.E. Scarascia Mugnozza (1996), Seasonal net carbon dioxide exchange of a Beech forest with the atmosphere, Global Change Biol., 2, 199-207. Vourlitis, G.L., and W.C. Oechel (1999), Eddy covariance measurements of CO2 and energy fluxes of an Alaskan tussock tundra ecosystem, Ecology, 80, 686-701. Wallin, G., S. Linder, A. Lindroth, M. Räntfors, S. Flemberg, and A. Grelle (2001), Carbon dioxide exchange in Norway spruce at the shoot, tree and ecosystem scale,

Figure 1 :

 1 Figure 1: a) Histogram of optimum daily Light-Use Efficiencies (LUE) for the 42 sites with

Figure 2 :

 2 Figure 2: b) Same as 2 a), but for canopy normalized photosynthesis (GEE*,µmol s -1 m -2 ) for

Figure 3 :

 3 Figure 3: a) Optimum daily LUE versus leaf nitrogen content N, mass basis.

Figure 3 :

 3 Figure 3: b) Same as a, but for canopy normalized photosynthesis (GEE*).

Figure 4 :

 4 Figure 4: Leaf nitrogen N versus annual mean temperature.

Figure 5 :

 5 Figure 5: a) Optimum daily LUE versus linear combination of annual mean temperature and

Figure 5

 5 Figure 5: b) GEE* versus linear combination of annual mean temperature and leaf nitrogen N.

Figure 6 :

 6 Figure6: a) LUE versus[START_REF] Green | Foliar morphology and canopy nitrogen as predictors of light-use-efficiency in terrestrial vegetation[END_REF] canopy index I G.

Figure 6 :

 6 Figure 6: b) Normalized canopy photosynthesis (GEE*) versus Green et al. [2003] canopy

Figure 7 b

 7 Figure 7 b: Same as 7 a, but for GEE*.

Figure 8 :

 8 Figure 8: Canopy fPAR versus leaf nitrogen N.

Figure 9 :

 9 Figure 9: a) Optimum daily LUE as a function of latitude for |lat| >45° .

Figure

  Figure 9 b) GEE* as a function of latitude for |lat| >45°.

a

  data from FLUXNET web site, b data from BOREAS CD (revised 2004), c data from CARBODATA CD, d data from EUROSIB project database.* values from[START_REF] Lindroth | Long-term measurements of boreal forest carbon balance reveal large temperature sensitivity[END_REF]

  is to state that, for different canopies, daily GEE is proportional to GEEmax, the maximum rate of canopy photosynthesis. Thus LUE defined as the ratio of daily GEE to daily absorbed PAR writes:

			LUE	=	PAR GEE I fPAR .	∝	PAR max I . fPAR GEE	Eq. 11
	Note that the use of GEE* as a proxy for LUE is based on the same assumption (GEE* being
	close to eq 11 right-hand-side). Assuming the maximum canopy photosynthesis rate is
	proportional to the integral of leaf level maximum photosynthesis rate (on a surface
	basis,	max s A	), we have
			GEE max	∝	LAI ∫	s A max	dl .

Table 1

 1

	References	Name		Species or canopy type PFT	Lat	MAT °C	LAI -	FPAR -	N (%)	LUE mol/mol	GEE* µmolm -2 s -1
	Harazono et al. Oechel et al 2000 Vourlitis and Oechel Wallin et al. 2001 Markkanen et al 2001 Lloyd et al. 2002 Lohila et al. 2004 Lindroth et al 1998 Milyukova et al. 2002 Goulden et al. 1997 McCaughey et al 1997 Moore et al., 2000 Pilegaard et al. Yang et al 1999 Jarvis et al. 1997 Baldocchi et al 1997 Dolman et al. 2002 Kowalski et al. 2000 Grünwald and Bernhofer 2007 Tharandt Barrow a Upad a Happy valley Flakaliden c Hyytiala c Zotino d Joikonnen Barley a Norunda c* Fyodorovskoye d BN-OBS b BN-YJP b BN-OJP b Soroe c BS-OA b BS-OBS b BS-OJP b Loobos c Braschaat c c Aubinet et al. 2001 Vielsalm c Klemm and Mangold 2001 Weidenbrunnen c Granier et al 2000 Hesse c Davis et al. 2003 Park Fall a Hollinger et al. Howland a Berbigier et al 2001 Landes c Law et al 2001 Metolius-old a Nakai et al 2003 Japan Forest a Valentini et al 1996 Collelongo c Wofsy et al. 1993 Harvard forest a Reichstein et al. 2002 Castelporziano c Turner et al 2003 Bondville a Turner et al 2003 Bondville a Monson et al 2002 Niwot a Goldstein et al. Blodgett a Suyker & Verma 2001 Shidler a Hanan et al. 2002 Ponca City a Wilson and Baldocchi 2000 Walker Branch a Katul et al 2001 Duke a Meyers 2001 Little Washita a Stylinski et al. 2002 Sky Oaks-young a Stylinski et al. 2002 Sky Oaks-old a Campbell et al. Rice	wet sedge tundra wet sedge tundra moist tussock tundra Norway Spruce Scots Pines Scots pine Barley Spruce Pine Norway Spruce old black Spruce Young jack Pine old jack Pine Beech forest old Aspen old black Spruce old jack Pine Scots pine Scots Pine Norway spruce fir spruce pine beech Norway spruce Beech maple aspen pine Spruce dominated maritime pine ponderosa pine Birch Oak Beech Oak Maple Hemlock Quercus ilex Corn Soybean Fir Spruce Pine ponderosa Pine Tallgrass prairie wheat Oak maple Pine grassland chaparral chaparral Rice	▲ ▲ ▲ + ▲ ▲ ▲ ▲ ▲ ○ ○ ▲ ▲ ▲ ◆ ▲ ◆ ▲ ○ ◆ ▲ ▲ ▲ ○ ○ ○ • * + ▲ ▲ * + ○ ▲ * • • +	70.3 70.3 69.0 64.1 61.8 61.0 60.9 60.0 56.0 56.0 56.0 56.0 55.5 54.0 54.0 54.0 52.2 51.3 50.3 50.3 50.0 48.0 45.9 45.2 44.7 44.7 43.0 42.0 42.0 42.0 40.0 40.0 40.0 39.0 37.0 37.0 36.0 36.0 15.5 -12.4 1.5 -8.2 1.5 -11.7 1.5 1.0 2.2 3.2 4.0 -1.5 -3.9 5 5.5 4.0 3.8 4.3 -2.9 --2.9 --2.9 -8.1 4.8 1.0 -1.0 -1.0 -10.3 3.0 10.2 3.1 8.0 6.0 7.4 4.5 5.8 5.3 9.2 5.0 6.6 5.0 6.7 5.3 12.5 3.1 7.6 2.1 6.5 4.5 7.0 4.5 8.5 3.4 15.3 3.5 11.2 5.5 11.2 6.7 4.0 4.2 10.4 3.1 14.7 2.8 15.3 5.0 14.5 6.0 5.2 35.0 16.0 2.5 33.0 12.2 1.1 33.0 12.2 3.0 29.2 20.0 5.5	0.64 0.64 0.63 0.73 1.15 0.0184 -0.0157 -0.0065 -0.0126 0.86 1.20 0.0190 0.70 0.99 0.0129 0.91 3.62 0.026 0.86 -0.0099 0.88 -0.0190 0.86 0.66 0.0128 0.65 0.93 0.0114 0.76 0.90 0.0068 0.90 -0.0234 0.90 2.50 0.0189 0.85 0.70 0.0131 0.78 1.00 0.0106 0.78 1.81 0.0232 0.80 -0.0170 0.92 -0.0276 0.89 -0.0224 0.91 -0.0157 0.90 2.50 0.0258 0.89 -0.0168 0.90 1.1 0.0194 0.85 1.10 0.0176 0.63 1.35 0.0171 0.86 2.25 0.0217 0.86 2.40 0.0238 0.83 1.87 0.0234 0.80 1.50 0.0212 0.90 2.30 0.0326 0.92 -0.0162 0.87 0.99 0.0126 0.76 1.25 0.0127 0.73 -0.0266 0.88 -0.0227 0.92 1.75 0.0207 0.88 1.08 0.0146 0.69 -0.0163 0.41 0.80 0.0128 0.74 -0.0092 0.89 3.50 0.0386	21.1 11.7 15.2 25.7 25.5 17.7 35.2 14.0 24.4 15.6 18.4 15.0 35.3 30.3 19.5 21.8 32.1 26.7 35.1 28.2 23.1 34.8 23.6 25.7 27.2 25.2 37.4 37.9 32.2 28.5 57.8 26.6 18.5 25.0 45.5 35.7 33.6 27.4 28.4 23.5 15.5 67.6
	Lloyd 2001	Ny Alesund,		Tundra, semi-desert		80.0	-6.0	0.3	0.23	-	-	12.7
	Nordstoem et al 2001	Zackenberg fen	fen		74.5	-9.5	1.1	0.56	-	-	27.8
	Soegaard et al 2000	Zackenberg heath	Heath tundra		74.5	-9.5	0.2	0.14	-	-	26.8
	Laurila et al 2001	Zackenberg willow	Willow tundra		74.5	-9.5	0.5	0.31	-	-	26.0
	Williams et al. 2000	AFS 1		coastal wet tundra		70.5	-8.2	-	0.57 1.14	-	6.7
	Williams et al. 2000	AFS 13		Alder tussock tundra		70.5	-8.2	-	0.55	1.0	-	12.7
	Williams et al. 2000	AFS 14		acidic tussock tundra		70.5	-8.2	-	0.50	1.0	-	12.5
	Williams et al. 2000	AFS 3		non-acidic tundra		70.5	-8.2	-	0.75 0.87	-	6.2
	Williams et al. 2000	AFS 4		acidic tussock tundra		70.5	-8.2	-	0.83 0.78	-	8.0
	Williams et al. 2000	AFS 6		acidic tussock tundra		70.5	-8.2	-	0.59	1.0	-	16.5
	Williams et al. 2000	AFS 9		Shrub tundra		70.5	-8.2	-	0.52 2.05	-	30.8
	Williams et al. 2000	AFS heath		Heath tundra		70.5	-8.2	-	0.65 0.94	-	6.6
	Laurila et al 2001	Kevo		fen		69.8	-2.0	0.7	0.38	-	-	24.8
	Aurela et al. 2001 Laurila et al 2001 Aradóttir et al. 1997 Röser et al 2002	Petsikko Kaamanen Gunnarsholt Zotino-birch	c d	mountain Birch fen black Cottonwood birch	○ ○ ○	69.5 69.1 63.8 61.0	-2.0 -2.0 5.0 -1.5	2.8 0.7 1.4 2.6	0.83 0.38 0.57 0.75 2.14 ---	----	22.5 24.5 37.3 37.5

Table 1 :

 1 List of sites and references and derived information. The first 42 rows contain Type I data (see text) sorted by latitude, the 35 rows below are for type II data. PFT is for Plant Functional Type, Lat is latitude, MAT is mean annual temperature, LAI is Leaf Area Index, fPAR is the fraction of absorbed PAR, N is for leaf nitrogen on a mass basis, LUE is optimum daily light-use efficiency and GEE* is normalized canopy photosynthesis.

Table 2

 2

	Plant types	LUE	CV	GEE*	CV
	Deciduous Broadleaved	.0225	10 (n=7)	34.5	14 (n=13)
	Evergreen Broadleaved	.0144	43 (n=3)	22.5	24 (n=4)
	Mixed forests	.0187	17 (n=3)	27.2	10 (n=4)
	Evergreen Needleleaved	.0155	32 (n=19)	23.6	24 (n=25)
	Deciduous Needleleaved	-	-	21.7	71 (n=2)
	Tundra, Wetlands	.0116	41 (n=3)	18.0	48 (n=19)
	C3 Grasses and Crops	.0270	42 (n=3)	35.8	51 (n=5)
	C4 Grasses and Crops	.0245	28 (n=4)	43.6	27 (n=5)
	All Grasses and Crops	.0256	32 (n=7)	39.7	38 (n=10)
	All plant types	.0182	37(n=42)	26.2	42 (n=77)

Table 2 :

 2 Optimum daily Light-Use Efficiency (LUE, mol/mol) and canopy normalized photosynthesis (GEE*, µmol m -2 s -1 ): average, coefficient of variation (CV) and number of data, grouped by Plant Functional Types.

Acknowledgements

This work would not have been possible without the considerable efforts of a large group of people and many funding agencies (see acknowledgements for each site in the corresponding reference). Each flux measurements site usually requires several scientists and students to be run on the long term. Sites in remote places, in the arctic for instance, are among the most challenging places to run flux systems. The authors would like to thank all these people for sharing their data under "fair-use policy", as well as providing unpublished information Special thanks to Dennis Baldocchi, who provided energy, ideas and enthusiasm to the FLUXNET community.


*No country => laboratory experiment Culicidae species Country* Culicidae species

Keywords: Argentina, India, Nigeria, Sweden, Thailand, Zimbabwe quinquefasciatus France, India, Italy, Tanzania, Thailand Anopheles annularis India Culex sp. Anopheles arabiensis, Kenya, Tanzania Anopheles labranchiae Toxorhynchites splendens Anopheles lesteri Verrallina carmenti Anopheles marajoara

• 34 animal species: on land (cow, poultry, etc.) and aquatic (carp, shrimp, etc.)

• Human being

• 65 crop species Distribution of the 46300 uses per health

Uses /health and species protected by using a plant

  Knowledge location(Knomana, Nov. 2021)

	In the World Plant name	Animal health	Environnemental health	Human health	Public health	Plant health	Total
	Azadirachta indica	248	39	42	20	710	1059
	Annona senegalensis	117		913	4	17	1051
	Securidaca longipedunculata	1		874		20	895
	Ximenia americana Detarium microcarpum	61 57		810 770	871 West African countries 827
	Uvariodendron calophyllum			736			736
	Parkia biglobosa	57		586		3	646
	Bauhinia reticulata	1		620			621
	Adansonia digitata	115		500			615
	Vitellaria paradoxa	59		482			541
	Ocimum gratissimum	80	5	130	23	278	516
	Sclerocarya birrea	1		472			473
	Tamarindus indica	64		370		6	440
	Gardenia erubescens			432			432
	Carica papaya	112		184		130	426
	Guiera senegalensis	6		370		4	380
	Entada africana			374			374
	Ricinodendron heudelotii			350			350
	Khaya senegalensis	64		270	4	8	346
	Allium sativum	133	2	2	5	194	336
	Senna alata	206		132			338
	Vernonia amygdalina	281				54	335
	Zanthoxylum beecheyanum				1		1
	Zanthoxylum fagara				1		1
	Zanthoxylum oxyphyllum				1		1
	Ziziphus abyssinica	1					1

ICPP 3, Yamoussoukro, Cote d'Ivoire, July 25-29, 2022 … ICPP 3, Yamoussoukro, Cote d'Ivoire, July 25-29, 2022

The Project Knomana (2017-2018, GLOFOODS Meta-program, funding Cirad)

Partners

Burkina Faso -Univ. Ouaga

Plant name East African countries

West African countries

North African countries Plant name East African countries

West African countries

North African countries July 25-29, 2022 215 targeted species, among which 28 within at least 2 regions of Africa

Targeted species East African countries

West African countries

North African countries


Nick Marriner
email: nick.marriner@univ-fcomte.fr

David Kaniewski

Majid Pourkerman

Benoît Devillers

Anthropocene tipping point reverses long-term Holocene cooling of the Mediterranean Sea: a meta-analysis of the basin's Sea Surface Temperature records

Keywords: Sea Surface Temperatures, Holocene, Mediterranean, Anthropocene

The Mediterranean is facing numerous socio-environmental challenges linked to global change, frequently compounded by rapid population growth. Within this framework, regional-scale Holocene temperature reconstructions are key to placing industrial-era warming into the perspective of natural climatic variability. Here, we present a new Mediterranean Sea Surface Temperature (SST) stack based on 54 records for the last 11,750 years, to evaluate millennial-to-centennial-scale climate variability and to contextualize present and future changes. The Holocene thermal maximum is reconstructed 9,400-3,000 years ago and is estimated to have been an average of 0.47 ± 0.2°C warmer than the mean for 1900-1960, followed by the onset of long-term cooling beginning around 3000 years BP. The coolest temperatures during the last ten millennia are recorded during the Little Ice Age. We find that Mediterranean Sea temperatures have risen from near the coldest to warmest 2 levels of the Holocene within the past century, with decadal warming rates that are ~16 times greater than the closest interglacial analogue. The basin's SSTs have consistently exceeded the full distribution of Holocene warmth since 2011.

Introduction

The Mediterranean comprises one of the world's most prominent and vulnerable climate change "hotspots" due to its rapid response to atmospheric forcings [START_REF] Tuel | Climate pacing of millennial sea-level change variability in the central and western Mediterranean[END_REF]; current change and future scenarios consistently highlight the substantial and growing risks that the basin will face in coming decades [START_REF] Guiot | Climate change: The 2015 Paris Agreement thresholds and Mediterranean basin ecosystems[END_REF][START_REF] Tramblay | Challenges for drought assessment in the Mediterranean region under future climate scenarios[END_REF]. Within this context, Sea Surface Temperature (SST) is an important component of the Mediterranean climate system [START_REF] Macias | Understanding the causes of recent warming in Mediterranean waters. How much could be attributed to climate change[END_REF] and knowledge of past, present and future trends in its SSTs is crucial for future climate scenarios and to improve understanding of thermal and dynamical interactions between the Mediterranean Sea, the atmosphere [START_REF] Alexander | Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans[END_REF][START_REF] Pastor | Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982-2016)[END_REF] and steric-driven sea-level changes (Vacchi et al., 2021).

Climate change constitutes one of multiple drivers affecting Mediterranean anthroposcapes and human health [START_REF] Cramer | Climate change and interconnected risks to sustainable development in the Mediterranean[END_REF], either directly (e.g. through temperature extremes, drought and water security, storms) or indirectly (modifications in food provision and quality, air pollution, creation of urban heat islands). Future warming in the Mediterranean region is forecasted to exceed global rates by 25 %, with summer warming outpacing the global mean by 40 % [START_REF] Lionello | The relation between climate change in the Mediterranean region and global warming[END_REF]. In 2019, Mediterranean countries hosted >520 million people (United Nations, 2019) and population exposure to heat is increasing due to climate change and rapid rates of urbanization. For example, from 2000-2019, >65,000 people died due to Mediterranean heatwaves (EM-DAT, 2020). This figure is particularly alarming because the frequency of Mediterranean heatwave days is projected to evolve from an average of about two days per summer for the period to around 13 days for 2021-2050 and 40 days for 2071-2100 [START_REF] Fischer | Consistent geographical patterns of changes in highimpact European heatwaves[END_REF]. The Mediterranean coastline, in particular, is extremely sensitive to heatwaves and strong changes in ambient temperature and sea temperatures, notably the basin's densely populated urban centers.

Mediterranean SST data covering the Holocene can furnish crucial insight into natural decadal to centennial time-scale changes and put recent climate change into a longer-term perspective [START_REF] Mcgregor | Robust global ocean cooling trend for the pre-industrial Common Era[END_REF][START_REF] Abram | Early onset of industrial-era warming across the oceans and continents[END_REF]. This is particularly important because semi-enclosed basins like the Mediterranean Sea are poorly resolved by the ~1° resolution of many global climate models. The Mediterranean's rich archaeological record also emphasizes the importance of recurring socio-cultural changes/transitions and demonstrates that the region's past societies faced climate-related challenges at various points during the Holocene [START_REF] Kaniewski | Climate change and water management in the Biblical city of Dan[END_REF]. Here, we place today's regional SST changes into the broader temporal context of the Holocene using a consistent framework based on spatially averaged estimates of Mediterranean SSTs. Mediterranean Sea temperatures are now ~1.4°C higher than at the turn of the 20 th century, compared with an increase of ~1.2°C worldwide (NOAA, 2020). Understanding future trends in SSTs is, therefore, of paramount importance for Mediterranean climate adaptation policies, including public health, land-use and the conservation of biodiversity.

Methods

The SST database gathers 54 records from 44 locations in the Mediterranean Sea (see Table 1). Figure 1 displays the spatial distribution of the archives used in the database. The records derive from marine cores and are based on the following proxy methods (Figures S1 andS2): alkenones (n=34), long-chain diols (n=2), Mg/Ca (n=11), TEX86 (n=5), δ18Ow foraminifera (n=1) and Ri OH-GDGTs (n=1).

They range in temporal length from 0 to 244,350 years BP, but only the Holocene section of each record was retained for the database. Details regarding the collection, analysis and interpretation of individual records are provided in the original publications. A list of sites in the database, including basic metadata, is presented in Table 1. To facilitate comparison of information on a common chronological grid and for shared spectral resolutions, all records were annualized using nearest-neighbor interpolation. Each initial record was converted into SST anomalies (in °C) by subtracting the mean temperature from each sequence. This method also helps to reduce analytical biases associated with individual geochemical reconstruction techniques. The time series were subsequently summed and averaged to generate a spatially averaged Holocene record of Mediterranean SSTs. To quantify analytical uncertainties in the time series, we calculated Confidence Intervals, integrating the number of records at a given point in time, the reconstructed annual mean and the associated standard deviation (Figure S3). The mean CI for the entire time series is 0.23°C. We also incorporated the varying chronological uncertainties (2σ) associated with each of the 54 time series used to construct the SST stack, by means of the initial age models. To quantify the potential role of geographic location in influencing the overall signal we applied the following three-step analysis.

(1) The initial 54 records were divided into three separate entities East (21°E to 36°E), Central (10°E to 20.9999°E) and

West (-4.7496°W to 9.9999°E) Mediterranean. The SST data were transformed into percentages of the total signal. For each calculation, the mean was subtracted by geographic entity using these percentages.

(2) Next, we took the number of sequences by geographic entity (East, Central and West) and transformed these numbers into percentages of the total signal. For each calculation, we also subtracted the mean (per geographic entity) using these percentages.

(3) To weight the overall signal using the three different zones, we multiplied the percentages produced in step 1 with those from step 2. The level of signal variation is low, with the exception of the first ~200 years of the Holocene, giving us confidence in the overall quality of the SST stack as a robust palaeoclimate record (Figure S5).

A homogeneity test was applied to the Holocene record of SSTs to detect shifts in the long-term dynamics. Each discordant period was categorized and its average indicated by "mu" (Figure 2). A polynomial regression was then applied (Figure 2) and a sinusoidal regression was calculated to model potential periodicities in the time series. The link between insolation and Mediterranean SSTs was tested, ranking the insolation scores in ascending order and retaining the associated SST values.

Averages were calculated and are plotted with their standard deviations (Figure 3).

The same process was applied in testing the relationship between the Agassiz Ice Cap melt record and the SSTs (Figure 4A All of the raw data tables used to draft the figures are reported in the Excel file (Supplementary Material) accompanying the manuscript. the Mediterranean [START_REF] Peyron | Precipitation changes in the Mediterranean basin during the Holocene from terrestrial and marine pollen records: a model-data comparison[END_REF]. Our reconstruction demonstrates that cooling only became well expressed in the past three millennia, accompanied by a marked transition in seasonality that is clearly attested in terrestrial records from around the Mediterranean, concomitant with a gradual precession-driven decrease in summer insolation versus increases in winter values [START_REF] Peyron | Holocene seasonality changes in the central Mediterranean region reconstructed from the pollen sequences of Lake Accesa (Italy) and Tenaghi Philippon (Greece)[END_REF][START_REF] Kaniewski | Recent anthropogenic climate change exceeds the rate and magnitude of natural Holocene variability on the Balearic Islands[END_REF].

Results and discussion

Holocene evolution of Mediterranean SSTs

Superimposed on the insolation-based Holocene cooling of Mediterranean SSTs, the reconstruction manifests considerable variability linked to second-order forcing agents. Six periods of marked SST cooling are delineated at 8360-8100 B2K, 5400-5100 B2K, 4220-3900 B2K, 3130-2800 B2K, 1470-1250 B2K and 670-140 B2K

(Figures 4 and5). Our analysis shows that these rapid Mediterranean cooling phases mesh with internal oceanic shifts in the North Atlantic during which ice rafting and meltwater outburst events from Greenland and North America cooled and freshened ocean surface waters, slowing and modifying the geography of deep-water formation and disrupting the northerly transport of warm low-latitude waters by the thermohaline circulation [START_REF] Fisher | Recent melt rates of Canadian arctic ice caps are the highest in four millennia[END_REF]; Figure 4). These major changes in the Atlantic Meridional Overturning Circulation (AMOC) significantly affected SSTs in the Mediterranean with broader implications for the basin's palaeoclimates [START_REF] Fletcher | Mid-Holocene emergence of a low-frequency millennial oscillation in western Mediterranean climate: Implications for past dynamics of the North Atlantic atmospheric westerlies[END_REF]. Terrestrial-based records demonstrate that the cooling phases were frequently associated with reductions in precipitation and temperatures, and had wider social, cultural, and political consequences for some Mediterranean and West Asian societies at key points during the mid to late Holocene [START_REF] Weiss | Global megadrought, societal collapse and resilience at 4.2-3.9ka BP across the Mediterranean and west Asia[END_REF][START_REF] Sinha | Role of climate in the rise and fall of the neo-assyrian empire[END_REF]. For instance, the Late Bronze Age to Early Iron Age transition in the Eastern Mediterranean was characterized by a 30-40% reduction in precipitation which appears to have engendered food shortages in parts of the Levant, Egypt and Greece, and marked the onset of the Iron Age Dark Ages [START_REF] Kaniewski | Environmental Roots of the Late Bronze Age Crisis[END_REF][START_REF] Kaniewski | Climate change and water management in the Biblical city of Dan[END_REF]. The archaeological record documents significant cultural and socio-economic shifts in the Levant and Egypt during the Late Bronze Age, and several sites from Syria, Israel and the Nile Delta show a transition to cooler and more arid conditions at ~1200 BC [START_REF] Kaniewski | Cold and dry outbreaks in the eastern Mediterranean 3200 years ago[END_REF]. Egypt withdrew from its province in Canaan and experienced food shortages [START_REF] Shaw | The Oxford History of Ancient Egypt[END_REF]. In Greece, the population was reduced, and the world of organized state armies, kings, officials, and redistributive systems disappeared. During the Early Iron Age, Greece was divided into independent regions organized by kinship groups and the oikoi or households, the origins of the later poleis. This model appears to have provided citizens with greater opportunities for political participation and rendered these societies more resilient to climate change. Later, around 1500-1250 B2K, rapid climate cooling of 0.27°C coincided with the transformation of the eastern Roman empire into the Byzantine empire [START_REF] Büntgen | Cooling and societal change during the Late Antique Little Ice Age from 536 to around 660 AD[END_REF] and marked the end of a period of relatively stable climatic conditions during which Rome extended its hegemony over great swathes of Europe, the Near East and North Africa.

The lowest SSTs during the past 10,000 years occurred during the 1600 to 1800s. At this time, many Mediterranean glaciers attained their maximum Holocene extent and even existed as far south as the High Atlas of Morocco and the Sierra Nevada of southern Spain [START_REF] Hughes | Little Ice Age glaciers and climate in the Mediterranean mountains: a new analysis[END_REF]. All of the solar minima of the Little Ice Age are clearly captured by the Mediterranean SST stack. These cold episodes mesh with abrupt cooling events in the Bosphorus and colder winters in Istanbul (Turkey), both identified in historical data [START_REF] Yavuz | The Black Sea Flood Question: Changes in Coastline, Climate, and Human Settlement[END_REF]; Figure S7). Rapid temperature deviations are therefore important in fully comprehending the complex interplay between Mediterranean climate, landscapes and its societies.

Climate change is not only manifest in mean SST trends but also in shifts in the anatomy of past abrupt warmings and coolings recorded in the Mediterranean SST stack. We probed the duration and amplitude of cooling/warming transitions in the SST record, focusing on four warming phases and nine cooling episodes (Figure 6).

We note large variability in the transition duration from one event to the other. The These past warm periods are particularly relevant for modelling future changes in Mediterranean SSTs. Our reconstruction shows that Mediterranean SSTs have consistently exceeded the full distribution of Holocene warmth since 2011 (Figure 7E). If the rates of temperature increases observed over the past five decades were to remain constant for the rest of the twenty-first century, Mediterranean SSTs could attain anomalies of around 3.59 ± 0.08°C by 2100. This analysis suggests that Industrial-era Mediterranean SST warming rates are unprecedented at the scale of the Holocene, with Mediterranean SSTs projected to warm ~16 times faster than the closest analogue during the past ~11,000 years.

Conclusion

Our analysis provides further evidence that low carbon emissions are critical to mitigate contributions of Mediterranean SST increases to local and regional sea-level rise. Our multi-millennial assessment also clearly proves the need to assimilate pretwentieth-century data into comprehensive assessments of industrial-era warming. At the scale of the Mediterranean, societal and economic impacts are likely to propagate in a nonlinear manner as climate-related hazards reach tipping points beyond which the affected physiological, anthropogenic, or ecological systems function less effectively or break down altogether. Many of these effects are likely to become even stronger in the near future because of the Mediterranean's growing human population levels and economic activity, particularly in coastal areas. Our analysis demonstrates that there are no direct Holocene analogues to assess the resilience of human societies in the face of such rapid current warming. SSTs. The numbers correspond to the references in Table 1. Table 1: Basic metadata of the 54 records used in the Mediterranean SST compilation. B.

Supplementary figures

A B ]-3;-2] C ]-2;-1] D ]-1;0] E ]0;1] F ]1;2] G ]2;3] H ]
I N D U S T R I A L -E R A W A R M I N G M E D I E V A L C L I M A T E A N O M A L Y H O L O C E N E W A R M P E R I O D W O L F S P O R E R D A L T O N H O L O C E N E C L I M A T E O P T I M U M M A U N D E R 3 . 2 E V E N T 5 . 2 E V E N T 4 . 2 E V E N T 8 . 2 E V E

  Figure S4 plots the 2σ error average of all records used to construct the SST record for each time point. The data are plotted at a resolution of 50 years for clarity. Mediterranean SSTs for 1854-2019 were extracted from //icoads.noaa.gov; 55). All SST data are reported as average temperature anomalies. For data plotted on the B2K timescale, the year 2000 CE represents 0. Negative time values are post-2000 (e.g. 2010 = -10).

  -B). Warming and cooling phases during the Holocene (expressed in °C per decade; Figures 5-7) were calculated by selecting the highest and lowest values for each climate episode. Rates were defined by subtracting the high and low values and dividing by time. A linear model was applied to present-day values to project warming values up to 2100, based on each of the past Holocene scenarios (with the associated standard deviation; Figure 7E). A short-time Fourier transform was applied to the last two millennia (window = rectangle; Figure S7) to determine the sinusoidal frequency and the phase content of sections of the SST signal over time. Boxplots of Mediterranean SST variability during the Holocene (Figure S8) were plotted to highlight the extent of Global Warming since the 19 th century AD. Each chronological window was defined by a series of homogeneity tests.

Figure 2

 2 Figure2plots average SST anomalies on the B2K timescale. The record has successfully captured both first-and second-order pacemakers. Change-point analyses highlight three statistically significant periods. (1) The reconstruction shows a strong SST warming trend between 11,700 to 9400 B2K characterized by warming of ~2.8°C at an average rate of 0.12°C per century. The temperature anomalies range from -2.75 ± 0.59°C to 0.03 ± 0.26°C with a mean of -1.02 ± 0.39°C. This early Holocene period was characterized by a much greater range of variability than seen over later millennia. (2) Around 9400 years ago, the rates of SST change slowed rapidly. Between 9400 to 3000 B2K, temperatures plateaued in a phase of sustained warming with SST anomalies ranging from -0.01 ± 0.2°C to 0.58 ± 0.18°C, with an average anomaly of 0.32 ± 0.2°C. Maxima in the SST data occur at 4700-4950 B2K (average = 0.53 ± 0.23°C) and 7100-7200 B2K (average = 0.54 ± 0.19°C). (3) After

  longest warming transition was for the Holocene Warm Period with a warming of 0.43 ± 0.17°C over ~510 years. The longest cooling-phase transition was the 3.2 Event characterized by cooling of -0.38 ± 0.19°C over ~320 years which contrasts markedly with the short cooling-phase transition during the Dalton Minimum of the Little Ice Age with cooling of -0.45 ± 0.23°C in just ~45 years. The absence of a systematic pattern in the duration of SST transitions could result from internal climate variability superimposed on broader forcing mechanisms. Nonetheless, our data do suggest that warming and cooling phases have been particularly rapid and of high amplitude during the past 1000 years.Present and future SSTs trends and implicationsOur results demonstrate that the mean of Mediterranean SSTs for the decade 2010-2019 exceeds the warmest temperatures of the early to mid-Holocene (9000 to 3000 BP) by 0.31 ± 0.09°C. By contrast, the decadal SST mean of the early 20 th century(1900)(1901)(1902)(1903)(1904)(1905)(1906)(1907)(1908)(1909) was cooler than 86% of the Holocene distribution. Mediterranean SSTs have therefore risen from near the coldest to the warmest levels of the Holocene within the past century (Figures6 and 7), reversing the long-term cooling trend that began ~3000 years ago.During the past 46 years, Mediterranean SSTs have increased by ~1.54°C ± 0.09°C at an average warming rate of 0.33°C per decade. Prior to the ongoing Industrial-era warming, phases of rapid warming played out at 1070-705 B2K, 5350-4840 B2K and 8300-7150 B2K (Figures6 and 7), although, as we highlight above, the tempo of warming rates during these periods contrast markedly with the recent Industrial period. For instance, average warming rates were (i) 0.021°C per decade during the Medieval Climate Anomaly (0.36 ± 0.15°C in 172 years), (ii) as low as 0.008°C per decade during the Holocene Warm Period (0.43 ± 0.17°C in 510 years) and (iii) 0.015°C per decade during the Holocene Climate Optimum (0.5 ± 0.2°C in 330 years). To test to what extent current warming rate is unparalleled in relation to the last 11,750 years, we calculated temperature changes using moving 46-years windows for the entire sequence (FigureS9). The 46-year window corresponds to the present ongoing phase of rapid Industrial-era warming (1973 to 2019); our analysis confirms that the current rate of Mediterranean Sea warming is without precedent at the Holocene timescale.

Figure 1 :

 1 Figure 1: Location of the 54 records included in the Mediterranean database of

Figure 2 :

 2 Figure 2: Stack of Mediterranean Sea Surface Temperature (SST) anomalies for the Holocene. SST anomalies are the spatial average of the SST minus the mean of each individual time series in the database and are plotted on the B2K timescale. Change-point analysis was used to separate the time series into three periods: 11,750 to 9426 B2K (mu3 = -1.03), 9425 to 2951 B2K (mu2 = 0.32) and 2950 B2K to present (mu1 = 0.01). mu1-3 denote the averages for each statistically significant period. The light blue shading denotes the 68% Confidence Interval.

Figure 3 :

 3 Figure 3: A. Mediterranean SSTs for the past ~10,000 years. B. Mediterranean SSTs plotted against June-July-August (JJA) insolation (orange line and shading) and December-January-February (DJF) insolation (green line and shading). C. Correlation between grouped SSTs and insolation for JJA and DJF. These analyses underline the role of changing Holocene seasonality in driving SST changes.

Figure 4 :

 4 Figure 4: A. Mediterranean SST temperature anomalies (purple lines) plotted against Agassiz and Renland average temperature change record (in °C, green lines, record from Fisher et al., 2012) and Agassiz melt record (% melt, light-blue lines, record from Fisher et al., 2012). B. Grouped Mediterranean SST temperature anomalies versus grouped melting rates from the Agassiz record. The analysis demonstrates that increased meltwater inputs into the North Atlantic are correlated with cooling of Mediterranean SSTs.

Figure 5 :

 5 Figure 5: Industrial-era warming compared and contrasted with other Holocene phases of rapid cooling (blue) or warming (orange).

Figure 6 :

 6 Figure 6: A. Average annual rates of warming/cooling phases (in °C) for key Holocene periods. The analysis demonstrates that warming and cooling phases have been particularly rapid and of high amplitude during the past 1000 years. There are no direct Holocene analogues for the current rapid warming of Mediterranean SSTs. B. Onset and endpoints (dots) of key Holocene climate transitions (oblique lines) in Mediterranean SSTs. The vertical amplitude between the onset and the end of each

Figure 7 :

 7 Figure 7: A-D. Industrial-era SST warming rates compared to other periods of rapid Holocene warming, including the Medieval Climate Anomaly (B), the Holocene Warm Period (C) and the Holocene Climate Optimum (D). E. Modelled Mediterranean SSTs up to 2100 based on current and, for comparison, Holocene warming rates. The data suggest that decadal warming rates are ~16 times greater than the closest Holocene analogue and that Mediterranean SSTs have persistently exceeded the full distribution of Holocene warmth since 2011.

Figure S1 :

 S1 Figure S1: Location of the 54 records in the Mediterranean database, grouped by proxy type.

Figure S2 :

 S2 Figure S2: A. Temporal length and proxy type of the 54 records used to construct the Mediterranean SST stack. B. Number of records used in the Mediterranean SST stack through time.

Figure S3 :

 S3 Figure S3: A. Stack of Mediterranean Sea Surface Temperature (SST) anomalies for the Holocene, plotted on the B2K timescale. The light blue shading denotes the 68% Confidence Interval. The red line is a 200-year resampling, based on the coarsest chronological resolution of all the 2σ error margins used to construct the SST stack. The yellow line represents the 68% Confidence Interval associated with the 200-year time step. B. Same as (A) but excluding the annual resolution and associated Confidence Intervals.

Figure S4 :

 S4 Figure S4: Stack of Mediterranean Sea Surface Temperatures (SST) for the Holocene, plotted at 50-year intervals. The light blue error bars denote the 68% Confidence Intervals. The green error bars denote the average 2σ error of all records

Figure S5 :

 S5 Figure S5: Comparison between SST minus mean average (cold colours) and SST spatial average (warm colours).

Figure S6 :

 S6 Figure S6: Potential role of geographic location in influencing the overall SST signal for the Holocene. A. Each SST record (West, Central and East Mediterranean) is presented as deviations from the mean. B. The number of sequences used per geographic location is shown as deviations from the mean. C. The weight of each geographic location in the final SST curve is subtracted (using A. x B.) and the resulting three curves (West, Central and East Mediterranean) are compared and contrasted with each other.

Figure S7 :

 S7 Figure S7: A. Mediterranean SST anomalies during the past 2000 years and broad climatic periods. The stars denote freezing events in the Bosphorus and cold winters in Istanbul, based on historical records (Yavuz et al., 2007). B. Short-time Fourier transform of the data highlighting the periods of marked climatic variability.

Figure S8 :

 S8 Figure S8: Boxplots of Mediterranean SST variability during the Holocene, highlighting the magnitude and rate of ongoing industrial-era warming. Recent warming contrasts markedly with long-term Holocene cooling of the Mediterranean Sea.

Figure S9 :

 S9 Figure S9: Holocene changes in Mediterranean SST°C using moving 46-years windows for the complete sequence. The 46-year window corresponds to the current phase of rapid Industrial-era warming (1973 to 2019). The first value covers the temperature rise between 1973 and 2019, the second between 1972 and 2018, the third between 1971 and 2017 and so forth for the entire sequence. The associated confidence interval comprises the average of the confidence intervals for each 46-yr time-span.

Figure S1 :

 S1 Figure6

Figure S4 :Figure S5 :

 S4S5 Figure S4: Stack of Mediterranean Sea Surface Temperatures (SST) for the Holocene, plotted at 50-year intervals. The light blue error bars denote the 68% Confidence Intervals. The green error bars denote the average 2σ error of all records used to reconstruct the SST record at time point x. The grey inset is a boxplot of the 2σ chronological errors in the initial 54 records (in years) used to construct the SST stack.

Acknowledgments

This work was supported by MITI CNRS Evénements rares (2022-2023) project AQUASANMARCO. We wish to thank an anonymous reviewer for constructive suggestions that helped to significantly improve earlier versions of the manuscript.

Figure and table captions

Number of records SST record

A. A.

B.

SST anomaly in

°C

B.

-