ORCHIDEE (Organizing Carbon and Hydrology in Dynamic EcosystEm) is a dynamic global vegetation model (Krinner et al., 2005; Maignan et al., 2011) that consists of two main parts: the carbon module STOMATE (Saclay-Toulouse-Orsay Model for the Analysis of Terrestrial Ecosystems) and the surface vegetation atmosphere transfer scheme SECHIBA (Schématisation des échanges hydriques à l'interface biosphere-atmosphère, in English: mapping of hydrological exchange at the biosphere/atmosphere interface).

STOMATE describes processes such as photosynthesis, carbon allocation, litter decomposition, soil carbon dynamics, maintenance and growth respiration. A completely prognostic plant phenology including leaf critical age, maximum LAI (leaf area index), senescence, plant tissue allocation, and leaf photosynthetic efficiency, which varies depending on the leaf age, is also taken into account. The soil water budget and the exchanges of energy and water between the atmosphere and the biosphere are calculated in SECHIBA (Krinner et al., 2005). The Choisnel hydrological scheme is used with a 2 m soil column represented by two moisture layers: a superficial layer and a deep layer (Ducoudré et al., 1993). The biogenic emission scheme, of which we present a new version, is embedded in this module (Lathière et al., 2006).

In ORCHIDEE, ecosystems are represented by 13 plant functional types (PFTs, listed in Table 1). Each PFT is representative of a specific set of plant species that are grouped according to plant physiognomy (tree or grass), leaf shape (needleleaf or broadleaf), phenology (evergreen, summergreen or raingreen) and photosynthesis type for crops and grasses (C3 or C4). The main biophysical and biogeochemical processes for each PFT are described in Krinner et al. (2005) and in Maignan et al. (2011). For our study, the global vegetation distribution is prescribed for all runs using appropriate forcings, as described in Sect. 2.4.

The BVOC module is extensively updated, considering recent findings regarding emission schemes and field measurements. The new BVOC emission scheme is a development of the module implemented in ORCHIDEE by Lathière et al. (2006), and is based on the model presented by Guenther et al. (2012a). It now provides a multi-layer canopy model, where radiation is calculated following the scheme proposed by Spitters (1986) and Spitters et al. (1986) and the one already used in ORCHIDEE for the calculation of photosynthesis. The canopy is considered to be split vertically into several LAI layers, the number of which (up to 17) depends on the LAI value. Emissions are calculated for each layer through consideration of the sunlit and shaded leaf fractions and the light extinction and light diffusion through canopy. In a second step they are vertically summed, providing a single value for each PFT and grid point.

The emission flux F of a specific biogenic compound c, for a given PFT i at a LAI layer l is calculated following Eq. (1): Fc,i(l)=LAIi(l)⋅SLWi⋅EFc,i⋅CTLc(l)⋅Lc, where LAIi(l) is the leaf area index expressed in m2 m-2 at a particular LAI layer and PFT, SLWi is the specific PFT leaf weight in g m-2, EFc,i is the basal emissions at the leaf level for an individual compound and PFT at standard conditions of temperature (T= 303.15 K) and photosynthetically active radiation (PAR = 1000 µmol m-2 s-1), expressed in µgC g-1 h-1. CTLc is the emission activity factor, depending on the emitted compounds, which takes the deviation from the standard conditions related to temperature and PAR into account, and it is extensively described in the second part of the present paragraph. Lc is the activity factor simulating the impact of leaf age on emissions, and is considered for isoprene and methanol. The total emission per grid cell is obtained by summing Fc,i(l) over the layer l and averaging the emission contribution of each individual PFT, weighted by PFT fractional land coverage. Further details on the original version of the emission module are given in Lathière et al. (2006).

Table 2 summarizes the principal modifications compared to the previous module version. In particular, we (i) added new emitted compounds, (ii) estimated the emissions using a multi-layer radiation scheme that calculates diffuse and direct components of light at different LAI levels, (iii) inserted a dependence on light for almost all compounds and (iv) updated the EFs.

Eight speciated monoterpenes (α-pinene, β-pinene, limonene, myrcene, sabinene, camphene 3-carene, t-β-ocimene) and bulk sesquiterpenes are now included in the updated ORCHIDEE emission module. We chose these compounds because measurements have shown that they are emitted from vegetation in the greatest abundance and because of their importance in atmospheric chemistry, in particular regarding secondary organic aerosol formation.

We mentioned that the emission module has also been modified to include a light dependency for almost all compounds emitted. In the previous module version, indeed, isoprene was the only compound dependent on both light and temperature, while the others were only dependent on temperature. As detailed in Sect. 1, most recent field campaigns highlight, for a large number of plants, the dependency of monoterpenes, sesquiterpenes and oxygenated BVOC emissions on radiation as well. Adopting a detailed parameterization is not yet possible because of the lack of data at global scale. Therefore, in the new emission module we consider the approach described in Guenther et al. (2012a), even if it is rather oversimplified. BVOCs are now modelled to consider both light-dependent and light-independent emission processes, and the response to temperature and light (CTL) is calculated for individual compounds at each LAI layer (l): CTLcl=1-LDFc⋅CTLIc+LDFc⋅CTLD⋅CLl.

LDFc is the light-dependent fraction of the emission, specified for each compound emitted (Table 2). To choose the LDF value for monoterpenes, we rely on Dindorf et al. (2006), Holzke et al. (2006), Guenther et al. (2012a) and Šimpraga et al. (2013). Other LDF values were based on Guenther et al. (2012a). CTLIc is the temperature-dependent emission response that is not light-dependent and depends on individual compounds. CTLD and CL are the temperature and light responses for the light-dependent fraction, respectively, and are the same functions as in the previous version of the emissions module. For all details we refer to Guenther et al. (1995) and Lathière et al. (2006). CTLI is equal to CTLI=expβT-T0, where β is the empirical coefficient of the exponential temperature response, and it is now defined as in Guenther et al. (2012a) (Table 2).

The Model of Emissions of Gases and Aerosols from Nature (MEGAN) is a modelling system for the estimation of emission fluxes of biogenic organic compounds from terrestrial vegetation. The basis of the model is a simple mechanistic approach established by Guenther et al. (1991, 1993, 1995), which links emissions with the main environmental driving factors such as solar radiation and leaf temperature. Further development of the algorithm led to the inclusion of leaf ageing, soil moisture impact on the emissions and effects of the loss and production of compounds within a forest canopy (Guenther et al., 2006). The current version of the model, MEGANv2.1, also includes a full canopy module. The model calculates light and temperature conditions inside a canopy by evaluating the energy balance on five canopy levels. Additionally, emissions of each compound are considered to have light-dependent and light-independent components defined by the light-dependent fraction (LDF). For a detailed description of emission equations and parameterization we refer to Sect. 2 in Sindelarova et al. (2014) and Guenther et al. (2012a).

MEGANv2.1 is available either as a stand-alone version or embedded in the Community Land Model version 4 (CLM4) (Lawrence et al., 2011) of the Community Earth System Model (CESM) (Gent et al., 2011). When operating in the stand-alone version, the driving variables, such as meteorological input data, vegetation description and leaf area index, need to be provided by the user. When running MEGAN inside CLM4, the input data can be provided by the CESM atmospheric and land surface models online at each time step. In this work, we use the stand-alone model version of MEGANv2.1, hereafter simply referred to as MEGAN.

MEGAN estimates emissions of 19 chemical compound classes, which are then redistributed into 147 final output model species, such as isoprene, monoterpene and sesquiterpene species, methanol, carbon monoxide, alkanes, alkenes, aldehydes, ketones, acids and other oxygenated VOCs. Although the input parameters, such as vegetation description and emission potentials, can be defined by the user, MEGAN comes with a default definition of PFTs and the emission factors assigned to them. The vegetation distribution is described with fractional coverage of 16 PFT classes, consistent with those of the CLM4 model (Lawrence and Chase, 2007). The emission potential of each modelled species is calculated based on the PFT coverage and emission factor of each PFT category. For several VOC compounds, emission potentials can be defined in the form of input maps. Emission potential maps with global coverage and high spatial resolution for isoprene, main monoterpene species and MBO are provided together with the MEGAN code.

MEGAN is widely applied for the estimation of biogenic VOC emissions at both regional and global scales (e.g. Guenther et al., 2006, 2012a; Müller et al., 2008; Millet et al., 2010; Sindelarova et al., 2014; Situ et al., 2014; Stavrakou et al., 2014), and serves for the evaluation of the impact of BVOCs on atmospheric chemistry by coupling the model with chemistry transport models (e.g. Heald et al., 2008; Pfister et al., 2008; Emmons et al., 2010; Fu and Liao, 2012; Tilmes et al., 2015).

The objectives of the group of simulations are (i) to provide global estimates of BVOC emissions for a large variety of compounds over the 2000–2009 period, (ii) to investigate the differences and similarities between the ORCHIDEE and MEGAN results regarding the spatial, inter-annual and inter-seasonal variability of emissions and (iii) to analyse the response of BVOC emissions to the variation of some key variables and parameters such as the LAI and LDF. Table 5 summarizes the simulations performed in this study and their principal characteristics.

We carried out a total of five sets of runs:

two simulations for the 2000–2009 period performed by both models using each model's standard configuration, but with the same climatology (ORC_CRU and MEG_CRU);

In ORCHIDEE, the activity factor (Lc) is kept as in Lathière et al. (2006), considering four leaf age classes (new, young, mature and old leaves). For methanol, Lc is equal to 1 for new and young leaves and equal to 0.5 for mature and old leaves, while for isoprene, Lc is equal to 0.5 for new and old leaves and equal to 1.5 for young and mature leaves. In MEGAN, the Lc values are taken from Table 4 in Guenther et al. (2012a); in particular, for isoprene, Lc is equal to 0.05, 0.6, 1 and 0.9, and for methanol it is equal to 3.5, 3.0, 1.0 and 1.2 for the four leaf age classes. For both models, no soil moisture activity factor is taken into account. The annual CO2 concentration varies along the simulation from a value of 368 ppm in 2000 to 385 ppm in 2009. In ORCHIDEE, the variation of CO2 concentration can indirectly impact on the BVOC emission as it affects leaf growth, while in MEGAN, a CO2 inhibition factor on isoprene emission based on Heald et al. (2009) is activated. As the CO2 variation in this 10-year simulation is low, the inhibition effect is considered insignificant (Sindelarova et al., 2014) in this context. For ORCHIDEE, LDF and the β coefficient values are given in Table 2. For MEGAN, the values of LDF and β are those presented in Table 4 in Guenther et al. (2012a).

While starting from a similar approach, the ORCHIDEE and MEGAN emission modules differ significantly in their parameterization and variable description and calculation. We list the main differences below.

One of the principal differences in the two emission schemes is the approach on LAI. ORCHIDEE calculates the LAI at each model time step for each PFT and grid cell, taking a full plant phenology scheme and the environmental condition (temperature, radiation, precipitations, CO2, etc.) into account, while the MEGAN stand-alone version used in this study does not compute the LAI; rather, it has to be provided as an external forcing averaged over the vegetated part of the grid cell.

As already discussed at the end of the Introduction, the validation of BVOC emissions at the global scale is a complex issue because of the poor data coverage in many regions and the general lack of year-round measurements. Satellite observations provide very useful information, especially regarding the order of magnitude and the seasonal and regional variability of emissions, but the most abundant VOC species are not directly measured (such as isoprene and monoterpenes). Satellite measurements are also subject to large uncertainties arising from difficulties in the retrieval of the atmospheric concentration of short-lived compounds from space or in separation of the different sources (for instance, terrestrial biogenic, anthropogenic, oceanic) and the various compounds themselves. Global emission estimates are generally performed using models, or from the application of inverse modelling techniques that combine the measurements (from satellite, ground or aircraft measurements) and models, providing emissions for compounds such as methanol (Jacob et al., 2005; Millet et al., 2008; Stavrakou et al., 2009; Hu et al., 2011; Wells et al., 2012, 2014) and acetaldehyde (Jacob et al., 2002; Millet et al., 2010). Isoprene emissions have also been inferred from satellite formaldehyde concentration (Shim et al., 2005; Palmer et al., 2006; Stavrakou et al., 2011; Barkley et al., 2013; Bauwens et al., 2013; Stavrakou et al., 2014).

At the global scale, the main way to evaluate the results obtained in the present study is to compare them with the most recent emission budgets derived either from other model runs or from the inversion of satellite data. We have compared emissions from a large number of estimates published so far, over the 1980–2010 period, with the global emission budgets obtained from ORC_CRU and MEG_CRU simulations, the results of which are summarized in Fig. 1. The emissions, calculated by the earlier version of the emission module (black squares, Fig. 1) (Lathière et al., 2006), are particularly high, as already pointed out by Sindelarova et al. (2014). Methanol (106.1 Tg C yr-1) and acetaldehyde (42.2 Tg C yr-1) emissions are twice as large, and formaldehyde emissions (10.0 Tg C yr-1) are up to 5 times greater than the other estimates. The results of the new module version (ORC_CRU, green stars) are more in the range of other published estimates. Although the MEG_CRU simulation was carried out using the same MEGAN version as in Guenther et al. (2012a) (blue hexagons, Fig. 1), there is a noticeable difference between the two emission budgets (especially for isoprene, monoterpenes and acetaldehyde), even when considering results for the same year (e.g. 2000). Using reanalysis provided by Qian et al. (2006) as climate forcings for the year 2000, Guenther et al. (2012a) report BVOC emissions of 472 Tg C yr-1 for isoprene, 124 Tg C yr-1 for monoterpenes (considering the speciated monoterpenes accounted in this work) and 11.5 Tg C yr-1 for acetaldehyde. Our MEG_CRU simulation estimates for 2000 are 410, 72 and 8.3 Tg C yr-1 for isoprene, monoterpenes and acetaldehyde, respectively. As was already pointed out by Arneth et al. (2011), our results confirm that the differences between existing meteorological forcings can lead to substantial differences in the emission estimates (green triangles, first plot of Fig. 1).

Table 6 shows the annual emissions calculated by ORCHIDEE and MEGAN (ORC_CRU and MEG_CRU simulations) at the global scale and for the northern (lat: 0–30∘ N) and southern (lat: 30∘ S–0∘) tropics, the northern (lat: 30–60∘ N) and southern (lat: 30–60∘ S) temperate latitudes and the northern boreal (lat: 60–90∘ N) regions, averaged over the 2000–2009 period. At the global scale, the two models are in a good agreement. Isoprene is the main compound emitted with a global amount of 465 Tg C yr-1 for ORCHIDEE, accounting for 61 % of total BVOC emissions (estimated to 757 Tg C yr-1), and 428 Tg C yr-1 for MEGAN, accounting for 64 % of total BVOCs (estimated at 666 Tg C yr-1). The following most abundant compounds are monoterpenes, accounting for 12 % of the total for ORCHIDEE and 11 % for MEGAN, and methanol, accounting for 5 % of the total BVOC emissions for ORCHIDEE and 6 % for MEGAN. Acetone, sesquiterpenes and acetaldehyde each represent 1 to 4 % of the total BVOCs for both models, while other compounds contribute to less than 0.5 %.

Compared to ORCHIDEE, MEGAN global emissions are 8 % lower for isoprene, 8 % higher for methanol, 17 % lower for acetone, 18 % lower for monoterpenes, 39 % lower for sesquiterpenes and 25 % for MBO. Regarding speciated monoterpenes, major differences arise from α-pinene (around 40 %), while the relative difference between ORCHIDEE and MEGAN is between -8 and +16 % for other compounds. The highest contribution to total emissions is attributed to the tropical regions ranging between 34 % and 50 % for the southern tropics and between 31.5 and 39.5 % for the northern tropics, depending on the compound (except MBO). Both models calculate the contribution of northern temperate regions to the total emission ranging from 6 to 24 % and a contribution of less than 5 % for southern temperate regions and northern boreal regions. For MBO, field campaigns only measured significant emissions for a few plant types such as Ponderosa and Scots pine (Kim et al., 2010; Tarvainen et al., 2005; Harley et al., 1998). The EF values in the ORCHIDEE and MEGAN models are consequently only significant for the PFTs representing these plants (TeNeEv and BoNeEv), leading to notable emissions in the temperate northern latitudes, and contributing 88 % for ORCHIDEE and 63 % for MEGAN of the global MBO emissions.

At the regional scale, the largest differences between ORCHIDEE and MEGAN in terms of absolute values appear in the northern temperate regions for isoprene, where emissions are 21 Tg C yr-1 higher in ORCHIDEE. Indeed, the marked seasonal cycle of emissions for northern temperate latitudes implies that the largest differences between ORCHIDEE and MEGAN occur in summer. The differences between the two models are, in this case, directly linked to discrepancies in the EFs and in the occupying surface of the PFTs at these latitudes (see Fig. 3, plots in the last row). In particular, in northern temperate regions the highest discrepancies are mainly due to the different PFT surface coverage for grass and crop and the higher EFs values in ORCHIDEE in comparison to MEGAN. Actually, in ORCHIDEE C3Gr covers the 42 % of vegetated surface with an EF = 12 µgC g-1 h-1 and C3Ag covers the 18 % with an EF = 5 µgC g-1 h-1, while in MEGAN the C3GrCool occupies the 20 % with an EF = 2 µgC g-1 h-1, C3GrCold the 6 % with an EF = 4 µgC g-1 h-1, C3GrCool the 20 % with an EF = 2 µgC g-1 h-1 and Crop the 23.2 % with an EF = 0.12 µgC g-1 h-1. This example raises an important issue. Considering the EF assigned to C3Gr, we lowered its value with respect to the previous version, from 16 to 12 µgC g-1 h-1. These is a compromise value, chosen so that we do not excessively bias the emissions in other areas. C3Gr is, indeed, strongly present in other regions: 13 % of northern tropical areas, 22 % of southern tropical areas and 32 % of the total vegetation surface. A more detailed description of the different crop and grass (in other words with a larger number of PFTs) could lead to more accurate results. The same consideration could be done for almost all the other PFTs.

This illustrates the strong impact of different choices in EF allocation, not only regarding global estimates, but also for geographical variation in emissions. For the other species the largest differences occur in tropical regions. For example, the emission differences between ORCHIDEE and MEGAN in the northern and southern tropics are -2.2 and -2.1 Tg C yr-1 for methanol, 4.3 and 10.2 Tg C yr-1 for monoterpenes and 3.9 and 4.9 Tg C yr-1 for sesquiterpenes.

Figure 2 shows the annual and monthly global emission budgets of ORC_CRU and MEG_CRU. The models have very similar annual trends and monthly variations for almost all compounds, illustrating that climate variables, in particular temperature and solar radiation, are the major driving factors at the global scale for inter-annual and inter-monthly variability.

Nevertheless, large differences appear for isoprene. The emissions in ORC_CRU present a clear seasonal cycle, with an emission maximum in July and August that is not simulated in MEG_CRU results. Indeed, the major differences can be identified in July and August, when global emissions in MEG_CRU are, on average, lower by 11.5 and 9.0 Tg C month-1 compared with ORC_CRU. The monthly zonal average for tropical, temperate and northern boreal latitudes regions are shown in Fig. 3. We observe, as mentioned in Sect. 3.1, that the ORCHIDEE emissions are significantly higher in northern temperate regions compared with MEGAN, with a marked seasonal cycle and the largest differences between the two models occurring in summer. In July (August) in particular, calculated isoprene emissions in ORC_CRU are about 4 Tg C month-1 (5.5 Tg C month-1) higher than in MEG_CRU. In July (August), a further important contribution to the global emission peak is attributed to the northern and southern tropics, where ORCHIDEE isoprene emissions are higher, in total, by about 4 Tg C month-1 (5 Tg C month-1) in comparison to MEGAN in July (August), (Fig. 3, first plot, left column).

MEGAN isoprene emissions are indeed dominant from the tropical regions, leading to an overall stable global emission budget throughout the year (Fig. 2). The northern and southern tropics have an opposite seasonal cycle, with isoprene emissions coming mostly from the northern tropics between March and October and from the southern tropics for the rest of the year (Fig. 3). The overall stable global emission budget is generally characteristic of the compounds for which tropical regions are strong emitters all year round, such as sesquiterpenes (Table 3 and Fig. 3). On the other hand, the global BVOC emissions for which temperate regions are strong emitters will have a more marked seasonal cycle (Fig. 2), such as for methanol and isoprene in ORCHIDEE.

Indeed, the two models exhibit a very different inter-seasonal variation in terms of isoprene global emissions. Sindelarova et al. (2014) compared the monthly isoprene emissions time series from different data sets, showing, for some of them, an inter-seasonal variation similar to ORCHIDEE, and, for others, no seasonal cycle. Based on our current knowledge, we cannot establish which is the best representation because of the lack of long-term observations at the global scale. However, we can extensively investigate why the differences between the two models occur, by performing sensitivity simulations and looking at the various processes modelled. This is the main purpose of the next section.

Additionally, Fig. 3 shows that in northern and southern temperate and northern boreal regions, the seasonal cycle is very similar between the two models, even if ORCHIDEE calculates higher emissions than MEGAN, especially for isoprene.

The spatial patterns of BVOC emissions in winter and summer for ORC_CRU and MEG_CRU simulations are presented in Figs. 5–9 for isoprene, monoterpenes, methanol, acetone and sesquiterpenes. To better assess the impact of EFs on emissions, we show the resulting emission potential for each grid cell, summing the EFs, each weighted by the cell area occupied by each PFT. In MEGAN, emission potentials are already provided per grid cell for isoprene, monoterpenes and MBO (see Sect. 2.3). Emission potentials per grid cell can be interpreted as the average EFs associated with the ecosystem present in the grid cell.

For a particular compound, the formula to convert the ORCHIDEE EF (µgC g-1 h-1) in the potential emission (µg m-2 h-1) consistent to those provided by MEGAN are, for emission not depending on light (LDF = 0), EP=∑iEFi⋅MMMCarbonMCarbon⋅LAIREF⋅SLWi⋅Ai, and for light-dependent emissions (LDF = 1), EP=∑iEFi⋅MMMCarbonMCarbon⋅LAIREF⋅SLWi⋅Ai⋅CCE, where i is the index related to PFTs, MCarbon and M are the molar mass of carbon and the compound, respectively, LAIREF equals 5.0 m2 m-2, which is the LAI in MEGAN standard conditions, SLW is the MEGAN specific leaf weight depending on PFTs, A is the PFT grid fraction and CCE is the canopy environment coefficient, a scaling factor dependent on the canopy radiation module, which equals 0.57 in this MEGAN configuration (Guenther et al., 2012a).

In general, for every compound, we observe a similar geographical distribution. High emission areas are identified in Brazil, equatorial Africa, southeastern Asia and southeastern United States for both models, with values for ORCHIDEE (MEGAN) ranging between: 5.0–12.0×1010 kg C m-2 s-1 (3.0–9.0×1010 kg C m-2 s-1) for isoprene, 0.8–2.0×1010 kg C m-2 s-1 (0.6–1.3×1010 kg C m-2 s-1) for monoterpenes, 0.3–1.2×1010 kg C m-2 s-1 (0.2–0.7×1010 kg C m-2 s-1) for methanol, 0.2–0.5×1010 kg C m-2 s-1 (0.1–0.3×1010 kg C m-2 s-1) for acetone and 0.4–0.6×1010 kg C m-2 s-1 (0.2–0.3×1010 kg C m-2 s-1) for sesquiterpenes, respectively. For methanol, in summer, high emitting areas also appear in Europe and Russia, with values of 0.3–0.5×1010 kg C m-2 s-1 for ORCHIDEE and 0.1–0.3×1010 kg C m-2 s-1 for MEGAN. Indeed, these regions are populated by temperate and boreal needleleaf evergreen trees, which are strong methanol emitters (Table 3 and Fig. 7, last row).

In southeastern China and southeastern United States, for methanol, acetone and, to a lesser extent, monoterpenes, ORCHIDEE emission estimates are higher than MEGAN. This is directly linked to the larger fraction of temperate needleleaf evergreen trees (TeNeEv) in ORCHIDEE in comparison to MEGAN (not shown), which are strong emitters of these compounds. The emission potentials (last row, Figs. 6–8) show the same geographical pattern that is mainly driven by the PFT distribution in these regions.

Other notable differences between the two models appear in South America for isoprene, directly in relation with the EP distribution. The pattern of isoprene emission in MEGAN has higher values in western Brazil, Bolivia and northern Argentina, while in ORCHIDEE the values are more homogeneous, with higher emissions in central Brazil. The same pattern differences are detected in the emission potential (Fig. 5, last row on the right), and we therefore infer that the EP distribution drives the isoprene emission geographical distribution. The same conclusion also holds for monoterpenes, where lower emissions along the Amazonian river follow the lower EPs in this area perfectly. In general, comparing the emission geographical distribution for each compound and the corresponding emission potential, we can state that, in both models, emission spatial patterns are mostly affected by the EF and PFT distributions.

In this section, we investigate the differences between the two models arising from LAI in detail, and we explore to what extent LAI can affect BVOC emission estimates.

Figures 4 and 10 show large differences in the geographical distribution and global average of ORCHIDEE LAI and MODIS LAI (Yuan et al., 2011). As illustrated in Fig. 10, the global monthly mean LAI calculated by ORCHIDEE is 1.5–2 m2 m-2 higher compared to the LAI used in MEGAN and based on MODIS data sets. In addition the LAI peaks at different times throughout the year in ORCHIDEE and MEGAN. We investigate the contribution of different areas and we observe that whilst in northern temperate regions, the MODIS LAI peaks in July and afterwards decreases quite quickly, the ORCHIDEE LAI peaks in both July and August. Furthermore, in the boreal region, the ORCHIDEE LAI peaks 1 month later (August) than the MODIS LAI (July). Therefore, the time shift observed globally is due to the greater persistence of the growing season provided by ORCHIDEE in the northern temperate area and its delay in the northern boreal region compared with what is detected by MODIS.

Furthermore, in the tropics, the MODIS LAI exhibits quite a clear seasonal cycle, especially in Amazonia, central Africa and Indonesia, which is not simulated by ORCHIDEE (Fig. 4).

The differences between these LAI estimates are significant, but our current state of knowledge does not allow us to establish which estimate is more reliable. Field and satellite data provide very useful and complementary information regarding the order of magnitude and the seasonal and the geographical variability of LAI. Nevertheless, inferring values for LAI on small or large regional scales is particularly challenging, and data available from either field or satellite measurements also have significant uncertainties. Satellites, for instance, do not measure the real LAI, but the effective LAI obtained from indirect optical methods and strongly determined by the a priori assumptions necessary for the inversion procedure. Even starting from the same input reflectance, diverse retrieval methods can lead to LAI values that are highly different (Garrigues et al., 2008; Fang et al., 2013). The effective LAI can be very dissimilar to the LAI directly measured in situ, and relative differences can reach 100 % (Fang et al., 2012a, b).

The transition from effective to real LAI is only possible when additional information about the vegetation structure is available (Pinty et al., 2011), increasing the risk of inaccuracy. The sources of uncertainties are numerous (Garrigues et al., 2008). First, foliage clumping is, in general, not taken into account, leading to underestimates of LAI of up to 70 % over the coniferous forest. Second, the forest understory is not systematically taken into account since the satellite LAI product is derived from a vertical integrated radiation signal. Third, in dense canopies, such as broadleaf tropical forests, the optical signal can saturate, leading to an underestimate of the effective LAI in comparison with the true value with a saturation limit of 3.0 m2 m-2 (Pinty et al., 2011). Fourth, the presence of ice and snow can strongly upset the retrieval, making it very difficult to estimate LAI in boreal and mountain regions.

Conversely, in a validation study using satellite-derived vegetation index time series, Maignan et al. (2011) pointed out some weaknesses in the ability of ORCHIDEE to correctly model the LAI seasonal cycle, especially in the equatorial forest (Amazonia, central Africa, Indonesia) where a poor correlation of model output with satellite data was demonstrated. In general, quite large and comparable incertitude is found when different LAI databases are compared. Krinner et al. (2005) found that the difference between ORCHIDEE and MODIS satellite LAI (Myneni et al., 2002) is as much as the difference between the satellite data that they used and an alternative satellite vegetation cover data set (Tucker et al., 2001). Therefore given the many existing limitations, we cannot precisely estimate to which extent ORCHIDEE LAI is reliable. It is likely that the ORCHIDEE LAI modelization has room for improvement, and a possible component to be upgraded is the allocation of the different carbon stocks, but further investigations are needed. Performing a robust evaluation of the model's ability to simulate the LAI, especially at the global scale, still remains challenging, and is beyond the scope of our study.

In this context, model inter-comparison and sensitivity tests provide an essential insight to assess the impact of different LAI estimates and their uncertainties on BVOC emissions.

As described in Sect. 2.2, the LDF parameter sets the light-dependent fraction of emissions for each compound. Many experimental studies point out for several plant species that, if emissions can be totally light-independent for some BVOCs, the emissions of most of them are actually light-dependent to a degree that depends on the compound (Jacob et al., 2002, 2005; Hansen and Seufert, 2003; Dindorf et al., 2006; Holzke et al., 2006; Harley et al., 2007; Millet et al., 2008, 2010; Hu et al., 2011; Wells et al., 2014). Since the results of these studies are highly heterogeneous, assigning a single LDF value to each compound is as difficult as assigning the EFs to each PFT (Sect. 2.2). Hence, the LDF uncertainty could be even higher than the uncertainties associated with EFs, as there have been fewer less quantitative studies on this subject published to date.

The objective of this section is to quantify, for both ORCHIDEE and MEGAN, the relative contribution of the light-dependent and light-independent part to the total emissions, and consequently to determine the impact of LDF-attributed values on emission estimates, giving clues to better understand the different behaviours of the two models.

For the fully light-dependent (isoprene: LDF = 1) or largely light-dependent compounds (methanol: LDF = 0.8) (Figs. 5 and 7), we observe that a higher EP in ORCHIDEE than in MEGAN does not necessarily lead to higher emissions in ORCHIDEE. In the case of a LDF close to 1, even when the same EP value is used in both models, the emissions calculated by MEGAN are higher compared to ORCHIDEE, suggesting a different emissions response to light. Indeed, this effect is less important for compounds that are less dependent on light such as monoterpenes (LDF = 0.5) (Fig. 6) and sesquiterpenes (LDF = 0.6) (Fig. 9), and indeed are even negligible for acetone (LDF = 0.2) (Fig. 8). It therefore seems that the choice of LDF parameter can be crucial in the emission estimate and in the sensitivity to EF variation.

To isolate the signal related to the LDF, we investigate the hourly variation of two “test compounds”, the first defined as light-independent (LDF = 0) and the second defined as totally light-dependent (LDF = 1). All EFs are set to 1 µgC g-1 h-1 for each PFT. The other settings are specified as in the reference run, and are the same for the two test compounds (for further details see Table 5). We refer in the text to the first compound as orcldf0 if it is calculated by ORCHIDEE and as megldf0 if it is calculated by MEGAN, while we refer to the second compounds as orcldf1 and megldf1, respectively.

In order to quantify the contribution of the light-dependent part in comparison to the light-independent one, we use the LDF index, which we define as the ratio between the light-dependent and the light-independent test compound, multiplied by 100 (orcldf1/orcldf0 ⋅ 100, megldf1/megldf0 ⋅ 100). Using the LDF index we can easily compare the behaviour of the two models, avoiding the complication arising from the mismatch between the two land covers. Indeed, the direct comparison of the absolute values of orcldf and megldf compounds could be affected by the differences between the PFT distributions in the two models, and the signal due to LDF change could therefore not be well isolated.

In Fig. 14 the daily profile averaged over each month of the LDF index is presented to investigate the daily and annual variations. At the global scale (left panel), we observe that the LDF index associated with MEGAN is much higher (up to 20 %) than the index associated with ORCHIDEE. At the regional scale, in the southern tropics, for example (second panel), the index reaches up to 70 % and is twice as large the index calculated for ORCHIDEE. The light-dependent part of the emissions in MEGAN is therefore more important than ORCHIDEE, with important impacts on emission estimates. Firstly, we show that based on the same EF value, the MEGAN emissions are higher than in ORCHIDEE for compounds associated with high LDF, as expected from Sect. 3.3.

Secondly, the variable orcldf0 (megldf0) represents the emissions when LDF is zero, while orcldf1 (megldf1) represents the emissions when LDF is 1; thus, they define the interval spanned by emissions as LDF varies. Therefore, a low LDF index is associated with a greater variability of emissions for equal light-independent emissions. Consequently, ORCHIDEE results are more sensitive to LDF variation than MEGAN, as the ORCHIDEE LDF index is lower than the MEGAN index. Furthermore, the LDF index provides an evaluation of error due to a diverse choice of LDF values. The LDF index is always less than 100, meaning that the light-independent component of the emission is always bigger than the light-dependent part. Therefore, if LDF in the model is greater than it should be, emissions will be underestimated, while if it is less, emissions will be overestimated. At regional scale, tropical areas, which are associated to a high LDF index, will be less sensitive to LDF variation than other regions.