A new version of the biogenic volatile organic compounds (BVOCs) emission
scheme has been developed in the global vegetation model ORCHIDEE (Organizing
Carbon and Hydrology in Dynamic EcosystEm), which includes an extended list
of biogenic emitted compounds, updated emission factors (EFs), a dependency
on light for almost all compounds and a multi-layer radiation scheme. Over
the 2000–2009 period, using this model, we estimate mean global emissions of
465 Tg C yr
The terrestrial biosphere emits large amounts of volatile organic compounds (VOCs) in particular terpenoids, such as isoprene, monoterpenes and sesquiterpenes, and oxygenated hydrocarbons such as methanol, acetone, formaldehyde, acetaldehyde, acetic acid or formic acid (Laothawornkitkul et al., 2009; Guenther et al., 2012a; Penũelas and Staudt, 2010). On the global scale, the ecosystem contribution to VOC emissions is significantly higher than the anthropogenic one, and accounts for 75–90 % of the total emission (Guenther et al., 1995; Lamarque et al., 2010). Biogenic volatile organic compounds (BVOCs) play a central role in atmospheric chemistry, influencing the oxidative capacity of the atmosphere (Arneth et al., 2011; Taraborrelli et al., 2012), leading to the production of tropospheric ozone in the presence of nitrogen oxides (Von Kuhlmann et al., 2003; Mao et al., 2013), and influencing the tropospheric carbon monoxide budget (Pfister et al., 2008). Additionally, BVOCs and their oxidation products lead to the formation and growth of more than 50 % of the secondary organic aerosols (SOAs) (Kanakidou et al., 2005; Goldstein and Galbally, 2007; Van Donkelaar et al., 2007; Engelhart et al., 2008; Hallquist et al., 2009; Acosta Navarro et al., 2014; Tsigaridis et al., 2014). Under appropriate atmospheric conditions, BVOCs can contribute to a significant fraction of particles that evolve into cloud condensation nuclei (Riipinen et al., 2012), even enhancing the droplet number concentration in clouds (Topping et al., 2013).
Despite numerous measurements and the progressive understanding of the processes underlying their production, BVOC emission estimates are still highly uncertain, and vary significantly (Steiner and Goldstein, 2007; Arneth et al., 2008; Simpson et al., 2012; Sindelarova et al., 2014).
Over the last 20–25 years, two main methods have been developed to derive
BVOC inventories: a top-down approach based on the inversion of satellite
measurements, which allows BVOC emissions to be indirectly derived (Palmer et
al., 2006; Barkley et al., 2013), and a bottom-up approach. The latter
approach is the most widely used method for local-, regional- or global-scale
studies, and can be divided into two main categories: (i) an empirical
method, based essentially on Guenther et al. (1995), where the response of
leaf emissions to environmental changes is modelled using algorithms combined
in a multiplicative way (Guenther et al., 2006, 2012a; Lathière et al.,
2006, 2010; Steinbrecher et al., 2009; Oderbolz et al., 2013); hereafter we
refer to it simply as the
BVOC emission modelling at the global scale is a complex issue, especially because of the number of variables and processes influencing the emission of these compounds, generally characterized by strong temporal and geographical variations. A critical point is the lack of information available at the global scale related to the various biomes, making an accurate representation of the geographical distribution and of the seasonal variation of BVOC emissions difficult (Peñuelas and Staudt, 2010). The basal emission factor (EF), for instance, defined as the emission at the leaf level under standardized environmental conditions of temperature and solar radiation (Guenther et al., 1995; Steinbrecher et al., 2009), shows large variability from one plant species to another. Nowadays, there is an increasing number of field campaigns that investigate, in addition to isoprene and bulk monoterpenes, many other important compounds for atmospheric chemistry, especially regarding the SOA formation, such as speciated monoterpenes and sesquiterpenes. More data and information are therefore available, allowing EF estimates for a wider range of BVOCs, despite the limitations for modelling purposes which will be discussed in Sect. 2.2.1. To calculate BVOC emissions, a single EF is usually assigned to each plant functional type (PFT), where one PFT represents a group of plants having the same phylogenetic, phenological and physical characteristics (Prentice et al., 1992). The choice of one single value for each PFT is especially difficult, as each PFT actually corresponds to several plant species, and EFs show, in general, a wide range of values among different plants (Kesselmeier and Staudt, 1999; Niinemets et al., 2011). Moreover, several measurements show that the emission factors are significantly sensitive to many processes and parameters that are difficult to isolate and linked to plant stress, such as drought periods, ozone exposure, insects, herbivores and pathogen attacks (for a review see Laothawornkitkul et al., 2009, and Niinemets et al., 2010), making it not easy to set EFs, even for a single plant. In addition, the link between EF variation and plant phenology is in general not taken into account, or is roughly described, especially in models that adopt the empirical approach.
In the early works focusing on BVOCs, isoprene was the only compound
considered to be both light- and temperature-dependent, while the other
compounds were considered to be only temperature-dependent. More recent
papers show a growing evidence of the dependency of monoterpenes (Dindorf et
al., 2006; Holzke et al., 2006; Šimpraga et al., 2013), sesquiterpenes
(Hansen and Seufert, 2003) and oxygenated BVOCs (Jacob et al., 2002, 2005;
Harley et al., 2007; Millet et al., 2008, 2010; Hu et al., 2011; Wells et
al., 2014) on radiation. As proposed in Guenther et al. (2012a), a general
approach is now to consider, for each emitted compound, an emission fraction
that depends on both temperature and solar light, as done for isoprene, with
the remaining fraction dependent only on temperature. The Guenther et
al. (2012a) approach considers only one value per emitted compound, whilst it
has been shown that the LDF also depends on the plant species. For example,
measurements of the diurnal cycle for monoterpenes above Amazonian rainforest
(Rinne et al., 2002; Kuhn et al., 2002) suggest that emissions are dependent
on both light and temperature, whilst the role of light in influencing
monoterpene emissions from boreal Scot pine forest is less clear (Taipale et
al., 2011). Moreover, Staudt and Seufert (1995) and Loreto et al. (1996) show
that monoterpene emissions from coniferous trees are principally influenced
by the temperature, while those from Holm oak are predominantly controlled by
a light-dependent mechanism. Owen et al. (2002) find that, in the
Mediterranean region, emissions of all compounds from
Another crucial component in the estimation of BVOC emissions is the LAI, which can be either simulated using a vegetation model, or prescribed using values retrieved from satellite data or field measurements. Significant differences in terms of temporal and spatial distribution are found between the LAI estimated by measurements and the LAI calculated by models, with discrepancies of up to 100 % at the global scale and more than 150 % for specific biomes types (Garrigues et al., 2008; Pinty et al., 2011; Fang et al., 2012a, b). Consequently, the high uncertainty related to LAI affects the predicted regional and seasonal distribution of BVOC emissions.
According to our knowledge, most papers investigating BVOC emission
sensitivity focus on the response of emissions to different experimental
set-ups, changing, for instance, climate forcing and land use. For example,
Oderbolz et al. (2013) pointed out the importance of the differences between
the land-cover inventories, and of the uncertainties in the classification of
land cover. Arneth et al. (2011) compared three vegetation models, changing
the experimental set-up, such as the vegetation distribution and the climate
forcings. Depending on the experiment considered, the total annual isoprene
emissions were found to increase or decrease by more than 30 %. Ashworth et
al. (2010) investigated the impact of varying the climate forcing temporal
resolution of isoprene emission in the MEGAN model, finding a variation of
isoprene emissions of up to 7 % at the global scale and up to 55 % in
some locations. Keenan et al. (2009) investigate the effect of canopy
structure using different canopy models, and they conclude that larger
differences in the final emissions can be attributed to the use of different
canopy models, rather than different emission model approaches. Nevertheless,
very few studies have investigated the impact of the uncertainty of key
parameters/variables, such as LAI, on emissions. One example is the work by
Sindelarova et al. (2014), in which several simulations were performed with
the MEGAN model to assess the sensitivity of isoprene emissions to many
parameters and processes such as LAI, emission factors (EFs), CO
In the present work, our objectives are to (i) present the updated version of the emission module embedded in the dynamic global vegetation model ORCHIDEE (Organizing Carbon and Hydrology in Dynamic EcosystEm), (ii) provide present-day estimates of global BVOC emissions for several relevant compounds (isoprene, monoterpenes, sesquiterpenes, methanol, acetone, formaldehyde, acetaldehyde, acetic acid, formic acid and the main speciated monoterpenes) using the new emission scheme, (iii) compare the ORCHIDEE results to the widely used emission model MEGAN, putting the two models under the same forcing conditions, but retaining their particular characteristics (see Sect. 2.5), in particular the emission scheme, classes and distribution of PFTs and LAI processing and (iv) explore, at global and regional scales, the BVOC emission sensitivity to EFs, LAI and LDF in ORCHIDEE and MEGAN, and to understand the reasons behind these discrepancies. ORCHIDEE is designed to provide past, present and future scenarios of emissions from vegetation, studying the links between climate, the plant phenology and emissions. It is therefore essential that the internal variability, weaknesses and inaccuracies of the emission module are extensively investigated. The proper way to assess the correctness of a model would be to evaluate it against observations, as it is done, for example, for organic aerosols by Mann et al. (2014) and Tsigaridis et al. (2014) and for tropical mountain forest carbon store by Spracklen and Righelato (2014). The evaluation of BVOC emission models against observations has already been carried out at local and regional scales (i.e. Karl et al., 2007; Kuhn et al., 2007; Lathière et al., 2010; Smolander et al., 2014), demonstrating a good performance of the Guenther formulation. Nevertheless, given the ecosystem biodiversity, the huge variability of the parameters involved and the poor spatial and temporal coverage of BVOC emission observations, it is extremely difficult to infer a robust evaluation at global scale. In such a context we can rely on model inter-comparison and sensitivity tests in order to assess the limitations and uncertainties of BVOC emission estimates, to relate them to particular key parameters/variables and to investigate their origin. In Sect. 2, the ORCHIDEE model and the updates from the previous version (Lathière et al., 2006), the MEGAN model and the technical details of the simulations are described. The comparison with other published estimates, the inter-comparison between the two models and the sensitivity tests carried out are extensively described in Sect. 3. The conclusions and future directions are provided in Sects. 4 and 5.
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).
Plant functional types in ORCHIDEE and MEGAN and corresponding
occupied surfaces in 10
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
The emission flux
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.
Comparison between the old and new versions of the biogenic emission module in ORCHIDEE: list of emitted compounds, principal parameters for emission equations, radiation model type and compounds for which the leaf emission activity is activated.
Eight speciated monoterpenes (
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 (
LDF
EF determination represents one of the greatest sources of uncertainty in the quantification of BVOC emissions (Niinemets et al., 2011). Several measurement campaigns were carried out over the last decade, providing important new insights and information for re-examining the emission factors used in the emission module and correcting them accordingly. Nevertheless, the methodology to assess EFs is still under debate within the scientific community. Assigning EFs, especially on the global scale, is very tricky. In the ideal case, for each compound emitted, we should consider the EFs of all plants belonging to one particular PFT and the land cover of each plant. We could then, for each PFT and compound, make averages weighted by plant land cover, thus obtaining an average EF for each PFT and emitted compound. Unfortunately, there are not yet enough observations available to use such a methodology.
Several aspects make it difficult to find a good strategy to assign EFs. First, sources of information regarding EFs are very heterogeneous such as bibliographical reviews, articles presenting punctual or fairly widespread measurement campaigns and modelling experiments, making the selection of papers to use especially tricky. When a large range of EF values is documented for one particular plant species, it is not obvious whether this range is actually representative of a natural (geographical or species-to-species) variability, and can therefore be considered as valid, or originates from technical difficulties or improvements (and, in this case, if preference should be given to more recently published papers). A further difficulty is linked to the high number of plant species that can be combined together into one PFT, in comparison to the relatively small proportion of plant species and/or measurement sites worldwide that could be investigated, despite numerous and crucial field studies. Moreover, our EF review shows that EFs are highly variable from one plant to another, even if the plants belong to the same PFT. In this context, it is difficult to assign a single EF per each PFT, which integrates this variability adequately. Lastly, the procedure itself used to determine EFs from field measurements adds another source of uncertainty. Indeed, EFs are derived by adjusting the measured flux at leaf level in standard conditions of photosynthetically active radiation (PAR) and temperature, using algorithms such as Guenther et al. (1995). However, there is no universal agreement on the parameterization of these algorithms (Tarvainen et al., 2005; Duhl et al., 2008; Kim et al., 2010; Bracho-Nunex et al., 2011; Fares et al., 2011).
All these aspects underline the challenge and uncertainty of assigning one fixed EF value for each PFT in global models (Kesselmeier and Staudt, 1999; Niinemets et al., 2010; Arneth et al., 2011), also considering that the emission estimates are very sensitive to changes in EF.
In this particular context, we try to establish a sufficiently consistent
methodology, and we follow the guidelines below to update the EFs in the
ORCHIDEE emission scheme. All the values and related references used to
define the new EFs are provided in Tables S1–S10 (one table for each
compound) of the Supplement.
First, we select only papers that provide EFs per leaf biomass and for
standard conditions such as defined in ORCHIDEE (PAR When the most recent papers agree on a specific EF range, we discard the
old references if the EF value is significantly different. In other cases
all the works collected are taken into account. First for each paper we gather all the values available per ORCHIDEE PFT
and per emitted compound. In there are more values per paper, we calculate
the average in order to have one EF per compound, PFT and paper. Then, for each compound and each PFT, we choose an EF that is in the
range of the collected values, and is the closest to the average and median
calculated. When one EF value cannot be clearly assigned, we take a value
between the average or the median and the previous ORCHIDEE EF values
(Lathière et al., 2006). Considering the high sensitivity of the
emission module to EF variation, in order to avoid unreliable estimate, in
the case of ambiguity, for the highly emitted compounds, in particular for
isoprene, a more conservative approach is adopted, and the EF values of the
previous version are kept. In choosing the new EFs, in the case of very little or inconclusive
information, EF variability between the different PFTs of the old version of
ORCHIDEE (Lathière et al., 2006) and/or MEGAN (Guenther et al., 2012a) is
taken into account. For each compound we check a posteriori that the new set of EFs
provides a regional distribution that is consistent with the orders of
magnitude expected and given in the literature. Only for monoterpenes for
tropical PFTs do we replace the first value selected (2.5
Table 3 shows the new and old EFs used in the emission module, and Table 4
shows the EF values for each speciated monoterpene as a percentage of the
bulk monoterpene EF value. As shown in Table 3, the revision leads to the
modification of almost all EFs. In some cases, the EF differences in
comparison with the previous version are very significant. Regarding
isoprene, boreal needleleaf deciduous PFT is now recognized as a less
important emitter (EF
Emission factors (EFs,
Furthermore, we consider boreal broadleaved deciduous trees to be a higher
emitter of isoprene than in the previous model version (now
EF
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).
Percentage of speciated monoterpene EFs with respect to the PFT bulk monoterpene EF (fourth line, in bold the Table 3) in the new version of the ORCHIDEE emission module.
Configuration of simulations performed by ORCHIDEE and by MEGAN.
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); one simulation for the 2000–2009 period with MEGAN using the LAI
estimated by ORCHIDEE (MEG_CRULAI); four simulations for the year 2006 by both models, using the ORCHIDEE
LAI scaled by a factor 0.5 and 1.5, respectively (ORC_LAI05, ORC_LAI15,
MEG_LAI05 and MEG_LAI15); one simulation for the year 2006 forcing ORCHIDEE with the MODIS LAI
used in MEGAN standard configuration; two simulations for the year 2006 performed by both models, where we
output two test species, the first one totally dependent on light
(LDF
All simulations are performed at the global scale with a spatial resolution
of 0.5
In ORCHIDEE, the activity factor (
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, CO In ORCHIDEE, the formulation of CTLD and CL is the same as in Guenther
et al. (1995) (see Eqs. 9 and 10), while in MEGAN it is defined by Eqs. (8),
(9) and (10) in Guenther et al. (2012a). In particular in Guenther et
al. (2012a) the parameters of the CTLD formulation vary according to the
average solar radiation over the past 24 and 240 h, and this dependence is
different for diffuse and direct radiation. We calculate the CTLD obtained
with this formulation considering different incoming solar radiations, and we
observe that the CTLD for direct light is around twice that for diffuse
light. In ORCHIDEE the CTLD parameters are fixed, and are the same for
diffuse and direct radiation. The radiation scheme in ORCHIDEE and MEGAN is based on the same approach
(Spitters, 1986; Spitters et al., 1986), but the parameterization and formulation used are
different. For example, the number of vertical layers and their distribution
over the LAI significantly differ between the two models: up to 17 in
ORCHIDEE and up to 5 in MEGAN. MEGAN also takes the infrared
radiation into account in emission calculation. The PFT classes and their distribution are not the same in the two
models (Table 1), and they are not interchangeable without significantly
modifying the models. In ORCHIDEE, emissions are calculated for each PFT using the associated
EF and LAI. Next, they are averaged over the grid cell, considering the PFT
land-cover surface, as described in Sect. 2.2. In MEGAN, vegetated emission
potential (EP) is calculated over the grid cell and multiplied by the average
LAI over the vegetated part of the grid cell. In MEGAN, vegetated potential
emission maps are provided for isoprene, In the ORCHIDEE model, the dependence of the light-independent emission
on LAI is linear, as shown in Eqs. (1) and (2) of the present work, whereas in MEGAN, the dependence on LAI is given by the In MEGAN, leaf age classes are derived from consideration of the
variation between the LAI value of the current and preceding month, following
a highly parameterized scheme. In ORCHIDEE, leaf age classes are calculated
online considering the plant leaf growth and leaf turnover at each model
time step (30 min). In ORCHIDEE, hydrological processes are explicitly calculated, as
briefly described in Sect. 2.1. In ORCHIDEE, the air temperature is used to compute emissions, while in
MEGAN the leaf temperature is considered.
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).
Global emission budgets (Tg C yr
Emission budget (Tg C yr
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
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
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
Monthly global (solid lines) and yearly averaged (dashed lines)
emission budgets in Tg C month
Zonal mean for northern and southern tropics (left column),
northern and southern temperate and northern boreal latitudes (right column)
of the monthly emission budget (Tg C month
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
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
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
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.
Leaf area index (LAI) considered for BVOC emission estimates in
ORCHIDEE (LAI calculated on line) and in MEGAN (MODIS retrieval) in summer
(June, July, August) and winter (December, January, February), averaged over
the 2000–2009 period (m
Emissions in winter (first row) and summer (second row) in
10
The same as Fig. 5, but for monoterpenes.
For a particular compound, the formula to convert the ORCHIDEE EF
(
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.
The same as Fig. 5, but for methanol.
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.
The same as Fig. 5, but for acetone.
Mean emission budgets (Tg C yr
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.
The same as Fig. 5, but for sesquiterpenes.
Global monthly mean LAI (m
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 m
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
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 m
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.
LAI has an important role in driving the seasonal cycle of emissions. To show this, we perform an extra 10-year simulation following the same configuration as in the previous runs, but forcing MEGAN with the ORCHIDEE LAI (MEG_CRULAI simulation, Table 5), and we compare the results with MEG_CRU and ORC_CRU simulations.
First of all, we observe that, for the MEG_CRU simulation, the isoprene emission seasonal cycle in the tropics (particularly in the south) is more marked than for ORC_CRU simulation (Fig. 11). This behaviour is principally related to the differences in seasonal variation between the MODIS and the ORCHIDEE LAI (Fig. 4), since the ORCHIDEE LAI presents smaller variations between winter and summer in tropical regions, in particular in Amazonia, (Fig. 4, left column) in comparison with MODIS LAI (Fig. 4, right column), whereas the two models have a similar inter-seasonal variability when they are driven by the same LAI (MEG_CRULAI and ORC_CRU). Moreover, the MEG_CRULAI simulation gives a lower peak in the northern tropics April and May emissions than MEG_CRU (Fig. 11), being more similar to ORC_CRU.
Zonal mean of monthly emission budgets (Tg C month
Generally, for every compound, we observe a better agreement between the MEG_CRULAI and the ORC_CRU simulations than between MEG_CRU and ORC_CRU, especially in the tropical regions.
The global and zonal emission budgets (Table 7) in the MEG_CRULAI simulation are not significantly different than those determined in MEG_CRU, even if the ORCHIDEE LAI is significantly higher than MODIS LAI, suggesting a low sensitivity of MEGAN to LAI size. Indeed, at the regional scale, in boreal and temperate regions, the MEG_CRULAI emissions are slightly higher than those in MEG_CRU, and in the tropics they are even slightly lower for some compounds. As proposed by Sindelarova et al. (2014), a possible reason for the emission decrease calculated in the tropics by MEGAN is to the strengthened effect of leaf self-shading caused by an increase in LAI in locations characterized by a dense vegetation (e.g. in central Africa or Amazonia). This effect can be predominant for compounds for which biogenic emissions are strongly dependent on light, such as isoprene or methanol.
Indeed, for the other compounds the MEG_CRU and MEG_CRULAI emission budgets
are very similar. We could foresee that these results are linked to the leaf
self-shading effect on leaf temperature. In contrast to ORCHIDEE, where the
air temperature is used, in MEGAN the leaf temperature is calculated for
shaded and sunlit leaves. If the leaf self-shading effect was crucial even
for light-independent compounds, we would expect a much higher leaf
temperature for sunlit leaves than for shaded leaves. Calculating the
difference in hourly leaf temperature between sunlit and shaded leaves in the
case of dense vegetation (TrBrEv and TrBrDe), we estimate differences of
about 1–1.5
We therefore investigate in more detail whether models show the same response to a particular change in LAI. We perform two extra simulations for each model, using the ORCHIDEE LAI multiplied by a factor of 0.5 or 1.5. The scaling factors considered are consistent with the LAI uncertainties (see the beginning of Sect. 3.4). Figure 12 shows the four simulations: MEGLAI05, ORC_LAI05 (ORCHIDEE LAI multiplied by 0.5) and MEG_LAI15 and ORC_LAI15 (ORCHIDEE LAI multiplied by 1.5), for the year 2006 (details in Table 5). Only the zonal average for the tropics and southern and northern temperate areas, for isoprene and monoterpenes, are displayed, but they are also representative of other regions.
Zonal average of changed emissions in the different LAI
sensitivity tests: ORC_CRU and MEG_CRULAI using ORCHIDEE LAI (solid line),
ORC_LAI05 and MEG_LAI05 using ORCHIDEE LAI
Regarding isoprene, we observe that ORCHIDEE and MEGAN present a similar
response to LAI variation. When the LAI is multiplied by a factor of 0.5
(1.5), the change in emissions compared to the reference runs (MEG_CRULAI,
ORC_CRU) reaches
Monoterpene emissions show a different response in terms of sensitivity to
LAI. In the southern tropics, the relative difference in monoterpene emission
budget between ORC_LAI05 (ORC_LAI15) and ORC_CRU is
Table 8 shows the total emission budget calculated for MEG_LAI05,
ORC_LAI05, MEG_LAI15 and ORC_LAI15 simulations for every compound. In
general in ORCHIDEE, the lower the light dependence, the higher the
sensitivity to LAI, while for MEGAN, the sensitivity to LAI does not
significantly change with LDF. The explanation for this difference in
emission response lies in the different formulation for light-independent
emissions in the two models. Such differences are detailed in point 6 of
Sect. 2.5. In particular, in ORCHIDEE, the light-independent emission
linearly depends on LAI, whereas in MEGAN it is determined by the
Considering the high sensitivity of BVOC emissions to the LAI and the high
differences between ORCHIDEE and MODIS LAI, we perform an additional
simulation, forcing ORCHIDEE with the LAI provided by MODIS (ORC_CRUMOD) for
the year 2006. Details of ORC_CRUMOD are provided in Table 5. In Fig. 13, we
present the differences between the seasonal averages of ORC_CRUMOD and
ORC_CRU for monoterpenes and isoprene emissions. In ORC_CRUMOD, isoprene
emissions significantly decrease in the tropics, up to 3–
Differences between the ORC_CRUMOD and ORC_CRU simulation for isoprene and monoterpenes emissions in summer and winter for 2006.
Figure 13 also illustrates the seasonal variation for both isoprene and
monoterpene emissions in the tropics, and clearly shows that the use of MODIS
LAI implies a seasonality in tropical and equatorial emissions, which is
almost not present in the ORC_CRU simulation. Confirming the results presented
in Sect. 3.4.2, monoterpene emissions show higher sensitivity to LAI
variations than isoprene, with the monoterpene annual global budget for
ORC_CRUMOD being 32 % lower than for ORC_CRU, while for isoprene, the
annual global budget is 6 % lower. Considering the other species, the
impact of using the MODIS LAI is stronger for species with a lower LDF. The
relative difference between ORC_CRUMOD and ORC_CRU is
Annual emission budgets (Tg C yr
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
To isolate the signal related to the LDF, we investigate the hourly variation
of two “test compounds”, the first defined as light-independent
(LDF
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
Global (left plot) and southern tropical (right plot) average of the LDF index for ORCHIDEE and MEGAN. The LDF index is provided as the hourly daily profile averaged over each month.
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.
The main objectives of this study were to (i) present the new version of the BVOC emission module embedded in the ORCHIDEE model, (ii) provide BVOC emission estimates for the 2000–2009 period for a large diversity of compounds, (iii) compare the ORCHIDEE model results to emissions calculated by MEGAN in terms of global, regional and seasonal patterns and (iv) investigate how the uncertainty linked to some key variables or parameters such as the LAI and the LDF could affect the BVOC emission estimate in the two models.
The new ORCHIDEE emission module now considers many speciated monoterpenes and bulk sesquiterpenes, which have been shown to be important regarding SOA formation, uses updated EFs and includes developments in the physical processes related to BVOC formation, such as the emission dependence on light for almost all compounds, a multi-layer calculation of diffuse and direct radiation and shaded and sunlit leaves over LAI layers.
The ORCHIDEE emission estimates are within the range of the published
emission budgets. The ORCHIDEE global budgets averaged over the period
investigated (2000–2009) are 465 Tg C yr
More generally, considering the geographical distribution of emissions for each compound and the corresponding emission potential, we show that, in both models, EF and PFT distributions are the main drivers of the geographical emission pattern. In terms of seasonal variation, the differences between the two models in the tropics are mostly due to the different seasonal cycles of LAI between MODIS and ORCHIDEE, while the large discrepancy in northern temperate regions is attributed to differences in the EF distribution.
The LAI calculated by ORCHIDEE is 1.5–2 m
We investigated the contribution of the light-dependent and light-independent part of emissions and consequently the impact that a different choice of LDF can have on emissions. In MEGAN, the light-independent part of emissions is more important than in ORCHIDEE, reaching a factor of 2 in the southern tropics. We find that ORCHIDEE estimates are more sensitive to LDF variation than MEGAN. Moreover, we showed that overestimation (underestimation) of the LDF value leads to emission underestimation (overestimation).
Model inter-comparison and sensitivity tests are extremely useful to define which parameters/variables mainly affect BVOC emissions, what is the cause of this sensitivity and how estimates can be improved. Previous works have already investigated the impact of different experimental set-ups (climate forcing and vegetation distribution) (Arneth et al., 2011), differences in the canopy structure description (Keenan et al., 2009) and land-cover classification (Oderbolz et al., 2013) on emissions.
In the present work, we focused on the impact of LAI, LDF, EFs and PFT
distribution. Our results underline that the high uncertainties in the variables/parameters
involved and the different choices in modelling
processes result in a high variability of BVOC emission estimates. The
outcome of this analysis provides some guidelines for future developments of
BVOC emission models at the global scale. In particular, the following
issues should be carefully addressed.
LAI uncertainties are still extremely high, and have a considerable impact
on emissions. Improvements in LAI modelling or estimation at the global scale
are essential. EF allocation is a big concern because of its high variability. A proper way
to assign statistically robust values at a global scale has not yet been
found. Significant improvement can only be achieved by increasing the
observation data coverage of many regions and performing long-term
measurements. LDF parameterization is still oversimplified, and has a significant impact on
emissions. Future developments should, therefore, improve LDF
parameterization accuracy, for example, by including PFT dependency. As for
EFs, more reliable results can only be achieved by increasing observation
coverage. The rather low number of PFTs in global models is a limiting factor in
accurate emission estimates.
Further analysis will certainly be needed to include other important
parameters/variables in the investigation, for example, leaf temperature
vs. air temperature usage, leaf age classes, parameters in the Guenther
formulation and the soil moisture activity factor.
Finally, it is worth mentioning that, besides model inter-comparison, there is a strong need to evaluate model results against emission observations. This has already been done in other domains, for example, in atmospheric chemistry modelling (Mann et al., 2014; Tsigaridis et al., 2014). In the case of BVOCs, however, observational data are very challenging to acquire, especially on the long-term scale. Therefore, for BVOC emission modelling, a robust validation of model results against observations is still lacking.
The ORCHIDEE model code, input data, ORCHIDEE and MEGAN outputs are archived
in the CEA (Commissariat à l'énergie atomique et aux énergies
alternatives) high-performance computing centre TGCC and are available upon
request. The source code of the MEGAN model can be downloaded from
We thank Cathy Nangini for her very useful comments and corrections, J.-Y Peterschmitt for his help in data visualization and A. Guenther for meaningful discussions. We gratefully acknowledge support from the project ÉCLAIRE (grant agreement no. 282910), PEGASOS (grant agreement no. 265148), MACC II project (grant agreement no. 283576) and MACC-III (grant agreement no. 633080) funded under the EC Seventh Framework Programme. This work was partly funded by the DGAC under the TC2 project. This work was performed using DSM-CCRT resources under the GENCI (Grand Equipement National de Calcul Intensif) computer time allocation and the GENCI project.Edited by: J. Rinne Reviewed by: two anonymous referees