2.1. General Remarks
 ORCHIDEE is based on two different existing models and one newly developed model:
 1. The SVAT SECHIBA [Ducoudré et al., 1993; de Rosnay and Polcher, 1998] has been developed as a set of surface parameterizations for the LMD (Laboratoire de Météorologie Dynamique, Paris) atmospheric general circulation models (AGCM). SECHIBA describes exchanges of energy and water between the atmosphere and the biosphere, and the soil water budget. In its standard version, SECHIBA contains no parameterization of photosynthesis. Time step of the hydrological module is of the order of 30 min.
 2. The parameterizations of vegetation dynamics (fire, sapling establishment, light competition, tree mortality, and climatic criteria for the introduction or elimination of plant functional types) have been taken from the dynamic global vegetation model (DGVM) LPJ [Sitch et al., 2003]. The effective time step of the vegetation dynamics parameterizations is 1 year.
 3. The other processes such as photosynthesis, carbon allocation, litter decomposition, soil carbon dynamics, maintenance and growth respiration, and phenology form together a third model called STOMATE (Saclay Toulouse Orsay Model for the Analysis of Terrestrial Ecosystems). STOMATE essentially simulates the phenology and carbon dynamics of the terrestrial biosphere. Treating processes that can be described with a time step of 1 day, STOMATE makes the link between the fast hydrologic and biophysical processes of SECHIBA and the slow processes of vegetation dynamics described by LPJ. Innovative features of STOMATE comprise a completely prognostic plant phenology (leaf out dates, maximum LAI, senescence) and plant tissue allocation including a carbohydrate reserve, and time-variable photosynthetic capacity depending on leaf cohort distribution. This newly developed model is described in section 2.3, and in more detail in Appendix A.
 SECHIBA will be referred to as the “hydrological module” in the following, while STOMATE and the included parameterizations of vegetation dynamics will be referred to as the “carbon module.”
 ORCHIDEE can be run in different configurations, depending on the type of problem to be addressed. These are as follows:
 1. In the hydrology only case, the carbon module is entirely deactivated and leaf conductance is calculated as by Ducoudré et al.  without using any parameterizations of photosynthesis. Vegetation distribution is prescribed, and LAI is either prescribed (using satellite observations) or diagnostically calculated as a function of temperature [Polcher, 1994].
 2. In the hydrology and photosynthesis case, the parameterizations of photosynthesis (following Farquhar et al.  and Collatz et al. ) and stomatal conductance (following Ball et al. ) are activated, but vegetation distribution is prescribed and LAI is either prescribed or diagnosed as a function of temperature.
 3. In the case of hydrology and carbon cycle with static vegetation, the carbon cycle is fully activated. Soil, litter, and vegetation carbon pools (including leaf mass and thus LAI) are prognostically calculated as a function of dynamic carbon allocation. However, LPJ is deactivated; instead, the vegetation distribution is prescribed after Loveland et al. .
 4. In the case of hydrology and carbon cycle with dynamic vegetation, all three submodels are fully activated and the model makes no use of satellite input data that would force the vegetation distribution, so that vegetation cover, with its seasonal and interannual variability and dynamics, is entirely simulated by the model.
 Global simulations using the latter two configurations will be presented later in this article. In any of these configurations, ORCHIDEE can be run in stand-alone mode, that is, forced by climatological or experimental data (global or local), or it can be run coupled to an AGCM.
 Like LPJ, ORCHIDEE builds on the concept of plant functional types (PFT) to describe vegetation distributions. This concept allows grouping of species with similar characteristics into functional types in ways which maximize the potential to predict accurately the responses of real vegetation with real species diversity [Smith et al., 1997]. ORCHIDEE distinguishes 12 PFTs (of which 10 are natural and two agricultural; see Table 1), for which Table 1 gives the values of the most important pertinent biogeochemical parameters (except those already defined by Sitch et al. ).
Table 1. PFTs and PFT-Specific Parameters in ORCHIDEEa
|NC3||70||27.5 + 0.25Tl||2.5||0.25||0.20||0.2||120||4||0.2|
|AC3||90||27.5 + 0.25Tl||6||0.25||0.18||0.4||150||10||0.2|
 The different PFTs can coexist in every grid element. The fraction of the element occupied by each PFT is either calculated (and thus variable in time) or prescribed when LPJ is deactivated. The fractional area occupied by agricultural PFTs can be fixed such that vegetation dynamics does not act on the agricultural fraction of the grid element. Stomatal resistances are calculated separately for each PFT (and so is the resistance of bare soil). Water reservoirs are calculated for each PFT separately, but the lower soil reservoirs are mixed instantaneously [de Rosnay and Polcher, 1998].
 In ORCHIDEE grasses cannot grow below trees. This idealized assumption simplifies several parameterizations, for example, photosynthesis, transpiration, and light competition.
 Carbon dynamics is described through the exchanges of carbon between the atmosphere and the different carbon pools in plants and soils. There are eight biomass pools: leaves, roots, sapwood above and below ground, heartwood above and below ground, “fruits” (plant parts with reproductive functions: flowers, fruits, etc.), and a plant carbohydrate reserve; four litter pools: structural and metabolic litter, above and below the surface; and three soil carbon pools: active, slow, and passive soil carbon. Turnover time for each of the soil carbon and litter pools depends on temperature, humidity, and quality. The relatively high number of biomass pools is necessary because, first, ORCHIDEE distinguishes aboveground and belowground litter, which induces the need for distinguishing aboveground and below ground biomass; second, the cost of reproductive processes, which represents about 10% of the global NPP [Sitch et al., 2003], needs to be taken into account and therefore requires a corresponding carbon pool; and third, the plant carbohydrate reserve is needed to represent carbon translocation at leaf onset. The litter and soil carbon pools are treated separately on the agricultural and natural part of each grid cell because of the large differences in soil carbon dynamics on agricultural and natural ground. Within each of these two parts of a grid cell (agricultural and natural) the PFTs are supposed to be well mixed so that the soil carbon is not calculated separately below each PFT. The parameterizations of litter decomposition and soil carbon dynamics essentially follow Parton et al. .
 The basic state variables in the carbon modules of ORCHIDEE are the various carbon reservoirs and the density of individuals ρ (in m−2) of each PFT. The maximum fractional cover vmax of each PFT is calculated from these state variables through
where crown area c (in m2) of an individual plant is obtained using allometric relationships between c and the biomass of an individual [Huang et al., 1992]. The density of individuals ρ is the result of plant death (through “natural” mortality, competition, and disturbances) and sapling establishment when the vegetation dynamics is activated; it is calculated such that ρc = vmax if vmax is prescribed (that is, if the vegetation dynamics is not activated). Note that vmax, which represents the part of the grid cell that is covered by the crowns of a given PFT, does not depend on the leaf mass. For herbaceous PFTs, an individual is defined as a tuft of c = 1 m2.
 The foliage projective cover v (i.e., the fraction of the ground effectively covered by the leaves of the PFT) is calculated through
Here k = 0.5 is the extinction coefficient within the canopy [Monsi and Sæki, 1953] and λ is the leaf area index (LAI), defined as the ratio between the PFT's total leaf surface and vmax. The total leaf surface of a PFT is calculated from the amount of leaf biomass, which is a prognostic variable of the model, and the prescribed specific leaf area. For high leaf area indices, limλ∞v = vmax.
 The role of nitrogen is represented implicitly in the photosynthesis (section 2.3.1) and carbon allocation (section 2.3.3) parameterizations. In ORCHIDEE, fires are the main disturbance affecting the terrestrial vegetation, but a simple parameterization of regular herbivory following McNaughton et al.  is included in ORCHIDEE. Other natural disturbances (such as wind throw) are not taken into account.
 Unlike the original formulation in LPJ, where the time step of vegetation dynamics is 1 year, these calculations are carried out in ORCHIDEE with the time step (Δt = 1 day) of the other parts of the carbon module, and the corresponding variables are updated at this time step. However, in most cases, slowly (i.e., annually) varying input variables are used in these slow vegetation dynamics processes in order to maintain consistency with the basic hypotheses of the parameterizations of LPJ (the following paragraph describes how these variables, such as monthly or seasonal air temperatures, are calculated efficiently in ORCHIDEE). Therefore, for most of the parameterizations of vegetation dynamics in ORCHIDEE, the effective temporal resolution, determined by the temporal inertia of the parameterizations' input variables, is still 1 year. This procedure guarantees a smooth temporal evolution of the variables affected by vegetation dynamics and prevents sometimes dramatic, instantaneous changes which could occur if the LPJ parameterizations were only called once per year. The increase of computational cost corresponding to the higher frequency of vegetation dynamics calculations is very weak. The computationally most expensive part of the model remains the hydrology, which is calculated at a time step of 30 min. The time step of the “slower processes,” leading to a daily update of the corresponding variables seen by the hydrology (primarily LAI and fractional vegetation cover), is also sufficiently small to allow a smooth temporal evolution of the hydrological variables.
 In order to reduce the computer memory requirements, short-term variables Xs (e.g., daily temperatures) are not kept in memory in order to sum them up to obtain long-term variables Xl (e.g., monthly temperatures). Instead, long-term variables Xl are updated at every time step Δt using a linear relaxation method,
where τ is a time constant depending on, and generally somewhat shorter than, the length of the period which Xl is to represent. For example, arithmetic mean temperatures over the preceding 30-day period are best approximated with this relaxation method when τ = 18 days (see Figure 1); weekly arithmetic means are best approximated with τ = 5 days. Of course the “long-term” variable Xl with a given τ will not be exactly the same as the running mean of Xs over the corresponding period, particularly in cases where there is a strong high-frequency variability. For this reason, some parameterizations had to be retuned for the inclusion in the model. Compared to the running mean method, it is possible that this way of calculating long-term variables might in many cases actually be more suitable for parameterizing the physiological processes in plants.
Figure 1. An example presenting the linear relaxation method used to calculate long-term “mean” temperatures in ORCHIDEE. Temperature time series at a model grid point in Siberia: daily mean temperatures (thin solid line); arithmetic average over the preceding 30-day period (thick solid line); “monthly” mean temperature using the relaxation method presented here with τ = 18 days (thick dashed line).
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2.4. Vegetation Dynamics
 The parameterizations of vegetation dynamics have been taken from the model LPJ [Sitch et al., 2003] with minor modifications. A brief description of the minor modifications applied to these parameterizations is given here. The reader is referred to Sitch et al.  for a description of LPJ.
 The basic vegetation dynamics parameterizations included in ORCHIDEE are the introduction/elimination of PFT using climatic criteria, sapling establishment, light competition, fire occurrence and impact on vegetation, and tree mortality. A PFT is declared adapted to a given climate if the instantaneous minimum surface air temperature during the last 12 months has not fallen below a PFT-specific threshold (some PFTs, for example, boreal needleleaf summergreen trees, have no such threshold and are thus regarded as totally insensitive to frost). In this case, the PFT can be introduced if it is not already present. In the opposite case, it will be eliminated. In the original version of LPJ the instantaneous minimum temperature was parameterized as a function of the mean temperature of the coldest month. In ORCHIDEE, with its time step of 30 min for the processes involving surface energy exchanges, the actual instantaneous minimum temperature is taken. This means that the meteorological input data have to capture nighttime minimum temperatures; that is, they must have at least a temporal resolution of a few hours. A further difference to LPJ is that warm season temperatures Tws (Tws is the “seasonal” surface air temperature calculated with the relaxation method presented in section 2.1 using τ = 60 days) must exceed 7°C for trees to be declared adapted to the given climate (no such criterion is applied in LPJ). The motivation for applying this criterion is the observed strong correlation between warm season isotherms and treeline position, which is thought to be due to growth limitation of tree-specific tissue types at low temperatures [Körner, 1998]. However, Kaplan et al.  argued that the use of NPP as a limit on tree growth might be more mechanistic. In any case, the inclusion of the warm season temperature criterion allows ORCHIDEE to simulate correctly the present boreal treeline.
 Sapling establishment increases the density of individuals ρ of a PFT, but in ORCHIDEE, unlike LPJ, no increase of biomass is associated with sapling establishment except if the number of newly established saplings is not negligible compared to the number of already present individuals (in the latter case, this biomass increase is taken into account as carbon flux from the atmosphere to the biosphere such that the total mass of carbon in atmosphere plus biosphere is conserved). Biomass is redistributed between the existing and the newly established plants, and the plant characteristics (LAI, height, etc.) are then recalculated.
 The formulation of fire occurrence [Thonicke et al., 2001] in LPJ follows an intermediate approach between the fire history concept (using statistical relationships between the length of the fire season and the area burnt) and a process-oriented methodology (estimation of fire conditions based on litter quantity and moisture): the length of the fire season is first calculated from daily litter quantity and moisture and is then used to determine the area burnt in 1 year. As in the other parameterizations of LPJ, the time step of the original formulation of fire occurrence [Thonicke et al., 2001] is 1 year. However, in ORCHIDEE it is desirable to dispose of a formulation that explicitly simulates the seasonal variations of fire occurrence, including human-induced fires, as the effect of fires on radiative properties of the atmosphere (via aerosol injection) or on surface conditions during the dry season can be regionally important when large areas burn in short periods [e.g., Hobbs et al., 1997; Ross et al., 1998]. A good simulation of this seasonality is particularly desirable as ORCHIDEE is coupled to an atmospheric GCM. However, due to the strong nonlinearity of the formulations of Thonicke et al. , it is not possible to simply increase the effective temporal resolution of the LPJ fire parameterizations above 1 per year without obtaining unrealistic fire fractions. The solution that was adopted was to increase the effective temporal resolution of the fire parameterizations to 1 month while introducing a corrective term in the calculated fire extents which ensures that the effective annual mean fire extent calculated in this way is equal to the annual mean fire extent that would have been obtained with the original formulation. Using this approach, calculated fire occurrence does exhibit a clear seasonality as a function of drought and litter, and simulated fire return times are reasonable. A parameterization of the transformation of biomass into black carbon, which can be regarded as totally inert at the timescales ORCHIDEE is designed for (a few thousand years at most), has been introduced into the fire subroutine following the work of Kuhlbusch et al. .