wiki:Documentation/TrunkFunctionality4

Version 96 (modified by luyssaert, 7 months ago) (diff)

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ORCHIDEE trunk following r6610

Good to know

The trunk following r6610 is a combination of three code bases: the trunk (branched as ORCHIDEE 3) as it was in 2020, the ORCHIDEE-CN branch in 2016 and the key functionality of ORCHIDEE-CAN.

Consider the model as two parts: 1) core processes that affect fluxes and pools on the per square meter level, and 2) peripheric processes that involve everything else. In this picture, the functionality that is not yet included depends on core processes like vegetation growth, soil hydrology or the energy budget, but it does not affect these processes at the pixel level. The current trunk can be considered a light version of the ORCHIDEE trunk because some of the functionality present in previous trunks has not yet committed (see below). Nevertheless, this light version is believed to contain all functionality that affects the parameterization of the core of the model.

  • The available functionality is described in more detail below
  • Missing functionalities compared to the trunk: DGVM, IPCC wood harvest, prescribed LAI, FORCESOIL, and fire disturbances
  • Missing functionalities compared to ORCHIDEE-CN: none
  • Missing functionalities compared to ORCHIDEE-CAN: multi-layer energy budget
  • Coupling: ORCHIDEE has been coupled to LMDz. Given that several key parameters for the coupling have changed: albedo, roughness (through vegetation dynamics), and more landscape heterogeneity when using age classes, it is expected that lots of testing will be required to come to a satisfying results with LMDzOR.

Fair use policy

Before downloading and using the trunk, please, read and adhere to the ''FAIR USE POLICY''.

Warranty

ORCHIDEE (milestone 4) comes with a 12 month warranty (starting on March 10th 2019)! This warranty is valid for the head of the trunk if no code changes were made and for the default configurations (analytical spinup, FG1, FG2, FG4 and FG5). The warranty applies to model crashes, ipsl errors and obvious bugs (e.g., spikes, sudden changes on January 1st, crazy values, …). Following the establishment of the problem, you need to extract a 1 pixel or 3x3 pixel test case that reproduces the crash on Obelix. A script to extract the restart files for this kind of restricted test cases can be downloaded from svn ( download here). Once the TRUNK FORCE has received the test case, the first response time is 5 working days (outside the holiday period). If your problem falls outside the warranty we can still give assistance and advice.

How do I run this version?

The instructions to download and extract ORCHIDEE are found here. When you check-out the model, you also check-out OCRHIDEE_OL, where OOL_SEC_STO_FG1trans, OOL_SEC_STO_FG2 and SPINUP_ANALYTIC_FG1 have been updated. Use configuration comes with a choice of parameter files that can be used for runs with 3, 13, 15 (default), 28, 37 or 64 PFTs. OOL_SEC_STO_FG4 and OOL_SEC_STO_FG5 are configurations in which classic CAN functionality is used: species, age classes, forest management and litter raking.

Functionalities (alphabetical order)

Age classes

Describes r6614. Age classes were introduced to better handle heterogeneity at the landscape level. The feature allows us to distinguish between different successional stages of the same PFT (e.g., a newly grown forest vs. a mature forest). Age classes are independent of the number of diameter classes. Using age classes adds a lot of details to both the biophysics and the biogeochemistry following natural disturbances, forest management and land cover change. If half of a grassland is afforested with a PFT that already exists in the pixel, previous versions of ORCHIDEE will combine this newly forest land and the existing forest in a single PFT. This will result in, for example, a low albedo, a high roughness, and other properties. When age classes are used, the newly afforested and the existing forest will end up in separate PFTs. One will have a high albedo, the other a low albedo, and other properties may differ significantly as well. In CAN with age classes, PFTs are only merged if the youngest age class for a PFT already has biomass.

Age classes are defined as separate PFTs. Different age classes of the same PFT could therefore be, in principle, run with different parameters. This option has not been tested yet because it is expected to result in discontinuities when the biomass is moved from one age class to another. The number of age classes is fixed for the whole simulation, but for each PFT it can be decided whether age classes are used or not. In other words, if the user chooses four age classes for the simulation, each PFT can have either 1 or 4 age classes. This adds a lot of flexibility to the model. ORCHIDEE trunk, for example, has been run with 64 PFTs, using age classes for European forest and using no age classes for all forests outside the domain of interest. Setting-up a simulation with age classes will require some thinking when creating the orchidee_pft.def. A Python-script was written to create this kind of run.def and is stored in config/ORCHIDEE_OL/MAKE_RUN_DEF. Increasing the number of PFTs has important consequences for the speed of the model and the memory use, although ORCHIDEE trunk does make extensive use of "CYCLE" statements to avoid calculations where no biomass is present. Because a single run can contain PFTs with and PFTs without age classes, processing of the simulation output needs to account for the relationship between PFTs of the same species but a different age class.

It does not make sense to use age classes for runs ignoring land cover change and forest management. Age classes can be used for site level simulations that involve land cover change. Only use age classes if you really need them (e.g., land cover change, forest management, other disturbances), as not using age classes will make post processing of the simulation results considerably easier.

The number of age classes is defined by the parameter NAGEC. Setting this parameter to 1 is a good start unless you have a special interest in using age classes. When NAGEC is set to more than 1, PFT_TO_MTC', AGEC_GROUP and PFT_NAME will all need to be carefully defined. See the different orchidee_pft.defs that come with the standard configurations in config/ORCHIDEE_OL for functional examples. See below for some principles:

  • NAGEC = 4
  • Assume we want to use four age classes for all forests. We will end-up with 37 PFTs: one each for bare soil, three C3 grasses, C4 grass, C3 crop and C4 crop and 4 times 8 for the 8 forest PFTs. Thus NVM = 39
  • Because we still use the 13 default MTCs we can use the default maps. Let the model know how many MTCs it should find on the maps: NVMAP=15
  • If you want to use IMPOSE_VEG=y then vegetation should only be placed in the youngest age class. ORCHIDEE will update the vegetation fractions during the simulations
    SECHIBA_VEG_01=0.0769230769231
    SECHIBA_VEG_02=0.0769230769231
    SECHIBA_VEG_03=0.0
    SECHIBA_VEG_04=0.0
    SECHIBA_VEG_05=0.0
    ...
    SECHIBA_VEGMAX_01=0.0769230769231
    SECHIBA_VEGMAX_02=0.0769230769231
    SECHIBA_VEGMAX_03=0.0
    SECHIBA_VEGMAX_04=0.0
    SECHIBA_VEGMAX_05=0.0
    ...
    
  • Link PFTs to MTCs
    PFT_TO_MTC_01=1
    PFT_TO_MTC_02=2
    PFT_TO_MTC_03=2
    PFT_TO_MTC_04=2
    PFT_TO_MTC_05=2
    ...
    
  • Tell ORCHIDEE which PFTs have a successional relationship
    AGEC_GROUP_01=1
    AGEC_GROUP_02=2
    AGEC_GROUP_03=2
    AGEC_GROUP_04=2
    AGEC_GROUP_05=2
    ...
    
  • Give all PFTs a name for clarity
    PFT_NAME__01=Soilbare
    PFT_NAME__02=Broadleavedevergreentropical_age01
    PFT_NAME__03=Broadleavedevergreentropical_age02
    PFT_NAME__04=Broadleavedevergreentropical_age03
    PFT_NAME__05=Broadleavedevergreentropical_age04
    ...
    

Albedo

Describes r6614. ORCHIDEE trunk makes use of a two stream radiative transfer scheme through the canopy, extended to multiple canopy levels (https://doi.org/10.5194/gmd-2016-280). The scheme is based on Pinty et al 2006. This approach accounts not only for the leaf mass but also for the vertical and horizontal distribution of the leaf mass (=canopy structure), calculating an effective LAI based on the solar angle. Light from collimated (black sky) and diffuse (white sky) sources are used, and both are weighted equally as information about this partitioning is not yet available in forcing data. In ORCHIDEE trunk the same scheme is used to simulate the reflected, transmitted and absorbed light, of which the absorbed light as a function of canopy level is passed to the photosynthesis routines and used in place of the exponential LAI layering found in older versions of the TRUNK (see the section Photosynthesis). This implies that albedo and photosynthesis are now fully consistent as well as the light reaching the forest floor (the latter is used in for example recruitment). ORCHIDEE trunk cannot revert to previous approaches for calculating albedo. The radiative transfer through the canopy is controlled by 3 parameters for each wavelength/band: single leaf scattering leaf_ssa_xxx, forward scattering leaf_psd_xxx and background reflectance bgrd_ref_xxx. At present, both the visible (VIS) and near-infrared (NIR) spectra have been parameterized. Parameterization is based on running an inverse radiation scheme on the MODIS albedo product while accounting for the different land cover types. The inverted parameters are provided by the JRC as the JRC TIP product. Seasonal variation in the background albedo was observed but small and therefore not accounted for.

When snow is present in a pixel, all snow is assumed to reach the ground and the background albedo and the snow albedo (calculated as a function of snow age) are weighted according to their cover fractions (see Background albedo).

Albedo (background)

Describes r6614. If covered by snow, the background albedo is calculated in the VIS and NIR wavelengths by the snow module and accounts for snow age and snow density. If not covered by snow, the background albedo is not simulated but prescribed by the parameters bgd_reflectance(:,ivis) and bgd_reflectance(:,inir). If the background is partly covered by snow, the snow albedo and the background albedo are merged, which allows snow to settle under the canopy, reflecting In deciduous forest, grasslands and croplands, the background albedo is known to be strongly affected by the phenology and senescence of the understory vegetation. ORCHIDEE trunk has two options to prescribe the background albedo:

  • The background albedo is prescribed per PFT but is constant throughout the year. This option has been evaluated over Europe. Set alb_bg_modis = n.
  • The background albedo is constant across PFTs. This option reads background maps. Given that those maps are based on the JRC TIP product, they should be compatible with the new albedo scheme. This option, however, has not been evaluated yet. Set alb_bg_modis = y.

Albedo (snow)

Describes r6614. The snow albedo could be either prescribed (the value of fixed_snow_albedo should then be set in the run.def and will be read in src_parameters/constantes_var.f90) or calculated following Chalita and Treut (1994) ok_snow_albedo_clm3 = n or calculated following CLM3 ok_snow_albedo_clm3 = y (recommended). The difference between the latter two methods has not been tested yet. The CLM method was implemented in ORCHIDEE-CN-CAN and has been tested in combination with the other changes to the albedo calculations. The Chalita and Treut method was added in parallel to the ORCHIDEE trunk 3.0. When merging both versions, we ended up with two option.

Allocation

Describes r7066. ORCHIDEE trunk uses the allometric allocation as developed in O-CN. In ORCHIDEE trunk the approach was adjusted to work for more than one diameter class. Since it was developed this allocation has been used in ORCHIDEE-CN and ORCHIDEE-CNP. In those branches only a single diameter class was used. Except for the way the reserves and labile pools are calculated (incl. the pseudo sugar loading), the allocation scheme remained rather similar between the aforementioned versions. The model is, however, very sensitive to the way the reserves and labile pools are calculated. The allocation makes use of a labile pool for which the activity is calculated based on the temperature. This sensitivity is important at the start and the end of the growing seasons when temperatures may be low. As such the model addresses the sink/source discussion initiated by Körner. Whereas this approach resulted in an acceptable interannual variability in for example NPP in ORCHIDEE-CAN, adding N seems to have dampen the interannual variability a lot/too much. This dampening was observed in ORCHIDEE-CN and ORCHIDEE-CN-CAN. IN ORCHIDEE-CNP the temperature relationship was removed (hence NPP and GPP are strictly coupled) because the interannual variability became unrealistic.

One of the big challenges for all deciduous PFTs is to grow first (in spring), restore the reserves towards the end of the growing season (late summer) and still have enough reserves to grow leaves the next year. To do so a phenological stage called pre-senescence was introduced. This is justified by the observation that tree rings stop growing weeks before senescence starts. Basically allocation occurs when the plant_status is icanopy. When the ratio between Rm and GPP (weekly mean value) drops below a PFT-specific trhreshold, plant_status is changed to ipresenescence. This phenological phase is used to restore the labile and reserve pools. The relationship between allocation and plant status is detailed in the table below.

Table. Allocation is a function of the plant status.

Process Plant_status Snipets of the code and comments
Fresh GPP destination ibudbreak & icanopy GPP goes to labile pool
ipresenescence GPP goes to reserve pool. Divert fresh GPP to reserve pool so that it’s not readily available for allocation. But still be available after rebalance between reserve and labile pool.
isenescent GPP goes to reserve pool
idormant GPP is zero
Calculating bm_alloc_tot ibudbreak & icanopy (1) gtemp > 0; (2) IF use_reserve, shift 5% of reserve to labile pool; and (3) bm_alloc_tot > 0. bm_alloc_tot is taken from labile carbon. bm_alloc_tot(ipts,j) = gtemp(ipts,j)*tmp_bm(ipts,j,ilabile,icarbon)
ipresenescence (1) gtemp > 0; (2) suppress the shift of 5% of reserve to labile pool; and (3) bm_alloc_tot > 0. bm_alloc_tot is taken from labile carbon. bm_alloc_tot(ipts,j) = gtemp(ipts,j)*tmp_bm(ipts,j,ilabile,icarbon)
isenescent gtemp = 0 => bm_alloc_tot = 0. bm_alloc_tot is taken from labile carbon. bm_alloc_tot(ipts,j) = gtemp(ipts,j)*tmp_bm(ipts,j,ilabile,icarbon)
idormant gtemp = 0 => bm_alloc_tot = 0. bm_alloc_tot is taken from labile carbon. bm_alloc_tot(ipts,j) = gtemp(ipts,j)*tmp_bm(ipts,j,ilabile,icarbon)
Maintenance Respiration (Rm) Irrespective of plant_status Rm deficit will be paid by reserve if Rm>bm_alloc_tot, otherwise limit Rm to “bm_alloc_tot + reserve”. Rm could deplete all reserves or reduce it to a too low value, giving trouble to leaf onset in next growing season.
ibudbreak & icanopy Rm deficit will be paid by reserve if Rm>bm_alloc_tot, otherwise limit Rm to “bm_alloc_tot + reserve”.
ipresenescence Rm deficit will be paid by reserve if Rm>bm_alloc_tot, otherwise limit Rm to “bm_alloc_tot + reserve”. Stop tissue allocation but do not truncate Rm. If Rm>bm_alloc_tot: it will be paid by labile; Otherwise, if Rm<bm_alloc_tot, the extra bm_alloc_tot will not go to tissue but go back to reserve. Divert the gpp not consumed by Rm to reserve pool, in order to increase pool.
isenescent Rm deficit will be paid by reserve if Rm>bm_alloc_tot, otherwise limit Rm to “bm_alloc_tot + reserve”. Rm = 0. Suppress Rm to not to lose the accumulated reserve pool.
idormant & ibudavail Rm deficit will be paid by reserve if Rm>bm_alloc_tot, otherwise limit Rm to “bm_alloc_tot + reserve”. Rm = 0. Suppress Rm to not to lose the accumulated reserve pool.
Rebalance between reserve and labile pool Independent of plant_status Allow exchange between reserve and labile pool to reach their optimal share

There are no options to revert to the allocation based on resource limitation (Friedlingstein et al. 1999). All references and parameters for allocation based on resource limitation have been removed from the code (those that were overlooked can be removed). Allometric allocation makes use of the following PFT-specific parameters: sla, longevity_root, longevity_leaf, longevity_sap, pipe_density, tree_ff, pipe_tune_x, k_latosa_max, and k_latosa_min. In addition to this set of parameters that mainly describe the allometric relationships and the longevity of the different tissues, the calculation of the allocation coefficients makes use PFT-specific tissue conductivities, i.e., k_sap, k_belowground, and k_leaf (see also plant water stress). Details on the parameters can be found in the SI of Naudts et al 2015 in GMD or in src_parameters/constantes_mtc.f90.

Previously there was a functional link between C and N-allocation and the hydraulic architecture of a plant because both approaches used the same parameter k_root. In ORCHIDEE-CAN k_root described the conductivity of the fine roots and the soil. In ORCHIDEE trunk this joined conductivity has been split in a fine root conductivity and a soil to root conductivity. Allocation should make use of both conductivities but soil to root conductivity cannot be easily calculated when needed in the allocation. This is subject to future developments. Accounting for the soil to root conductivity in the allocation would imply an adaptation of plant growth to its environment.

Crown area is calculated from tree diameter by making use of pipe_tune1 and pipe_tune_exp_coeff. Since r6933, pipe_tune1 and pipe_tune_exp_coeff can be calculated as a function of respectively pipe_tune2 and pipe_tune3. This has became the default setting. For that reason pipe_tune1 and pipe_tune_exp_coeff are undefined but the parameters crown_to_height and crown_to_height_exp need to be set. If one wants the crown diameter to be 66% of the tree height, crown_to_height needs to be set to 0.66 and crown_to_height_exp to 2. As long as crown_to_height_exp equals to 2, crown_to_height should range between 0 and 0.99. If a different values for crown_to_height_exp it should be checked that the crown diameter never exceeds the tree height as this will make the model crash with a clear error message.

Anthropogenic species change

Describes r6614. Following a disturbance (which could be a clear cut), tree species changes and forest management change can be prescribed or read from a map in ORCHIDEE trunk. Set ok_change_species = y, read_species_change_map = y, and read_desired_fm_map = y and specify the paths of those maps in the COMP/stomate.card. A example of such a configuration can be found in config/ORCHIDEE_OL/OOL_SEC_STO_FG5. This functionality replaces the DGVM in areas where humans rather than nature govern species distribution, for example, Europe. Note that there are some constraints on the possible species changes. If the forest is unmanaged (fm=1), the code assumes that nature will determine the species rather than humans. Anthropogenic species change has not been developed to work together with land cover change. For the moment it is one or the other. When testing this functionality read_species_change_map and/or read_desired_fm_map could be set to n. The new forest management strategy can then be simply prescribed by setting the parameter fm_change_force to one of the four fm strategies. Likewise the new species can be prescribed by setting the parameter species_change_force to the desired PFT number.

Bare soil

Describes r6783. The flag ok_bare_soil_new controls how the bare soil is perceived and calculated. If set to FALSE the total bare soil is still calculated veget_max_1 + sum(veget_max_i - veget_i) with i from 2 to the number of PFTs. When a deciduous PFT sheds its leaves, the gaps in the forest will thus contribute to the bare soil fraction in the grid. Although this approach was introduced a long time ago to get acceptable evaporation estimates from forest, the approach also resulted in using the albedo of PFT1 deserts as the background albedo of forest gaps. In ORCHIDEE v2.1 the background albedo has been reparameterized and this issue may have been largely resolved now if alb_bg_modis = y (more details). From many points of view a dynamic bare soil fraction is strange, e.g., bare soil has its own water column so when moving the forest gaps from to forest to PFT1 the soil water content, soil carbon content, litter layer, etc all changes temporary. If ok_bare_soil_new is set to TRUE, canopy gaps no longer contribute to the bare soil. The new albedo scheme (see Albedo and Background albedo) considers a specific background albedo for each PFT and calculates the albedo of the PFT including the canopy gaps but the calculation of bare soil evaporation underneath a canopy would be problematic. For that reason ok_bare_soil_new is recommended only to be used with the multi-layer energy budget (when run with more than 1 layer). The multi-layer energy budget accounts for within canopy turbulence and can therefore deal with evaporation from beneath a canopy. At present the settings for ok_bare_soil_new are included in the energy_control flag (more details). The default settings combine the new albedo scheme with the single layer energy budget (enerbil) and ok_bare_soil_new = n.

Bark beetles

Describes r6614. The bark beetle module was developed such that it interacts with the windthrow module. If you want to activate it use the flag OK_PEST=y in the run.def. To specify which PFT may be affected by bark beetles use BEETLE_PFT_xxx= TRUE where XXX is the pft you interested in. Note that for the moment bark beetles in ORCHIDEE is parameterized only to work with Picea abies.

C13

Describes r6614. The concentration of C13 in the leaves can be calculated by setting ok_c13 to y in the run.def. This calculation follows Farquhar's approach and is currently limited to the fractionation in the leaves. Following changes to the recalculation of GPP under plant water stress the calculation of Ci is no longer accurate. This may have broken the functionality to calculate C13. Needs to be tested.

Configurations

Describes r7144. The model comes with the following well-tested configurations (config/ORCHIDEE_OL). Well-tested means that these configuration have been tested on different servers and some are part of the trusting:

  • SPINUP: used for single pixel FLUXNET runs.
  • ENSEMBLE: used for single pixel FLUXNET runs (more details).
  • SPINUP_ANALYTIC_FG0(nd): 2x2 degrees CRU-JRA forcing cycles over 1901-1910 between 1801 and 1900. (about the forcing). Start from scratch. 15 PFTs, no land cover changes, nitrogen input for 1850, 1860 or 1900 depending on the species, and fixed CO2 concentrations. During this pre-spinup each forested PFT is clear cut once. This makes the age structure more heterogeneous. Clear cutting is prescribed according to a map such that the process is reproducible. (details and context).
  • SPINUP_ANALYTIC_FG1(nd): 2x2 degrees CRU-JRA forcing cycles over 1901-1910 between 1901 and 2240 (about the forcing). Restart from SPINUP_ANALYTIC_FG0. 15 PFTs, no land cover changes, nitrogen input for 1850, 1860 or 1900 depending on the species, and fixed CO2 concentrations (details and context).
  • OOL_SEC_STO_FG1trans(nd): 2x2 degrees CRU-JRA forcing cycles over 1901-1910 between 1861 and 1900 (about the forcing). Restart from SPINUP_ANALYTIC_FG1. 15 PFTs, no land cover changes, nitrogen input for 1900, and annual CO2 concentrations.
  • OOL_SEC_STO_FG2(nd): 2x2 degrees annual CRU-JRA forcing between 1901 and 2010 (about the forcing). Restart from FG1trans. 15 PFTs, annual land cover changes, annual nitrogen input and annual CO2 concentrations.
  • OOL_SEC_STO_FG3: 0.5x0.5 degrees annual WFDEI_GPCC forcing between 1979-2009 (about the forcing). Start from scratch. 15 PFTs, annual land cover changes, annual nitrogen input and annual CO2 concentrations.
  • OOL_SEC_STO_FG3nd: 0.5x0.5 degrees annual WFDEI_GPCC forcing between 1979-2013 (about the forcing). Start from scratch. 15 PFTs, annual land cover changes, annual nitrogen input and annual CO2 concentrations.
  • OOL_SEC_STO_FG4: ~0.5x0.5 degrees annual CRU-JRA forcing between 1901 and 2010 (about the forcing). Start from scratch. 64 PFTs, European forest are defined at the species level with 4 age classes, forests outside of Europe are defined at the MTC level with 1 age class, annual land cover and tree species changes, annual input deposition, annual CO2 concentrations, annual forest management, and annual litter raking.
  • OOL_SEC_STO_FG5: 1x1 degrees annual IPSL RCP 4.5 forcing between 1911 and 2100. Start from OOL_SEC_STO_FG4. 64 PFTs, no land cover and changes, annual input deposition, annual CO2 concentrations, prescribed species and management changes following annual a stand replacing disturbance, litter raking for 2010.
  • OOL_SEC_STO_FG8nd: a configuration for more detailed studies over France.
  • OOL_SEC: 2x2 degrees annual CRU-JRA forcing between 1901 and 2010 (about the forcing). Start from scratch. 15 PFTs prescribed by reading in a biomass map. Land cover changes, input deposition, CO2 concentrations, and all other biogeochemical and ecological processes are deactivated. This configuration is under development, waiting for a global LAI map (in case of CN-CAN this has become biomass map) to be tested.
  • FORCESOIL: was restored for ORCHIDEE trunk 3 but was not maintained for ORCHIDEE trunk (see ticket #684).

Consistency checks

Describes r6947. The code distinguishes between three options to check for mass and surface conservation. These options are controlled by the parameter err_act. Always use err_act = 3 when developing and testing the code. Note that in addition to checking for mass balance closure ORCHIDEE trunk will also check for the conservation of veget_max and frac_nobio. This is useful to make sure no surface area is lost when moving biomass from one PFT to another following natural disturbances, forest management, land cover changes and when using age classes. In some parts of the code, for example, modules that deal with disturbances, it is assumed that the tallest trees are stored in the last diameter class. When the difference in diameter between diameter classes becomes very small, this assumption could be violated. Therefore, the diameter classes are sorted to enforce the assumed order and where needed the order is checked.

  • err_act = 1 is recommended when running global long-term simulations. Under this option, mass balance closure is checked for all biogeochemical processes but only at the highest level thus stomate.f90 and stomate_lpj.f90. Although the mass balance checks are not very expensive in terms of computer time, skipping the numerous lower level checks is expected to save some time. Under this option the total mass balance error is only written to the history file. No information is provided in which subroutine the problem occurred.
  • err_act = 2 is recommended when developing and testing the model. Now the mass balance is explicitly checked in stomate.f90, stomate_lpj.f90 and all its subroutines. Under this option the mass balance error is written to the history file and if the mass balance is not closed, the warning message will indicate in which subroutine the problem likely originated.
  • arr_act = 3 is recommended when having a problem with mass balance closure. The mass balance is explicitly checked in stomate.f90, stomate_lpj.f90 and all its subroutines. If a mass balance error occurs, the model is stopped. It is expected that within the time step of the subroutine a precision of 1e-8 is achieved. If not the model will crash. Additionally consistency between flux-based and pool-based NBP is checked. This check represent an integrated check which give confidence for the technical implementation of the C and N cycles. Comparing the flux and pool based nbp estimates is an instantaneous check that covers just a single day in the model (thus 48 time steps for the soil and litter calcuations which are called from stomate.f90). The cross-consistency check for the flux and pool based NBP requires mass balance closure and area preservation from all all subroutines being called from stomate.f90 including stomate_lpj.f90. This check should always be satisfied, if not there is a bug somewhere! The mass balance of individual subroutines teaches us about the calculations performed within that subroutine. The flux and pool based NBP consistency check teaches us of how these fluxes and pools should be accounted for, passing this check requires additional understanding of the flow of the code (i.e., bm_to_litter) and how the different time steps come together.

As an additional consistency check the time integral of the NBP (from 0 -> t) should equal the C and N stock at time step t. Comparing the time integral of NEP vs the all pools includes all the accumulated precision errors. If those errors would be random there would be no problem but they are more likely biases. Hence, when the fluxes and pools increase several order of magnitudes (e.g toward 1e4) and a small bias (1e-12) is integrated over 10,000 days (1e4) the divergence between the time integral of NEP and the pools will approach/exceed 1-8 and woukd stop the model. This is a fundamental problem related to machine precision and the way we do the calculations (think of the so called catastrophic rounding errors that may occur with floating numbers). For this reason this check is written to the history files but does not make the model crash. The user will have to decide whether (s)he is facing a precision error or a real bug. Fixing precision errors will require to improve the precision of the calculations in one or several subroutines.

Croplands

Describes r6614. Makes use of crop_planting and crop_harvest in the module sapiens_agriculture. Harvest goes into harvest_pool (just like wood) and its decomposition is accounted for in the fluxes from the product use pool.

CWRR vs Choinel

Describes r6614. Following ORCHIDEE trunk 3, the Choinel code is no longer available in ORCHIDEE trunk. The hydrological schemes were not touched during the development of ORCHIDEE trunk. The hydraulic architecture in ORCHIDEE trunk relies on CWRR.

Diameter classes

Describes r6614. Diameter classes were introduced to better simulate the canopy structure, they are a tool to simulate heterogeneity within a single PFT. Given that the canopy is the interface between the land and the atmosphere this feature has effects well beyond forest management. Stand structure was observed to affect albedo, transpiration, photosynthesis, soil temperature, roughness length, and recruitment. Using diameter classes adds very little complexity to setting-up the simulations as well as to the output files. The complexity is mostly within the code.

The computational cost of using diameter classes is negligible and when a reasonable low number of diameter classes is used, the memory cost remains very small as the dimensions of only two variables are increased. The number of diameter classes is the same for all PFTs and is set by the parameter NCIRC. ORCHIDEE-CN, and ORCHIDEE-CNP are coded and used for NCIRC = 1. ORCHIDEE trunk was coded and tested for NCIRC = 3. Until further testing, three diameter classes are considered sufficient although for some specific application, i.e., simulating tree ring width NCIRC = 5 resulted in slightly better model performance. The branch ORCHIDEE-CN-CAN that preceded this trunk version has been run with one diameter class as well (this should be confirmed for ORCHIDEE trunk).

Given earlier choices in the ORCHIDEE model, we either need to define the boundaries of each diameter class or the diameter distribution. While developing the code, we considered the second approach the most flexible. To allow maximal flexibility, each diameter class needs to be defined separately by the variable CIRC_CLASS_DIST_0000X, where X is the number of the diameter class. The smallest number presents the smallest diameter class. From literature it is known that a truncated exponential distribution is a good first guess:

CIRC_CLASS_DIST_00001=9
CIRC_CLASS_DIST_00002=5
CIRC_CLASS_DIST_00003=1 

The above declaration implies that 9/15th of the trees will always be in the smallest diameter class, 5/15th will be in the medium class and 1 tree out of 15 will be in the largest diameter class. These ratios are kept throughout the simulations and the boundaries of the diameter classes are adjusted to respect this constraint. Consequently, an even-aged stand will be simulated with three diameter classes where the diameter of the first class may be, for example, 20.3 cm, the diameter of the second class 20.4 cm and the diameter of the third class 20.5 cm. The same code and set-up allows to simulate, in the same simulation, an uneven-aged stand for the same PFT but in a different pixel with, for example, the smallest diameter 7 cm, the medium diameter 25 cm and the largest diameter 45 cm.

Forced clear cut

Describes r6614. FORCED_CLEAR_CUT is a flag used to force ORCHIDEE trunk to clearcut a forest after one year of simulation. This flag is set to TRUE in order to start a new stand at the beginning of the FIN step in ENSEMBLE runs. It helps us to control the stand age at the end of the HIST step. If you want to use this flag for other purposes, do not forget that a clearcut means that the majority of the living biomass is removed (circ_class_biomass for sapwood and heartwood), but the other pools are transferred in the litter pool (leaf, branches, fruit, fine root). Note that if FORCED_CLEAR_CUT is used, the model will clearcut at the end of every year in that run. The typical set-up should be: 300 years of spin-up with FORCED_CLEAR_CUT set to FALSE, 1 year with FORCED_CLEAR_CUT set to TRUE, a simulation with the length similar to the age of the forest with FORCED_CLEAR_CUT set to FALSE.

Forest management and management changes

Describes r6614. 70% of the global forests are managed, which contradicts the assumption in previous versions of ORCHIDEE that forests are long-lived natural vegetation. Forest management, inspired by ORCHIDEE-FM, was implemented in ORCHIDEE-CAN. Owing to the allometric allocation scheme, the introduction of diameter classes and a canopy structure, only the principles of ORCHIDEE-FM, i.e., the allocation rule of Deleuze and Dhote that allocates carbon to different diameter classes based on the basal area of the tree, and relative-density index (RDI)-based management which enforces thinning and harvest operations based on the current tree density and the self-thinning density, were retained.

The forest management strategy can either be forced as a single value to all PFTs and grid cells, or be read from an input map to allow for spatially and temporally varying strategies. If the forest management strategy is not specified the default value "unmanaged" (FM = 1) is used. This implies that the stand is never thinned or harvested. Once the stand density drops below the threshold or the tree diameter exceeds a different threshold, a stand replacing disturbance occurs and a new stand is prescribed in the next time step. Therefore, the biomass pools in ORCHIDEE trunk no longer depend on a prescribed longevity.

When developing and testing the model, a single forest management strategy can be applied for all pixels and PFTs. Set read_fm_map to n and specify the desired management strategy (1-4) through forest_managed_forced. ORCHIDEE trunk distinguishes 4 different strategies:

  • FM=1 unmanaged
  • FM=2 high stand management: with RDI based thinnings and density/diameter based final harvest
  • FM=3 coppice
  • FM=4 short rotation coppice with willow or poplar

For applications that focus on forestry or require landscape heterogeneity, a PFT-specific management strategy can be read from a spatially explicit map. Thus, the same PFT in different pixels can be assigned a different management strategy. However, within a pixel a single PFT can only have one management strategy. Unless, one wants to run forest management over Europe the user will have to create his/her forest management maps first (see ticket #647). Set read_fm_map to y and specify the location of the forest management map in COMP/stomate.card. Check OOL_SEC_STO_FG4 for an example of how the existing map should be defined and used.

The details of each of the 4 management strategies can be refined through a set of PFT-specific parameters. Note that not every management strategy makes use of all parameters. For more details see the SI of Naudts et al 2015 (last table). The different management strategies require parameter values for : harvest diameter max_harvest_dia, coppice diameter coppice_diameter, rotation length src_rot_length, number of rotations src_nrots, fuelwood diameter fuelwood_diameter and the minimum and maximum alpha and beta (thus 4 parameters) specifying the RDI range alpha_rdi_upper, alpha_rdi_lower, beta_rdi_upper and beta_rdi_lower.

According to economic theory, high-stand forest are harvested when the actual growth drops below the long-term growth. This has been implemented in ORCHIDEE trunk. This feature was found to be very sensitive to the time frame for which actual increment was calculated. This option can be by-passed by setting this period unrealistically high, for example, n_pai =1000. Persons interested in further testing/developing this feature should set this parameter (unit: years) to 5 or 10 and go from there.

Following a disturbance (which could be a clear cut), tree species changes and forest management change can be prescribed or read from a map in ORCHIDEE trunk. Set lchange_species = y, read_species_change_map = y, and read_desired_fm_map = y and specify the paths of those maps in the COMP/stomate.card. This functionality replaces the DGVM in areas where humans rather than nature govern species distribution, for example, Europe. Note that there are some constraints on the possible management changes. If the species is a conifer, for example, the new management strategy cannot be coppicing (fm=3). Anthropogenic species change has not been developed to work together with land cover change (this would require some good bookkeeping for veget_max). Forest management change has not been tested together with land cover change but because they affect different variables, i.e., forest_managed and veget_max combining both functionalities seems not overly complex.

Good to know: the forest management approach is embedded in the carrying capacity of a stand which has been formalized through the self-thinning relationship which makes use of two parameters alpha_self_thinning and beta_self_thinning. As a fail-safe option the longevity of a stand is still defined but will only be used when all other criteria fail to kill the stand (not observed). Longevity is defined by the parameter residence_time.

Grasslands

Describes r6614. In terms of grassland phenology ORCHIDEE trunk follows branch 3 as closely as possible. For the moment ORCHIDEE trunk is also using Nicolas's patch that enforces senescence if too many of the leaves are in the oldest leaf age class. This patch is controlled by the LNVGRASSPATCH flag, and is set to T in the standard run.def.

Part of the turnover goes into the harvest pool (harvest_fraction is now hard coded to 0.5 for sapwood and leaves and 0 for roots in stomate_turnover.f90 - see ticket #687). This simple parameter should be replaced by a grassland management module but for the moment 50% of the turnover goes into the harvest_pool (just like wood) and its decomposition is accounted for in the fluxes from the product use pool.

Harvest

Describes r6614. All biomass harvest is recorded in a harvest variable harvest_pool, this variable also stores the mass and dimensions of the harvest/mortality (absolute numbers thus accounting for the pixel area!). Related variables were introduced: harvest_type, harvest_cut, and harvest_area. Wood product pools and fluxes from wood and biomass decomposition are calculated in a separate module dedicated to wood use. The dimension of the wood use pools were externalized and can be changed to better address regional differences when aiming for regional simulations. The default 1, 10 and 100 year pools were replaced by 2, 17 and 50 years which is closer to the reality for Europe. For most parts of the world a 100 year wood pool is very optimistic.

Height (of vegetation)

Describes r7089. Vegetation height is dynamic; as the PFT (in particular forests) grow, the height will grow through the allocation. When multiple circumferences classes are used, height has an impact on where the carbon goes. Due to intra-stand competition, more carbon gets allocated to taller circumference classes. Disturbances can change the average stand height. Self-thinning is a natural process in which small trees die because they are out-competed for resources. Environmental mortality is another natural process in which larger trees are killed as they are assumed to be more sensitive to environmental stress. Windthrow is an (optional) process which will disproporationally impact taller trees. In runs using forest management (not currently the default), forests can be thinned from above or below. Forests thinned from above lose tall trees to harvest, while those thinned from below lose small trees. Both actions change the average height of the stand.

Vegetation height has an impact on roughness height (aerodynamic properties of the surface).

PFTs are prescribed by choosing the height of the saplings, which determines initial biomass through allometric relations.

Two variables are output for each PFT: "HEIGHT", which is the average height of the vegetation (averaged across all circumference classes), and "HEIGHT_DOM", which is the dominant height (the height of the largest circumference class). A switch was introduced in r7083 which allows one or the other to be used in the calculation of the roughness height.

Land cover change (with age classes)

Describes r6614. Land cover change now accounts for age classes. It is controlled by veget_update. Set veget_update = 0Y if land cover change should be disabled. The wood pool and its subsequent fluxes were moved from the land cover change routine to a separate routine. Furthermore, land cover change also deals with the change of biological land uses to non biological land uses (of which the most important change is probably urbanization). If urbanization happens, all the carbon an nitrogen are stored in a series of variables burried_xxx where xxx stands for a different pool, e.g., litter, soil, .... Burried_xxx are cumulative variables thus increasing over time . There is a place holder in sapiens_lcchange.90 to also develop the release of the buried carbon and nitrogen following de-urbanization (see ticket #616). The series of the burried_xxx variables are not yet written to an output file but this could be easily added (they are already defined in the xml files).

An interesting parameter is min_vegfrac. When reading in a land cover map, PFTs with a fraction below min_vegfrac are removed. Likewise the fraction cover of a PFT after a land cover change should not be less than min_vegfrac either. This requirement seems to have been solely established to avoid ending up with too many PFTs with very small fractions. Because the the non-biological and biological fraction covers of each pixel should sum up to one, removing even these very small fractions implies that these fractions need to be added to one of the remaining PFTs. First it is tried to add the fraction to the bare soil (this will only be accepted if the new fraction of the bare soil exceeds min_vegfrac), then the code tries to allocate the residual fraction to the largest vegetated fraction. If age classes are used this should be the largest vegetated fraction in the first age class of a PFT. If all of the above failed, the residual fraction is added to frac_nobio irrespective of whether frac_nobio exceeds min_vegfrac. Everytime this happens, the failure to meet the min_vegfrac criterion is registered in the variable failed_vegfrac. This variable is not yet added to an output file.

Note that the min_vegfrac criterion could be the reason of why very small land cover changes occur. Another consequence is that the land cover fractions in the model are not exactly the same as those read in from the maps. Deviations should remain small and should not accumulate over time. Assume that in y0 the fraction of PFT2 = 0. In y1 the map tells us the fraction is half of min_vegfrac. The model will keep the PFT fraction to zero. The model and the map will no longer be in line with each other. In y2 the map tells us the fraction is twice min_vegfrac. The model will now accept the change. The model and the map will be in line with each other.

Leaf area

Describes rXXXX. There is no longer a parameter for maximum LAI in the trunk. Given that LAI is a key variable that links the biogeochemistry to the biophysics in ORCHIDEE it deserves some background information. LAI is now calculated as a prognostic variable and is the net result of growth, allocation and turnover. There are few parameters in stomate that do not affect the LAI but three parameters stand out:

  • Growth is largely determined by GPP and Ra. (a) GPP is mainly driven by the LAI so this results in an interesting feedback. If the LAI is very low, GPP will be low, growth will be low, LAI will remain low, etc. When GPP is too low to make the LAI, the vegetation will die. This could be rather quick, i.e., within a year, or it could be rather slow, i.e., several years. Given this is a feedback loop there is no single parameter that can be used to control this feedback. (b) The ratio of NPP/GPP or the fraction of GPP that is used in autothropic respiration and especially maintenance respiration will to a large extent determine how much C is availble for growth. If the LAI is very high or low, always check the NPP/GPP ratio. Ra and thus the NPP/GPP ratio can be tuned through the MAINT_RESP_SLOPE_xxx variables and the COEFF_MAINT_INIT.
  • Allocation distributes the growth across the different plant components. The parameter that controls the fraction that ends up in the leaves is K_LATOSA_xxx. This variable can be used to tune LAI but the effect of increasing K_LATOSA_xxx decreases for larger values (possibly due to N-limitation). The range of observed K_LATOSA_xxx is quite large. The model shows a nice sensitivity within the observed range. Note that allocation is a "zero sum game", hence, allocating less C to other components will result in more C going to the leaves. Note that the leaves (GPP) and roots (N-uptake) are necessary to grow in the first place (see section Leaf and roots).
  • Turnover will decrease the leaf area. The parameter that controls the turnover is LONGEVITY_LEAF. For evergreen PFTs the relationship between longevity_leaf and turnover is straightforward. For deciduous PFTs the relationship is more confusing (see section Leaf longevity and turnover). Turnover also affects the leaf age and young leaves have a higher VCMAX than old leaves. The age-effect on vcmax is strong enough to make that a small young canopy may have a higher GPP than a large old canopy.

Leaf area index map

Describes r6614. Four flags have been identified that control the model behavior in terms of lai: ok_stomate, ok_pheno, impveg and read_lai. There is a 5th implicit flag which is whether restart files are used or not. If a restart file is used, the lai values will come from the sechiba restart file which is read at t=48. Given that each flag can take two values, we have 32 configurations in total. Out of these 32 configurations 10 are defined of which about 5 to 7 seem to be intended (for more details see Start and restart - Table 1). Many of the remaining 22 settings are inconsistent (i.e. running stomate to calculate a lai and reading an lai_map to prescribe lai), duplicate other settings, or would require further developments to work properly. Furthermore, the current code does not stop or warn when inconsistent settings are selected. The table (see Start and restart) proposes a scheme with 2 flags which can run with our without restart files, thus resulting in 8 different ways to control the lai in sechiba or the initial lai in stomate. The remaining 2 combinations are inconsistent and will stop the model.

In previous ORCHIDEE trunks, canopy structure is prescribed by a single variable called lai and the assumption of a turbid medium (Lambert-Beer). Consequently reading an lai value suffice to prescribe the entire canopy. In ORCHIDEE trunk, however, canopy structure has become a 3D property that can be calculated from the leaf biomass, stem biomass, the number of individuals and the assumptions that the trees follow a Poisson distribution in the horizontal plain, that the crowns are spherical and that the leaf biomass is uniformly distributed within the crowns.

The functionality to simply prescribe an lai value (either through impose or by reading a map) will need to be replaced by functionality that prescribes or reads the biomass, leaf age, and number of individuals. Given that the current lai map is based on a previous ORCHIDEE run, the same approach could be used to generate a spatially explicit canopy map that contains biomass, individuals and leaf age distribution. Nevertheless, reading an observed lai, i.e., from MODIS, and using it to force ORCHIDEE trunk would require a substantial number of assumptions to turn an aggregated 1D lai value into a disaggregated 3D canopy at the PFT level. For the moment the code (slowproc.f90) contains a simplified place holder that only works with NCIRC=3 and it doesn’t read leaf_age.

Leaf longevity and turnover

Describes r6908. Longevity_leaf is a prescribed parameter for the longevity of a typical leaf/needle at the average temperature for that PFT. For PFTs where a large range in longevity has been observed, this is accounted for in the calculation of leaf_age_crit. Leaf_age_crit is thus the location-specific longevity. It the age of a leaf/needle exceeds half the value for crit_leaf_age, leaf, root and sapwood turnover will start. Because of the way leaf_age_crit is calculated the simulated longevity differs from the prescribed longevity. For example, longevity_leaf = 220, leaf_age_crit = 165 at 25C, 220 at 15C and 330 at 5C. Note that turnover starts when leaf age is half this value. Thus at 110 days in the temperate zone which is within the observations.

Turnover will result in an imbalance between the vegetation components and this balance will be restored in the allocation routines. Leaf turnover will thus replace old leaves (with a low VCMAX) by young leaves (with a high VCMAX). Despite the fact that leaves are being killed, the GPP may increase and eventually the LAI may even increase.

For evergreen PFTs turnover from ageing is easy to understand as it is the only source of turnover. A longevity_leaf of 730 days will result in a two year canopy if the PFT is growing correctly. For deciduous PFTs the meaning of longevity_leaf is a bit harder to grasp. It affects the calculation of leaf_age_crit and once half the value of leaf_age_crit is reached it will determine the magnitude of the turnover but the age of the leaves is mostly driven by the phenology (senescence). For deciduous species turnover from ageing can thus be understood as the leaf turnover between plant_status = icanopy and plant_status = isenescent. The contribution leaf turnover from ageing can be monitored by two variables: LEAF_TURN_AGEING_c and LEAF_M_MAX_c. When expressed as a percentage, the within season turnover varies between 0 and 30% for trees and reaches 100 to 150% for grasses. This seems to be acceptable.

Litter decomposition

Describes r6614. After large-scale dieback events (with a closed n-cycle, i.e., impose_cn = n), so much soil mineral N becomes immobilized to decompose litter that too little N is left for plant regrowth. To address this, we implicitly represent the action of fungivores, which eat the decomposing fungi and release N for the plants and increase N turnover rates. We set aside a fraction of qd (stomate_litter.f90) which becomes available for plant uptake in nitrogen_dynamics. This fraction is calculated and is at its maximum when the litter pool is large compared to the biomass pool. The fraction is at its lowest when the living biomass is high compared to the litter biomass. The implemented principle mimics a Lokta-Volta dynamic where the predator are the fungivores and the prey the fungi. The share of the N contained in the decomposing fungi that is released as an excrement from the fungivores ranges between 0 and 1 and is calculated.

After a die-back (with a closed n-cycle, i.e., impose_cn = n) too little N is available to reach the target C:N ratio of the receiving soil pool, meaning that carbon litter cannot decompose. If the C:N ratio is not controlled it spins out of control reaching values of more than 600. The target C:N ratio, which is a variable in stomate_litter.f90, can now vary between 1 and max_cn where the latter can be set in the run.def. Good results were obtained by setting max_cn to 250. max_cn is bound between 1 and 400, the C:N ratio of wood which is the pool with the highest C:N ratio (and biomass that contributes to the litter). During the simulation the target C:N ratio varies and a couple of decades after the dieback reaches the expected value between 5 and 20. The ecological justification behind a max_cn above the C:N ratio of the fungi is that mycorrhizae receive their C from the plant photosynthesis products. Thus, litter can still decompose even though C is not incorporated into the plant biomass. max_cn can be set in the run.def:

MAX_CN = 250

ORCHIDEE-CN allows negative n_mineralisation values to be passed to stomate_soilcarbon where they are treated as immobilization. If we have persistent negative n_mineralisation, however, we can reach a situation where we completely deplete our soil_n_min pools to satisfy the demand for immobilization. The model cannot recover from such a condition. This tends to happen when there is no plant recruitment, in which case a generation of trees will all reach their maximum diameter and die at the same time. This creates an enormous amount of litter, meaning our som_input values become relatively large and when we subtract som_input from n_mineralisation, the values become negative. To remedy this, we'll truncate som_input based on the current mineralisation pool, but we'll also limit the occurrence of negative values. We'll set a minimum min_n for n_mineralisation so we can keep some N in the soil (and so the simulation can recover). This problem was discovered and fixed before adding the fungivores and max_cn. It is not clear whether this fix is still needed but it looks like a good problem-catcher. min_n can be set in the run.def:

MIN_N = .0001

Litter raking

Describes r6614. Tree litter was collected from the forest and used in the winter in stables instead of straw. In spring the litter and manure was spread on the croplands. This lateral flow of C and N between PFTS in the same pixel can be accounted for in ORCHIDEE trunk by setting use_litter_raking = y. If litter raking is to be used, the model will search for annual maps. The path of these maps needs to be specified in COMP/stomate.card. Litter raking maps were prepared for Europe. Unless liter raking is your research topic set use_litter_raking = n. An example of to set up the model to make use of the historical litter raking maps can be found in config/ORCHIDEE_OL/OOL_SEC_STO_FG4

Litter resistance to evaporation

Describes r6783. The default for the flag do_rsoil has changed to TRUE. When set to TRUE the flag reduces the bare soil evaporation because it accounts for the moisture content of the litter layer (Sellers et al., 1992). The presence of litter always acts as an extra barrier (resistance) to bare soil evaporation. If the litter layer dries out the resistance will even further increase. As in previous version, ORCHIDEE calculates the bare soil fraction as PFT1 + SUM(veget_max_i - veget_i) where i goes from 2 to the number of PFTs. veget is now calculated by using Pgap which resulted in more bare soil, the extra resistance from litter helps to keep to evapotranspiration in check.

Mortality

Describes r6614. ORCHIDEE trunk distinguishes 2 types of natural mortality: (1) explicitly considering mortality from disturbances and self-thinning, and (2) implicitly considering background mortality. Ideally, approach (1) should be further developed such that all underlying agents driving background mortality are represented in the model (i.e., gap-scale mortality, pests, disease, windthrow, etc.) such that it can replace approach (2). Two options of background mortality may be chosen: constant background mortality and dynamic background mortality. To use the first option, set the flag constant_mortality = y. The background mortality of a forests is calculated as a constant, prescribed fraction. In trunk, this fraction is given by residence_time (see also forest management). Otherwise set constant_mortality = n, the dynamic background mortality of a forest is a function of its net primary production (npp). If npp decreases, mortality will increase Both options have been developed but only constant_mortality = y has been tested in ORCHIDEE trunk.

However, because of the introduction of self-thinning in ORCHIDEE trunk, constant_mortality = y became the default setting. In ORCHIDEE trunk, the total mortality is the maximum of the background mortality and the mortality from self-thinning. Only if self-thinning is absent or too low, background mortality will play a role. This approach implies that when constant_mortality = y is used in combination with self-thinning, background mortality will only play a role in the first years to decade before self-thinning starts (the latest calculations of RDI - see Prescribe - the role of the background mortality has further decreased). Despite its limited use, it represents an essential process: owing to background mortality, the number of individuals decreases, the remaining individuals grow faster and thus manage to reach self-thinning in a reasonable amount of time.

ORCHIDEE trunk calculates the number of individuals and uses this as a criterion to initiate a stand replacing disturbance. This approach, guided by the self-thinning relationship, avoids the need for a stand-level turnover time. ORCHIDEE-CN, and ORCHIDEE-CNP still make use of stand-level turnover. Note that the meaning of residence_time is very different between the CAN branch and the trunk. In the trunk biomass has no age and thus the residence time accounts for all forest dynamics including self-thinning, pests, diseases and windthrow. In the CAN branch, biomass does have an age and self-thinning is explicitly accounted for, hence, the residence time should be much higher as it only accounts for pest, diseases and windthrow. Even the latter is not exact because as long as those disturbances are small scale they are probably accounted for in the parametrization of self-thinning.

Nitrogen cycle

Describes r6614. ORCHIDEE trunk strictly follows ORCHIDEE trunk 3 where it concerns the implementation of the N-cycle. Following mass balance problems caused by negative N mineralization and followed by negative immobilization, the code has been slightly adjusted to ensure mass balance closure. First the flag stomate_ok_ncycle needs to be set to y, to run the model with a N-cycle. Subsequently the parameter impose_cn is used to control the N-cycle calculations. If set to y, C/N ratios are calculated but whenever N appears to be limiting, it is taken from the atmosphere to satisfy this need. This is the preferred setting when testing/developing the code without a proper spin-up. N-limitation will only be accounted for when setting impose_cn = n. With this setting the N-cycle is closed (checked when checking for mass balance closure) it requires a spin-up to produce reasonable results.

The paths of the N-inputs from atmospheric N-deposition, fertilization, and biological nitrogen fixation (Nammonium_FILE, Nnitrate_FILE, Nfert_FILE , NManure_FILE and Nbnf_FILE) are set in the stomate.card. Moreover, the N inputs you wish to include in your simulation can be specified in the run.def. If set to NONE, the given N input files will not be read.

Nammonium_FILE= ndep_nhx.nc
Nnitrate_FILE= ndep_noy.nc
Nnitrate_VAR=noy
Nammonium_VAR=nhx

Nfert_cropland_FILE = nfert_cropland.nc
Nfert_cropland_VAR = nfer
Nmanure_cropland_FILE = nmanure_cropland.nc
Nmanure_cropland_VAR = Nmanure

Nfert_pasture_FILE = nfert_pasture.nc
Nfert_pasture_VAR = Nfer
Nmanure_pasture_FILE = nmanure_pasture.nc
Nmanure_pasture_VAR = Nmanure

Nbnf_FILE= bnf.nc
Nbnf_VAR= BNF_MGN_PERM2_PERYR

NINPUT_UPDATE=_AUTO_
INPUT_SUFFIX_YEAR = n

Nitrogen reserve pools

Describes r6904. As for carbon, the model simulates two non-structural nitrogen pools: labile and reserve. Nitrogen uptake is stored in the labile pool, and after the growth, nitrogen in excess of the calculated target labile pool is moved to reserve nitrogen. Before r6825, nitrogen limitation during plant growth was rather common but was solely based on the labile nitrogen pool. It was rather common that the labile pool was empty but that at the same time the reserve pool was very well filled. Plant growth experienced n-stress despite having a good amount of N in the reserves. To deal with this issue, r6825 also considers the reserve nitrogen for estimating nitrogen limitation. The parameter use_reserve_n controls the functionality along with nitrogen uptake feedback which was added r6869 (See Root nitrogen uptake).

Parameter files

Describes r6614. A model run now requires three different parameter files: run.def, orchidee.def and orchidee_pft.def. LibIGCM reads these files in COMP/orchidee_ol.card.

  • The run.def contains all the information for the driver. This is also the file where the default global domain can be reduced (LIMIT_N, LIMIT_S, LIMIT_E and LIMIT_W). When the same experiment is to run in off-line and coupled mode, only the run.def should be changed.
  • The orchidee.def contains the details of the experiment together with the settings of the orchidee_ol, sechiba and stomate cards in COMP. Each of the predefined configurations in config/ORCHIDEE_OL will have different settings. These files need to set up manually because each experiment will differ. Copy the best matching predefined experiment from config/ORCHIDEE_OL and use it as a template to set-up your own simulation experiment.
  • The orchidee_pft.def is available in different configurations. The configuration depends on the interest of the user in vegetation and its dynamics. The default setting for the moment is the same as the trunk, i.e., 15 pfts with a single age class. Nevertheless other useful orchidee_pft.def files are available. Users can make their own files by making use of the script in the MAKE_RUN_DEF folder. Note that different numbers of PFTs will require matching land cover maps. Also the order of the PFTs in the run.def should follow the order of the PFTs in the map. There is no quality control to check whether definition files and input maps following the same PFT order. If the PFT maps are available, users can run each of the predefined simulation experiments (config/ORCHIDEE_OL) with all available orchidee_pft.def files. The number of PFTs largely determines the speed of the model.

How to use a different orchidee_pft.def file?

  • Go to COMP/orchidee_ol.card and change the value of DefSuffix. DefSuffix = 15pft.1ac will make use of orchidee_pft.def_15pft.1ac, change DefSuffix = 3pft.1ac to have the model use orchidee_pft.def_3pft.1ac. You can also use your own customized orchidee_pft.def, just make sure the follow the naming guideline.
  • make sure the PFT map has the correct number of units nvm vs. nvmap. These numbers are set in orchidee_pft.def but affect the expectation of the model when reading the land cover map. The correct land cover map is set in COMP/sechiba.card
  • orchidee_pft.def with 3 PFTs make for a powerful test-case. Most of the output variables in stomate have their units in something per square meter of vegetation. So two identical PFTs with different vegetation fractions should still result in the same stomate outputs. If this is not the case, something went wrong with the units (and likely the way veget and/or veget_max are used in the model. To use this test case remember to set impose_veg = y (PARAM/orchidee.def) and veget_update = 0Y (COMP/sechiba.card). The test case can be run over large spatial domains which may also help to find bugs.
  • orchidee_pft.def with 64 PFTs demonstrates how MTCs, PFTs, species and age classes can all be combined in a single set-up. The species parameters are limited to the European domain, the MTCs are used outside of Europe. In Europe 4 age classes are distinguished, outside of Europe a single age is used. This is the set-up that was used in Naudts et al 2016 and Luyssaert et al 2018.

Parameters - good to know

k_latosa

  • While parameterizing CAN, the response to k_latosa appeared almost linear. More recent tests with r6818 and a focus on PFT4 showed a clear nonlinear relationship between k_latosa and annual mean LAI. LAI saturated when k_latosa increased: 2500 to 4500 in steps of 500.
  • Around r7124 we think we understand this saturation. GPP is the input of carbon. We can only allocate to tissue if we have enough N. If we don't, it goes to reserve pool. If we have too much in the reserves, then we reduce GPP with sugar loading. Higher LAIs will initially result in higher GPP but if the system lacks N to allocate the feedback through sugar loading may reduce the GPP on the long term. The model needs a way to "burn off" the GPP that could not be allocated. Leaching of labile carbon to the soil (thought to be responsible for up to 20-30% of the NPP - textbook by Chapin 2012) comes to mind. As leaching has not been implemented yet, we could add leaching artificially by turning over the roots. So now we have high GPP, same amount of C goes to tissue, but rather than the excess going to the reserves (and decreasing GPP through the sugar loading) it turns over the roots. This could also increase the soil carbon, which is still desirable around r7113.

Parameter compensations

  • When setting up factorial experiments to quantify model sensitivity, we often assume additive effect and ignore the interactions between parameters. Tests with r6818 and a focus on PFT4 demonstrated clear non-additive effects. Five sets of parameters for PFT4 were stepwise replaced with the parameters for PFT7.

set1: k_latosa, longevity_leaf, sla
set2: arjv, vcmax25, coeff_maint_init
set3: pipe_density, pipe_tune_*, tree_ff, alloc_min
set4: psi_50, psi_leaf, k_sap, r_froot
set5: leaf_psd_*, leaf_ssa_*, bgrd_ref_*, nir, vis

Changes of set1 and set1 combined with set2 to set 5 (set1+set2, set1+set3, set1+set4 and set1+set5) increased LAI much (~2.5 -> ~7). However, change of all sets (set1+set2+set3+set4+set5) showed a moderate increase in LAI (~2.5 -> ~4) which implies interactions between parameters.

Allocation and plant hydraulic architecture

  • When tuning allocation to match observed wood or root masses productivity or their stocks, it may be necessary to change longevity_sap and longevity_root. Note that this will affect the ratio between leaf and root mass because Mr = Ml / LF where LF = c0_alloc * KF and c0_alloc = sqrt(k_belowground(pft)/k_sap(pft)*longevity_sap/longevity_root*sapwood_density). Likewise when tuning the hydraulic architecture to match observed transpiration, c0_alloc will change and so will allocation. Note that in c0_alloc k_belowground is used whereas the calculation of hydraulic architecture uses k_root. The difference is explained in the code.

Phenology

Describes r6918. The pft-specific parameter always_init controls whether the phenology depends on the reserves (set to .FALSE.) or is forced (set to .TRUE.). Note that a forced phenology (thus always_init = .TRUE.) has no ecophysiological basis, it is a numerical approach to stabilize the vegetation cover. A stable vegetation cover is particularly welcome in coupled simulations but likely hides real vegetation dynamics (especially under future climate conditions) or problems in other routines or parameter settings. If a PFT keeps dying in an area where it is currently present, this would hint at a problem with the current model/parameters. If a PFT keeps dying under future conditions, it may be a real response (depending on the PFT). If phenology is always initialized, plants will develop an initial canopy in phenology irrespective of whether the plant had sufficient carbon and nitrogen reserves and for evergreen species irrespective of whether the canopy was viable at all. This setting basically overcomes a mortality event at the expense of taking up carbon and nitrogen from the atmosphere. When used in combination with impose_cn = n, an inconsistency is introduced: impose_cn = n reflect the desire to close the nitrogen cycle, always_init = y opens a backdoor in the nitrogen cycle.

From a conceptual point of view, ORCHIDEE trunk is all about vegetation dynamics and thus instabilities in the vegetation cover. In ORCHIDEE trunk there are two processes that can deal with dying PFTs including evergreens PFTs. First, ok_recruitment could used. If ok_recruitment = .TRUE. a decrease in the canopy cover will result in more light reaching the forest floor which in turn should trigger recruitment of -for the moment- the same PFT. Generations (cohorts) can take over from each other without loosing the canopy cover entirely. Second, if there are insufficient reserves to grow leaves, there will be no or insufficient gpp, the carbon reserves will be consumed by respiration processes, the plants will be killed, the biomass transferred to the litter pools and the same or another PFT (see section on species change) will be replanted. ORCHIDEE trunk was developed to work with always_init = .FALSE. so this has become the default value, contrary to previous versions of the trunk where always_init = .TRUE. is the default.

Temperature is a very consistent climatological variable in the sense that each year the summer is warmer than the winter (even in so-called cold years). This implies that temperature driven phenology is very robust in the sense that the PFT will develop a canopy each year. Soil moisture, however, is much less predictable and in very dry or very yet years the seasonal patterns may differ a lot. Especially when soil moisture criteria are combined with other criteria (pheno model: moigdd) there are years that the criteria are never met and that the PFT never develops leaves. This is not at all observed. Even farmers would plant crops when the conditions are far from optimal (typically because they have to plant the crops before they know that the conditions will be far from optimal). If set the yes, the flagok_force_pheno will start the growth of a canopy a predefined number of days (force_pheno_mtc) after the average budbreak data for that PFT at that location. Test simulations over the Americas showed that over a 30 year period forced phenology was used 5 to 10% of the years between 1901 and 1930 for PFT10, 12 and 13. This flag did little or nothing to the phenology of the other PFTs with the pheno model moigdd. The variable qc_pheno_event registers if budbreak happened during the year. The variable qc_forced_pheno_event registers if the phenology event had to be forced rather then being simulated as a function of the climatological variables. The ratio of these output variables can be used to plot in which regions and years phenology had to be forced. Not surprisingly it is most used in dry regions of the PFT distribution.

Photosynthesis

Describes r6614. Photosynthesis is calculated as in ORCHIDEE trunk, and ORCHIDEE trunk 3 but the way the canopy levels are defined has changed. The reason of this change is in the albedo and energy budget for which physical layers are required. For this reason the space between the bottom and the top of the canopy is divided into x layers. x is set by the parameter nlevels_photo. Applications with ORCHIDEE trunk used nlevels_photo = 10. Contrary to previous trunk versions of ORCHIDEE where each canopy layer contained 0.5 units of LAI, in ORCHIDEE trunk, each canopy layer will have the same depth (in m) but will contain a different amount of LAI because tree canopies are assumed spherical.

Plant water stress

Describes r6614. With ORCHIDEE trunk there are two option to calculate waters stress:

  • Same as in branch 3 and previous ORCHIDEE trunks, where a soil moisture stress functions limits C assimilation through constraints on the carboxylation capacity.
  • The second possibility takes hydraulic architecture of plants into account, when calculating the plant water supply. This scheme, based on Hickler et al (2006), calculates the water supply as the ratio of the pressure difference between the soil and leaves. The plant water supply is the amount of water the plant can transport from the soil to the stomate accounting for resistance of water transport in the roots, sapwood and leaves. The resistances are inversely proportional to the conductivities in these different tissues, with the sapwood conductivity decreasing when cavitation occurs. If transpiration rates exceeds plant water supply, stomatal conductance is reduced. If you wish to make simulations with the hydraulic architecture choose the correct setting for energy_control (see section on Single layer vs. multi-layer energy budget for more elaboration on these flags).

The following PFT dependent parameters are needed for the calculations accounting for plant hydraulic architecture; minimal leaf water potential psi_leaf, sapwood leaf water potential that causes 50 % loss of xylem psi_50, maximum sapwood conductivity k_sap, root conductivity k_root, leaf conductivity k_leaf, specific root lenght srl, fine root radius r_froot, minimum root water potential psi_root. Moreover, as a further refinement to the hydraulic architecture, a more detailed description of the soil to root resistance has been included. This overcomes the need of psi_soil_tune in ORCHIDEE trunk as needed in ORCIDEE-CAN. Psi_soil is weighted by the fraction of evapotranspiration supplied by each soil layer, which depends on the soil to root resistance per layer that accounts for different root properties and hydraulic conductivity. In ORCHIDEE-CAN k_root described the conductivity of the fine roots and the soil. In ORCHIDEE trunk this joined conductivity has been split in a fine root conductivity and a soil to root conductivity. k_root is solely used in the calculation of the hydraulic architecture (because k soil to root is calculated separately in that subroutine), k_belowground is solely used in allocation. Hence, allocation and hydraulic architecture are no longer intimately linked.

The soil hydrology model distinguishes three water columns, the so called soil tiles. The first soil tile simulates the water contained under PFT1, the second the soil water contained under forest PFTs and the third the water contained under short vegetation PFTs (grasses and crops). PFTs are linked to soil tiles by means of the parameter pref_soil_veg (in src_parameters/constantes_mtc.f90). Note that both transpiration (veget) and evaporation (frac_bare_ns) is taken from the same soil tile.

Prescribe initial vegetation

Describes r6614. At the start of the model run or after a die-back or clear-cut new vegetation needs to be planted as ORCHIDEE does not grow vegetation from seeds. The initial dimensions of the vegetation are thus prescribed. Given that the allocation follows allometric relationships, any of the tree dimensions or any mass of any component could have been used to prescribe. The variable diameter was chosen because it is easy to (mentally) visualize the prescribed vegetation. In the run.def dia_init_min and dia_init_max need to be prescribed. Typical values are 1 to 3 cm. If more than one diameter class is used, dia_init_max needs to larger than dia_init_min. The larger the difference between the min and max, the more vegetation layers the canopy will be composed from. Grasses are prescribed by the parameters height_init_min and height_init_max because we have no diameter-height relationship for grasses. Note that height_init_min and height_init_max are the same. The fact that we have two values is a left over from previous versions of stomate_prescribe.f90 (see ticket #688).

In addition, the initial number of seedlings needs to be prescribed as well. For this a parameter needs to be set. This is a critical parameter to obtain acceptable model behaviour. If it is too high, lai saturates but the stand-level GPP will be distributed over too many individuals, each individual will grow very little and so it will take very long before self-thinning is reached. If it is set too low, LAI will be too low resulting in a too low GPP and thus very slow growth. A good starting values is a bit below self-thinning. That way the vegetation starts growing, individual are killed thanks to the background mortality and within 10 to 20 years self-thinning is reached. Why not starting at self-thinning? During code development it was tried to have the model start at the exact number of trees at which self-thinning will start given the diameter of the tree. One issues was that when prescribing small individuals (1 to 3 cm) the calculated number of trees could in the millions and so the GPP had to be distributed over too many individuals.

Depending on whether a stand is managed or not, the initial number of trees is calculated in a different way (see stomate_prescribe.f90). For managed stands (FM = 2, 3 and 4) alpha_rdi_upper and beta_rdi_upper are used to calculate a dynamically changing RDI. For unmanaged forest (FM = 1), grad_thin, alpha_rdi_upper, beta_rdi_upper are used to calculate RDI as long as the stand has not reached self-thinning. For better model performance the parameter grad_thin should be made PFT-dependent (see ticket #686)

Pseudo sugar loading

Describes r6614. The model code does not control the C/N ratio of the labile pool hence, even if there is a strong N-limitation, the model can accumulate lots of carbon in the labile pool. The first CN-version was indeed doing this as the plant could easily store several 1000 gC m-2. As this was considered unrealistic, the excess C in the labile pool was burned-off by some excess respiration. Although this luxury/wastage respiration has been suggested in the literature (see Amthor et al 2000 and Chamber et al 2004) it is not confirmed by many observations. It was therefore decided to control the size of the labile pool. The model already had an estimate of the optimal pool size of the labile and carbres pools. If the plant has more labile carbon than the optimal, GPP is downregulated because too much sugars in the leaves will increase the viscosity and hamper the sapflow in the phloem. The viscosity can be decreased again by closing the stomata and transpiring less of the sapflow in the xylem. By closing the stomata, GPP will be downregulated (Holtta et al 2017 doi:10.1093/treephys/tpx011). Because ORCHIDEE has no sapflow, turgor and viscosity yet, we used a simple ratio to downregulate NUE. Hence, the feature was called pseudo sugar loading to make clear this is not the real thing yet.

The regulation is smoothened by setting boundaries to avoid sudden decreases in GPP (which are not apparent in the data). Smoothing is taken care of in stomate_vmax.f90. If the plant has less carbon in its labile and carbres pools than wanted, the NUE is upregulated. Up regulation is also capped to avoid crazy NUE values and high frequency changes between up and downregulation. Up and downregulation are done in stomate_vmax.f90. The parameters than control the smoothing are sugar_load_min and sugar_load_max and can be set in the run.def.

Sugar loading can have a huge effect on the GPP even up to the point that realist LAIs come with way too low GPPs. This issues was eventually solved by setting the roots, leaves and sapwood longivity to observed values (for sapwood turnover this is not a very well constrainted parameter). Most important is to aim for a good NPP/GPP ratio. Interestingly when approaching the values reported in Campiolli et al 2015 and Vicca et al 2012, the reserves drop to more realistic values. At this point sugar loading is becoming much less dominant. From r7135 onwards, sugar loading cannot reduce the GPP by more than 25%. This implies that reserves could become crazy again and should be monitored regularly.

Recruitment

Describes r6614. When stands grow old the tree density decreases and under certain conditions the LAI can no longer cover the ground area. When this happens productivity will start to decrease. In nature the decrease in LAI comes with an increase in the amount of light reaching the forest floor which enables seedlings to grow and to restore the LAI. This process is known as recruitment. Note that in managed forest and forest with a lot of stand replacing disturbances (for example, fire) the forest may never reach the stage where the canopy sufficiently opens up to enable recruitment.

ORCHIDEE trunk can simulate recruitment for each PFT separately by setting recruitment_pft to true or false. When using age classes it makes sense to have the same setting for all age classes of the same species. When developing and testing the code it was considered too time consuming to change the settings at the PFT level so a flag ignoring the setting for recruitment_pft was introduced. If the ok_recruitment flag is set to true, the settings of recruitment_pft will be used. If the ok_recruitment flag is set to false, the settings of recruitment_pft will be ignored and the model runs without recruitment.

Recruitment has been developed, tested and validated for tropical forests. There is no reason why it shouldn't work for other forests. Initial test for temperate regions show that it works there as well. Also forest productivity at higher ages seems relatively sensitive to recruitment. At present recruitment was introduced at the PFT level. It probably makes more sense to link it to the management strategy than to the PFT. This needs to be checked.

River routing

Describes r6614. This functionality works has not been evaluated yet for ORCHIDEE trunk, although it technically runs. Unless rivers are your main interest, set river_routing to n. The functionality still has problem when combined with the zoomed grids because pixels far away from the zoom become so big that some rivers can no longer reach the sea. Stand alone applications of ORCHIDEE trunk with river routing being activated require that the whole watershed in include in the simulation domain. If not the model will crash right at the start.

Root nitrogen uptake

Describes r6904. The calculation of root nitrogen uptake in nitrogen_dynamice has two parts. First, the maximum amount of root nitrogen uptake is calculated based on soil chemical properties and long-term root carbon mass. And then, nitrogen uptake is adjusted by the feedback from nitrogen reserve; if the long-term ratio of actual to potential nitrogen reserve pool is over one, then root nitrogen uptake decreases. The uptake does not decrease further than p_n_uptake times maximum root nitrogen uptake. The adjustment was added due to avoid carbon-limited growth (evident from too much nitrogen in the plants) which became more common after the code was changed such that the reserve nitrogen was used as much as possible. This setting is described in Nitrogen reserve pools), and it is controlled by the parameter use_reserve_n to y. A better use of the reserve nitrogen in the code was implemented after observing that the labile nitrogen pool was empty resulting in n-limited growth whereas the reserve pool was still well filled.

Root profile

Describes r713X. There are two different ways of looking at a root profile in the code. It could reflect "structure" or "function". Root structure is probably how most of us think about roots (i.e. digging a whole and observing where the roots are). When thinking at root structure the profile should be relatively constant over time. (A logic time integrator to determine this constancy is longevity_root as the profile cannot grow faster then the roots grow and die -> in ORCHIDEE we simply fixed it over time).

In ORCHIDEE root structure is used in the calculation of k_lab which is water infiltration along roots (accounted for in hydrol.f90) and the input of soil carbon and nitrogen at depth due to the turnover of roots which is accounted for stomate_soil_carbon_discretization.f90. Furthermore, it is used to calculate the root temperature in stomate_resp.f90.

When thinking about root function it is not so important where the roots are located but it is more important at which depth the roots will be active. The function approach is used in the dynamic root profile where the plants can take most of the soil water from the layers where the soil water happens to be (feature added by D.Zhu in r4363. I'm not sure which versions this is in but we have it in the 4.x). This way of looking at the roots is similar to how we look at the canopy where we have a lot of leaves at places in the canopy where little light can penetrate and where a large part of the photosynthesis is taken care of by the leaves in the top layers of the canopy.

The exponentially decreasing root mass with depth which is the default in ORCHIDEE, is based on root mass observations and, therefore, reflects a structural approach. ORCHIDEE uses the same root profile to calculate plant water uptake, infiltration and som inputs. Hence, root function and root structure are considered to be the same.

The dynamic root profile in ORCHIDEE follows a functional approach based on soil water availability. This seems a fair approach. Plants probably have way more roots than they need exactly to be able to quickly adjust to changes in the soil moisture environment (likewise they have way too many leaves to adjust to the fact that the Earth is rotating and leaves are continuously moving from the wind). If we pass this functional root profile (which is hyper dynamic as it may change every 30 minutes) to the rest of the code, we would introduce scientific inconsistencies in the calculation of infiltration, temperature and som_input. Note: yet another functional approach could be based on nutrient availability as that is another function of the roots.

Ideally the code should thus use a different root profile depending on what we want to use it for.

Root_profiles can and is calculated in the absence of vegetation. Having a root profile even in the absence of the PFT is convenient for when land cover changes happen. The downside is that the history files become unintuitive. To have the best of both worlds we calculate it for every pixel but will do some cleaning before writing the history files which is done in hydrol_main.

The approach that has been used in previous versions of the trunk describes the root profile as a truncated exponentially decreasing function of depth. The function is truncated at a prescribed depth (maxdepth). The shape of the root profile is prescribed by the pft-dependent parameter humcste. The nodes and interfaces of the soil layers follow the scheme proposed by De Rosnay (PhD thesis). In r4363 a second option was added and a dynamic root profile can be calculated by setting the flag ok_dynroot to y. The dynamic root profile optimizes soil moisture usage and was modified from Dan Zhu's initial implementation to account for soil layer depth. The root profile that is now calculated in hydrol.f90 is passed to the other subroutines that make use of it.

Table The root profile interacts with the use of hydraulic architecture and soil discretization. Each of these combinations should be checked and tested.

ok_dynroot ok_hydraul_arch ok_soil_discretization Comments
- - - Default (r7135). Nodes and interfaces of the discretization have been checked. Extensively tested at the global scale.
- - + Nodes and interfaces of the discretization have been checked. Tested for a limited test case.
- + - Nodes and interfaces of the discretization have been checked. Tested for a limited test case.
- + + Nodes and interfaces of the discretization have been checked. Tested for a limited test case.
+ - - To be checked.
+ - + Model made to crash. The dynamic root profile may conflict with the way som_input is distributed.
+ + - To be checked.
+ + + Model made to crash. The dynamic root profile may conflict with the way som_input is distributed.

run.def

Describes r6614. The format of the run.def has been modified to use the same structure for both coupled and off-line runs. The run.def is split between its driver settings (written in run.def), general items which control the ORCHIDEE model (in the orchidee.def file), and PFT-dependent parameters (in the orchidee_pft.def file). A script is now included with the config/ORCHIDEE_OL directory called MAKE_RUN_DEF. This scripts generates different orchidee_pfts.def files. Default model configurations (and thus different orchidee.def files) can be found in config/ORCHIDEE_OL. If one is interested in running two identical simulations with the land-only and the land-atmosphere model, only the run.def needs to be replaced. If one is interested in running the model in the same mode (land-only or land-atmosphere) but change the model functionality, only orchidee.def needs to be changed. If one is interested in running the same mode and same model functionality but only wants to change the PFTs tho orchidee_pft.def file will have to be changed. This modularity is expected to enhance the transparency of the model configuration.

Soil maps

Describes r6614. ORCHIDEE trunk runs for two soil maps: zobler (3 soil types) and usda (12 soil types). Although the usda map has been the default for a while, ORCHIDEE trunk is back at using Zobler due to yet to resolve issues with the usda map. The code has been adjusted in hydraulic_architect.f90 and stomate_windthrow.f90 so it works with Zobler and usda. The soil map can be selected run.def by setting the value for soiltype_classif:

SOILTYPE_CLASSIF= usda
SOILTYPE_CLASSIF= zobler

or The file names of the maps that need to be read can be set in the COMP/sechiba.card:

 (${R_IN}/SRF/SOIL/File_name, soil_bulk_and_ph.nc), \
 (${R_IN}/SRF/SOIL/File_name, soils_param.nc)

Single vs multi layer energy budget

Describes r6614. There are still some issues with the multi-layer energy budget, and it is currently only possible to run for one PFT. Thus, we suggest you use the single layer energy budget. In this can, however, be done by two different methods that in theory should give the exact same results: A - Use the energy scheme as found in the original enerbil.f90 B - Use the multi-layer energy scheme for a single canopy layer. This should reduce the multi-layer energy scheme to the original enerbil code but this is no longer the case in r6614. This needs to be restored (see ticket #276, #379, #457)

Several flags control which code is used to calculate the energy budget a functionality that is intimately linked to the way the plant water stress has to be calculated. For simplicity, a flag has been made to automatically control several of the flags related to the energy budget and the plant water stress, called energy_control.

Energy_control has 5 possible settings:

  • Single layer, using the multi layer energy scheme (i.e. choice B above)
  • Multi-layer energy budget
  • User specific as set in the run.def
  • Single layer, using the original enerbil scheme (i.e. choice A above)

1 - DEFAULT uses the enerbil module in combination with the hydraulic architecture (ok_hydrol_arch and ok_gs_feedback true, while ok_mleb, ok_impose_canopy_structure and ok_bare_soil_new are set to false). 2 - option to use enerbil module and original water stress (not hydraulic architecture). 3 - The energy budget is calculated using the multi-layer energy scheme with a single layer: ok_hydrol_arch, ok_gs_feedback, ok_impose_canopy_structure and ok_mleb all TRUE, but The energy budget is only calculated for a single layer (jnlvls is 1,jnlvls_under is 0,jnlvls_canopy is 1,jnlvls_over is 0). No mleb output, ok_mleb_history_file and ok_bare_soil_new are set to FALSE. 4 - multi-layer energy budget: ok_hydrol_arch, ok_gs_feedback and ok_mleb all TRUE. ok_impose_canopy_structure is False, and the energy budget is calculated for multiple layers (jnlvls is 29,jnlvls_under is 10,jnlvls_canopy is 10,jnlvls_over is 9). No mleb output, ok_mleb_history_file is set to FALSE. ok_bare_soil_new is set to TRUE 5 - user specific: user specific settings for these controls and layers as defined in the run.def by the user.

The default setting of energy_control in ORCHIDEE trunk is 1. This is well tested and considered safe to use. More details of the flags controlling the energy budget and water stress calculations can be found in src_parameters/control.f90. The flags controlled by the energy_control are:

  • ok_hydrol_arch: Flag that activates the hydraulic architecture routine (true/false). Previous trunk versions of ORCHIDEE used soil water as a proxy for water stress and applied the stress to Vcmax. When set to true the hydraulic architecture of the vegetation is accounted for to calculate the amount of water that can be transported through the plant given the soil and leaf potential and the conductivities of the roots, wood and leaves. Water supply through the plant is compared against the atmospheric demand for water. If the supply is smaller then the demand, the plant experiences water stress and the stomata will be closed (water stress is now on gs rather than Vcmax). Note that whether stomatal regulation is used or not is controlled by a separate flag: ok_gs_feedback.
  • ok_gs_feedback: Flag that activates water stress on stomata (true/false). This flag is for debugging only! It allows developers to calculate GPP without any water stress. If the model is used in production mode and ok_hydrol_arch is true this flag should be true as well.
  • ok_mleb: Flag that activates the multilayer energy budget (true/false). The model uses 10 (default) canopy layers to calculate the albedo, transmittance, absorbance and GPP. These canopy layers can be combined with 10 (default) layers below and 9 layers above the canopy to calculate the energy budget (ok_mleb=y). If set to no, this flag will make the model use 10 layers for the canopy albedo, transmittance, absorbance and GPP and just a single layer for the energy budget.
  • ok_impose_can_structure: This flag is for debugging only! It allows developers to use a prescribed canopy structure rather then the structure calculate by ORCHIDEE. The flag activates the sections of code which directly link the energy budget scheme to the the size and LAI profile of the canopy for the respective PFT and age class that is calculated in stomate, for the albedo. If set to TRUE and the multi-layer budget is activated the model takes LAI profile information and canopy level heights from the run.def. If set to FALSE, and the multi-layer energy budget is used the profile information and canopy levels heights comes from the PGap-based processes for calculation of stand profile information in stomate.
  • ok_mleb_history_file: Flag that controls the writing of an output file with the multi-layer energy simulations (true/false). Note that this is a large file and writing slows down the code.
  • ok_bare_soil_new: This flag is described in a separate section.

Specific leaf area

Describes r6614. Similar to branch 3.0, ORCHIDEE trunk users can choose to use a constant specific leaf area (SLA) or a dynamic (SLA) by setting the flag dyn_sla. SLA is a fundamental parameter in the allocation scheme and it is well established that SLA is dynamic in nature especially during leaf onset. Nevertheless, the effect of this flag is much more limited than one could expect based on the perceived importance of sla.

Spin-up

Describes r6614. The analytical spin-up works with ORCHIDEE trunk. To ensure growth at the onset of the analytic spin-up for all PFTs initial values are needed for the SOM pools, and that is irrespective of whether IMPOSE_CN is y or n. The initial pools are set in stomate_io.f90, and they can be specified in the run.def by:

SOM_INIT_ACTIVE = 1000
SOM_INIT_SLOW = 3000
SOM_INIT_PASSIVE = 3000
SOM_INIT_SURFACE = 1000

The initial values of carbon for the four SOM pools are used globally. Sensitivity test have shown that the analytic spin-up is not sensitive to the actual size of the initial values. The different ecosystems will after a while settle into their own states (N limited or saturated) in spite of having the same initial state. Convergence appears to be faster if initial values are set.

Start and restart

Describes r6614. The table below shows typical configurations to start and restart ORCHIDEE trunk

Sechiba stomate
Restart impveg impsoil laimap ok_stomate restart Comment
- - - - + - 1. Typical for a spin-up from scratch
- - - + - n.a. 2. Typical for a spin-up with sechiba only
- + + - + - 3. Typical for a site-level spin-up
- + - - + - 4a. Typical for a test run with prescribed PFTs
- - + - + - 4b. Typical for a test run with prescribed soil properties
+ - - - + + Typical for a simulation following the spinup described in 1
+ - - + - n.a. Typical for a simulation following the spin-up described in 2
+ + + - + + Typical for a simulation that will be compared against site-level observations and that was spun-up with 3
+ + - - + + Typical for restarting a test run following a test run described in 4a
+ - + - + + Typical for restarting a test run following a test run described in 4a

The code allows for many more combinations than described above. Some of those configuration could be useful in specific cases but be aware of the following:

  • If the restart file for sechiba does not come from the same simulation/year as the restart file for stomate, the values for the variables that are stored in both sechiba and stomate will be overwritten by the values stored in the stomate restart file because that file is read later in the initialization phase. Some variables are duplicated because that adds the flexibility to the configuration that can be used to restart the model.
  • When read_lai is true, ok_stomate should be false (but this is not yet enforced by the model - see ticket #685). It may work but it is flawed from a conceptual point of view. read_lai was developed for cases where the model cannot simulate its own canopy. ok_stomate was developed so the model would simulate its own canopy.
  • Combining a sechiba restart from with impveg, impsoil or laimap may result in values from the restart file being overwritten because impveg, impsoil and laimap are checked after reading the sechiba restart file.
  • NCO commands could be used to hack into the restart file. This could be powerful when stomate is used and one wants to restart the model after a spin-up but also want that the forest starts growing the first year of the simulation. In that case circ_class_biomass and circ_class_n should be set to zero. This approach is straightforward when no age classes are used. When age classes are used many more variables need to be moved to the correct PFT to avoid conflicts.

Sechiba vs stomate

Describes r6614. In ORCHIDEE trunk the links between sechiba and stomate have been strengthened compared to previous ORCHIDEE trunks. As in previous versions, stomate makes use of variables calculated in sechiba but in ORCHIDEE trunk, sechiba requires information from stomate to work properly. It is possible to prescribe the canopy structure and thus only run sechiba. Set lai_map = y, the data for a canopy structure map need to come from an ORCHIDEE trunk run with stomate. All the required information is stored in the sechiba restart file to enable restarting the model without stomate.

Tree ring width

Describes r6721. Tree ring width is always calculated and is not controlled by any flags or additional parameters. The basal area increment is central in the allocation scheme when making use of the Deleuze and Dhote rule to account for within stand competition between trees with different dimensions. It is saved to the output variable CCDELTABA (mm2 day-1). Basal area increment can be used to calculate the tree ring width when assuming that the cross-section of a tree trunk is circular. The output variable CCTRW represents the radial increase of individual trees for each circumference class (mm day-1). Since CCTRW is simply sqrt(ba_later/pi) - sqrt(ba_beginning/pi), it reconstructs trees from zero at year 1 removing the effect of BA_init. An implication of this approach is that the value at the first time step does not only include the growth calculated in stomate_growth_fun_all but also the basal area that was prescribed in stomate_prescribe.f90. The contribution of the initial tree dimensions prescribed in stomate_prescribe becomes more important if bigger trees are prescribed. Both CCDELTABA and CCTRW are written to stomate_4dim.nc.

Vertical discretization soil carbon

Describes r6721. To be completed.

Wind throw

Describes r6614. The calculation of wind storm damage can be activated by setting ok_windthrow to y in the orchidee.def. This module calculates the critical wind speed of a stand taking stand and soil properties into account. If the stand is managed, the damaged trees are salvaged logged. If the stand is unmanaged the damaged trees are left on-site to decompose. The default setting for ok_windthrow is n. The damaged fractions of the stands are replanted and moved to the first age class (if more than 1 age class is used).