6.10. jules_vegetation.nml

This file sets the vegetation options. It contains one namelist called JULES_VEGETATION.

6.10.1. JULES_VEGETATION namelist members

JULES_VEGETATION::l_trait_phys
Type:

logical

Default:

F

Switch for using trait-based physiology.

TRUE

Vcmax is calculated based on observed leaf traits. Leaf nitrogen (nmass: kgN kgLeaf-1) and leaf mass (LMA: kgLeaf m-2) can be based on observations from the TRY database. Vcmax (umol CO2 m-2 s-1) is based on linear regressions as in Kattge et al. 2009. Two additional parameters are needed: vint and vsl - the intercept and slope, respectively, that relate the leaf nitrogen to vcmax. Sigl is replaced with LMA (sigl=LMA*Cmass, where Cmass is the kgC kgLeaf-1 and is 0.4).

FALSE

Vcmax is calculated based on parameters nl0 (kgN kgC-1) and neff.

JULES_VEGETATION::l_phenol
Type:

logical

Default:

F

Switch for vegetation phenology model.

TRUE

Use phenology model.

FALSE

Do not use phenology model.

JULES_VEGETATION::l_triffid
Type:

logical

Default:

F

Switch for dynamic vegetation model (TRIFFID) except for competition.

TRUE

Use TRIFFID. In this case soil carbon is modelled using four pools (biomass, humus, decomposable plant material, resistant plant material).

FALSE

Do not use TRIFFID. A single soil carbon pool is used.

JULES_VEGETATION::l_veg_compete
Type:

logical

Default:

T

Switch for competing vegetation.

Only used if l_triffid = TRUE.

TRUE

TRIFFID will let the different PFTs compete against each other and modify the vegetation fractions.

FALSE

Vegetation fractions do not change.

JULES_VEGETATION::l_ht_compete
Type:

logical

Default:

F

Only used if l_triffid = TRUE.

TRUE

Use height-based vegetation competition (recommended).

This allows for a generic number of PFTs. When l_trif_eq = TRUE, this is implemented by lotka_eq_jls.F90. When l_trif_eq = FALSE, it is implemented in lotka_noeq_jls.F90 when l_trif_crop = FALSE and in lotka_noeq_subset_jls.F90 when l_trif_crop = TRUE.

FALSE

Use the vegetation competition described in HCTN24.

This is hard-wired for 5 PFTs (BT, NT, C3, C4, SH, in that order) with co-competition for grasses and trees in lokta_jls.F90.

JULES_VEGETATION::l_nitrogen
Type:

logical

Default:

F

Only used if l_triffid = TRUE.

TRUE

Enable Nitrogen limitation of carbon uptake. A nitrogen deposition field should be provided otherwise no N deposition is assumed.

FALSE

No Nitrogen limitation. Nitrogen fluxes are calculated as diagnostics only.

JULES_VEGETATION::l_trif_eq
Type:

logical

Default:

T

Switch for equilibrium vegetation model (i.e., an equilibrium solution of TRIFFID).

Only used if l_triffid = TRUE.

TRUE

Use equilibrium TRIFFID.

FALSE

Do not use equilibrium TRIFFID.

JULES_VEGETATION::phenol_period
Type:

integer

Permitted:

>= 1

Default:

None

Period for calls to phenology model in days. Only relevant if l_phenol = TRUE.

JULES_VEGETATION::triffid_period
Type:

integer

Permitted:

>= 1

Default:

None

Period for calls to TRIFFID model in days. Only relevant if one of l_triffid or l_trif_eq is TRUE.

JULES_VEGETATION::l_gleaf_fix
Type:

logical

Default:

T

Switch for fixing a bug in the accumulation of g_leaf_phen_acc.

This bug occurs because veg2 is called on TRIFFID timesteps and veg1 is called on phenol timesteps, but veg1 did not previously accumulate g_leaf_phen_acc in the same way as veg2.

TRUE

veg1 accumulates g_leaf_phen_acc between calls to TRIFFID. This is important if triffid_period > phenol_period.

FALSE

veg1 does not accumulate g_leaf_phen_acc between calls to TRIFFID.

JULES_VEGETATION::l_bvoc_emis
Type:

logical

Default:

F

Switch to enable calculation of BVOC emissions.

TRUE

BVOC emissions diagnostics will be calculated.

FALSE

BVOC emissions diagnostics will not be calculated.

JULES_VEGETATION::l_o3_damage
Type:

logical

Default:

F

Switch for ozone damage.

TRUE

Ozone damage is on.

Note

Ozone concentration in ppb must be prescribed in prescribed_data.nml.

FALSE

No effect.

JULES_VEGETATION::l_stem_resp_fix
Type:

logical

Default:

F

Switch for bug fix for stem respiration to use balanced LAI to derive respiring stem mass. The switch is included for backwards compatibility with existing configurations. Future updates should include this change.

TRUE

Respiring stem mass is derived allometrically.

FALSE

Respiring stem mass varies with seasonal LAI.

In the case of a Broadleaf tree in the winter (no leaves) this would mean stem respiration is scaled to 0.

JULES_VEGETATION::l_scale_resp_pm
Type:

logical

Default:

F

Scale whole plant maintenance respiration by the soil moisture stress factor, instead of only scaling leaf respiration.

TRUE

Soil moisture stress reduces leaf, root, and stem maintenance respiration.

FALSE

Soil moisture stress only reduces leaf maintenance respiration.

JULES_VEGETATION::fsmc_shape
Type:

integer

Permitted:

0,1

Default:

0

Shape of soil moisture stress function on vegetation (fsmc).

  1. Piece-wise linear in vol. soil moisture.

  2. Piece-wise linear in soil potential. Currently only allowed when const_z = T and l_use_pft_psi = T.

Note

The option fsmc_shape = 1 is still in development. Users should ensure that results are as expected, and provide feedback where deficiencies are identified.

JULES_VEGETATION::l_use_pft_psi
Type:

logical

Default:

F

Switch for parameters in the soil moisture stress on vegetation function (fsmc).

TRUE

Fsmc is calculated from psi_close_io and psi_open_io.

FALSE

Fsmc is calculated from sm_wilt and sm_crit in JULES_SOIL_PROPS and fsmc_p0_io.

Note

Soil respiration and surface conductance of bare soil respectively will depend on sm_wilt and sm_crit in JULES_SOIL_PROPS, regardless of the setting of fsmc_shape.

Note

The option l_use_pft_psi = T is still in development. Users should ensure that results are as expected, and provide feedback where deficiencies are identified.

JULES_VEGETATION::l_vegcan_soilfx
Type:

logical

Default:

F

Switch for enhancement to canopy model to allow for conduction in the soil below the vegetative canopy, reducing coupling between the soil and the canopy.

TRUE

Allow for conduction in the soil.

FALSE

No effect.

JULES_VEGETATION::l_leaf_n_resp_fix
Type:

logical

Default:

F

Switch for bug fix for leaf nitrogen content used in the calculation of plant maintenance respiration. The switch is included for backwards compatibility with existing configurations. Runs with can_rad_mod = 1, 4 or 5 are affected.

TRUE

Use correct forms for canopy-average leaf N content.

FALSE

No effect.

JULES_VEGETATION::l_landuse
Type:

logical

Default:

F

Switch for using landuse change in conjunction with TRIFFID

Only used if l_triffid = TRUE.

TRUE

Land use change is implemented within TRIFFID. Litter fluxes are split between soil and wood product pools. Requires additional prognostics covering the product pools and the agricultural fraction from the previous TRIFFID call.

FALSE

All litter fluxes enter the soil

JULES_VEGETATION::l_recon
Type:

logical

Default:

T

Switch for reconfiguring vegetation fractions. Also initialises vegetation and soil biogeochemistry at land ice points. With the ECOSSE soil model this switch also ensures that the initial condition for soil biogeochemistry is internally consistent.

TRUE

For soil points (land points with no ice) ensure vegetation fractions are at least a minimum value and reduce other fractions accordingly.

FALSE

Do not apply the minimum vegetation fractions. This is useful when some points are 100% lake and urban, in which case reconfiguration leads to a total surface tile fraction of greater than 1.

JULES_VEGETATION::l_prescsow
Type:

logical

Default:

F

Switch that determines how crop sowing dates are defined. Only used if ncpft > 0.

TRUE

Sowing dates prescribed in JULES_CROP_PROPS are used.

FALSE

Sowing dates are determined by the model.

JULES_VEGETATION::l_trif_crop
Type:

logical

Default:

F

Switch controlling the treatment of agricultural PFTs. Where agricultural PFTs are defined by the crop_io parameter.

TRUE

In the non-agricultural area natural PFT competition is calculated by a call to a new version of the lotka routine and in each agricultural area agricultural-PFT competition is calculated by an additional call to the new version of the lotka routine. Crop and pasture areas are defined by the frac_agr and frac_past variables respectively. Additionally, to represent harvesting, a fraction of crop litter is added to the fast wood products pool instead of the soil carbon pools.

FALSE

Vegetation competition is calculated for natural and crop PFTs together, with natural PFTs excluded from the agricultural area that is defined by the frac_agr variable. Agricultural PFTs can also grow in natural areas where they are interpreted as natural grasses.

JULES_VEGETATION::l_trif_biocrop
type:

logical

default:

F

Allows for representation of bioenergy crops with continuous or periodic harvesting of agricultural PFTs at prescribed intervals. Requires l_trif_crop = TRUE.

TRUE

Crop, pasture, and bioenergy crop areas are defined by the frac_agr, frac_past, frac_biocrop variables respectively. Harvests are permitted from any land class and enabled for each PFT separately using the harvest_type_io variable. Harvesting may be continuous (as per the existing scheme in l_trif_crop, when harvest_type_io is 1), or performed at prescribed intervals defined using the harvest_freq_io and harvest_ht_io variables (when harvest_type_io is 2).

FALSE

Land use classes, PFT partitioning, and harvests are as defined by the l_trif_crop switch.

See also

References:

JULES_VEGETATION::l_ag_expand
Type:

logical

Default:

F

Allows for assisted expansion of agricultural crop areas. Requires l_landuse = TRUE.

TRUE

Automatically plant out new crop areas with target PFTs.

FALSE

No automatic increase of PFT fraction when land class fraction increases.

JULES_VEGETATION::can_model
Type:

integer

Permitted:

1-4

Default:

4

Choice of canopy model for vegetation:

  1. No distinct canopy (i.e. surface is represented as a single entity for radiative processes).

  2. Radiative canopy with no heat capacity.

  3. Radiative canopy with heat capacity. This option is deprecated, with 4 preferred.

  4. As 3 but with a representation of snow beneath the canopy. This option is preferred to 3.

Note

can_model = 1 does not mean that there is no

vegetation canopy. It means that the surface is represented as a single entity, rather than having distinct surface and canopy levels for the purposes of radiative processes.

JULES_VEGETATION::can_rad_mod
Type:

integer

Permitted:

1, 4, 5, 6

Default:

4

Options for treatment of canopy radiation.

  1. A single canopy layer for which radiation absorption is calculated using Beer’s law. Leaf-level photosynthesis is scaled to the canopy level using the ‘big leaf’ approach. Leaf nitrogen, photosynthetic capacity, i.e the Vcmax parameter, and leaf photosynthesis vary exponentially through the canopy with radiation.

  1. Multi-layer approach for radiation interception following the two-stream approach of Sellers et al. (1992). This approach takes into account leaf angle distribution, zenith angle, and differentiates absorption of direct and diffuse radiation. It has an exponential decline of leaf N through the canopy and includes inhibition of leaf respiration in the light. Canopy photosynthesis and conductance are calculated as the sum over all layers.

  2. This is an improvement of option 4, including:

    • Sunfleck penetration though the canopy.

    • Division of sunlit and shaded leaves within each canopy level.

    • A modified version of inhibition of leaf respiration in the light.

  3. This is an improvement of option 5, including an exponential decline of leaf N with canopy height proportional to LAI, following Beer’s law.

Note

can_rad_mod = 1 and 6 are recommended.

Note

When using can_rad_mod = 4, 5 or 6 it is recommended to use driving data that contains direct and diffuse radiation separately rather than a constant diffuse fraction.

See also

Descriptions of option 1 can be found in Jogireddy et al. (2006), and an application of option 4 can be found in Mercado et al. (2007). Options 1 to 5 are described in Clark et al (2011).

JULES_VEGETATION::ilayers
Type:

integer

Permitted:

>= 0

Default:

10

Number of layers for canopy radiation model. Only used for can_rad_mod = 4, 5 or 6.

These layers are used for the calculations of radiation interception and photosynthesis.

JULES_VEGETATION::photo_model
Type:

integer

Permitted:

1 or 2

Default:

none

Choice for model of leaf photosynthesis.

Possible values are:

  1. C3 and C4 plants use the models of Collatz et al., 1991 and 1992, respectively. These were used in the original JULES model.
  2. C3 plants use the model of Farquhar et al. (1980); C4 plants use the model of Collatz et al. (1992).

Warning

The Farquhar model can only be used if can_rad_mod = 1, 5 or 6. Code has not been written for other values of can_rad_mod.

See also

References:

  • Collatz et al., 1991, Physiological and environmental regulation of stomatal conductance, photosynthesis, and transpiration – a model that includes a laminar boundary layer, Agricultural and Forest Meteorology, https://doi.org/10.1016/0168-1923(91)90002-8.

  • Collatz et al., 1992, Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants, Australian Journal of Plant Physiology, https://doi.org/10.1071/PP9920519.

  • Farquhar et al., 1980, A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, https://doi.org/10.1007/BF0038623.

JULES_VEGETATION::stomata_model
Type:

integer

Permitted:

1, 2, OR 3

Default:

1

Choice for model of stomatal conductance.

Possible values are:

  1. The original JULES model, including the Jacobs closure - see Eqn.9 of Best et al. (2011).

  2. The model of Medlyn et al. (2011) - see Eqn.11 of that paper, and Medlyn et al (2012). Note that as implemented the model uses a single parameter (g1, assuming that g0 = 0).

  3. The SOX model of Eller et al. (2020)

Warning

Only the original (Jacobs) model can currently be used with the UM (Option 1).

JULES_VEGETATION::frac_min
Type:

real

Default:

1.0e-6

Minimum fraction that a PFT is allowed to cover if TRIFFID is used.

JULES_VEGETATION::frac_seed
Type:

real

Default:

0.01

Seed fraction for TRIFFID.

JULES_VEGETATION::pow
Type:

real

Default:

5.241e-4

Power in sigmodial function used to get competition coefficients.

See Hadley Centre Technical Note 24, Eq.3.

JULES_VEGETATION::l_inferno
Type:

logical

Default:

F

Switch that determines whether interactive fires (INFERNO) is used. This allows for the diagnostic of burnt area, burnt carbon and a variety of fire emissions.

TRUE

INFERNO is used to provide diagnostic fire variables

FALSE

INFERNO is not used.

JULES_VEGETATION::ignition_method
Type:

integer

Permitted:

1, 2, 3

Default:

1

Switch to determine the type of ignition used (ubiquitous or prescribed with population and lightning)

  1. INFERNO uses ubiquitous (constant) ignitions, of 1.67 fires km-2 s-1 (1.5 from humans, 0.17 from lightning).

  2. INFERNO uses prescribed lightning ignitions, either from an ancillary or the UM. Meanwhile humans are assumed to ignite 1.5 fires km-2 s-1.

  3. INFERNO uses prescribed ignition using Population Density and Lightning Frequency (Cloud-to-Ground). These must be provided as prescribed data to the JULES run.

JULES_VEGETATION::l_trif_fire
Type:

logical

Default:

F

Switch that determines whether interactive fire is used. This allows for burnt area to link with dynamic vegetation.

Only used if l_triffid = TRUE.

TRUE

Burnt area is calculated in INFERNO and passed to TRIFFID to calculate vegetation dynamics. Carbon is also removed from DPM and RPM pools in SOILCARB.

FALSE

Burnt area is zero unless prescribed via an ancillary file.

JULES_VEGETATION::l_vegdrag_pft
Type:

logical(npft)

Default:

F

Switch for using vegetation canopy drag scheme on each PFT.

TRUE

Use a vegetative drag scheme. This is based on Harman and Finnigan (2007).

FALSE

Do not use vegetative drag scheme.

JULES_VEGETATION::l_rsl_scalar
Type:

logical

Default:

F

Switch for using a roughness sublayer correction scheme in scalar variables. This is based on Harman and Finnigan (2008).

Only use if any l_vegdrag_pft = TRUE.

TRUE

Use a roughness sublayer correction scheme in scalar variables.

FALSE

Do not use a roughness sublayer correction scheme in scalar variables.

JULES_VEGETATION::c1_usuh
Type:

real

Permitted:

>= 0

Default:

None

u*/U(h) at the top of dense canopy. See Massman (1997).

Only use if any l_vegdrag_pft = TRUE.

JULES_VEGETATION::c2_usuh
Type:

real

Permitted:

>= 0

Default:

None

u*/U(h) at substrate under canopy. See Massman (1997).

Only use if any l_vegdrag_pft = TRUE.

JULES_VEGETATION::c3_usuh
Type:

real

Permitted:

>= 0

Default:

None

This is used in the exponent of equation weighting dense and sparse vegetation to get u*/U(h) in neutral condition. See Massman (1997). The default value is taken from Wang (2012).

Only use if any l_vegdrag_pft = TRUE.

JULES_VEGETATION::cd_leaf
Type:

real

Permitted:

0:1

Default:

None

Leaf level drag coefficient.

Only use if any l_vegdrag_pft = TRUE.

JULES_VEGETATION::stanton_leaf
Type:

real

Permitted:

0:1

Default:

None

Leaf-level Stanton number

Only use if l_rsl_scalar = TRUE.

JULES_VEGETATION::l_spec_veg_z0
Type:

logical

Default:

F

Switch for using specified values of the vegetation roughness length rather than being determined by the canopy height.

TRUE

Vegetation roughness lengths are specified for each PFT in z0v_io.

FALSE

Vegetation roughness lengths are calculated using canopy heights and parameter dz0v_dh_io.

JULES_VEGETATION::l_limit_canhc
Type:

logical

Default:

F

Switch for limiting the canopy heat capacity for vegetation, which is calculated from the canopy height.

Using the SIMARD canopy height ancillary gives very large heat capacities in the Amazon, so this switch limits the areal heat capacity to 1.15e5 J kg-1 m-2, which is the value calculated by the default broadleaf tree height of 19.01 m.

TRUE

Vegetation areal heat capacity limited.

FALSE

Vegetation areal heat capacity unlimited.

JULES_VEGETATION::l_sugar
Type:

logical

Default:

F

Switch for using the SUGAR carbohydrate model (Jones et al., (2020))

TRUE

Respiration is calculated using the SUGAR carbohydrate model

FALSE

SUGAR is not used

Note

The option l_sugar = T is still in development. Users should ensure that results are as expected, and provide feedback where deficiencies are identified.

Only used with the Farquhar model of leaf photosynthesis (photo_model = 2).

JULES_VEGETATION::photo_acclim_model
Type:

integer

Permitted:

0, 1, 2, or 3

Default:

None

Choice for model of thermal response of photosynthetic capacity. Possible values are:

  1. No adaptation or acclimation.
  2. Thermal adaptation - plant response to temperature varies geographically in response to a static “home” temperature.
  3. Thermal acclimation - plant response to temperature varies geographically and temporally in response to a dynamic “growth” temperature.
  4. Thermal adaptation and acclimation - plant response to temperature varies geographically and temporally in response to a static “home” temperature and a dynamic “growth” temperature.

Note

When photo_acclim_model = 1 or 3 is used, the user must supply the long-term home temperature as ancillary field t_home_gb in JULES_VEGETATION_PROPS. When photo_acclim_model = 2 or 3 is used, the user must supply the running mean growth temperature as initial condition t_growth_gb in JULES_INITIAL.

JULES_VEGETATION::photo_act_model
Type:

integer

Permitted:

1 or 2

Default:

None

Choice of model for the activation energies of Jmax and Vcmax.

  1. Activation energies vary by PFT but not by land point, and are NOT subject to acclimation.
  2. Activation energies vary by land point but not by PFT, and are subject to acclimation.

Note

When photo_act_model = 1 is used, activation energies are calculated using act_jmax_io and act_vcmax_io. When photo_act_model = 2 is used, activation energies are calculated using act_j_coef and act_v_coef.

Warning

A value of 1 (PFT-dependent) must be used if photo_acclim_model = 0 (no adaptation or acclimation).

JULES_VEGETATION::photo_jv_model
Type:

integer

Permitted:

1 or 2

Default:

None

Choice for model of for the variation of J25/V25.

  1. J25 is found by scaling V25 by the given ratio J25/V25, that is, all the variation in the ratio comes from varying J25 (while V25 remains fixed).
  2. J25 and V25 are calculated assuming that the total amount of nitrogen allocated to photosynthesis remains constant, thus any change in J25 requires a compensatory change in V25 - as used in Mercado et al. (2018).

Warning

A value of 1 (simple scaling) must be used if photo_acclim_model = 0 (no adaptation or acclimation).

Only used with photo_jv_model = 2.

JULES_VEGETATION::n_alloc_jmax
Type:

real

Default:

None

Constant relating nitrogen allocation to Jmax (mol CO2 m-2 s-1 [kg m-2]-1). This is 5.3 in Eq.5 of Mercado et al. (2018).

JULES_VEGETATION::n_alloc_vcmax
Type:

real

Default:

None

Constant relating nitrogen allocation to Vcmax (mol CO2 m-2 s-1 [kg m-2]-1). This is 3.8 in Eq.5 of Mercado et al. (2018).

Only used with thermal adaptation or acclimation of photosynthesis (photo_acclim_model = 1, 2 or 3).

The thermal adaptation/acclimation scheme in JULES is structured following Eq. 13 of Kumarathunge et al. (2019), in which C3 photosynthetic capacity is allowed to vary at each land point as a function of a static home temperature (Th) and a dynamic growth temperature (Tg). This is achieved by calculating five parameters used in the Farquhar photosynthesis scheme as functions of those temperature fields, rather than using fixed parameters from JULES_PFTPARM. Each parameter, Q, is calculated as a linear function of Th and Tg:

Q(Th, Tg) = Qcoef(0) + Qcoef(1) Th + Qcoef(2) Tg.

The following namelist members specify the coefficients, Qcoef, used for each parameter. Note that, in each case, the units for Qcoef(1) and Qcoef(2) have an extra factor K-1 relative to the units for Qcoef(0). This structure can be configured to represent the acclimation scheme of Kattge and Knorr (2007), as used by Mercado et al. (2018), and the scheme of Kumarathunge et al. (2019).

Note

If photo_acclim_model = 1 is used all Qcoef(2) must equal 0.0, and if photo_acclim_model = 2 is used all Qcoef(1) must equal 0.0.

JULES_VEGETATION::act_j_coef
Type:

real(3)

Default:

None

Coefficients for the activation energy for Jmax (J mol-1 and J mol-1 K-1). Replaces the use of act_jmax_io.

JULES_VEGETATION::act_v_coef
Type:

real(3)

Default:

None

Coefficients for the activation energy for Vcmax (J mol-1 and J mol-1 K-1). Replaces the use of act_vcmax_io.

JULES_VEGETATION::dsj_coef
Type:

real(3)

Default:

None

Coefficients for entropy factor for Jmax (J mol-1 K-1 and J mol-1 K-2). Replaces the use of deact_jmax_io.

JULES_VEGETATION::dsv_coef
Type:

real(3)

Default:

None

Coefficients for the entropy factor for Vcmax (J mol-1 K-1 and J mol-1 K-2). Replaces the use of deact_vcmax_io.

JULES_VEGETATION::jv25_coef
Type:

real(3)

Default:

None

Coefficients for the ratio J25/V25 (mol electrons [mol-1 CO2] and (mol electrons [mol-1 CO2] K-1). Replaces the use of jv25_ratio_io.

Only used with thermal acclimation of photosynthesis (photo_acclim_model = 2 or 3).

JULES_VEGETATION::n_day_photo_acclim
Type:

real

Default:

None

Time constant (days) for the exponential moving average of temperature that is used as the growth temperature. Given a step function as input, the smoothed output has fallen to 1/e (approx. 37%) of the initial value after this number of days.

JULES_VEGETATION::l_croprotate
Type:

logical

Default:

F

Switch that enables sequential cropping (crop rotations). Only used if ncpft > 0 and if l_prescsow = T.

TRUE

Sowing dates and latest harvest dates prescribed in JULES_CROP_PROPS are used. The method is implemented in Mathison et al. (2019).

FALSE

The crop model is used in its standard form with a single crop per year

6.10.2. JULES_VEGETATION references

  • Best et al., 2011, The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., https://doi.org/10.5194/gmd-4-677-2011.

  • Clark et al., 2011, The Joint UK Land Environment Simulator (JULES) model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701-722, https://doi.org/10.5194/gmd-4-701-2011

  • Eller et al. (2020), Stomatal optimization based on xylem hydraulics (SOX) improves land surface model simulation of vegetation responses to climate. New Phytologist 226: 1622–1637 https://doi.org/10.1111/nph.16419

  • Harman, I.N. & Finnigan, J.J. (2007), A simple unified theory for flow in the canopy and roughness sublayer. Boundary-Layer Meteorol. 123: 339. https://doi.org/10.1007/s10546-006-9145-6

  • Harman, I.N. & Finnigan, J.J. (2008), Scalar Concentration Profiles in the Canopy and Roughness Sublayer. Boundary-Layer Meteorol. 129: 323. https://doi.org/10.1007/s10546-008-9328-4

  • HCTN24, Hadley Centre Technical Note 24, available from the Met Office Library. For ease the direct link to this document is: HCTN24 “Description of the “TRIFFID” Dynamic Global Vegetation Model”.

  • Jogireddy, V., Cox, P. M., Huntingford, C., Harding, R. J., and Mercado, L. M.: An improved description of canopy light interception for use in a GCM land-surface scheme: calibration and testing against carbon fluxes at a coniferous forest, Hadley Centre Technical Note 63, Hadley Centre, Met Office, Exeter, UK, 2006. https://digital.nmla.metoffice.gov.uk/IO_7873ea05-61ec-4615-b030-6bc33397d675

  • Kattge, J. and Knorr, W., 2007, Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species, Plant, Cell and Environment, 30: 1176–1190, https://doi.org/10.1111/j.1365-3040.2007.01690.x.

  • Kattge, J. , Knorr, W. , Raddatz, T. and Wirth, C. (2009), Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale terrestrial biosphere models. Global Change Biology, 15: 976-991. https://doi.org/doi:10.1111/j.1365-2486.2008.01744.x

  • Kumarathunge, D. P. et al (2019), Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale, New Phytologist, 222: 768-784, https://doi.org/10.1111/nph.15668

  • Massman, W. J. (1997), An Analytical One-Dimensional Model of Momentum Transfer by Vegetation of Arbitrary Structure, Boundary-Layer Meteorol. 83: 407-421.

  • Medlyn, B. E., Duursma, R. A., Eamus, D. , Ellsworth, D. S., Prentice, I. C., Barton, C. V., Crous, K. Y., De angelis, P., Freeman, M. and Wingate, L. (2011), Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biology, 17: 2134-2144. https://doi.org/10.1111/j.1365-2486.2010.02375.x

  • Medlyn, B. E., Duursma, R. A., Eamus, D. , Ellsworth, D. S., Prentice, I. C., Barton, C. V., Crous, K. Y., De angelis, P., Freeman, M. and Wingate, L. (2012), Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biology, 18: 3476-3476. https://doi.org/10.1111/j.1365-2486.2012.02790.x.

  • Mercado, L. M., Huntingford, C., Gash, J. H. C., Cox, P. M., and Jogireddy, V.: Improving the representation of radiative interception and photosynthesis for climate model applications, Tellus B, 59, 553–565, 2007. https://doi.org/10.1111/j.1600-0889.2007.00256.x

  • Mercado et al., 2018, Large sensitivity in land carbon storage due to geographical and temporal variation in the thermal response of photosynthetic capacity, New Phytologist, 218: 1462–1477, https://doi.org/10.1111/nph.15100.

  • Sellers et al., 1992, Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sens. Environ., 42, 187-216, https://doi.org/10.1016/0034-4257(92)90102-P

  • Wang, W. (2012), An Analytical Model for Mean Wind Profiles in Sparse Canopies. Boundary-Layer Meteorol 142: 383. https://doi.org/10.1007/s10546-011-9687-0

  • Mathison, C , Challinor, A. J., Deva, C., Falloon, P., Garrigues, S., Moulin, S., Williams, K., and Wiltshire, A. (2019), Developing a sequential cropping capability in the JULESvn5.2 land–surface model, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-85, in review, 2019.

  • Jones et al., 2020, The impact of a simple representation of non-structural carbohydrates on the simulated response of tropical forests to drought, Biogeosciences, 17: 3589–3612. https://doi.org/10.5194/bg-17-3589-2020