Geosci. Model Dev., 8, 2035–2065, 2015
www.geosci-model-dev.net/8/2035/2015/
doi:10.5194/gmd-8-2035-2015
© Author(s) 2015. CC Attribution 3.0 License.
A vertically discretised canopy description for ORCHIDEE
(SVN r2290) and the modifications to the energy, water and carbon
fluxes
K. Naudts1,14, J. Ryder1, M. J. McGrath1, J. Otto1,10, Y. Chen1, A. Valade1, V. Bellasen2, G. Berhongaray3,
G. Bönisch4, M. Campioli3, J. Ghattas1, T. De Groote3,11, V. Haverd5, J. Kattge4, N. MacBean1, F. Maignan1,
P. Merilä6, J. Penuelas7,12, P. Peylin1, B. Pinty8, H. Pretzsch9, E. D. Schulze4, D. Solyga1,13, N. Vuichard1, Y. Yan3, and
S. Luyssaert1
1LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
2INRA, 21079 Dijon, France
3University of Antwerp, 2610 Wilrijk, Belgium
4MPI-Biogeochemistry, Jena, Germany
5CSIRO-Ocean and Atmosphere Flagship, 2600 Canberra, Australia
6METLA, Oulu, Finland
7CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del Valles, Spain
8European Commission, Joint Research Centre, Ispra, Italy
9TUM, Munich, Germany
10Helmholtz-Zentrum Geesthacht, Climate Service Center 2.0, Hamburg, Germany
11VITO, 2400 Mol, Belgium
12CREAF, Cerdanyola del Vallès, Spain
13CGG, 91341 Massy, France
14MPI-Meteorology, Hamburg, Germany
Correspondence to: K. Naudts (kim.naudts@lsce.ipsl.fr)
Received: 29 October 2014 – Published in Geosci. Model Dev. Discuss.: 05 December 2014
Revised: 04 May 2015 – Accepted: 22 May 2015 – Published: 13 July 2015
Abstract. Since 70 % of global forests are managed and
forests impact the global carbon cycle and the energy ex-
change with the overlying atmosphere, forest management
has the potential to mitigate climate change. Yet, none of
the land-surface models used in Earth system models, and
therefore none of today’s predictions of future climate, ac-
counts for the interactions between climate and forest man-
agement. We addressed this gap in modelling capability by
developing and parametrising a version of the ORCHIDEE
land-surface model to simulate the biogeochemical and bio-
physical effects of forest management. The most significant
changes between the new branch called ORCHIDEE-CAN
(SVN r2290) and the trunk version of ORCHIDEE (SVN
r2243) are the allometric-based allocation of carbon to leaf,
root, wood, fruit and reserve pools; the transmittance, ab-
sorbance and reflectance of radiation within the canopy; and
the vertical discretisation of the energy budget calculations.
In addition, conceptual changes were introduced towards a
better process representation for the interaction of radiation
with snow, the hydraulic architecture of plants, the represen-
tation of forest management and a numerical solution for the
photosynthesis formalism of Farquhar, von Caemmerer and
Berry. For consistency reasons, these changes were exten-
sively linked throughout the code. Parametrisation was re-
visited after introducing 12 new parameter sets that represent
specific tree species or genera rather than a group of often
distantly related or even unrelated species, as is the case in
widely used plant functional types. Performance of the new
model was compared against the trunk and validated against
independent spatially explicit data for basal area, tree height,
canopy structure, gross primary production (GPP), albedo
and evapotranspiration over Europe. For all tested variables,
Published by Copernicus Publications on behalf of the European Geosciences Union.
2036 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
ORCHIDEE-CAN outperformed the trunk regarding its abil-
ity to reproduce large-scale spatial patterns as well as their
inter-annual variability over Europe. Depending on the data
stream, ORCHIDEE-CAN had a 67 to 92 % chance to re-
produce the spatial and temporal variability of the validation
data.
1 Introduction
Forests play a particularly important role in the global car-
bon cycle. Forests store almost 50 % of the terrestrial or-
ganic carbon and 90 % of vegetation biomass (Dixon et al.,
1994; Pan et al., 2011). Globally, 70 % of the forest is man-
aged and the importance of management is still increasing
both in relative and absolute terms. In densely populated re-
gions, such as Europe, almost all forest is intensively man-
aged by humans. Recently, forest management has become a
top priority on the agenda of political negotiations to mitigate
climate change (Kyoto Protocol, http://unfccc.int/resource/
docs/convkp/kpeng.pdf). Because forest plantations may re-
move CO2 from the atmosphere, if used for energy produc-
tion, harvested timber is a substitute for fossil fuel. Forest
management thus has great potential for mitigating climate
change, which was recognised in the United Nations Frame-
work Convention on Climate Change and the Kyoto Protocol.
Forests not only influence the global carbon cycle, but they
also dramatically affect the water vapour and energy fluxes
exchanged with the overlying atmosphere. It has been shown,
for example, that the evapotranspiration of young plantations
can be so great that the streamflow of neighbouring creeks
is reduced by 50 % (Jackson et al., 2005). Modelling studies
on the impact of forest plantations in regions that are snow-
covered in winter suggest that because of their reflectance
(the so-called albedo), forest could increase regional temper-
ature by up to four degrees (Betts, 2000; Bala et al., 2007;
Davin et al., 2007; Zhao and Jackson, 2014). Management-
related changes in the albedo, energy balance and water cycle
of forests (Amiro et al., 2006a, b) are of the same magni-
tude as the differences between forests, grasslands and crop-
lands (Luyssaert et al., 2014). Moreover, changes in the wa-
ter vapour and the energy exchange may offset the cooling
effect obtained by managing forests as stronger sinks for at-
mospheric CO2 (Pielke et al., 2002). Despite the key implica-
tions of forest management on the carbon–energy–water ex-
change, there have been no integrated studies on the effects
of forest management on the Earth’s climate.
Earth system models are the most advanced tools for pre-
dicting future climate (Bonan, 2008). These models repre-
sent the interactions between the atmosphere and the sur-
face beneath, with the surface formalised as a combination
of open oceans, sea ice and land. For land, five classes are
distinguished: glacier, lake, wetland, urban and vegetated.
Vegetation is typically represented by different plant func-
tional types. ORCHIDEE is the land-surface component of
the IPSL (Institut Pierre Simon Laplace) Earth system model.
Hence, by design, the ORCHIDEE model can be run cou-
pled to the LMDz global circulation model. In this coupled
set-up, the atmospheric conditions affect the land surface and
the land surface, in turn, affects the atmospheric conditions.
Coupled land–atmosphere models thus offer the possibility
to quantify both the climatic effects of changes in the land
surface and the effects of climate change on the land sur-
face. The most advanced land-surface models used, for in-
stance, in Earth system models to predict climate changes
(see the recent CMIP5 exercise), account for changes in veg-
etation cover but consider forests to be mature and ageless,
e.g. JSBACH (Reick et al., 2013), CLM (Stöckli et al., 2008),
MOSES (Cox et al., 1999), ORCHIDEE (Krinner et al.,
2005) and LPJ-DVGM (Bonan et al., 2003). At present, none
of the predictions of future climate thus accounts for the
essential interactions between forest management and cli-
mate. This gap in modelling capability provides the motiva-
tion for further development of the ORCHIDEE land-surface
model to realistically simulate both the biophysical and bio-
geochemical effects of forest management on the climate.
The ORCHIDEE-CAN (short for ORCHIDEE-CANOPY)
branch of the land-surface model was specifically developed
to quantify the climatic effects of forest management.
The aim of this study is to describe the model devel-
opments and parametrisation within ORCHIDEE-CAN and
to evaluate its performance. ORCHIDEE-CAN is validated
against structural, biophysical and biogeochemical data on
the European scale. To allow comparison with the standard
version of ORCHIDEE, ORCHIDEE-CAN was run with
a single-layer energy budget. A more detailed description
and evaluation of the new multi-layer energy budget and
multi-level radiative transfer scheme is given by Ryder et al.
(2014), Chen et al. (2015) and McGrath et al. (2015b). A
new forest management reconstruction, which is needed to
drive forest management in ORCHIDEE-CAN, is presented
in McGrath et al. (2015a), and the interactions between forest
management and the new albedo scheme have been discussed
by Otto et al. (2014).
2 Model overview
2.1 The starting point: ORCHIDEE SVN r2243
The land-surface model used for this study, ORCHIDEE, is
based on two different modules (Krinner et al., 2005, their
Fig. 2). The first module describes the fast processes such as
the soil water budget and the exchanges of energy, water and
CO2 through photosynthesis between the atmosphere and the
biosphere (Ducoudré et al., 1993; de Rosnay and Polcher,
1998). The second module simulates the carbon dynamics of
the terrestrial biosphere and essentially represents processes
such as maintenance and growth respiration, carbon alloca-
tion, litter decomposition, soil carbon dynamics and phenol-
Geosci. Model Dev., 8, 2035–2065, 2015 www.geosci-model-dev.net/8/2035/2015/
K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2037
ogy (Viovy and de Noblet-Ducoudré, 1997). The trunk ver-
sion of ORCHIDEE describes global vegetation by 13 meta-
classes (MTCs) with a specific parameter set (one for bare
soil, eight for forests, two for grasslands and two for crop-
lands). Each MTC can be divided into a user-defined number
of plant functional types (PFTs) which can be characterised
by at least one parameter value that differs from the param-
eter settings of the MTC. Parameters that are not given at
the PFT level are assigned the default value for the MTC to
which the PFT belongs. By default, none of the parameters
is specified at the PFT level; hence, MTCs and PFTs are the
same for the standard ORCHIDEE-trunk version. A concise
description of the main processes in the ORCHIDEE-trunk
version and a short motivation to change these modules in
ORCHIDEE-CAN is given in Table 1.
Before running simulations, it is necessary to bring the
soil carbon pools into equilibrium due to their slow fill rates,
an approach known as model spin-up (Thornton and Rosen-
bloom, 2005; Xia et al., 2012). For a long time, spin-ups
have been performed by brute force, i.e. running the model
iteratively over a sufficiently long period which allows even
the slowest carbon pool to reach equilibrium. This naïve ap-
proach is reliable but slow (in the case of ORCHIDEE it takes
3000 simulation years) and thus comes with a large com-
putational demand, often exceeding the computational cost
of the simulation itself. Alternative spin-up methods calling
only parts of the model, e.g. subsequent cycles of 10 years
of photosynthesis only followed by 100 year cycles of soil
processes only, have been used for ORCHIDEE to reduce
the computational cost in the past. These approaches, how-
ever, tend to lead to instabilities in litter and carbon pools.
In recent years, semi-analytical methods have been proposed
as a cost-effective solution to the spin-up issue (Martin et al.,
2007; Lardy et al., 2011; Xia et al., 2012). A matrix-sequence
method has been implemented in ORCHIDEE following the
approach used by the PaSim model (Lardy et al., 2011). The
semi-analytical spin-up implemented in ORCHIDEE relies
on algebraic methods to solve a linear system of equations
describing the seven carbon pools separately for each PFT.
Convergence of the method and thus equilibrium of the car-
bon pools is assumed to be reached when the variation of the
passive carbon pool (which is the slowest) drops below a pre-
defined threshold. The net biome production (NBP) is used
as a second diagnostic criterion to confirm equilibrium of the
carbon pools. In order to optimise computing resources, the
semi-analytical spin-up will stop before the end of the run
once the convergence criteria are met. ORCHIDEE’s imple-
mentation of the semi-analytical spin-up has been validated
on regional and global scales against a naïve spin-up, and
has been found to converge 12 to 20 times faster. The largest
gains were realised in the tropics and the smallest gains in
boreal climate (not shown).
Plant water
supply
Hydraulic
architecture
Canopy 
structure
1 day
Carbon 
allocation
Radiation 
scheme
30 min
30 min
30 min
30 min
30 min 1 day
Water
stress
Phenology
Photo-
synthesis
Soil 
hydrology
30 min
Energy 
budgetTranspirationdemandTranspiration
Mortality
1 day
30 min
1 day
1 day
(1)
(2)
(7)
(3)
(4)
(5)
(6)
Figure 1. Schematic overview of the changes in ORCHIDEE-CAN.
For the trunk the most important processes and connections are indi-
cated in black, while the processes and connections that were added
or changed in ORCHIDEE-CAN are indicated in red. Numbered
arrows are discussed in Sect. 2.2.
2.2 Modifications between ORCHIDEE SVN r2243
and ORCHIDEE-CAN SVN r2290
One major overarching change in the ORCHIDEE-CAN
branch is the increase in internal consistency within the
model by adding connections between the different processes
(Fig. 1, red arrows). A more specific novelty is the intro-
duction of circumference classes within forest PFTs, based
on the work of Bellassen et al. (2010). For the temperate
and boreal zone, tree height and crown diameter are cal-
culated from allometric relationships of tree diameter that
were parametrised based on the French, Spanish, Swedish
and German forest inventory data and the observational data
from Pretzsch (2009). The circumference classes thus al-
low calculation of the social position of trees within the
canopy, which justifies applying an intra-tree competition
rule (Deleuze et al., 2004) to account for the fact that trees
with a dominant position in the canopy are more likely to in-
tercept light than suppressed trees, and, therefore, contribute
more to the stand level photosynthesis and biomass growth.
To respect the competition rule of Deleuze et al. (2004),
a new allocation scheme was developed based on the pipe
model theory (Shinozaki et al., 1964) and its implementation
by Sitch et al. (2003). The scheme allocates carbon to dif-
ferent biomass pools (leaves, fine roots, and sapwood) while
www.geosci-model-dev.net/8/2035/2015/ Geosci. Model Dev., 8, 2035–2065, 2015
2038 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
T
able
1.Concise
description
ofthe
m
odulesin
the
standard
O
RCH
ID
EE
v
ersion
w
ith
the
m
otiv
ation
to
change
the
m
odulesin
O
RCH
ID
EE-CA
N
.
M
odule
D
escription
M
otiv
ation
for
change
A
lbedo
F
o
r
each
PFT
the
total
albedo
for
the
grid
square
is
co
m
puted
as
a
w
eighted
av
erage
of
the
v
egetation
albedo,the
soil
albedo,
and
the
sn
o
w
albedo.
The
schem
e
o
v
erlooks
the
effect
of
v
egetation
shading
bare
soil
for
sparse
can
opies
and
giv
es
the
ground
in
allPFTs
the
sam
e
reflectance
properties
asbare
soil.
Soilhydrology
V
ertical
w
aterflo
w
in
the
soilisbased
o
n
theF
okk
er
–Planck
equation
that
resolv
es
w
ater
diffusion
in
n
o
n
-saturated
co
nditionsfrom
the
Richards
equation(Richards
,1931).The
2
m
soil
colum
n
co
n
sists
of11
m
oisture
layers
w
ith
an
exponentially
increasing
depth
(D
’O
rgev
al
et
al.
,2008).
N
o
change
Soiltem
perature
The
soiltem
peratureis
co
m
puted
acco
rding
to
the
F
o
u
rier
equation
u
sing
afinitediffer
-
en
ce
im
plicit
schem
e
w
ith
sev
en
n
u
m
erical
n
odes
u
n
ev
enly
distrib
uted
betw
een
0
and
5.5
m
(H
ourdin
,1992).
N
o
change
Energy
b
udget
The
co
upled
en
ergy
balance
schem
e,
and
its
ex
change
w
ith
the
atm
osphere,is
based
o
n
that
ofD
ufresne
and
G
hattas(2009).The
su
rface
isdescribed
as
a
single
layer
that
includesboth
the
soil
su
rface
and
any
v
egetation.
A
big
leaf
approach
does
n
ot
acco
u
ntfor
w
ithin
can
opy
transport
of
carbon,
w
ater
and
en
ergy
.Further
,itis
inconsistent
w
ith
the
cu
rrent
m
ulti-layerphotosynthesis
approach
and
the
n
ew
m
ulti-layer
albedo
ap-
proach.
Photosynthesis
C3
and
C4
photosynthesis
is
calculated
follo
w
ing
F
arquhar
et
al.(1980)
and
Collatz
et
al.(1992),
respectiv
ely
.Photosynthesis
assigns
artificialLA
Ilev
els
to
calculate
the
carbon
assim
ilation
ofthe
can
opy
.These
lev
els
allo
w
for
a
saturation
ofphotosynthesis
w
ith
LA
I,b
uthav
e
n
o
physical
m
eaning.
The
schem
e
u
ses
a
sim
ple
B
eerlaw
transm
ission
oflightto
each
lev
el,
w
hich
isinconsistent
w
ith
the
n
ew
albedo
schem
e.
A
utotrophic
respiration
A
utotrophic
respiration
distinguishes
m
aintenance
and
gro
w
th
respiration.M
aintenance
respiration
o
ccu
rs
in
living
plant
co
m
partm
ents
and
is
a
function
of
tem
perature,
biom
ass
and,theprescribed
carbon/nitrogen
ratio
of
each
tissue(R
uim
y
et
al.
,1996).A
prescribed
fraction
of28%
of
the
photosynthates
allocated
to
gro
w
th
is
u
sed
in
gro
w
th
respiration(M
cCree
,1974).The
rem
aining
assim
ilates
are
distrib
uted
am
o
ng
the
v
ari-
o
u
splant
o
rg
an
s
u
sing
an
allocation
schem
e
based
o
n
reso
u
rce
lim
itations(see
alloca-
tion).
N
o
change
Carbon
allocation
Carbon
is
allocated
to
the
plant
follo
w
ing
reso
u
rce
lim
itations
Friedlingstein
et
al.
(1999).Plants
allocate
carbon
to
theirdifferent
tissues
in
response
to
externallim
ita-
tions
of
w
ater
,light
and
nitrogen
av
ailability
.W
hen
the
ratios
of
these
lim
itations
are
o
ut
ofbounds,prescribed
allocation
factors
are
u
sed.
The
reso
u
rce
lim
itation
appro
ach
requires
capping
LA
I
at
a
predefined
v
alue.D
ue
to
this
cap,the
allocation
rules
are
m
o
st
often
n
ot
applied,
reducing
the
schem
e
to
prescribing
allocation.
Phenology
A
t
the
end
of
each
day
,
the
m
odel
checks
w
hether
the
co
nditions
for
leaf
o
n
set
are
satisfied.The
PFT
-specific
co
nditions
arebased
o
n
long-
and
short-term
w
arm
th
and/or
m
oisture
co
nditions(B
otta
et
al.
,2000).
N
o
change
M
ortality
and
turno
v
er
A
llbiom
asspoolshav
e
a
turno
v
ertim
e.Living
biom
assis
transferred
to
the
litterpool;
litterisdecom
posed
o
rtransferred
to
the
soilpool.
This
approach
is
n
ot
capable
of
m
odelling
stand
dim
ensions.
Soil
and
litter
carbon
and
heterotrophic
respi-
ration
F
ollo
w
ing(P
arton
et
al.
,1988),prescribed
fractions
of
the
differentplant
co
m
ponents
go
to
the
m
etabolic
and
structurallitterpoolsfollo
w
ing
sen
escen
ce,turno
v
er
o
r
m
o
rtal-
ity
.The
decay
of
m
etabolic
and
structurallitteris
co
ntrolled
by
tem
perature
and
soil
o
r
litterhum
idity
.F
o
r
structurallitter
,itslignin
co
ntent
also
influences
the
decay
rate.
N
o
change
F
o
rest
m
an
agem
ent
A
n
explicitdistrib
ution
of
individual
trees(B
ellassen
et
al.
,2010)
is
the
basis
for
a
process-based
sim
ulation
of
m
o
rtality
.The
abo
v
eground
stand-scale
w
o
od
increm
ent
is
distrib
uted
o
n
a
yearly
tim
e
step
am
o
ng
individual
trees
acco
rding
to
the
rule
of
(D
eleuze
et
al.
,2004):the
basal
area
of
each
individualtree
gro
w
sproportionally
to
its
circum
ference.
The
co
n
cept
ofthe
o
riginalim
plem
entation
w
ere
retained,ho
w
ev
er
,the
im
plem
entation
w
as
adjusted
for
co
n
sistency
w
ith
the
n
ew
allocation
schem
e
and
to
hav
e
a
largerdiv
ersity
of
m
an
agem
ent
strategies.
Geosci. Model Dev., 8, 2035–2065, 2015 www.geosci-model-dev.net/8/2035/2015/
K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2039
respecting the differences in longevity and hydraulic conduc-
tivity between the pools. In addition to the biomass of the
different pools, leaf area index (LAI), crown volume, crown
density, stem diameter, stem height and stand density are cal-
culated and now depend on accumulated growth. The new
scheme allows for the removal of the parameter that caps the
maximum LAI (Table 1).
The calculation of tree dimensions (e.g. sapwood area and
tree height) that respect the pipe theory supports making use
of the hydraulic architecture of plants to calculate the plant
water supply (Fig. 1, arrow 1), which is the amount of wa-
ter a plant can transport from the soil to its stomata. The
representation of the plant hydraulic architecture is based on
the scheme of Hickler et al. (2006). The water supply is cal-
culated as the ratio of the pressure difference between soil
and leaves, and the total hydraulic resistance of the roots,
leaves and sapwood, where the sapwood resistance is in-
creased when cavitation occurs. Species-specific parameter
values were compiled from the literature. As the scheme
makes use of the soil water potential, it requires the use of the
11-layer hydrology scheme of de Rosnay (2002) (Table 1).
When transpiration based on energy supply exceeds transpi-
ration based on the water supply, the latter restricts stomatal
conductance directly, which is a physiologically more real-
istic representation of drought stress than the reduction of
the carboxylation capacity (Flexas et al., 2006) done in the
standard version of ORCHIDEE (further also referred to as
the “trunk” version). In line with this approach, the drought
stress factor used to trigger phenology and senescence is now
calculated as the ratio between the transpiration based on wa-
ter supply and transpiration based on atmospheric demand
(Fig. 1, arrow 2).
The new allocation scheme also drastically changed the
way forests are represented in the ORCHIDEE-CAN branch.
Although the exact location of the canopies in the stand is
not known, individual tree canopies are now spherical ele-
ments with their horizontal location following a Poisson dis-
tribution across the stand. Each PFT contains a user-defined
number of model trees, each one corresponding to a cir-
cumference class. Model trees are replicated to give realistic
stand densities. Following tree growth, canopy dimensions
and stand density are updated (Fig. 1, arrow 3). This for-
mulation results in a dynamic canopy structure that is ex-
ploited in other parts of the model, i.e. precipitation inter-
ception, transpiration, energy budget calculations, a radiation
scheme (Fig. 1, arrow 4) and absorbed light for photosynthe-
sis (Fig. 1, arrow 5). In the trunk version these processes are
driven by the big-leaf canopy assumption. The introduction
of an explicit canopy structure is thought to be a key develop-
ment with respect to the objectives of the ORCHIDEE-CAN
branch, i.e. quantifying the biogeochemical and biophysical
effects of forest management on atmospheric climate.
The radiation transfer scheme at the land surface benefits
from the introduction of canopy structure. The trunk version
of ORCHIDEE prescribes the vegetation albedo solely as a
function of LAI. In the ORCHIDEE-CAN branch each tree
canopy is assumed to be composed of uniformly distributed
single scatterers. Following the assumption of a Poisson dis-
tribution of the trees on the land surface, the model of Haverd
et al. (2012) calculates the transmission probability of light to
any given vertical point in the forest. This transmission prob-
ability is then used to calculate an effective LAI, which is a
statistical description of the vertical distribution of leaf mass
that accounts for stand density and horizontal tree distribu-
tion. The complexity and computational costs are largely re-
duced by using the effective LAI in combination with the 1-
D two-stream radiation transfer model of Pinty et al. (2006)
rather than resolving a full 3-D canopy model. By using the
effective LAI, the 1-D model reproduces the radiative fluxes
of the 3-D model. The approach of the two-stream radia-
tion transfer model was extended for a multi-layer canopy
(McGrath et al., 2015b) to be consistent with the multi-layer
energy budget and to better account for non-linearities in
the photosynthesis model. The scattering parameters and the
background albedo (i.e. the albedo of the surface below the
dominant tree canopy) for the two-stream radiation transfer
model were extracted from the Joint Research Centre Two-
stream Inversion Package (JRC-TIP) remote sensing product
(Sect. 4.7). This approach produces fluxes of the light ab-
sorbed, transmitted, and reflected by the canopy at vertically
discretised levels, which are then used for the energy bud-
get (Fig. 1, arrow 6) and photosynthesis calculations (Fig. 1,
arrow 5).
The canopy radiative transfer scheme of Pinty et al. (2006)
separates the calculation of the fluxes resulting from down-
welling direct and diffuse light, with different scattering pa-
rameters available for near-infrared (NIR) and visible (VIS)
light sources. The snow albedo scheme in the trunk does
not distinguish between these two short-wave bands. There-
fore, the snow scheme of the Biosphere-Atmosphere Transfer
Scheme (BATS) for the Community Climate Model (Dickin-
son et al., 1986) was incorporated into the ORCHIDEE-CAN
branch, since it distinguishes between the NIR and VIS radi-
ation. The radiation scheme of Pinty et al. (2006) requires
snow to be put on the soil below the tree canopy instead of
on the canopy itself. The calculation of the snow coverage of
a PFT therefore had to be revised according to the scheme
of Yang et al. (1997), which allows for snow to completely
cover the ground at depths greater than 0.2 m. The parameter
values of Yang et al. (1997) were used in the ORCHIDEE-
CAN branch.
The ORCHIDEE-CAN branch differs from any other land-
surface model by the inclusion of a newly developed multi-
layer energy budget. There are now subcanopy wind, tem-
perature, humidity, long-wave radiation and aerodynamic re-
sistance profiles, in addition to a check of energy closure
at all levels. The energy budget represents an implementa-
tion of some of the characteristics of detailed single-site, it-
erative canopy models (e.g. Baldocchi, 1988; Ogee et al.,
2003) within a system that is coupled implicitly to the at-
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2040 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
mosphere. As an enhancement to the trunk version of OR-
CHIDEE (Table 1), the new approach also generates a leaf
temperature, using a vegetation profile and a vertical short-
wave and long-wave radiation distribution scheme (Ryder
et al., 2014), which will be fully available when parametri-
sation of the scheme has been completed across test sites
corresponding to the species within the model (Chen et al.,
2015). As with the trunk version, the new energy budget is
calculated implicitly (Polcher et al., 1998; Best et al., 2004).
An implicit solution is a linear solution in which the surface
temperature and fluxes are calculated in terms of the atmo-
spheric input at the same time step, whereas an explicit so-
lution uses atmospheric input from the previous time step to
calculate the surface temperature and fluxes. Although it is
less straightforward to derive, the implicit solution is more
computationally efficient and stable, which allows the model
to be run over a time step of 15 min when coupled to the
LMDz atmospheric model – much longer than would be the
case for an explicit model. Parameters were derived by op-
timising the model against the observations from short-term
field campaigns. The new scheme may also be reduced to the
existing single layer case, so as to provide a means of com-
parison and compatibility with the ORCHIDEE-trunk ver-
sion.
The combined use of the new energy budget and the hy-
draulic architecture of plants required changes to the calcula-
tion of the stomatal conductance and photosynthesis (Fig. 1,
arrow 7). When water supply limits transpiration, stomatal
conductance is reduced and photosynthesis needs to be re-
calculated. Given that photosynthesis is among the compu-
tational bottlenecks of the model, the semi-analytical proce-
dure as available in previous trunk versions (r2031 and fur-
ther) is replaced by an adjusted implementation of the analyt-
ical photosynthesis scheme of Yin and Struik (2009), which
is also implemented in the latest ORCHIDEE-trunk version.
In addition to an analytical solution for photosynthesis, the
scheme includes a modified Arrhenius function for the tem-
perature dependence that accounts for a decrease in car-
boxylation capacity (kVcmax) and electron transport capacity
(kJmax; see Table 2 for variable explanations) at high temper-
atures and a temperature-dependent kJmax/Vcmax ratio (Kattge
and Knorr, 2007). The temperature response of kVcmax and
kJmax was parametrised with values from reanalysed data in
the literature (Kattge and Knorr, 2007), whereas kVcmax and
kJmax at a reference temperature of 25 ◦C were derived from
observed species-specific values in the TRY database (Kattge
et al., 2011). As the amount of absorbed light varies with
height (or canopy depth), the absorbed light computed from
the albedo routines is now directly used in the photosynthesis
scheme, resulting in full consistency between the top of the
canopy albedo and absorption. This new approach replaces
the old scheme which used multiple levels based on the leaf
area index, not the physical height.
ORCHIDEE-CAN incorporates a systematic mass balance
closure for carbon cycling to ensure that carbon is not getting
created or destroyed during the simulation. Hence, budget
closure is now consistently checked for water, carbon and
energy throughout the model.
The trunk uses 13 PFTs to represent vegetation globally:
one PFT for bare soil, eight for forests, two for grasslands,
and two for croplands. The ORCHIDEE-CAN branch makes
use of the externalisation of the PFT-dependent parameters
by adding 12 parameter sets that represent the main Euro-
pean tree species. Species parameters were extracted from a
wide range of sources including original observations, large
databases, primary research and remote sensing products
(Sect. 4). The use of age classes is introduced through ex-
ternalisation of the PFT parameters as well. Age classes are
used during land cover change and forest management to
simulate the regrowth of a forest. Following a land cover
change, biomass and soil carbon pools (but not soil water
columns) are either merged or split to represent the various
outcomes of a land cover change. The number of age classes
is user defined. Contrary to typical age classes, the bound-
aries are determined by the tree diameter rather than the age
of the trees.
Finally, the forest management strategies in the
ORCHIDEE-CAN branch were refined from the origi-
nal forest management (FM) branch (Bellassen et al., 2010).
Self-thinning was activated for all forests regardless of
human management, contrary to the original FM branch.
The new default management strategy thus has no human
intervention but includes self-thinning, which replaces the
fixed 40 year turnover time for woody biomass. Three
management strategies with human intervention have been
implemented: (1) “high stands”, in which human intervention
is restricted to thinning operations based on stand density
and diameter, with occasional clear-cuts. Aboveground
stems are harvested during operations, while branches
and belowground biomass are left to litter; (2) “coppices”
involve two kinds of cuts. The first coppice cut is based
on stem diameter and the aboveground woody biomass is
harvested, whereas the belowground biomass is left living.
From this belowground biomass, new shoots sprout, which
increases the number of aboveground stems. In subsequent
cuts the number of shoots is not increased, although all
aboveground wood biomass is still harvested; and (3) “short
rotation coppices”, where rotation periods are based on
age and are generally very short (3–6 years). The different
management strategies can occur with or without litter
raking, which reduces the litter pools and has a long-term
effect on soil carbon (Gimmi et al., 2012). All management
types are parametrised based on forest inventory data, yield
tables and guidelines for forest management. The inclusion
of forest management resulted in two additional carbon
pools, branches and coarse roots (i.e. aboveground and
belowground woody biomass) and therefore required an
extension to the semi-analytical spin-up method (Sect. 2.1).
The semi-analytical spin-up is now run for nine C pools.
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K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2041
Table 2. Variable description. Variables were grouped as follows: F =flux, f = fraction, M = pool, m=modulator, d = stand dimension,
T = temperature, p = pressure, R = resistance, q = humidity, g = function.
Symbol in text Unit Symbol in ORCHIDEE-CAN Description
Frm gC m−2 s−1 resp_maint Maintenance respiration
Frg gC m−2 s−1 resp_growth Growth respiration
FLW,i W m2 r_lw Long-wave radiation incident at vegetation level i
FSW,i W m2 r_sw Short-wave radiation incident at vegetation level i
FTrs m s−1 Transpir_supply Amount of water that a tree can get up from the soil to its leaves for transpiration
Ta,i K temp_atmos_pres,
temp_atmos_next
Atmospheric temperature at the “present” and “next” time step, respectively, at
level i
TL,i K temp_leaf_pres Leaf temperature at level i
qa,i kg kg−1 q_atmos_pres, q_atmos_next Specific humidity at the “present” and “next” time step, respectively, at level i
qL,i kg kg−1 q_leaf_pres Leaf-specific humidity at level i
Ml gC plant−1 Cl Leaf mass of an individual plant
Ms gC plant−1 Cs Sapwood mass of an individual plant
Mh gC plant−1 Ch Heartwood mass of an individual plant
Mr gC plant−1 Cr Root mass of an individual plant
Mlinc gC plant−1 Cl_inc Increment in leaf mass of an individual plant
Msinc gC plant−1 Cs_inc Increment in sapwood mass of an individual plant
Mrinc gC plant−1 Cr_inc Increment in root mass of an individual plant
Mtotinc gC b_inc_tot Total biomass increment
Minc gC plant−1 b_inc Increment in plant biomass of an individual plant
Mswc m
3 m−3 swc Volumetric soil water content
mw – wstress_fac Modulator for water stress as experienced by the plants
mψ MPa psi_soil_tune Modulator to account for resistance in the soil-root interface
mNdeath – scale_factor Normalisation factor for mortality
mLAIcorr – lai_correction_factor Adjustable parameter in the calculation of gap probabilities of grasses and crops
dh m height Plant height
dl m−2 – One-sided leaf area of an individual plant
ds m
−2
– Sapwood area of an individual plant
dhinc m delta_height Height increment
ddbh m dia Plant diameter
dba m2 plant−1 ba Basal area
dbainc m2 plant−1 delta_ba Basal area increment
dcirc m circ Stem circumference of an individual plant
dind trees n_circ_class Number of trees in diameter class l
dc m
2 crown_shadow_h Projected area of an opaque tree crown
dcsa m
2 csa_sap Projected crown surface area
dLAI m
2
leafm
−2
ground – Leaf area index
dLAIeff – laieff Effective leaf area index
dLAIabove – lai_sum Sum of the LAI of all levels above the current level
dA,i m2 – Cross-sectional area of vegetation level i
dhl,i m delta_h Vegetation height of level i
dV,i m3 – Volume of vegetation level i
drd – root_dens Root density
dλ ind m2 – Inverse of the individual plant density
pdelta MPa delta_P Pressure difference between leaves and soil
pψsr MPa psi_soilroot Bulk soil water potential in the rooting zone
pψs MPa psi_soil Soil water potential for each soil layer
Rr MPa s m−3 R_root Hydraulic resistance of roots
Rsap MPa s m−3 R_sap Hydraulic resistance of sapwood
Rl MPa s m−3 R_leaf Hydraulic resistance of leaves
Rtemp MPa s m−3 – Hydraulic resistance of roots, sapwood or leaves adjusted for temperature
Ra,i s m
−1 big_r Aerodynamic resistance of vegetation at level i in the canopy
Rs,i s m
−1 big_r_prime Sum of the stomatal and leaf boundary layer resistance terms for latent heat
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2042 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
Table 2. Continued.
Symbol in text Unit Symbol in ORCHIDEE-CAN Description
fPwc – Pwc_h Porosity of a tree crown
f treesPgap – PgapL Gap probability for trees
f
gc
Pgap – PgapL Gap probability for grasses and crops
f bsPgap – PgapL Gap probability for bare soil
f icirdeath – mortality Mortality fraction per circumference class
fKF – KF Leaf allocation factor
fLF – LF Root allocation factor
fγ – gamma Slope of the intra-specific competition
fs m s Slope of linearised relationship between height and basal area
frl – leaf_reflectance Reflectance of a single leaf
ftl – leaf_transmittance Transmittance of a single leaf
fRbgd – bdg_reflectance Reflectance of the ground beneath the canopy
f
fR
Coll,veg – Collim_alb_BB,
Isotrop_alb_BB
Reflected fraction of light to the atmosphere which has collided with canopy
elements, separated for direct and diffuse sources, respectively
f
fR
UnColl, bgd – Collim_alb_BC,
Isotrop_alb_BC
Reflected fraction of light to the atmosphere which has not collided with any
canopy elements, separated for direct and diffuse sources, respectively
f TUnColl, veg – Collim_Tran_Uncoll Transmitted fraction of light to the ground which has not collided with any
canopy elements
f
fR
Coll, bgd,1 – – Reflected fraction of light which has struck the background a single time and
has collided with vegetation
f
fR
Coll, bgd,n – – Reflected fraction of light which has struck the background multiple times and
has collided with vegetation
z m z_array Height above the soil
θz radians solar_angle Solar zenith angle
θµ radians – Cosine of the solar zenith angle
gG – – Leaf orientation function
gσ – sigmas Cut-off circumference of the intra-specific competition, calculated as a function
of kncirc
3 Description of the developments
3.1 Allocation
Following bud burst, photosynthesis produces carbon that is
added to the labile carbon pool. Labile carbon is used to sus-
tain the maintenance respiration flux (Frm), which is the car-
bon cost to keep existing tissue alive (Amthor, 1984). Main-
tenance respiration for the whole plant is calculated by sum-
ming maintenance respiration of the different plant compart-
ments, which is a function of the nitrogen concentration of
the tissue following the Beer–Lambert law and subtracted
from the whole-plant labile pool (up to a maximum of 80 %
of the labile pool).
The remaining labile carbon pool is split into an active and
a non-active pool. The size of the active pool is calculated
as a function of plant phenology and temperature and was
formalised following Ryan (1991), Sitch et al. (2003) and
Zaehle and Friend (2010). The remaining non-active pool is
used to restore the labile and carbohydrate reserve pools ac-
cording to the rules proposed in Zaehle and Friend (2010).
The labile pool is limited to 1 % of the plant biomass or 10
times the actual daily photosynthesis. Any excess carbon is
transferred to the non-respiring carbohydrate reserve pool.
The carbohydrate reserve pool is capped to reflect limited
starch accumulation in plants, but carbon can move freely
between the two reserve pools. After accounting for growth
respiration (Frg), i.e. the cost for producing new tissue ex-
cluding the carbon required to build the tissue itself (Amthor,
1984), the total allocatable C used for plant growth is ob-
tained (Mtotinc).
New biomass is allocated to leaves, roots, sapwood, heart-
wood, and fruits. Allocation to leaves, roots and wood re-
spects the pipe model theory (Shinozaki et al., 1964) and
thus assumes that producing one unit of leaf mass requires
a proportional amount of sapwood to transport water from
the roots to the leaves as well as a proportional fraction of
roots to take up the water from the soil. The different biomass
pools have different turnover times, and therefore at the end
of the daily time step, the actual biomass components may no
longer respect the allometric relationships. Consequently, at
the start of the time step carbon is first allocated to restore the
allometric relationships before the remaining carbon is allo-
cated in the manner described below.The scaling parameter
between leaf and sapwood mass is derived from:
dl = kls×mw× ds (1)
where dl is the one-sided leaf area of an individual plant, ds
is the sapwood cross-section area of an individual plant, kls
a parameter linking leaf area to sapwood cross-section area,
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K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2043
and mw is the water stress as defined in Sect. 3.2. Alterna-
tively, leaf area can be written as a function of leaf mass (Ml)
and the specific leaf area (ksla):
dl =Ml× ksla. (2)
Sapwood mass Ms can be calculated from the sapwood
cross-section area ds as follows:
Ms = ds× dh× kρs, (3)
where dh is the tree height and kρs is the sapwood density.
Following substitution of Eqs. (2) and (3) into Eq. (1), leaf
mass can be written as a function of sapwood mass:
Ml = (Ms× fKF)/dh, (4)
where,
fKF = (kls×mw)/
(
ksla× kρs
)
, (5)
where kls is calculated as a function of the gap fraction as
supported by site-level observations (Simonin et al., 2006):
kls = klsmin+ fPgap, trees× (klsmax− klsmin). (6)
klsmin is the minimum observed leaf area to sapwood area
ratio, klsmax is the maximum observed leaf area to sapwood
area ratio and fPgap,trees is the actual gap fraction. By using
the gap fraction as a control of kls more carbon will be allo-
cated to the leaves until canopy closure is reached.
Following Magnani et al. (2000), sapwood mass and root
mass (Mr) are related as follows:
Ms = ksar× dh×Mr, (7)
where the parameter ksar is calculated according to Magnani
et al. (2000) (their Eq. 17):
ksar =
√
(krcon/kscon)× (kτ s/kτ r)× kρs, (8)
where krcon is the hydraulic conductivity of roots, kscon is the
hydraulic conductivity of sapwood, kτ s is the longevity of
sapwood and kτ r is the root longevity. Following substitution
of Eq. (4) into Eq. (7) and some rearrangement, leaf mass can
be written as a function of root mass:
Ml = fLF×Mr, (9)
where,
fLF = ksar× fKF. (10)
Parameter values used in Eqs. (1) to (9), i.e. klsmax, klsmin,
ksar, ksla, kρs, krcon, kscon, kτ s and kτ r, are based on literature
review (Tables S1, S2 and S3 in the Supplement). The allo-
metric relationships between the plant components and the
hydraulic architecture of the plant (Sect. 3.2) are both based
on the pipe model theory; hence, both the allocation and the
hydraulic architecture module use the same parameter values
for root and sapwood conductivity.
In this version of ORCHIDEE, forests are modelled to
have kncirc circumference classes with dind identical trees in
each one. Hence, the allocatable biomass (Mtotinc) needs to
be distributed across l diameter classes:
Mtotinc =
∑
(l)[dind(l)×Minc(l)], (11)
whereMinc(l) is the biomass that can be allocated to diameter
class l. Mass conservation thus requires:
Minc(l) =Mlinc(l)+Mrinc(l)+Msinc(l), (12)
where Mlinc(l), Mrinc(l) and, Msinc(l) are the increase in leaf,
root and wood biomass for a tree in diameter class l, respec-
tively. Equations (4) and (9) can be rewritten as
(Ml(l)+Mlinc(l))/(Ms(l)+Msinc(l))= fKF/(dh(l)
+ dhinc(l)) (13)
(Ml(l)+Mlinc(l))= (Mr(l)+Mrinc(l))× fLF (14)
An allometric relationship is used to describe the relation-
ship between tree height and basal area (Pretzsch, 2009):
dh(l) = kα1× (4/pi × dba(l))(kβ1/2). (15)
The change in height is then calculated as
dhinc(l) = [kα1×(4/pi×(dba(l)+dbainc(l)))(kβ1/2)]−dh(l), (16)
where dba(l) and dbainc(l) are the basal area and its increment,
respectively. kα1 and kβ1 are allometic constants relating tree
diameter and height. The distribution of C across the l di-
ameter classes depends on the basal area of the model tree
within each diameter class. Trees with a large basal area are
assigned more carbon for wood allocation than trees with a
small basal area, according to the method of Deleuze et al.
(2004).
dbainc(l) = fγ ×
(
dcirc(l)− km · gσ+√
(km× gσ + dcirc(l))2− (4× gσ × dcirc(l))
)
/2, (17)
where km is a parameter, fγ and gσ are calculated from pa-
rameters and dcirc(l) is the circumference of the model tree in
diameter class l. gσ is a function of the diameter distribution
of the stand at a given time step.
Equations (10) to (16) need to be simultaneously solved.
An iterative scheme was avoided by linearising Eq. (15),
which was found to be an acceptable numerical approxima-
tion as allocation is calculated at a daily time step, and hence
the changes in height are small and the relationship is locally
linear:
dhinc(l) = dbainc(l)/fs, (18)
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2044 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
where fs is the slope of the locally linearised Eq. (15) and is
calculated as
fs =kstep/(kα1× (4/pi · (dba+ kstep))(kβ1/2)
− kα1× (4/pi × dba)(kβ1/2)). (19)
Equations (10), (11), (12), (13), (14), (16), (17) and (18)
are then solved for fγ . fγ distributes photosynthates across
the different diameter classes and as such controls the intra-
species competition within a stand. fγ thus depends on the
total allocatable carbon and needs to be optimised at every
time step. Once fγ has been calculated, Mlinc(l), Mrinc(l) and
Msinc(l) can be calculated.
3.2 Hydraulic architecture
The representation of the impact of soil moisture stress on
water, carbon and energy fluxes has been identified as one of
the major uncertainties in land-surface models (De Kauwe
et al., 2013). Neither the empirical functions nor the soil
moisture stress functions, which are commonly used in land-
surface models, fully capture stomatal closure and limitation
of C uptake during drought stress (Bonan et al., 2014; Ver-
hoef and Egea, 2014). Therefore, we replaced the soil mois-
ture stress function which limits C assimilation through a
constraint on kVcmax in the ORCHIDEE trunk, with a con-
straint based on the amount of water plants can transport
from the soil to their leaves.
The model calculates plant water supply according to the
implementation of hydraulic architecture by Hickler et al.
(2006). Plant water supply is the amount of water the plant
can transport from the soil to its stomata, accounting for
the resistances to water transport in the roots, sapwood and
leaves. If transpiration rate exceeds plant water supply, the
stomatal conductance is reduced until equilibrium is reached.
The water flow from the soil to the leaves is driven by a
gradient of decreasing water potential. Using Darcy’s law
(Slatyer, 1967; Whitehead, 1998), the supply of water for
transpiration through stomata can be described as
FTrs = pdelta/
(
Rr+Rsap+Rl
)
, (20)
where pdelta is the pressure difference between the soil and
the leaves; and Rr, Rsap and Rl are the hydraulic resistances
of fine roots, sapwood and leaves, respectively. pdelta is cal-
culated following Whitehead (1998):
pdelta = pψsr− kψ l− (dh× kρw× kg) (21)
where kψ l is a PFT-specific minimal leaf water potential,
which means that plants are assumed to maximise water up-
take by lowering their kψ l to the minimum, if transpiration
exceeds FTrs (Tyree and Sperry, 1989). The product of dh,
kρw and kg accounts for the loss in water potential by lifting
a mass of water from the soil to the place of transpiration at
height dh, kρw is the density of water, and kg is the gravita-
tional constant. The soil water potential in the rooting zone
(pψsr) was calculated by adding a modulator (mψ ) to the bulk
soil water potential, which was calculated as the sum of the
soil water potential in each soil layer weighted by the relative
share of roots (drd) in the individual soil layer:
pψsr =
∑
(l)[pψs× drd] +mψ . (22)
The soil water potential for each layer pψsl is calculated
from soil water content according to Van Genuchten (1980).
pψs(l) = 1
kav
((
Mswc− kswcr
kswcs− kswcr
)−1/kmv
− 1
)1/knv
, (23)
where Mswc is the volumetric soil water content, kswcr and
kswcs are respectively the residual and saturated soil water
content and kav, kmv and knv are parameters.
Root resistance is related to the root mass and thus can be
expressed as (Weatherley, 1982):
Rr = 1
(krcon×Mr) , (24)
where krcon is the fine root hydraulic conductivity per unit
biomass. Sapwood resistance is calculated according to Mag-
nani et al. (2000):
Rsap = dh
(ds× kscon) , (25)
where kscon is the sapwood-specific conductivity, which is
decreased when cavitation occurs. The loss of conductance
as a result of cavitation is a function of pψsr and was imple-
mented by using an s-shaped vulnerability curve
kscon = kscon× e(−pψrs/kψ50)kc , (26)
where kψ50 is the pψsr that causes 50 % loss of conductance;
and kc is a shape parameter.
Rl is related to the specific leaf conductivity per unit leaf
area (kl) and the leaf area index:
Rl = 1
(klcon× dLAI) . (27)
The response of water viscosity to low temperatures in-
creases the resistance (Cochard et al., 2000). The relationship
is described as
Rtemp = R
(kα1v+ kα2v× T ) , (28)
where kα1v and kα2v are empirical parameters (Cochard et al.,
2000), Rtemp is the temperature adjusted Rl, Rsap or Rr, T is
air temperature for Rl and Rsap and T is soil temperature for
Rr.
If, for any time step, the transpiration calculated by the en-
ergy budget exceeds the amount of water the plant can trans-
port from the soil to its stomata, transpiration is limited to
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K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2045
the plant water supply. As the transpiration is now reduced,
the initial calculations of the energy budget and photosynthe-
sis, solely based on atmospheric information, are no longer
valid. As a result the energy budget and photosynthesis must
be recalculated for the time step in question. For this recal-
culation, stomatal conductance at the canopy level is calcu-
lated such that transpiration equals the amount of water the
plant can transport. Owing to the feedback between stomatal
conductance, leaf surface temperature and transpiration, this
calculation may require up to 10 iterations to converge, using
a stationary iterative method. When the multi-layer energy
budget is reduced to its single-layer implementation, how-
ever, canopy level stomatal conductance is decomposed to
obtain the stomatal conductance at each canopy layer assum-
ing that each layer is equally restricted by drought stress. Fi-
nally, the restricted stomatal conductance is used to calculate
CO2 assimilation rate according to the photosynthesis model
by Farquhar, von Caemmerer and Berry (Sect. 3.6).
3.3 Canopy structure
Stand structure controls the amount of light that penetrates
to a given depth in the canopy. For example, the amount
of light reaching the forest floor will be higher for a stand
with few mature trees compared to many young trees, even
if both stands have the same leaf area index. Where a big
leaf approach assumes a homogeneous block-shaped canopy
(as in the trunk version of ORCHIDEE) and can therefore
rely on the law of Beer and Lambert, a geometric approach
is required to calculate light penetration through structured
canopies. Light penetration needs to be simulated to calculate
albedo (Sect. 3.4), photosynthesis (Sect. 3.6), partitioning of
energy fluxes (Sect. 3.5) and the amount of light reaching
the forest floor (see for example Sect. 3.1). The gap fraction,
which is the basic information in calculating light penetration
at different depths in the canopy, is calculated following the
approach presented by Haverd et al. (2012) and formalised in
their semi-analytical model. Rather than a spatially explicit
approach, Haverd et al. (2012) follow a statistical approach
which reduces the memory requirements for the simulations
and limits the space requirements for storing the model out-
put files.
The model of Haverd et al. (2012) represents the canopy
by a statistical height distribution with varying crown sizes
and stem diameters for each height class. The crown canopies
are treated as spheroids containing homogeneously dis-
tributed single scatterers. Although this fPgap model can ex-
plicitly include trunks, we made the decision to exclude
them, as the spectral parameters for our radiation model
(Sect. 3.4) are extracted from remote sensing data (Sect. 4.8)
without distinguishing between leafy and woody masses.
This gives the gap probability for trees as a function of height
(z) and solar zenith angle (θz):
f treesPgap (θz,z)= e−dλ×dc(θz,z)×(1−fPwc(θz,z)), (29)
where dλ is the inverse of the tree density, dc is the pro-
jected crown area (for an opaque canopy), and fPwc is the
mean crown porosity. The overbar depicts the mean over the
tree distribution as a function of tree height or, in our case,
the mean over the l circumference classes. Following mi-
nor adaptations, the implementation of Haverd and Lovell
(Haverd et al., 2012) was incorporated into ORCHIDEE-
CAN. As there also exist crops, grasses, and bare soil in the
model, fPgap was adjusted for these situations as well. For
grasses and crops, the same formulation is used:
f
gc
Pgap(θz,z)= e−0.5×dLAIabove×mLAIcorr/cos(θz), (30)
where dLAIabove is the total amount of LAI above height z,
and mLAIcorr is a correction factor to account for the fact that
grasses and crops are treated as homogeneous blocks of veg-
etation with no internal structure, and is often referred to as
a clumping factor. Here it is treated as a tunable parameter
and therefore the term “correction factor” was used. For bare
soil, there is no vegetation to intercept radiation, and there-
fore f bsPgap(θz,z) is always unity.
3.4 Multi-layer two-way radiation scheme for tall
canopies
Species-specific radiation absorbance, reflectance and trans-
mittance by the forest canopy were calculated from a
radiation transfer model (Pinty et al., 2006) which was
parametrised by satellite-derived species-specific scattering
values (Sect. 4.8). Given the complexity of radiation trans-
fer, it remains challenging to accurately simulate radiation
transfer through structurally and optically complex vegeta-
tion canopies without using explicit 3-D models. The ap-
plied 1-D model belongs to the family of two-stream mod-
els (Meador and Weaver, 1980) and thus calculates transmit-
tance, absorbance and reflectance of both the incoming and
outgoing radiation. The calculation of the reflectance at the
top of the canopy due to a collimated source (i.e. the Sun) is
divided into three components:
1. scattering of radiation between the vegetated elements
with a black background
f
fR
Coll, veg = f (θmu,frl,ftl,gG,dLAIeff) (31)
2. scattering of radiation by the background with a black
canopy
f
fR
UnColl, bgd =fRbgd× e(−dLAIeff/(2×θmu))
× f TUnColl, veg (32)
3. multiple scattering of radiation between the canopy and
the background
f
fR
Coll, bgd = fRbgd×
[
f
fR
Coll, bgd,1+ f fRColl, bgd,n
]
(33)
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2046 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
Term (1) is widely used in cloud reflectance calculations,
and depends on the cosine of the solar zenith angle (θmu),
the reflectance and transmittance of the single leaves (frl and
ftl, respectively), the leaf orientation function (gG), and the
effective LAI (dLAIeff). The exact definition of this term is
given in Eq. (B2) in Pinty et al. (2006). In term (2), fRbgd
is the reflectance of the ground beneath the canopy and
f TUnColl, veg is the transmitted fraction of light to the ground
which has not collided with any canopy elements. In term
(3), f fRColl, bgd,1 is the fraction of light which has struck vege-
tation and collided with the background a single time, while
f
fR
Coll, bgd,n is the fraction which has collided multiple times
(n) with the background.
The sum of the three components results in the canopy
albedo (Pinty et al., 2006). Similar equations can be derived
for light originating from diffuse sources (e.g. clouds and
other atmospheric scattering) (Pinty et al., 2006). Implemen-
tations of the calculations of the canopy fluxes for a single
level are available from the JRC, and these implementations
were used as the basis of the routines put into ORCHIDEE-
CAN for both the single- and multi-level cases (McGrath
et al., 2015b). This implementation relies on the use of the
effective LAI, which is the LAI that needs to be used in a 1-
D process representation to obtain the same reflectance, ab-
sorbance and transmittance as would be obtained by a 3-D-
canopy representation (Pinty, 2004). In this study, the effec-
tive LAI was calculated by first computing the canopy gap
probability, i.e. the probability that light is transmitted to a
specified height in the canopy at a given solar angle. The gap
probability is then converted into the effective LAI by pass-
ing it as an input to the inverted Beer–Lambert law (with an
extinction coefficient of 0.5 to ensure compatibility with the
two-steam inversion of Pinty et al., 2011a).
dLAIeff =−2.0× cos(θz)× log(fPgap), (34)
where fPgap can be f treesPgap , f
gc
Pgap, f
bs
Pgap. Following the in-
troduction of multi-layer photosynthesis and energy budget
submodels, the approach proposed by Pinty (2004) had to
be adjusted such that it could be applied for every level for
which absorbance needs to be known to calculate photosyn-
thesis (Sect. 3.6) and reflectance needs to be known to calcu-
late the net short-wave radiation (Sect. 3.5). The multi-layer
approach basically applies the 1-D two-stream canopy radia-
tion transfer model by Pinty et al. (2006) to each canopy level
where the light transmitted by the overlaying level becomes
the input for the lower level.
As the multi-level approach is built around the solution of
the one-level scheme for each canopy level, no new equa-
tions are introduced. The method can be summarised by the
following algorithm for which the details are given in Mc-
Grath et al. (2015b). First, three fluxes are calculated for
each level independently: the fraction of light transmitted
through the layer without striking vegetation, the fraction of
light reflected after striking vegetation, and the fraction of
light transmitted through the layer after striking vegetation.
These three fluxes represent the only possible fate of light
(any light not taking one of these paths must be absorbed
for energy conservation). Next, an iterative approach is in-
voked which follows the path of a single photon entering the
top level. Based on the solutions for each single level, prob-
abilities can be calculated that the photon will be transmitted
to a lower level or reflected to a higher level. Any fraction
which is reflected upwards from the top level is added to
the total canopy albedo and is not considered further. The
fraction which is transmitted through the top level enters the
next highest level, and again the single-level solutions deter-
mine where this light goes. Any fraction reflected upwards
is considered in the next iteration as part of the light en-
tering the upper level. The steps continue until the bottom
canopy level is reached. Here, any fraction which is trans-
mitted into the soil is removed from consideration and added
to the total transmittance through the canopy. The algorithm
then proceeds to the above canopy level. Now the transmitted
fluxes are moving in the upwards direction towards to the sky,
while reflected fluxes are moving towards the ground. The
code continues towards the top level, taking as input from
below both the flux reflected by downwelling light from the
level below the current level and the flux transmitted from
the lower level by upwelling light. After each iteration (mov-
ing from the top of the canopy to the bottom and back to the
top), the total amount of light considered active has been re-
duced by light escaping to the sky or being absorbed by the
canopy or ground. Eventually, this “active” light falls below
a pre-defined threshold, and the calculation is considered to
be converged.
Due to the iterative procedure, energy is not strictly con-
served, although we have attempted to choose a threshold
which minimises this loss. The multi-level albedo calcula-
tion is currently the most expensive part of the model, due
to the iterations and the fact that it must be performed over
all canopy levels (currently set to 10), grid points, and PFTs
at every physical time step. Levels with no LAI are no less
expensive to compute, either, although we have arranged our
canopy levels to make sure no levels are empty in most cases.
3.5 Multi-layer energy budget
The present generation of land-surface models have difficul-
ties in reproducing consistently the energy balances that are
observed in field studies (Pitman et al., 2009; Jiménez et al.,
2011; de Noblet-Ducoudré et al., 2012). The ORCHIDEE-
CAN branch implemented an energy budget scheme that rep-
resents more than one canopy layer to simulate the effects of
scalar gradients within the canopy for determining more ac-
curately the net sensible and latent heat fluxes that are passed
to the atmosphere. As outlined in Polcher et al. (1998), the
use of an implicit solution for coupling between the atmo-
spheric model and the surface layer model is the only way
to keep profiles of temperature and humidity synchronised
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K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2047
across the two models when the coupled model is run over
large time steps (e.g. of 30 min). The difference between ex-
plicit and implicit schemes is that an explicit scheme will
calculate each value of the variable (e.g. temperature and hu-
midity) at the current time step entirely in terms of values
from the previous time step. An implicit scheme requires the
solution of equations written only in terms of those at the
current time step.
The modelling approach formalises three constraints that
ensure energy conservation. The three equations that de-
scribe the main interactions are the following.
1. The energy balance at each layer is the sum of incoming
and outgoing fluxes of latent and sensible heat and of
short-wave and long-wave radiation:
klhc,ikρv,i
δTL,i
δt
=
(
−kshckρa
(TL,i − Ta,i)
Ra,i
−kλ,LEρa qL,i − qa,i
Rs,i
+FSW,i +FLW,i
)
(
1
1dhl,i
)
, (35)
where FLW,i is the sum total of long-wave radiation, that
is, the net long-wave radiation absorbed into layer i, and
FSW,i is the net absorbed short-wave radiation as calcu-
lated by the radiation scheme in Sect. 3.4. kshc is the
specific heat capacity of air. The source sensible heat
flux from the leaf at level i is the difference between the
leaf temperature (TL,i) and the atmospheric temperature
at the same level (Ta,i), divided by Ra,i , which is the
leaf resistance to sensible heat flux (a combination of
stomatal and boundary layer resistance). Similarly, the
source latent heat flux from the leaf at level i is the dif-
ference between the saturated humidity in the leaf (qL,i)
and that in the atmosphere at level i (qa,i), divided by
Rs,i , which is the leaf resistance to latent heat flux. Ra,i
is calculated based upon the leaf boundary layer resis-
tance, and is described in the present model according
to Baldocchi (1988). Rs,i is an abbreviation for the sum
of the stomatal and leaf boundary layer resistance terms
for latent heat.
2. The sensible heat flux between the vegetation (“the
leaf”) and the surrounding atmosphere at each level, and
between adjacent atmospheric levels above and below,
is provided by the following expression:
δTa,i
δt
1dV,i = kk,i δ
2Ta,i
δz2
1dA,i −
(
TL,i − Ta,i
Ra,i
)
(
1
1dhl,i
)
1dV,i, (36)
where z denotes the height above the soil surface. We
have re-written the scalar conservation equation, as ap-
plied to canopies, in terms of the sensible heat flux, tem-
perature and source sensible heat from the vegetation at
each layer.
3. The latent heat flux between the vegetation and sur-
rounding atmosphere at each level, and between adja-
cent atmospheric levels above and below is described in
a form that is analogous to Eq. (36), above:
δqa,i
δt
1dV,i = kk,i δ
2qa,i
δz2
1dAi −
(
qL,i − qa,i
Rs,i
)
(
1
1dhl,i
)
1dVi ) (37)
In addition to these three basic equations, various terms
had to be parametrised. The 1-D second-order closure model
of Massman and Weil (1999) was used to simulate the ver-
tical transport coefficients kk,i within the canopy while ac-
counting for the vertical and horizontal distribution of LAI
(Sect. 3.3). This set of equations was then written in an im-
plicit form and solved by induction. More details on the im-
plicit multi-layer energy budget and a complete mathematical
documentation are given in Ryder et al. (2014).
To complete the energy budget calculations, the multi-
layer 1-D canopy radiation transfer model (Sect. 3.4) was
used to calculate the net short-wave radiation at each canopy
layer. Furthermore, the canopy radiation scheme makes use
of the Longwave Radiation Transfer Matrix (LRTM) (Gu,
1988; Gu et al., 1999). This approach separates the calcula-
tion of the radiation distribution completely from the implicit
expression. Instead, a single source term for the long-wave
radiation is added at each level. This means that the distribu-
tion of LW radiation is now explicit (i.e. makes use of infor-
mation only from the “previous” and not the “current” time
step), but the changes within the time step were small enough
not to affect the overall stability of the model. However, an
advantage of the approach is that it accounts for a higher or-
der of reflections from adjacent levels than the single order
assumed in the process above.
3.6 Analytical solution for photosynthesis
The photosynthesis model by Farquhar, von Caemmerer and
Berry (Farquhar et al., 1980) predicts net photosynthesis of
C3 plants as the minimum of the Rubisco-limited rate of CO2
assimilation and the electron transport-limited rate of CO2
assimilation (Farquhar et al., 1980). The ORCHIDEE-CAN
branch calculates net photosynthesis following an analytical
algorithm as described by Yin and Struik (2009). In addition,
the C4 photosynthesis is calculated by an equivalent version
of the Farquhar, von Caemmerer and Berry model that was
extended to account for noncyclic electron transport (Yin and
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2048 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
Struik, 2009). A detailed derivation of the analytical solution
of the Farquhar, von Caemmerer and Berry model is given in
Yin and Struik (2009). Although the exclusion of mesophyll
conductance from the photosynthesis model could lead to an
underestimation of the CO2 fertilization effect in Earth sys-
tem models (Sun et al., 2014), mesophyll conductance was
not included in ORCHIDEE-CAN, to maintain compatibil-
ity between the model formulation and its parametrisation.
Because values of kVcmax and kJmax differ between different
formulations of the photosynthesis model (Kattge and Knorr,
2007; Medlyn et al., 2002) and the parametrisation that was
used in ORCHIDEE-CAN did not include mesophyll con-
ductance, it was also not accounted for in the model formu-
lation. The analytical photosynthesis model implemented in
ORCHIDEE-CAN could be easily extended to include mes-
ophyll conductance, but that would require reparameterising
the photosynthesis model.
Owing to the canopy structure simulated in this model
version and the layering of the canopy, the amount of ab-
sorbed light now varies with canopy depth. This new ap-
proach replaces the old scheme which uses multiple levels
based on the leaf area index, not the physical height within
the canopy. Photosynthesis is now calculated at each ver-
tically resolved canopy level independently, using the total
amount of absorbed light calculated by the radiation trans-
fer scheme, which means that radiation transfer inside the
canopy and photosynthesis are now fully consistent. In the
new photosynthesis scheme, photosynthesis thus indirectly
depends on canopy structure.
3.7 Forest management and natural mortality
Although forest management has developed a wide range
of locally appropriate and species-specific strategies (Pret-
zsch, 2009), the nature of large-scale land-surface models
such as ORCHIDEE-CAN requires only a limited number
of contrasting strategies that are expected to be relevant on
the spatial scale (e.g. 50× 50 km) of global and regional
modelling studies. Four management strategies were imple-
mented based on their expected impact on biogeochemical
and biophysical processes.
1. In unmanaged stands self-thinning drives stand dynam-
ics and continues until too few trees are left on site.
Subsequently, a stand replacing disturbance moves all
standing biomass into the appropriate litter pools and a
new stand is established.
2. High stand management is characterised by regular
thinning and a final harvest cut. Thinning is decided on
the basis of the deviation between the actual and poten-
tial stand density for any given diameter. This approach
relates to the so-called relative density index (Fortin
et al., 2012), the land use disturbance index (Luyssaert
et al., 2011) or hemeroby and naturalness approaches
(Schall and Ammer, 2013). Exceeding a threshold di-
ameter results in a clear cut and the stand is replanted
in the next year. For both thinning and harvest, leaves,
roots and belowground wood are transferred to the ap-
propriate litter pools, whereas the aboveground woody
biomass is removed from the site and stored in a prod-
uct pool. Trees with a diameter below a species-specific
threshold are stored in a short-lived product pool which
mimics wood uses for fuel, paper and cardboard. Trees
with larger dimensions are moved to medium- and long-
lived product pools which mimic, for example, particle
boards and timber usages, respectively.
3. Coppicing of the aboveground biomass is decided on
stem diameter. At harvest, the root system is left intact
and, in between coppicing, no wood is harvested. Note
that at present it is not possible to simulate coppicing-
with-standards in ORCHIDEE-CAN.
4. In ORCHIDEE-CAN, stands under short rotation man-
agement are limited to poplar (Populus spp.) and willow
(Salix spp.) forests. Stands are harvested at a prescribed
age. Following a set number of harvest cycles, the root
system is uprooted and the whole stand is replanted.
Different age classes are distinguished to better account
for the structural diversity and its possible effects on the ele-
ment, energy and water fluxes. A clear hierarchy was estab-
lished for the mortality processes regarding the actual killing
of trees (i.e. move their biomass to the litter or harvest pools).
All of the processes determine first how much biomass they
would remove in the absence of all the other processes. Af-
terwards, the killing is arranged in the most realistic way pos-
sible. A clear-cut event has the highest priority, followed by
human thinning and finally natural mortality including self-
thinning. If, for example, a forest is scheduled to be clear-cut,
the entire forest biomass is subjected to the rules of the clear-
cut and no other mortality occurs in that time step.
In addition to forest management and natural prescribed
mortality, a variety of changes have been made to processes
involving vegetation mortality. A whole PFT within a grid
cell is now killed if, at the end of the day, the labile pool is
empty and there is no carbon available in the leaf or carbo-
hydrate reserve pool to refill it. In this situation, it will be
impossible for the plant to assimilate new carbon from the
atmosphere as it will not be able to grow new leaves and
thus initiate plant recovery. Furthermore, a forest can die if
the density falls below a certain prescribed value. In the next
time step a new young forest will be prescribed.
If a forest is thinned, it is assumed that the weakest trees
will be thinned, and therefore human thinning reduces or
even eliminates the natural mortality for that time step. Natu-
ral mortality still happens on a daily time step, while human-
induced mortality happens only at the end of the year. Self-
thinning, as described below, takes priority over environmen-
tal mortality, which is the mortality of individuals by in-
sects, lightening, wind, drought, frost and heart rot. Envi-
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K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2049
ronmental mortality is calculated by multiplying the stand
biomass by an assumed mortality fraction of 1/ktresid . Where
self-thinning is less than this assumed environmental mor-
tality, self-thinning is complemented by additional mortality
to reach the set environmental mortality. Where self-thinning
mortality exceeds the set environmental mortality, simulated
self-thinning is assumed to include environmental mortality.
The fire module that is available for the trunk but not for
the ORCHIDEE-CAN branch simulates stand replacing fires
rather than individual-tree-based mortality due to lightening.
The approach implemented in the ORCHIDEE-CAN branch
could therefore be extended with models that simulate stand
replacing mortality from fire, insects and storms.
The use of circumference classes adds a good deal of re-
alism and flexibility to the ORCHIDEE-CAN simulations,
but it also raises additional questions. For example, which
trees should be targeted by which mortality? Given that self-
thinning reflects the outcome of continuous resource com-
petition, the largest trees are expected to be most success-
ful when competing for resources, and therefore we assume
that the smallest trees die first to reduce the stand density.
Conversely, larger trees are more likely to die because of en-
vironmental stress factors, being more prone to cavitation,
wind damage, lightening, and, heart rot. Therefore, we select
more older trees to die from environmental mortality. While
doing this also trees in the other circumference classes were
killed based on the following recursive definition (cf. Bel-
lassen et al., 2010):
f icirdeath =
f icir-1death × k1−(kncirc−1)ddf
mNdeath
, (38)
where kddf is the death distribution factor, which is the factor
by which the smallest and largest circumference classes dif-
fer (e.g. kddf = 10 means that the largest circumference class
will lose 10 times as much biomass as the smallest as a re-
sult of the mortality),mNdeath is a normalisation factor so that
the sum of f icirdeath is unity, and f
1
death is set equal to unity be-
fore normalisation. As the stands are very close to even-aged,
we set the factor kddf to be equal to 1. This means the same
number of trees is killed in each circumference class. If, for
some reason, there is not enough biomass in a given class to
satisfy this distribution, the extra biomass is taken from the
next smallest class (in case the smallest class does not have
enough, it is taken from the largest class).
Related to mortality is the question of the circumference
class distribution. As mentioned above, trees in different cir-
cumference classes are preferentially killed by different pro-
cesses. If the simulation is long enough (or if the morality is
aggressive enough), eventually the number of trees in some
circumference classes may become 0. This would reduce the
numerical resolution of the allocation scheme. When only
one circumference remains populated, the scheme effectively
loses its meaning, as all the newly produced biomass is now
being allocated to the only remaining circumference class. In
order to maintain the same level of detail through the sim-
ulation, the distribution of all the circumference classes is
recalculated at the end of each day. A normalised target dis-
tribution is specified as an input parameter (an exponential
distribution is currently used), and this distribution is scaled
to produce a target distribution for the current number of in-
dividuals. All of the current individuals are placed in these
new classes until the target distribution is satisfied. The target
distribution now contains, however, trees of multiple sizes, so
we need to average them to find the new model tree for each
class. By changing the size of the model tree in each class,
we are able to preserve the total biomass of the stand as well
as the total number of individuals. Note that the boundaries
of each diameter class are recalculated at each time step; this
approach is a numerically efficient alternative to fixing the
boundaries of each diameter class with a varying distribu-
tion.
4 Description of the parametrisation
The ORCHIDEE-CAN branch was specifically developed
to quantify the climate effects of forest management over
Europe. Although the developments are sufficiently general
to be applied outside of Europe, the model was initially
parametrised for the boreal, temperate and Mediterranean cli-
mate zones and validation focused on Europe. Parametrisa-
tion of the tropical zone is subject of a follow-up study. The
parametrisation of the model, including parameter optimisa-
tion and tuning, consisted of five major steps:
1. Parameters related to carbon allocation (Sect. 4.2), for-
est management and mortality (Sect. 4.3), hydraulic
architecture (Sect. 4.4, canopy structure (Sect. 4.5)),
photosynthesis (Sect. 4.6), and canopy radiation trans-
fer (Sect. 4.7), and for which observations exist at the
species level (Sect. 4.1), were extracted from a wide
range of sources (Tables S1–S5). Using the extracted
species-level parameter values in ORCHIDEE with-
out further processing avoids hidden model tuning and
largely reduces the likelihood that simulation results
will be biased by hidden calibration owing to a poor tax-
onomic definition of PFTs (Scheiter et al., 2013).
2. The phenology-related parameters of the deciduous
MTCs were optimised by MacBean et al. (2015), us-
ing MODIS-derived NDVI data normalised to model
fAPAR over the 2000–2008 time period.
3. The modulator (mψ ) which accounts for processes in
the the soil-plant continuum that are currently not mod-
elled, was manually tuned against species distribution
maps (Sect. 4.4).
4. The coefficient for maintenance respiration was opti-
mised making use of Bayesian calibration (Sect. 4.8)
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2050 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
against a compilation of 100+ observations of biomass
production efficiency.
5. The leaf to sapwood area ratio was manually tuned
(Sect. 4.9) to match 100+ site-level gross primary pro-
duction (GPP) and LAI observations recorded over Eu-
rope.
4.1 Introducing 12 new PFTs
Similarly to the ORCHIDEE trunk, the ORCHIDEE-CAN
branch distinguishes 13 metaclasses (MTC) for vegetation.
Outside Europe the original MTC classification of OR-
CHIDEE was kept, while inside Europe 12 new parameter
sets representing the main European tree species were added.
The default vegetation distribution map in ORCHIDEE, i.e.
Olson et al. (1983), was replaced by an up-to-date global
MTC map which has been produced using the ESA CCI ECV
Land Cover map (http://www.esa-landcover-cci.org/) (Poul-
ter et al., 2015). The mapping from land cover to MTC ba-
sically followed Poulter et al. (2011), although Table 5 (the
“cross-walking” table) has been updated following discus-
sions with the LC-CCI team at Universite Catholique de Lou-
vain. For the European domain, the global MTC distribution
was overlaid by a tree species distribution map (Brus et al.,
2012).
This study focusses on tree species with a coverage of
more than 2 % in Europe, yielding seven species groups
covering in total 78.8 % of the European forest area: Be-
tula sp., Fagus sylvatica, Pinus sylvestris, Picea sp., Pinus
pinaster, Quercus ilex and a group combining Quercus robur
and Quercus petraea. For Pinus sylvestris, Picea sp. and Be-
tula sp. An additional distinction between boreal and tem-
perate forest was made for the species map and parametrisa-
tion: trees located in Norway, Sweden and Finland were con-
sidered boreal, while trees growing at lower latitudes were
categorised as temperate. Given the potential role of tree
species of the Salicacea genus in short rotation coppice man-
agement, a separate PFT was parametrised for Populus sp.
Furthermore, to improve the parametrisation of the MTC of
boreal needleaved deciduous forest, observations from Larix
sp. were included when possible.
For these 12 forest species, 12 new PFTs were created,
with each PFT belonging to a single MTC (Tables S2, S3 and
S4). Almost 79 % of the European forest was parametrised at
the species level. The remaining 21 % was reclassified into
four residual groups, i.e. a temperate and boreal needleleaf
evergreen and a temperate and boreal broadleaved residual
group. For use outside Europe, the original MTC classifica-
tion of ORCHIDEE was kept. The parameters of the resid-
ual groups and MTCs are the mean of the parameters of the
species-level PFTs that are in the MTC, with the exception
of albedo parameters that could be extracted from remote-
sensing products. Finally, separate PFTs were introduced for
boreal grasses and croplands, which allowed for a boreal
parametrisation of phenology, senescence and growth. This
approach, which distinguishes a total of 28 PFTs, allows a
higher taxonomic resolution over Europe, better defines for-
est types compared to the more general MTC approach and
facilitates the use of observations to derive parameters.
4.2 Allocation
The allocation scheme relies on the leaf to sapwood area
ratio (Sect. 4.9) and the relationship between diameter and
height. Following a logarithmic transformation of the more
than 150 000 data points from the national forest inventory
data of Spain, France, Germany and Sweden, the two param-
eters (i.e. kα1 and kβ1 ) describing the relationship between
diameter and height (Eq. 15) were fitted at the species level
making use of a least square regression. Parameter values for
MTCs were derived by grouping the species into MTCs and
fitting the parameters. Data sources and parameter estimates
are presented in Tables S2 and S3.
4.3 Forest management and mortality
Forest management and tree mortality are controlled by
(Sect. 3.7): (1) maximum tree diameter (no symbolic
notation; called largest_tree_diam in ORCHIDEE-CAN),
(2) minimum stand density (no symbolic notation; called
ntrees_dia_profit in ORCHIDEE-CAN), (3) environmental
mortality (no symbolic notation; called residence_time in
ORCHIDEE-CAN), (4) self-thinning (kα2 and kβ2 ) and,
(5) anthropogenic thinning (no symbolic notation; called
alpha_RDI_upper, alpha_RDI_lower, beta_RDI_upper and
beta_RDI _lower in ORCHIDEE-CAN) where the parame-
ters depend on the management strategy.
Maximum tree diameter was extracted from the French,
Swedish, German and Spanish forest inventories as the ob-
served 50 % quantile for diameter at breast height. The 50 %
quantile rather than the observed maximum was used to ac-
count for the fact that large-scale land-surface models are
expected to reproduce large-scale patterns rather than local
extremes. Minimum stand density was estimated as the ex-
pected stand density for the maximum tree diameter for a
stand under self-thinning. Although both criteria are related
to each other through the observed self-thinning relationship
(see below), the minimum number of trees is used to decide
when unmanaged forests should be replaced, whereas both
the maximum diameter and the minimum number are used
for managed sites as criteria to initiate a clear cut. Parame-
ters for anthropogenic thinning are based on the national for-
est inventory data and checked against the JRC database of
species-specific yield tables. Parameter values are presented
in Table S5. Resource competition between trees in the same
stand has been reported to result in the so-called self-thinning
relationship that relates the number of individuals within a
stand to the stand biomass (Reineke, 1933; Kira et al., 1953;
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Yoda et al., 1963):
(Ms+Mh)× kρs = kα × (dind)−kβ , (39)
where kα and kβ are the constants of the self-thinning rela-
tionship. Furthermore, stem volume can be written as a func-
tion of tree diameter (ddbh), tree height and stem form factor
(kα′ ) to account for the fact that the stem shape is not a per-
fect cylinder:
(Ms+Mh) · kρs = kα′ × (ddbh)2× dh. (40)
Following the allometric relationship given in Eq. (15),
tree height can be written as a function of tree diameter.
Hence, the self-thinning relationship can be re-written to re-
late stand diameter to stand density:
ddbh = kα2× (dind)−kβ2 , (41)
where, kβ2 relates to kβ1 (as in Eq. 15) as follows:
kβ2 =−3/2× (2+ kβ1) (42)
kα1 and kβ1 were estimated by fitting Eq. (15) to observed
diameter and height of individual trees from NFI of Swe-
den, Germany, France and Spain. kβ2 was calculated from
Eq. (42) and kα2 was estimated by fitting Eq. (41) to observa-
tions of the quadratic mean stand diameter and stand density
from NFI data.
4.4 Hydraulic architecture
Initial choices of parameters for this scheme were based on
the values and parameter sources listed by Hickler et al.
(2006). All data sources were revisited and the search was
extended to obtain values at the PFT rather than MTC level.
Given that plant hydrology is rather well studied, observed
parameters were available for most of the species. Data
sources are listed in Table S1, whereas the parameter val-
ues are shown in Table S3. Our implementation of hydraulic
architecture required the introduction of a tuning parameter
(mψ ) to account for processes that are currently absent in
the scheme, e.g. plant water storage and soil–root resistance.
A process-based description of these processes (i.e. Sperry
et al., 1998; Steppe et al., 2006) is being tested and should
reduce the effect of the tuning parameter and eventually al-
low its removal from the model.
For the time being, the modulator mψ was tuned manu-
ally against the species distribution map to obtain a match
between the simulated and observed species distributions.
When the modulator is set to zero, all PFTs experience ex-
cessive water stress resulting in large-scale plant mortality.
The modulator was increased until the prescribed vegeta-
tion distribution which was based on remote-sensing obser-
vations (Sect. 4.1), survived where it was prescribed. To this
aim, the model was run for 50 years, forced with v5.2 of
the CRU-NCEP climatology for Europe (Climatic Research
Unit, University of East Anglia). Note that the values of
the modulator depend on the climate data that are used to
force the model. Similarly the modulators may need to be re-
tuned when ORCHIDEE-CAN is coupled to an atmospheric
model.
4.5 Canopy structure
The relationship between diameter and projected crown sur-
face area follows the model proposed by Pretzsch (2009):
dcsa = kap× dkbpdbh (43)
with parameters estimated using the data set presented in
Pretzsch and Dieler (2012). This data set contains diame-
ter and projected crown surface areas observations for over
37 000 individual trees in Europe covering almost 30 species.
Following logarithmic transformation of the observations a
linear least square regression was used to fit species-specific
parameter values. Parameter values are shown in Table S2.
Parameter values for MTCs were derived by grouping the
species into MTCs and fitting the parameters. No observa-
tions were available for the boreal zone and temperate ever-
green deciduous species. For the boreal species, a subset of
the temperate observations (Pinus sylvestris, Picea abies and
Betula pendula) was used, i.e. the relationship between dcsa
and ddbh was fitted to all available data for Pinus sylvestris.
Next, all observations with a dcsa that falls below the pre-
dicted dcsa were selected as considered to represent a bo-
real subset. Given the importance of snow pressure on crown
structure, selecting observations with sub average dcsa is jus-
tifiable as a first approximation. Subsequently, the parame-
ters were fitted to this subset of data. For Quercus ilex no
data were available and parameters were tuned such that the
crown diameter was 0.85 m less than the tree height.
4.6 Analytical solution for photosynthesis
Three originally MTC-specific photosynthetic parameters
(kVcmax, kJmax and ksla) were derived at the species level by
obtaining weighted site means for each species from the TRY
global leaf trait database (Kattge et al., 2011) and addition-
ally from Medlyn et al. (2002). Only kVcmax and kJmax stan-
dardised to a common formulation and parametrisation of the
photosynthesis model by (Farquhar et al., 1980) were used.
Most kVcmax and kJmax values in the TRY database had al-
ready been standardised to a reference temperature of 25 ◦C
(Kattge and Knorr, 2007). Subsequently, a species-specific
kJmax,opt/kVcmax,opt ratio was calculated from the records
which included both kVcmax,opt and kJmax,opt measurements.
From this ratio, which was within a range of 1.91–2.47
for each species, kJmax,opt was calculated for records which
originally only included kVcmax. Only geo-referenced ob-
servations within Europe were used and the distinction be-
tween boreal and temperate forest was made similar to the
species map. Depending on the species this resulted in 5
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2052 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
to 183 observations for ksla and 11 to 173 observations for
kVcmax,opt and kJmax,opt. From these observations species-
specific means were calculated, weighted for differences in
the number of observations per site. The parameter values
are shown in Table S3.
4.7 Multi-layer two-way radiation scheme for tall
canopies
The radiation transfer scheme makes use of parameters de-
scribing leaf and background properties, i.e. leaf single scat-
tering and preferred scattering direction (for both visible
(VIS) and near-infrared (NIR) wavelengths) and the so-
called background albedo or the albedo of the surface be-
low the dominant tree canopy (VIS and NIR). All parameters
were taken from the Joint Research Centre Two-stream Inver-
sion Package (JRC-TIP) (Pinty et al., 2011a, b). This is a soft-
ware package (Pinty et al., 2007) which inverts a two-stream
model (Pinty et al., 2006) to best fit the MODIS broadband
visible and near-infrared white sky surface albedo from 2001
to 2010 at 1 km resolution (Pinty et al., 2011a). The inverse
procedure implemented in the JRC-TIP is shown to be ro-
bust, reliable, and compliant with large-scale processing re-
quirements (Pinty et al., 2011a). Furthermore, this package
ensures the physical consistency between sets of observa-
tions, the two-stream model parameters, and radiation fluxes.
Only parameter values for which the posterior standard
deviation of the probability density functions were signif-
icantly smaller than the prior standard deviation were se-
lected from the JRC-TIP optimisation (Pinty et al., 2011a),
since this condition ensures statistically significant values.
Species- and MTC-specific values were derived from JRC-
TIP by performing a multiple regression. This methods de-
termines, in an objective way, how the fractions of each MTC
or species explain the JRC-TIP parameter. The multiple re-
gression was performed separately for the six parameters: the
single scattering of leaves (for both VIS and NIR), the scat-
tering direction of leaves (VIS and NIR) and the background
albedo (VIS and NIR). Each JRC-TIP parameter was used
as the dependent variable and the independent variables con-
sisted of the fractions of each MTC (Poulter et al., 2015) or
species (Brus et al., 2012). These fractions were used to find
a linear function that best predicted each JRC-TIP parame-
ter. The corresponding slope of a regression of each MTC
or species fraction gives the MTC or species dependent JRC-
TIP value. The multiple regression was performed without an
intercept. To avoid pollution by the seasonal cycle, the multi-
ple regression was applied only for the pixels of the Northern
Hemisphere. Only pixels that were less than 10 % covered
by non-vegetative fractions where selected for the analysis
and only significant results following an F test and positive
r2 values were selected. The derived parameter values are
shown in Table S4.
4.8 Maintenance respiration
Both the trunk and ORCHIDEE-CAN branch reduce the
definition of net primary production to biomass production;
hence, carbon leaching from the roots, volatile organic emis-
sions from the leaves, dissolved and particulate carbon losses
through water fluxes and carbon subsidies to mycorryhzae
are not accounted for in the model. These fluxes are (incor-
rectly) accounted for in the modelled autotrophic respiration.
Modelled autotrophic respiration should therefore be consid-
ered an effective rather than a true value. For this reason, the
basal rate of autotrophic respiration was optimised against
126 site observations of the biomass production efficiency
(kcmaint) calculated as the ratio between annual biomass pro-
duction and annual photosynthesis (Vicca et al., 2012; Cam-
pioli et al., 2015), using a Bayesian optimisation scheme. The
scheme, for which more details are given in Santaren et al.
(2007), uses a standard variational method based on the iter-
ative minimisation of a cost function that measures both the
model data misfit and the parameter deviations from prior
knowledge (Tarantola, 2005).
The simulations that were used in the Bayesian optimi-
sation prescribed a 20 m tall vegetation for temperate tree
species, a 15 m tall vegetation for boreal tree species and a
10 m tall vegetation for Mediterranean tree species as its ini-
tial condition. This approach reduced the need for several
decades of simulations to a single year to grow a mature
forests. In total, the simulations were run for 10 years and
covered the European domain. The first year was discarded
and the ratio between modelled GPP and NPP was averaged
over the remaining 9 years. Prior to the optimisation, the ob-
servations were averaged for agricultural PFTs (0.57), and
deciduous (0.44) and evergreen (0.53) forest PFTs; the ob-
served uncertainty was 0.03. The parameter values were set
to range between 0.0032 and 0.160. The optimisation con-
verged within 11 iterations and the optimised parameter val-
ues are shown in Table S2.
It remains untested how well the simulated effective au-
totrophic respiration represents the (rarely) observed au-
totrophic respiration. Note that in the cases of both the
trunk and the ORCHIDEE-CAN branch of ORCHIDEE, a
match between effective and observed autotrophic respira-
tion should not be interpreted as evidence of desired model
behaviour because several components of net primary pro-
duction are not modelled yet.
After the optimisation of the maintenance respiration co-
efficient (kcmaint), the model simulates reasonable biomass
production efficiency for a unit of photosynthesis. Hence, the
final step of the parametrisation focussed on optimising the
leaf area, as this is one of the main drivers of photosynthesis.
4.9 Sapwood to leaf area ratio
The vegetation structure simulated by the ORCHIDEE-CAN
branch is sensitive to the value of kls which describes the ratio
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K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2053
between the leaf and sapwood area of an individual tree. The
available observations show a wide range within and across
forest species. Dependencies of kls on tree height (McDow-
ell et al., 2002; Novick et al., 2009), tree diameter follow-
ing stand thinning (Simonin et al., 2006) and CO2 (Pataki
et al., 2006) have been reported. Most observations, how-
ever, come from experiments where time was substituted by
space which hampers teasing apart the sources of variability.
Given the variation and uncertainty in the observations and
the model sensitivity to this parameter, we manually tuned
its value within the observed range, to match European-wide
observations of leaf area index as recorded in the Database of
Global Forest Ecosystem Structure and Function Luyssaert
et al. (2007).
This database was used to calculate a mean and maximum
observed leaf area index at the species level for the temperate
and boreal region. Initially 20 year long European-wide sim-
ulations were used to simulate leaf area index of a species,
when the large-scale leaf area index approached the mean
target value and did not exceed the maximum value, the sim-
ulations were extended to reach 100 years for checking the
temporal evolution of leaf area index. We deliberately opti-
mised the sapwood to leaf area ratio (kls) by making use of
stand-level data to reduce circularity with the model valida-
tion (see below).
Limited tests over a period of 100 years in a Scots pine for-
est at 51–52◦ N, 13–14◦ E (Fig. S1 in the Supplement) sug-
gested that optimising kcmaint and kls had the largest effect
on the maximum LAI, which decreased by almost 17 % after
optimisation compared to a simulation with prior parame-
ter values. Mean annual GPP, mean annual transpiration and
basal area decreased by, respectively, 6, 6 and 7 % compared
to a simulation with prior parameter values (Fig. S1).
5 Validation
ORCHIDEE-CAN is designed as the land-surface model to
be coupled to the LMDz atmospheric model. As such, fu-
ture applications of ORCHIDEE-CAN are expected to be re-
gional to global in the spatial domain and to span several
years in the temporal domain. Given its anticipated uses, the
ability of the model to reproduce large-scale spatial patterns
as well as their inter-annual variability is essential. The first
applications of the model, both offline and coupled to the at-
mosphere, will focus on Europe. The validation, therefore,
reports performance indices both over Europe as over eight
separate regions within Europe (Bellprat et al., 2012). These
eight regions, which partially overlap, are defined after Bell-
prat et al. (2012). Furthermore, the performance indices are
calculated for winter, spring, summer and autumn, and thus
allow one to evaluate the capacity of the model to reproduce
observed annual cycles.
In addition to the root mean square error, a land perfor-
mance index (LPI) based on the principles laid out for the
Climate Performance Index (Murphy et al., 2004, their SI)
was also calculated. LPI normalises the root of the squared
differences between the simulations and observations by the
observed spatial and temporal variance. The LPI was used
to estimate the likelihood that the simulated variable belongs
to the same population as the observed variable, defined as
exp(−0.5LPI2). An LPI equal to 1 indicates that the model
correctly reproduces the mean observed value and implies a
likelihood of 61 % (Murphy et al., 2004) that the simulations
and observations come from the same population. Similarly,
an LPI of 2 reduces this likelihood to 13 %. An LPI of less
than 0.32 has a likelihood of more than 95 % and therefore
indicates a statistically significant result.
While developing ORCHIDEE-CAN, the numerical ap-
proaches that added functionality to the code were selected
on the basis of their performance at the site level (see below).
Rather than running the same site-level tests for our imple-
mentation, we performed a complementary large-scale vali-
dation. The strength of our approach lies not in the details, as
is the case for site-level validation, but in its width by simul-
taneously testing model performance for structural variables
such as basal area (de Rigo et al., 2014), canopy structure
(Pinty et al., 2011a) and canopy height (Simard et al., 2011),
biogeochemical fluxes such as GPP (Jung et al., 2008), bio-
physical fluxes such as albedo (Schaaf et al., 2002) and fluxes
at the interface of biogeochemistry and biophysics such as
evapotranspiration (Jung et al., 2008). The selection of vari-
ables was limited by the availability of spatially explicit data-
derived products for Europe.
For the validation, both the trunk and ORCHIDEE-CAN
branch were run from 1850 to 1900 using CRU-NCEP cli-
mate forcing from 1901 to 1950 at 0.5 degree resolution.
From 1901 until 2012, the corresponding CRU-NCEP forc-
ing data for each year were used. Both versions used the
11 layer soil hydrology, the single-layer energy budget and
the same land cover map (Poulter et al., 2015). Given that no
European-wide, spatially explicit and data-derived products
were found for the validation of the net carbon flux, there
was no need for a carbon spin-up. For the ORCHIDEE-CAN
branch, the observed tree height and basal area were com-
pared against the simulation values at the end of 2010 (the
trunk does not simulate these variables). For both the trunk
and the ORCHIDEE-CAN branch, the observed GPP, evap-
otranspiration, effective LAI and VIS and NIR albedos were
compared against monthly means between 2001 and 2010.
5.1 Species versus PFTs
In ORCHIDEE-CAN the PFT concept was refined by
parametrising the main European tree species groups
(Sect. 4.1). To evaluate the effect of the species parametri-
sation, we performed a companion simulation for the config-
uration described above, but at the MTC level. Model perfor-
mance was barely affected by the use of the MTC parameters,
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2054 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
compared to the simulation with the species parameters (see
Fig. S2 for RMSE scores).
5.2 Allocation
In ORCHIDEE-CAN, functional relationships which vary by
species and light stress are used to allocate carbon among
the fine roots, foliage and sapwood. The allocation scheme
largely follows Zaehle and Friend (2010), who in turn was
inspired by Sitch et al. (2003). Approaches simulating allo-
cation based on functional relationships were found to out-
compete allocation schemes based on constant fractions or
resource limitation (De Kauwe et al., 2014). The ability of
these schemes to reproduce foliage, fine root and sapwood
reported in large observational data sets (for example, Luys-
saert et al., 2007) demonstrates that these schemes capture
the main observed features (Zaehle and Friend, 2010). In
addition, allocation schemes making use of functional rela-
tionships were also capable of simulating the observed ef-
fect of elevated CO2 on two mature forest ecosystems (De
Kauwe et al., 2014). Despite these successes, the schemes
were reported to be sensitive to their parametrisation. Dif-
ferences in parameters were reported to result in substan-
tial differences in the simulated allocation. The parameters
for the functional relationships used in ORCHIDEE-CAN
are given in Table S2. The main conceptual difference be-
tween the allocation scheme by Zaehle and Friend (2010) and
ORCHIDEE-CAN is that the latter was designed to simulate
one or more diameter classes.
Given that photosynthesis is still calculated at the stand
level (and thus not at the tree level) the allocation rule of
Deleuze et al. (2004) was integrated in the functional allo-
cation scheme to account for light and resource competition
within a stand. Where the functional relationships are used
to simulate carbon allocation within an individual tree of a
given diameter, the rule of Deleuze et al. (2004) allocates
carbon across the different diameter classes. The allocation
rule which models the radial increment for individual trees
in pure even-aged stands was successfully tested for Nor-
way spruce and Douglas fir stands in France (Deleuze et al.,
2004). A similar approach for modelling radial increment has
already been implemented in a version close to the trunk of
ORCHIDEE (Bellassen et al., 2010) and was able to suc-
cessfully simulate stand characteristics such as height, basal
area and stand diameter (Bellassen et al., 2011). This previ-
ous implementation differs from the current implementation
in its time resolution (which is now daily instead of yearly),
its analytical solution and the underlying allocation scheme
(which is now based on functional relationships instead of
resource limitation).
The aforementioned studies performed a detailed valida-
tion of the two approaches dealing with carbon allocation,
which were combined in ORCHIDEE-CAN. Complemen-
tary to these studies, we performed a European-wide vali-
dation of our implementation and parametrisation of these
well-tested schemes against a remote-sensing-based map of
tree height (Simard et al., 2011), upscaled eddy-covariance
observations for GPP (Jung et al., 2008) and a map of basal
area based on national forest inventory data (de Rigo et al.,
2014). The model’s ability to reproduce GPP is thought to
reflect its capacity to simulate the foliage biomass, a correct
simulation of height reflects the model’s capacity to simulate
aboveground woody biomass, and its capacity to reproduce
observed basal areas suggests that the interaction of stand
density and individual tree diameter are well captured.
The new implementation and parametrisation of the
within-tree and within-stand allocation schemes were found
to have a 91, 68 and 72 % chance that the simulations will
reproduce the observations for GPP, tree height and basal
area for Europe, respectively (Table 3). Given that basal
area and height are not available from the trunk version
of ORCHIDEE, we could not compare the performance of
model versions in this respect. With respect to GPP, the
ORCHIDEE-CAN branch was found to outperform the trunk
by 12 % and thus increased the likelihood that ORCHIDEE-
CAN is an unbiased simulator of the spatial and temporal
variability of GPP from 79 to 91 %. Improved performance
of the ORCHIDEE-CAN branch compared to the trunk is
observed for all regions in summer where the RMSE of GPP
was halved from 2.5–5 to 1–2 gC m−2 day−1 (Figs. 2, 3 and
4).
Although part of the high likelihood could be due to the
fact that the observed GPP was upscaled making use of sim-
ilar climatologies being used as the forcings of the mod-
els, this circularity could neither have contributed to the im-
proved performance between the trunk and the ORCHIDEE-
CAN branch nor to the decrease in RMSE. The improve-
ments are thought to be due to structural changes to the
model such as allocation, hydraulic architecture and canopy
structure as well as to the use of more consistent parametri-
sation.
5.3 Plant water supply
Our implementation of plant hydraulic architecture was
largely based on the scheme of Hickler et al. (2006), which
was tested globally and at site level. Global simulation re-
sults for actual evapotranspiration were found to reproduce
available data (Baumgartner and Reichel, 1975; Henning,
1989). At the site level, the model agreed well with the mag-
nitude and seasonality of eddy-covariance measurements of
actual evapotranspiration for 15 European forest sites (EU-
ROFLUX), with a tendency to slightly overestimate actual
evapotranspiration for 6 sites (Hickler et al., 2006).
The maximum amount of water that can be transported
by a tree relies on the hydraulic architecture of the tree and
therefore on the capacity of the model to simulate tree and
stand dimensions as well as on the model’s capacity to sim-
ulate soil water content. As an additional test, our imple-
mentation of the model was compared against the upscaled
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K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2055
Figure 2. Root mean square error of ORCHIDEE-CAN for gross primary production, evapotranspiration, visible and near-infra-red albedo,
effective leaf area index, basal area and height for different regions and periods (DJF: December–February, MAM: March–May, JJA: June–
August, SON: September–November). The gray-scale of the symbols indicates the number of pixels included in the calculation. The transition
from green to white indicates an RMSE of 100 %.
eddy-covariance measurements for GPP and actual evapo-
transpiration (Jung et al., 2008). The capacity to jointly re-
produce GPP and actual evapotranspiration is an indicator
that the model successfully reproduces the coupling between
CO2 and water exchange. Model validation showed 91 and
87 % chance (compared to 79 and 45 % for the trunk) that
ORCHIDEE-CAN reproduces the upscaled GPP and actual
evapotranspiration data (Table 3, Fig. 4). The RMSE for
actual evapotranspiration during summer dropped well be-
low 1 mm day−1 for most regions (Fig. 2), whereas it never
dropped below 1 mm day−1 for the trunk (Fig. 3).
5.4 Canopy structure
The canopy structure model by Haverd et al. (2012) was
previously validated against ground-based LIDAR data for
several test sites with varying density, structural complexity,
layering and clumping (Lovell et al., 2012). Model-derived
canopy gap probabilities compared with observations using
a one-sample t test were significant for 11 out of 12 test sites.
We considered this result to be a sufficient proof to use this
canopy structure model in the ORCHIDEE-CAN branch and
added to its validation by comparing the simulated canopy
structure model over Europe against a remote-sensing-based
map of tree height (Simard et al., 2011) and the JRC-TIP
effective LAI product (Pinty et al., 2011a). The effective
LAI value expresses the capability of the canopy to inter-
cept direct radiation, and is thus associated with the proba-
bility distribution function of the canopy gaps (Haverd et al.,
2012). Thus the effective LAI contains information about the
forest structure and leaf distribution of the canopy. In the
ORCHIDEE-CAN branch, canopy structure is used to cal-
culate the albedo, roughness length, absorbed light for pho-
tosynthesis and leaf area that is coupled to the atmosphere
for e.g. transpiration and interception of precipitation.
The ORCHIDEE-CAN branch is the first branch of OR-
CHIDEE that makes use of an effective LAI to calculate
the interaction between the canopy and the atmosphere. The
LPI and RMSE of the branch, therefore, cannot be compared
against the trunk. Overall, the combined implementation of
the allocation scheme and the canopy structure model shows
a 67 % chance to reproduce the satellite-based estimates for
effective LAI. Surprisingly, effective LAI is better simulated
in spring and autumn when dynamics within the canopy are
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2056 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
Figure 3. Root mean square error of ORCHIDEE trunk for gross primary production, evapotranspiration and visible and near-infrared albedo
for different regions and periods (DJF: December–February; MAM: March–May; JJA: June–August; SON: September–November). The grey
scale of the symbols indicates the number of pixels included in the calculation. The transition from green to white indicates an RMSE of
100 %.
  
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-2
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sa
l a
re
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Figure 4. Comparison between observations and simulations of
ORCHIDEE-CAN for gross primary production and basal area over
Europe. Gross primary production represents the mean for June–
August between 2001–2010 and basal area is the value at the end of
2010.
substantial due to leaf on-set and senescence. For the peri-
ods when the effective LAI is expected to be most stable,
i.e. summer and winter, LPI approached and frequently ex-
ceeded 1 (data not shown). Part of this shortcoming may be
due to the lack of shrubs in the land cover classification. In
the model, shrublands are replaced by forest and/or grass-
lands, likely resulting in differences between the observed
and simulated canopy structure. This lapse also appears in
the RMSE of effective LAI (RMSE higher than 0.8, Fig. 2)
5.5 Top of the canopy albedo
The radiation transfer model (Pinty et al., 2006) has
been validated extensively against realistic complex three-
dimensional canopy scenarios (Pinty et al., 2006) and as part
of the RAdiation transfer Model Intercomparison (RAMI)
project. The 1-D canopy radiation transfer model by Pinty
et al. (2006) was demonstrated to accurately simulate both
the amplitude and the angular variations of all radiant fluxes
with respect to the solar zenith angle (Widlowski et al.,
2011). In addition, the radiation transfer model and its ef-
fective values extracted from the JRC-TIP data set were suc-
cessfully applied to a single forest site (Pinty et al., 2011c).
Previously we reported on the capacity of the radiation
transfer model to simulate the effects of forest management
on albedo (Otto et al., 2014). For the latter, forest properties
were prescribed and the radiation transfer model was vali-
Geosci. Model Dev., 8, 2035–2065, 2015 www.geosci-model-dev.net/8/2035/2015/
K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2057
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–
–
–
dated against top-of-the-canopy albedo data from five ob-
servational sites. Differences in the spatial scales between
the observed and simulated albedo values were accounted
for by presenting the mean June albedo during 2001–2010
(Otto et al., 2014). The simulated summertime canopy albedo
falls within the range of observation. However, there occurs
a slight overestimation in the near-infrared wavelength band
compared to the single site measurement. Overly high near-
infrared single scattering albedo values for pine, as obtained
from the JRC-TIP product, are the most likely cause. The
observed deviation is not due to a shortcoming in the model
itself, but reflects the difficulties the JRC-TIP has with opti-
mising parameter values in the absence of field observations
in the specific case of sparse canopies (Otto et al., 2014).
For the spatial validation we use the white-sky albedo
(VIS and NIR) from Moderate Resolution Imaging Spectro-
radiometer (MODIS, Schaaf et al., 2002) at 0.5◦ resolution
(distributed in netCDF format by the Integrated Climate Data
Center (ICDC, http://icdc.zmaw.de) University of Hamburg,
Hamburg, Germany). Over large spatial and temporal do-
mains the ORCHIDEE-CAN branch reproduces the observed
VIS and NIR albedo and its variability; LPI for the albedo in
the visible light is especially satisfying with a likelihood of
92 % for the simulations to come from the same population
as the observations (Table 3). This high overall performance
index, however, hides performance issues over Scandinavia
and the Alps during the snow season. The RMSE for VIS and
NIR albedo without snow lies around 0.05, whereas during
the snow season the RMSE increases to 0.20 (VIS) and 0.18
(NIR) over these regions (Fig. 2). When the ORCHIDEE-
CAN branch is coupled to an atmospheric model, however,
these deviations will only have a minor effect on the climate,
owing to low incoming radiation during most of the snow
season, especially in Scandinavia.
Previous validation of the radiation transfer model showed
that the largest discrepancies were occurring in the near-
infrared domain with a snow-covered background (Pinty
et al., 2006). With the exception of the snow-covered season,
the new albedo scheme, which relies on the simulated canopy
structure, resulted in a substantial improvement of 0.05–0.15
compared to the trunk for the RMSE in both the VIS and NIR
range in Scandinavia and the Alps (Figs. 2 and 3). The Euro-
pean LPI-based likelihood that our model simulations come
from the same populations as the MODIS albedo increased
by a remarkable 11 and 23 % for, respectively, NIR and VIS
albedo (from 61 and 69 % for the trunk to 72 and 92 % for
the ORCHIDEE-CAN, Table 3).
Given that the parametrisation of the canopy radiation
transfer model used in ORCHIDEE-CAN relies on MODIS,
the high likelihood may not come as a surprise. However, our
implementation of the radiation transfer model also relies on
the simulated absorbed light, simulated GPP, simulated al-
location and simulated canopy structure (which depends on
mortality and forest management). In the absence of all these
processes our canopy radiation transfer model is expected
www.geosci-model-dev.net/8/2035/2015/ Geosci. Model Dev., 8, 2035–2065, 2015
2058 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
to reproduce the MODIS data with a probability of 100 %.
Hence, the likelihood of 72 and 92 % (for NIR and VIS, re-
spectively) could also be interpreted as a verification of the
aforementioned calculations; all calculations that determine
the canopy structure reduce the reproducibility of the data by
only 8–28 % (100 to 72 or 92 %).
5.6 Energy fluxes
The multi-layer scheme is in the process of a detailed eval-
uation across a range of test conditions (Ryder et al., 2014),
and further validation across a range of sites is ongoing. The
scheme is able to produce within-canopy temperature and hu-
midity profiles, and successfully simulates the in-canopy ra-
diation distribution, as well as the separation of the canopy
from the soil surface. However, in order to preserve a mea-
sure of continuity with previous evaluations of the model,
the multi-layer solution is here set to single-layer opera-
tion mode, which includes the effects of hydraulic limita-
tion (Sect. 3.2) and canopy structure (Sect. 3.3) on the energy
budget.
The single-layer set-up of the multi-layer solution makes
use of an improved albedo estimation and is therefore ex-
pected to better simulate the net radiation that needs to be re-
distributed in the canopy. This has been confirmed at a single
site with a sparse canopy (Ryder et al., 2014). Furthermore,
the improvements in actual evapotranspiration in addition to
the low RMSE (Fig. 2) are expected to be propagated in the
performance of the energy budget.
5.7 Forest management strategies
Model comparison has previously demonstrated that explic-
itly treating thinning processes is essential to reproduce lo-
cal and large-scale biomass observations (Wolf et al., 2011).
This finding justifies the implementation of generic ap-
proaches to forest management despite the difficulties asso-
ciated with defining and quantifying forest management and
its intensity (Schall and Ammer, 2013). Although the use
of so-called naturalness indices, in which the current state
of the forest in referenced against the potential state of the
forest, has been criticised because of difficulties in defining
the potential state of the forest (Schall and Ammer, 2013),
such approaches were demonstrated to correctly rank differ-
ent management strategies according to their intensity (Luys-
saert et al., 2011).
Naturalness indices making use of only diameter and stand
density or the so-called relative density index (RDI) have
been previously implemented at the stand level (Fortin et al.,
2012) as well as in large-scale models (Bellassen et al.,
2010). This approach was shown to successfully reproduce
the biomass changes during the life cycle of a forest (Bel-
lassen et al., 2011; Fortin et al., 2012). The implementation
of a forestry model based on the relative density index was
reported to perform better than simple statistical models for
0
5
10
15
20
25
B
io
m
a
ss
 (
10
3
 g
C
 m
−2
)
(A)
0
5
10
15
20
25
C
.W
.D
. 
(1
03
 g
C
 m
−2
)
(B)
1800 1850 1900 1950 2000
Year
0
5
10
15
20
25
30
T
re
e
 h
e
ig
h
t 
(m
)
(C)
1800 1850 1900 1950 2000
Year
0
10
20
30
40
50
60
C
u
m
. 
h
a
rv
e
st
 (
1
03
 g
C
 m
−2
) (D)
Figure 5. Impact of the different forest management strategies on an
oak forest for unmanged (green), high stand (orange) and coppice
(blue) compared to a Poplar short rotation coppicing (red) at 48◦ N,
2◦ E. The simulation was run without spin-up to better visualise car-
bon build-up in the coarse woody debris (C.W.D.) pool. Simulation
cycled of a single year (1990) of climate data to minimise the inter-
annual variability due to climatic year-to-year variability
stand-level variables such as stand density, basal area, stand-
ing volume and height (Bellassen et al., 2011). Although the
performance of the model was reported as less satisfying for
tree-level variables, the approach is nevertheless considered
reliable for modelling the effects of forest management on
biomass stocks of forests across a range of scales from plot
to country (Bellassen et al., 2011).
In the absence of forest management, ORCHIDEE-CAN
simulates that the stands develop into tall canopy (Fig. 5a),
with a high biomass (Fig. 5b), a substantial dead wood and
litter pool (Fig. 5c) and no harvest (Fig. 5d). High stand man-
agement reduces the height, standing biomass and litter pools
(Fig. 5a–c) but produces biomass for harvest (Fig. 5d). Un-
der coppicing, the reduction in forest age is reflected in a
shorter canopy and lower biomass and litter pools (Fig. 5a–
c) compared to high stand management. The harvest is more
evenly spread in time but falls below the harvest generated
by high stand management (Fig. 5d). Given the shorter ro-
tations, canopy height, standing biomass and litter pools are
lower for short rotation coppicing with poplar and willow
compared to all other management strategies applied on oak
forest (Fig. 5a–c). Short rotation coppice was harvested ev-
ery 3 years resulting in a quasi-continuous supply of woody
biomass (Fig. 5d).
The forestry model implemented in ORCHIDEE-CAN is
based on the RDI approach by Bellassen et al. (2010). We
complemented earlier validation of such an approach over
France (Bellassen et al., 2011) by a new European-wide val-
idation for basal area. On the European scale we verified the
simulated basal area and height against observed basal area
Geosci. Model Dev., 8, 2035–2065, 2015 www.geosci-model-dev.net/8/2035/2015/
K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE 2059
Figure 6. Root mean square error (RMSE) of tree diameter for dif-
ferent species (shown as different markers) for different regions
over France (shown as A to K). Open triangle, Pinus sylvestris;
open circle, Pinus pinaster; open square, Picea Sp.; filled diamond,
Quercus ilex/suber; filled triangle, Betula Sp.; filled circle, Fagus
sylvatica; filled square, Quercus robur/petraea.
from national forest inventories (de Rigo et al., 2014) and
height from remote sensing (Simard et al., 2011). With an
RMSE of 3–7 for height and 7–15 for BA, and a chance
of, respectively, 68 and 72 % to reproduce the data on the
European scale (Table 3), our model is capable of correctly
simulating the mean height and basal area but fails to cap-
ture much of the spatial variability (Fig. 4; temporal variabil-
ity was not considered because the data products were only
available for one time period).
Furthermore, we evaluated basal area and tree diameter at
the species level for 11 regions over France, which repre-
sents a finer spatial scale than targeted by the model devel-
opments and their parametrisation. The data were extracted
from the French forest inventory between 2005 and 2010 and
we used the same simulations as for the European validation
in the previous paragraph. We selected pixels included in the
French inventory data and for both simulations and obser-
vations we calculated a moving average for the diameter and
basal area per age class to then calculated the RMSE (Fig. 6).
To account for intrinsic species differences in diameter and
basal area, we normalised the RMSE. The normalised RMSE
was lower than 30 % of the mean tree diameter or mean basal
area for each region for Betula sp., Pinus pinaster and Quer-
cus ilex. For Fagus sylvatica, Pinus sylvestris, Picea sp. and
Quercus robur/petraea the normalised RMSE of diameter
and basal area exceeded 50 % for one to four regions for tree
diameter and basal area (not shown).
The inability to fully capture the observed spatial variabil-
ity in the simulation could be due to the simulation protocol
that started in 1850 with 2 to 3 m tall trees all over Europe. A
longer simulation accounting for the major historical changes
in forest management such as the reforestation in the 1700s
following an all time low in the European forest cover, the
start of high stand management at the expense of coppicing
in the early 1800s, and the reforestation programs following
World War II (Farrell et al., 2000) is expected to improve
the spatial variability in tree height and basal area. Regional
deviations such as those observed on the Iberian Peninsula
or over the entire Mediterranean (thus including part of the
Iberian Peninsula) may be due to the lack of shrubs in the
land cover map and parametrisation of the ORCHIDEE-CAN
branch. Therefore the models simulates a higher stand den-
sity and higher basal area for regions where in reality shrubs
occur (Fig. 4).
The parametrisation of the forestry module strongly de-
pends on the national forest inventories from Spain, France,
Germany and Sweden. Therefore verification against the
same data contains little information about the model qual-
ity. Nevertheless, no time-dependent relationships were used
in the ORCHIDEE-CAN branch; thus the model’s capacity
to reproduce the relationship between basal area and stand
age, diameter and stand age or wood volume and stand age
could be considered a largely independent test of the model
quality. These tests were performed over eight bioclimatic re-
gions of France and the ORCHIDEE-CAN branch was found
to largely capture the time dependencies of basal area, diam-
eter and wood volume (not shown).
6 Conclusions
ORCHIDEE-CAN (SVN r2290) differs from the trunk ver-
sion of ORCHIDEE (SVN r2243) by the allometric-based
allocation of carbon to leaf, root, wood, fruit and reserve
pools; the transmittance, absorbance and reflectance of ra-
diation within the canopy; and the vertical discretisation of
the energy budget calculations. Conceptual changes towards
a better process representation were made for the interac-
tion of radiation with snow, the hydraulic architecture of
plants, the representation of forest management and a numer-
ical solution for the photosynthesis formalism of Farquhar,
von Caemmerer and Berry. Furthermore, these changes were
extensively linked throughout the code to improve the con-
sistency of the model. By making use of observation-based
parameters, the physiological realism of the model was im-
proved and significant reparametrisation was done by intro-
ducing 12 new parameter sets that represent specific tree
species or genera rather than a group of phylogenetically of-
ten unrelated species, as is the case in widely used plant func-
tional types (PFTs). As PFTs have no meaning outside the
www.geosci-model-dev.net/8/2035/2015/ Geosci. Model Dev., 8, 2035–2065, 2015
2060 K. Naudts et al.: A vertically discretised canopy description for ORCHIDEE
scientific community, the species-level parametrisation of the
ORCHIDEE-CAN branch can deliver actionable information
to decision-makers and forest owners on the implications of
management strategies for the climate.
Model performance was tested against spatially explicit or
upscaled data for basal area, tree height, canopy structure,
GPP, albedo and evapotranspiration over Europe. The tested
data streams represented biogeochemical fluxes, biophysi-
cal fluxes and forest management related vegetation char-
acteristics. Enhanced process representation in ORCHIDEE-
CAN compared to the trunk version, was found to increase
model performance regarding its ability to reproduce large-
scale spatial patterns of all tested data streams as well as their
inter-annual variability over Europe. Although this validation
approach gives us confidence in the large-scale performance
of the model over Europe, additional validation is recom-
mended for other regional applications or higher resolution
studies.
Code availability
The code and the run environment are open source (http:
//forge.ipsl.jussieu.fr/orchidee). Nevertheless readers inter-
ested in running ORCHIDEE-CAN are encouraged to con-
tact the corresponding author for full details and latest bug
fixes.
The Supplement related to this article is available online
at doi:10.5194/gmd-8-2035-2015-supplement.
Author contributions. Developed and parametrised the
ORCHIDEE-CAN model: Kim Naudts, James Ryder, Matthew J.
McGrath, Juliane Otto, Sebastiaan Luyssaert, Nicolas Vuichard,
Didier Solyga.
Kim Naudts, James Ryder, Matthew J. McGrath, Juliane Otto and
Sebastiaan Luyssaert equally contributed to model development
and parametrisation.
Evaluated the performance of the ORCHIDEE-CAN model: Kim
Naudts, James Ryder, Juliane Otto, Matthew J. McGrath, Sebasti-
aan Luyssaert, Aude Valade, Yiying Chen, Fabienne Maignan.
Contributed Fortran code: Vanessa Haverd (canopy gaps), Bernard
Pinty (albedo), Valentin Bellassen (forestry).
Provided/shared observational data sets or tools for model
parametrisation: Hans Pretzsch, Päivi Merilä, Jens Kattge, Gerhard
Bönisch, Matteo Campioli, Josep Penuelas, Detlef Schulze, Toon
De Groote, Gonzalo Berhongaray, Yuan Yan, Philippe Peylin.
Developed driver data: Natasha MacBean.
Maintained and developed the run environment: Josefine Ghattas.
Acknowledgements. J. Ryder, Y. Chen, M. J. McGrath, J. Otto, K.
Naudts and S. Luyssaert were funded through ERC starting grant
242564 (DOFOCO), and AV was funded through ADEME (Bi-
CaFF). ESA ECV land cover also supported this work. The research
leading to these results has received funding from the European
Community’s Seventh Framework Programme (FP7/2007–2013)
under the Grant Agreement no. 284181-TREES4FUTURE. The
authors would like to thank Daniele de Rigo for providing a basal
area map for Europe.
Edited by: T. Kato
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