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Biospheric carbon stocks reconstructed at the Last
Glacial Maximum: comparison between general
circulation models using prescribed and
computed sea surface temperatures
Dominique Otto*, Daniel Rasse, Jed Kaplan, Pierre Warnant, Louis Franc�oisLaboratoire de Physique Atmospherique et Planetaire, Institut d’Astrophysique et de Geophysique, Universite de Liege,
allee du Six Aout B5c, B-4000 Liege, Belgium
Received 20 March 2001; received in revised form 27 August 2001; accepted 7 September 2001
Abstract
The terrestrial biosphere model Carbon Assimilation in the Biosphere (CARAIB) was improved by introducing two
vegetation storeys and implementing a new module which simulates the equilibrium distribution of the vegetation inferred
from physiological processes and climatic constraints. In this fourth version of CARAIB, we differentiate ground-level
grasses from tree canopies, which allows us to determine the light available to grasses as a direct function of the leaf area
index (LAI) of the forest canopy. Both of these storeys are potentially composed of several plant functional types (PFT).
The cover fraction of each PFT within each storey is estimated according to its respective net primary productivity (NPP).
A biome is assigned to each grid cell on the basis of three physiological criteria: (1) the cover fraction, (2) the NPP, and
(3) the LAI; and two climatic constraints: (1) the growing degree-days (GDD) and (2) the lowest temperature reached
during the cold season (Tmin), which are well-known indices of vegetation expansion boundaries. Total biospheric carbon
stocks (vegetation + soil) are reconstructed by forcing the model with eight climatic scenarios of the Last Glacial Maximum
(LGM, 21 ka BP), which were obtained from the Paleo-Modelling Intercomparison Project (PMIP) from four general
circulation models (MRI2, UGAMP, LMD4, and GEN2) using prescribed and computed sea surface temperatures (SSTs).
The model was also forced with a current climate together with a preindustrial atmospheric CO2 level of 280 ppm as
reference simulation. To validate the model, current biome distribution is reconstructed and compared, for the modern
climate, with two distributions of potential vegetation and, for the LGM, with pollen data. The model simulations are in
good agreement with broad-scale patterns of vegetation distribution. The results indicate an increase in the total biospheric
carbon stock of 827.8–1106.1 Gt C since the LGM. Sensitivity analyses were performed to discriminate the relative effects
of the atmospheric CO2 level (‘‘fertilization effect’’), the climate (present or LGM), and the sea level. Our results suggest
that the CO2 fertilization effect is mostly responsible for the total increase in vegetation and soil carbon stocks. The four
GCMs diverged in their predicted responses of continental climate to calculated SSTs. Only one of them, i.e., MRI2,
predicted a marked decline of the continental temperatures in response to lower calculated SSTs. For this GCM, the effect
of reduced SSTs on continental biospheric carbon stocks was a decrease of 544.1 Gt for the soil carbon stock and of 283.7
0921-8181/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.
PII: S0921 -8181 (02 )00066 -8
* Corresponding author. Tel.: +32-4-366-9780; fax: +32-4-366-9729.
E-mail address: otto@astro.ulg.ac.be (D. Otto).
www.elsevier.com/locate/gloplacha
Global and Planetary Change 33 (2002) 117–138
for the vegetation carbon stock, which means a decrease in the total biopsheric carbon stock of 827.8 Gt. D 2002 Elsevier
Science B.V. All rights reserved.
Keywords: prescribed and computed sea surface temperatures; Last Glacial Maximum; vegetation distribution; continental carbon stocks;
climate and vegetation models; asynchronous coupling
1. Introduction
The relationship between biospheric carbon stocks
and atmospheric CO2 concentrations during the gla-
cial– interglacial cycles, i.e., the Pleistocene epoch
during the past 2 million years, may help us under-
stand the mechanisms which will drive climate
change during the 21st century and beyond. Measure-
ments in ice and deep-sea sediment cores revealed
that the atmospheric CO2 concentration has fluctuated
widely in concert with temperature variations during
this period. From the Last Glacial Maximum (LGM,
21,000 years BP) to preindustrial time, atmospheric
CO2 concentration increased from 200 to 280 ppmv
(Petit et al., 1999). Earlier research hypothesised that
the reservoir size and turnover time of the ocean were
the sole responsible factors for this increase and
invoked a redistribution of carbon in the ocean–
atmosphere system attributable to changes in the
oceanic circulation (Broecker and Takahashi, 1984)
and nutrient cycles (Broecker and Peng, 1987). How-
ever, foraminifera data suggest that the d13C value of
oceanic carbon at the LGM was from 0.3x to
0.7xlower than at present (Shackleton, 1977;
Duplessy et al., 1988; Curry et al., 1988). This change
of the oceanic carbon isotopic composition implies
that a transfer of 470–1100 Gt of carbon from the
ocean to the biosphere occurred during deglaciation.
This calculation rests on the hypothesis that the
terrestrial biosphere is the only reservoir having
exchanged carbon with the ocean during the deglaci-
ation, at least at an isotopic signature different from
that of the ocean. It also assumes that the average
d13C fractionation of photosynthesis at the LGM was
the same as today. However, taking into account
possible variations of this photosynthetic fractionation
would only slightly modify this range probably
towards lower values, since C4 species (exhibiting a
lower fractionation factor) are thought to have been
more widespread at the LGM than today (Bird et al.,
1994; Franc�ois et al., 1998). Two additional methods
support the concept that massive amounts of carbon
were removed from the ocean to the land biosphere
during deglaciation. First, reconstructions of paleove-
getation from palynological and sedimentological
proxy data suggest that the increase in the biospheric
carbon stock from the LGM to the present ranges
from 700 to 1600 Gt C (Adams et al., 1990; Van
Campo et al., 1993; Crowley, 1995; Adams and
Faure, 1998). Second, biospheric models forced with
outputs of general circulation models (GCM) estimate
this change to range from 0 to 700 Gt C (Prentice and
Fung, 1990; Friedlingstein et al., 1992, 1995; Prentice
et al., 1993; Esser and Lautenschlager, 1994; Franc�oiset al., 1998, 1999). In addition to increased carbon
stocks in the land biosphere, the dissolution of ter-
restrial calcrete, i.e., soil carbonate, during deglacia-
tion appears as another potential sink of atmospheric
CO2 (Adams and Post, 1999). Both estimates of
change in biospheric carbon stock and calcrete imply
an increased efficiency of the oceanic mechanisms
increasing the atmospheric CO2 level during deglaci-
ation for the net effect to explain the observed 80
ppmv shift between the LGM and the pre-industrial
period.
Sea surface temperatures (SSTs) are pivotal to the
simulations of atmospheric global circulation models
(AGCM), which drive the biospheric models used for
estimating the evolution of LGM carbon stocks.
Tropical SST values during the LGM remain uncer-
tain, which propagates uncertainties to biospheric
model estimates of LGM carbon stocks. The first
global SST reconstructions for the LGM were gen-
erated by CLIMAP Project Members (1981). They
were prescribed based on the abundances of plank-
tonic fossils in deep-sea sediment cores (‘‘fixed
SST’’). CLIMAP reconstructions of tropical SSTs
are on average 1 jC cooler than present-day tropical
SSTs. A considerable controversy has arisen about the
validity of CLIMAP SST estimates. Numerous geo-
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138118
chemical studies indicate that the tropics were 4–6 jCcooler at the LGM than under present conditions and
provide some support for the hypothesis that glacia-
tions were global. These studies include: (1) lowered
tropical snowlines (Rind and Peteet, 1985); (2) fora-
miniferal records (Curry and Oppo, 1997); (3) Sr/Ca
ratios from Barbados corals (Guilderson et al., 1994);
(4) tropical ice cores (Thompson et al., 1995); (5)
noble gases in Brazil aquifer (Stute et al., 1995). This
new evidence of sensitivity of the tropics to climate
change could have dramatic implications for forecasts
of the future global warming. The CLIMAP results
implie that the tropics, which represents 40% of
Earth’s surface, might not be affected by the future
warming. Opposite to these results, geochemical stud-
ies described above suggest that the tropics might
experience similar warming to that of higher latitude
regions. On the other hand, analysis of the temper-
ature-dependent production of alkenone molecules by
marine organisms, which responds to changes in
water temperature by altering the molecular compo-
sition of their cell membranes (Eglinton et al., 1992;
Herbert and Schuffert, 1998), yields small temperature
changes of about 2 jC, closer to the CLIMAP
estimates. Recently, slab ocean models, i.e., atmos-
phere-mixed layer ocean models, (Broccoli, in press),
have simulated air–sea interactions and computed the
SST distribution at the LGM (‘‘calculated SST’’). The
computed tropical cooling is comparable to recon-
structions based on alkenones, but smaller than the
cooling inferred from other geochemical studies.
The objective of this research is to analyse the
sensitivity of LGM biospheric carbon stocks and
vegetation distribution to SSTs. For this purpose, we
used the biospheric model CARAIB (Warnant et al.,
1994; Warnant, 1999) forced with four sets of two
AGCM scenarios, which differed only on their SST
inputs: (1) SST are prescribed based on the CLIMAP
(CLIMAP Project Members, 1981) reconstructions,
and (2) SST are computed with a thermodynamic slab
ocean model integrated as a submodule in the AGCM.
Since most recent geochemical studies indicate that
tropical SST during LGM were cooler than SST
prescribed by CLIMAP, we aim at providing a more
realistic estimate of the change in biospheric carbon
stocks during the last deglaciation. For this study, we
hypothesized that colder SSTs resulted in decreased
continental temperatures.
2. The model
2.1. General structure
CARAIB is a global model of the carbon cycle in
the continental biosphere (Warnant et al., 1994;
Nemry et al., 1996). It calculates carbon fluxes
between the atmosphere and the terrestrial biosphere,
and estimates the evolution of carbon pools resulting
from these fluxes. Five pools are considered: (1) the
leaves (GC, for ‘‘green carbon’’); (2) the rest of the
plant, i.e., branches, stems and roots (Ludeke et al.,
1994) (RC, for ‘‘remaining carbon’’); (3) the litter
from GC (GL, for ‘‘green litter’’); (4) the litter from
RC (RL, for ‘‘remaining litter’’); (5) the humus, i.e.,
the product of litter decomposition (SC, for ‘‘soil
carbon’’). Eight plant functional types (PFTs) are
considered: (1) C3 grasses; (2) C4 grasses; (3) needle-
leaved evergreen trees; (4) needleleaved deciduous
trees; (5) temperate broadleaved evergreen trees; (6)
tropical broadleaved evergreen trees; (7) temperate
broadleaved deciduous trees; (8) tropical broadleaved
deciduous trees. Carbon contents and fluxes in and
out of each pool are estimated daily for each grid cell
and each PFT. The model contains no nutrient cycle. It
should be included in future. The different carbon and
water fluxes are described in the following subsec-
tions.
2.1.1. Soil water budget
The Improved Bucket Model (IBM) developed by
Hubert et al. (1998) is used to calculate the soil water
budget of a soil layer of a given thickness with a time
step of 1 day. This budget is described by the follow-
ing equation
dw
dt¼ P � E � D� SR ð1Þ
where w is the water content in mm of the soil layer
and the water fluxes P, E, D, and SR are respectively
precipitation, actual evapotranspiration, deep drain-
age, and surface runoff in units of mm year � 1.
Precipitation is an input of the model. Actual evapo-
transpiration is estimated as a fraction of the potential
evapotranspiration rate. This fraction depends on soil
wetness, while the potential evapotranspiration rate is
calculated from Penman’s equation (Mintz and
Walker, 1993). Deep drainage, i.e., the downward
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138 119
water flux at the bottom of the soil layer, is estimated
from the soil hydraulic conductivity parameterized as
a function of soil texture, i.e., % sand, % silt, and %
clay, and soil wetness, according to Saxton et al.
(1986). Surface runoff occurs when precipitation is
too high and exceeds maximum infiltration into the
soil. The model requires as inputs daily mean values
of air temperature, precipitation, cloudiness, relative
air humidity, and wind speed. The last variable is used
to calculate the aerodynamic resistance needed in
Penman’s equation. The balance between the two
water fluxes, i.e., precipitation, which is imposed by
the GCM data, and evapotranspiration, which is
driven by wind data, is critical for determining the
vegetation distribution and the extent of desertic
areas.
2.1.2. Photosynthesis
Photosynthesis of C3 and C4 plants is computed
according to the methodologies of Farquhar et al.
(1980) and Collatz et al. (1992), respectively. The
canopy is divided in several layers for computing the
absorption of the photosyntheticaly active radiation
(PAR). PAR is estimated for each layer following the
method of Goudriaan and van Laar (1994), which
separates the effects of direct and diffuse light. Photo-
synthetic fluxes are estimated on a 2-h time step, to
take into account the diurnal cycle of the solar
insolation. The stomatal conductance is from Leu-
ning’s (1995) model modified by Wijk et al. (2000) as
follows: the empirical coefficients of the original
stomatal conductance model from Leuning were opti-
mized by comparing modeled and measured transpi-
ration fluxes, and the stomatal response to soil water
content was incorporated in this formulation by multi-
plying the stomatal conductance by the standard
response function proposed by Jarvis (1976) and
Stewart (1988).
2.1.3. Autotrophic respiration
The autotrophic respiration is divided between two
fluxes: maintenance and growth respiration. Mainte-
nance respiration was parametrized as an exponential
function of temperature and a linear function of
carbon content of the GC or RC pools (Warnant,
1999). The growth respiration is assumed proportional
to the biomass increase (Raich et al., 1991; Parton et
al., 1993; Ruimy et al., 1996). For tree woody tissues,
only sapwood is respiring. The sapwood fraction was
estimated from data reported by Duvigneaud et al.
(1971), Dubroca (1983), Ryan and Waring (1992) and
collected by Ruimy et al. (1996).
2.1.4. Allocation of photosynthates and reserve use
The carbon assimilated during photosynthesis is
partitioned between the GC and the RC pools accord-
ing to the simulated environmental conditions. When
temperature falls between a minimum and a maximum
value and soil water content is above a critical value
(Table 1), which have been calibrated to reproduce a
reasonable vegetation distribution, one half of the
assimilated carbon is allocated to each pool. When
these conditions are not met, the assimilated carbon is
entirely allocated to the RC pool. The GC pool is
limited to a maximum value, defined by an allometric
relationship with the RC content (Ludeke et al., 1994).
A new leaf layer is created only if its productivity is
higher than its mortality rate, which prevents CAR-
AIB from generating leaf layers with a negative car-
bon budget. Budburst is simulated by a transfer of
carbon from the RC pool to the GC pool. The carbon
Table 1
Tresholds used for determination of stress conditions
PFTs Tmin1 Tmin2 Tmax1 Tmax2 SWmin1 SWmin2
(1) C3 grass � 55 � 55 40 40 0.1 0.0
(2) C4 grass 0 0 50 50 0.1 0.0
(3) Needle-leaved
evergreen
boreal/temperate
� 40 � 40 30 30 0.3 0.3
(4) Needle-leaved
summergreen
boreal/temperate
� 50 � 55 30 35 0.3 0.0
(5) Broad-leaved
evergreen
temperate
� 10 � 10 35 35 0.3 0.3
(6) Broad-leaved
evergreen tropical
0 0 45 45 0.2 0.2
(7) Broad-leaved
summergreen
boreal/temperate
0 � 25 40 40 0.3 0.0
(8) Broad-leaved
raingreen tropical
0 0 40 45 0.2 0.0
Tmin is the absolute minimum temperature (jC), Tmax is the absolute
maximum temperature (jC), SWmin is the minimum soil water
content, expressed as a fraction of the field capacity and limited to
the wilting point. Index 1 refers to leaves and index 2 refers to the
rest of the plant.
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138120
available for buds is computed as a fraction of the
maximum value of the GC pool of the previous year.
2.1.5. Litter production and mortality
Litter is producted by the GC and RC mortality,
resulting from three contributions: (1) seasonal leaf fall
for deciduous species controlled by three external
parameters, i.e., temperature, soil water content, and
PAR; (2) plant death due to the natural regeneration of
the canopy, with a mortality characteristic time depend-
ing on the type of plant, i.e., grass or tree, evergreen or
deciduous, and the reservoir, i.e., GC or RC; and (3)
plant death due to unfavourable climatic conditions
(see Table 1) with a characteristic time of 1 week.
2.1.6. Heterotrophic respiration
Heterotrophic respiration is due to organic matter
decomposition by soil bacteria. Litter decomposition
is computed as a function of temperature, soil water
content, and litter carbon content (GL or RL). The
adopted temperature dependence has been fitted by
Nemry et al. (1996) on the data reported by Raich and
Schlesinger (1992) for all major world’s ecosystems.
A fixed fraction (20%) of this flux contributes to the
SC formation (Johnson et al., 1987) and the rest is
returned to the atmosphere as CO2. The SC mineral-
ization is computed using an equation similar to that
for the litter decomposition, but with a proportionality
coefficient reduced by a factor of 100 (Esser, 1984).
2.2. The new biome prediction module
A biome prediction module was developed, using
the NPP and LAI outputs of the CARAIB model,
which allows us to determine the cover fractions of
the different PFTs and to assign a biome to each grid
cell. This assignment is made in three steps: (1) the
application of climatic constraints; (2) the simulation
of competition between PFTs; and (3) the assignment
of a biome.
2.2.1. Application of climatic constraints
Two climatic constraints, which are indices of the
geographical extent of vegetation, have been chosen
with the aim of selecting the species potentially
present in each grid cell (Table 2): the absolute
minimum temperature (Tmin), i.e., the average temper-
ature of the coldest day of the year, and the growing
degree-days based on a threshold temperature of 5 jC(GDD5), defined by
GDD5 ¼X365
i¼1ðbTi>5ÞðTi � 5Þ ð2Þ
where Ti is the mean temperature of day i (jC).According to Woodward (1987), there is a strong
correlation between the distribution of dominant spe-
cies of trees and their winter freezing resistance.
Tropical species die as soon as temperature falls
below 0 jC, while boreal and temperate species must
endure freezing temperatures. Broadleaved evergreen
species resist to � 10 jC. All plants require a period
with temperatures warm enough for growth. At high
latitudes, it is a severe limitation to the growth of
leaves, and therefore establishment of LAI. Two
values of GDD5 have been adjusted: (1) 50 simulates
Table 2
Climatic constraints applied to PFTs
PFTs Tmin GDD5 new PFTs
(1) C3 grass / / (1) C3 grass
(2) C4 grass / / (2) C4 grass
(3) Needle-leaved
evergreen
boreal/temp
< 0 [50, 1350
z 1350
(3) Needle-leaved
evergreen boreal
(4) Needle-leaved
evergreen temperate
(4) Needle-leaved
summergreen
boreal/temp
< 0 [50, 1350]
z 1350
(5) needle-leaved
summergreen boreal
(6) needle-leaved
summergreen
temperate
(5) Broad-leaved
evergreen
temperate
[� 10,0] > 50 (7) broad-leaved
evergreen temperate
(6) Broad-leaved
evergreen
tropical
> 0 > 50 (8) broad-leaved
evergreen tropical
(7) Broad-leaved
summergreen
boreal/temp
< 0 [50, 1350]
z 1350
(9) broad-leaved
summergreen boreal
(10) broad-leaved
summergreen
temperate
(8) Broad-leaved
raingreen
tropical
> 0 > 50 (11) broad-leaved
raingreen tropical
Tmin is the absolute minimum temperature and GDD5 is the growing
degree-days based on a threshold temperature of 5 jC.
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138 121
the limit of the ice sheet extent, and (2) 1350 simu-
lates the limit between temperate and boreal climates,
which allows us to generate three tree PFTs represent-
ing stages between temperate and boreal ecosystems,
and to raise the number of PFTs from 8 to 11 (see the
second column of Table 2). Further in this work, we
will adopt this new set of 11 PFTs, which contains two
grass PFTs and nine tree PFTs.
2.2.2. Simulation of competition between PFTs
Simulation of competition between PFTs results
from the introduction of two vegetation storeys. In
the previous version of the model, there was only one
vegetation storey, which means that all PFTs present in
any given grid cell received the solar irradiance avail-
able at the top of the canopy, and grew independently
from one another without being affected by the poten-
tial shading from surrounding plants. In this new
version, we differentiate ground-level vegetation from
tree canopies, which allows us to determine the light
available to grasses as a direct function of the LAI of
the forest canopy. The canopy is composed of a
maximum of 16 layers with a LAI of 0.5 each. The
bottom layer may be only partly filled with leaves if the
cumulated LAI is not a multiple of 0.5. The carbon
content is evaluated daily for each pool (GC and RC)
and each of the six tree PFTs. Using for each PFT a
constant value of the specific leaf area (SLA), it is then
possible to compute the LAI of all tree PFTs and,
hence, the number of layers occupied by their leaves
(ntree), from the respective GC contents. The net
primary productivity (NPP) is then estimated for each
tree PFT and each day. On the last day of each month,
the cover fractions (fractree,i) are estimated according to
fractree,i ¼NPPi
X8
j¼3
NPPj
ð3Þ
where the index i represents each of the six tree PFT
(see the first column of Table 2) and NPP refers to the
monthly NPP. The average number of layers occupied
by trees is obtained as
ntree ¼X8
i¼3
ntree,i*fractree,i ð4Þ
where ntree,i and ntree are not integers, since they are
derived from the LAI itself proportional to the GC
pool. Then the photosynthesis of grasses is calculated
with the PAR available under the ntree layers of tree
leaves. Finally, the NPP of the two grass PFTs is
estimated by limiting their canopy development to
16� ntree layers, i.e., a LAI of 8� ntree*0.5. Their
cover fraction is estimated such as for trees, i.e.
fracgrass,i ¼NPPi
X2
j¼1
NPPj
ð5Þ
The NPP of each PFT simulated by the model
reflects differences in metabolism between plants, and
their aptitude to use the available water and light
resources, and represents a physiological criteria of
competitivity among species, which is used for the
biomes determination described below.
2.2.3. Assignment of a biome
Biomes are determined according to the classifica-
tion scheme described in Table 3. The new set of 11
PFTs is used. The polar ice sheet and the tundra
biomes are assigned on a climatic criteria, i.e.,
GDD5, as done in other biospheric models (IBIS:
Foley et al., 1996; BIOME3: Haxeltine and Prentice,
1996). The value of 350 jC-day for the tundra biome
is the same as in the BIOME3 and IBIS models, and
the value 50 jC-day for the limit of the ice sheet has
been adjusted to reproduce as well as possible the two
available present potential natural vegetation maps
(Melillo et al., 1993; Matthews, 1983). The other
biomes are assigned on the basis of two outputs of
the CARAIB model, i.e., the LAI and the NPP of each
PFT, or of parameters directly derived from these two
variables. Let us define the LAI of grasses and the
LAI of trees as the weighted sum of the LAI of the
PFTs present in their respective storeys
LAIgrasses ¼X2
i¼1
LAIi*fracgrass,i
LAItrees ¼X11
i¼3
LAIi*fractree,i ð6Þ
These quantities may vary with the season, espe-
cially if deciduous species are present. Thus, in the
framework of the biome assignment criteria, the
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138122
maximum LAI reached by the PFT over the course of
the year is used for LAIi and the cover fractions are
defined on an annual basis. The total LAI of the pixel
is then defined as the sum of the LAI over both
vegetation storeys, i.e.
LAItot ¼ LAIgrasses þ LAItrees ð7Þ
In a similar way, for the NPP, we define
NPPgrasses ¼X2
i¼1
NPPi*fracgrass,i ð8Þ
NPPtrees ¼X11
i¼3
NPPi*fractree,i
where NPPi here is the annual NPP. The NPP of all
PFTs is given by
NPPtot ¼ NPPgrasses þ NPPtrees ð9Þ
The assignment of non-tundra biomes is then
performed as follows. Desert is assigned whenever
NPPtot is equal to 0 and semi-desert is assigned where
LAItot falls below 0.4. Grasslands and grassy savan-
nas are separated from forests and woody savannas
when NPPgrasses is higher than 3/2*NPPtrees, and
distinguished from each other on a LAItrees criteria
fixed at 0.5. The limit between temperate or tropical
forests and woody savannas is fixed at a LAItrees of
1.5. To reduce the number of biomes, the distinction
between grassy and woody savanna is not specified.
The temperate mixed forest is predicted whenever
the cover fraction of any of the temperate tree PFTs
present in the pixel does not reach 0.6. These
thresholds have been adjusted by comparing the
simulated present vegetation distribution with the
two present potential natural vegetation maps used
in this work, i.e., Matthews (1983) and Melillo et al.
(1993).
3. Input data
The climatic fields used to force the CARAIB
model for modern climate, i.e., air temperature, pre-
cipitation, and cloud cover, are monthly mean
observed fields from Cramer et al. (unpublished data,
1995), an updated version of the dataset of Leemans
and Cramer (1991). These climatic data correspond to
a long-term mean over the period 1931–1960. They
are widely used within the biospheric community, as
for instance in the Potsdam ’95 intercomparison of
Table 3
Biome assignment scheme
Biome GDD5 NPPtot LAItot R LAItrees D F
(1) Ice < 50
(4) Tundra [50, 350]
(2) Desert >350 = 0
(3) Semi-desert >350 p 0 < 0.4
(5) Grassland >350 p 0 z 0.4 >1.5 < 0.5
(6) Temperate/tropical savanna >350 p 0 z 0.4 >1.5 z 0.5
(13) Boreal evergreen forest/woodland >350 p 0 z 0.4 V 1.5 3
(14) Boreal deciduous forest/woodland >350 p 0 z 0.4 V 1.5 5 or 9
(6) Temperate/tropical savanna >350 p 0 z 0.4 V 1.5 < 1.5 all � {3,5,9}
(11) Temperate mixed forest >350 p 0 z 0.4 V 1.5 z 1.5 4,6,7 or 10 < 0.6
(12) Temperate conifer forest >350 p 0 z 0.4 V 1.5 z 1.5 4 or 6 z 0.6
(9) Temp broad-leaved evergreen forest >350 p 0 z 0.4 V 1.5 z 1.5 7 z 0.6
(10) Temp broad-leaved deciduous forest >350 p 0 z 0.4 V 1.5 z 1.5 10 z 0.6
(7) Tropical rainforest >350 p 0 z 0.4 V 1.5 z 1.5 8
(8) Tropical seasonal forest >350 p 0 z 0.4 V 1.5 z 1.5 11
GDD5 is the growing degree-days based on a threshold temperature of 5 jC.NPPtot and LAItot are the sum of the NPP and LAI of each PFT weighted by their respective cover fraction. R is the ratio between the total NPP
of the two grass PFTs (NPPgrasses) and the total NPP of the nine tree PFTs (NPPtrees). D is the dominant tree PFT. F is the cover fraction of the
dominant tree PFT. PFTs are numbered from 1 to 11 (see the new set of PFTs in Table 2).
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138 123
biospheric models (Cramer et al., 1999). Absolute
anomalies of these fields between the LGM and the
present are calculated from four different AGCM
models participating in the Paleo-Modelling Inter-
comparison Project (Joussaume and Taylor, 1995):
MRI2 (Meteorological Research Institute-Japan),
UGAMP (Universities Global Atmospheric Model-
ling Programme-UK), LMD4 (Laboratoire de Mete-
orologie Dynamique-France) and GEN2 (GENESIS
model, National Center for Atmospheric Research-
USA). Model descriptions can be found on the web
at http://www-pcmdi.llnl.gov/pmip. These anomalies
are then added to Cramer et al.’s (1999) present
dataset to obtain LGM fields of air temperature,
precipitation and cloud cover. Two sets of SST fields
were used as boundary conditions for each GCM: (1)
SST prescribed from estimates given by CLIMAP
Project Members (1981) and (2) SST computed
using coupled atmosphere-mixed layer ocean models
(Dong and Valdes, 1998). The CARAIB model also
requires surface horizontal wind speed and air rela-
tive humidity fields to calculate evapotranspiration
fluxes. For present-day conditions, these fields were
obtained from monthly average ECMWF data for
1991. The relative humidity data were missing from
the GCM outputs. The present-day field was used for
the LGM simulation after extrapolation to the LGM
grid, which is larger than the present-day one due to
a lower sea level, using a distance weighted average
procedure. The relative anomalies of wind speed
between the LGM and the present simulations of
the GCM were calculated and multiplied by the
observed field to obtain the corrected LGM one.
The relative anomaly Dqrel is calculated by
Dqrel ¼qLGM
qpresð10Þ
and the relative corrected field qrel,corr is given by
qrel,corr ¼ qobs*Dqrel ð11Þ
where qLGM and qpres are the values of the variable
in the GCM simulations for LGM and present
conditions, respectively, while qobs is the observed
present-day value from Cramer et al. (1999). In total,
eight LGM climatic fields were used to force the
CARAIB model, and three modern simulations were
performed. Their characteristics are summarized in
Table 4. The CARAIB model contains a stochastic
generator of meteorological variables (Hubert et al.,
1998), which transforms the monthly mean data into
diurnal values. In the procedure used, a normaliza-
tion is performed after the stochastic generation to
ensure that the monthly mean values of the variables
are not altered. GCM outputs were interpolated down
to a 0.5� 0.5j latitude–longitude resolution.
The glacial atmospheric CO2 level was set to 200
ppmv (Petit et al., 1999). For modern simulations, it
was set to its pre-industrial value, i.e., 280 ppmv,
while we consider that vegetation is clearly out of
equilibrium with the atmospheric CO2 level since its
rapid increase at the beginning of industrial times. To
be fully consistent, we should have used a pre-
industrial climate instead of a present-day one, but
such a dataset is not available. Anyway, the difference
between the pre-industrial and the modern climates
remains small compared to the change between the
LGM and the present.
The orbital parameters used to calculate the solar
radiation fluxes are the same as in the PMIP experi-
ments (listed in Franc�ois et al., 1999). The present
continental area is 132� 106 km2. At the LGM, the
sea level is lowered by 105 m (Tushingham and
Peltier, 1991) and the continental area is increased
by 22� 106 km2.
Soil texture data from Zobler (1986) were used for
the present. In the absence of any information on past
soil texture, the same dataset was used for the LGM
and extrapolated to the emerged shelf by assuming a
medium soil texture on these pixels.
Table 4
Characteristics and codes of the 11 simulations performed
Code Climate [CO2]atm(ppmv)
Sea level SST
PD280 present-day 280 present-day
PD200 + present-day 200 present-day
PD200� present-day 200 LGM
MRI2C LGM 200 LGM calculated
MRI2F LGM 200 LGM fixed
UGAMPC LGM 200 LGM calculated
UGAMPF LGM 200 LGM fixed
LMD4C LGM 200 LGM calculated
LMD4F LGM 200 LGM fixed
GEN2C LGM 200 LGM calculated
GEN2F LGM 200 LGM fixed
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138124
4. Validation of the pre-industrial simulation
The reference simulation for the present is the
PD280 one (Table 4), i.e., a simulation with present-
day climate and continent size, and a pre-industrial
atmospheric CO2 level of 280 ppmv.
4.1. Vegetation distribution
The reference simulation PD280 has been com-
pared with two maps of present potential natural
vegetation distribution (Fig. 2): (1) the map simulated
by the TEM (Melillo et al., 1993) adapted from the
maps of Matthews (1983), Olson et al. (1983), Isa-
chencko (1990) and Kuchler (1964) (Haxeltine and
Prentice, 1996), which we will call further the
‘‘Melillo adapted’’ map and (2) the map of Matthews
(1983) (Fig. 1). A comparison at the pixel level has
been performed. We assessed the agreement among
the three distributions at the pixel level, by comparing
each pixel to pixels included within a given radius r of
the original pixel. A radius of 0 km is equivalent to
treat grid cells independently. At the global scale, the
appropriate scale for comparison may be larger than a
0.5j cell, due to the relatively coarse effective reso-
lution of the interpolated climatic fields, and to the
limited accuracy of the potential natural vegetation
distribution. Since the mean size of a pixel of
0.5� 0.5j is approximately 50 km, we considered a
radius of 50 or 100 km, which corresponds to a circle
of 1 or 2 pixels of radius.
The CARAIB model correctly predicts the ice and
tundra distributions, since these biomes are assigned
on a GDD5 criteria, which has been adjusted. Deserts
are too extended at the expense of semi-deserts. Two
parameters are principally responsible for this deserti-
fication: wind and precipitation. Indeed, all the areas
where desert appears in the PD280 map and not in the
potential natural vegetation maps are characterised by
a strong wind and weak precipitations: Western North
America, South Argentina, Somalia, Southern Sahara,
Southern Arabia, Western Caspian Sea, Northern
China, and Mongolia. The tropical rainforest biome
is too extended. Seasonal forest has approximately the
same extension as in the natural maps, but is slightly
shifted because it is invaded by tropical rainforest and
it extends on the savanna area. For the North-Western
of India, tropical seasonal forest is replaced by grass-
land and semi-desert. This region is characterised by a
strong wind and weak precipitations. Savanna is
reduced and replaced by seasonal forest. In northeast-
ern Argentina and Uruguay, the model predicts trop-
ical forest instead of grasslands because precipitations
are sufficiently abundant. Haxeltine and Prentice
(1996) put forward the hypothesis that the cause of
this discrepancy may be connected with the occur-
rence of specific soil conditions in this region. For the
temperate region, the model tends to predict a uniform
mixed forest because all trees have approximately the
same productivity. The transition between grasslands
and closed forests is not as gradual as in the potential
natural maps because trees reach too rapidely a high
LAI. Boreal forest areas are correctly predicted,
except in some regions where forest is replaced by
grassland and semi-desert: North-Eastern of Russia
and North-Western of North America. These regions
are characterised by weak precipitations, and hence
small soil water contents.
It appears clearly that the vegetation simulated by
CARAIB is very sensitive to wind speed, which
affects evapotranspiration, and thus the soil water
content, by the way of the aerodynamic resistance.
The expression of this resistance may need to be
refined in the future.
The CARAIB model shows a better agreement
with the Melillo adapted map than with the Matthews
one. However, for the tropical regions, the agreement
is better with the Matthews map. A global comparison
on all biomes was performed (G in Fig. 2). The
overall levels of agreement with the Melillo adapted
and Matthews maps are both 61% for a radius of 100
km. A comparison between the two potential natural
vegetation maps has also been performed and shows a
global agreement of 74%.
4.2. Global carbon stocks values
Previous reconstructions of pre-industrial bio-
spheric carbon stocks (Adams et al., 1990; Prentice
and Fung, 1990; Van Campo et al., 1993; Prentice et
al., 1993; Crowley, 1995; Franc�ois et al., 1998, 1999)give carbon stocks that range from 1115 to 1379 Gt
for the soil carbon, with a mean of 1247 Gt. For the
vegetation carbon, these reconstructions range from
622 to 924 Gt, with a mean of 773 Gt, and for the total
biospheric carbon, the range is from 1891 to 2422 Gt,
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138 125
Fig. 1. Present-day vegetation distribution simulated with the CARAIB model (PD280) and two maps of present potential natural vegetation
distribution: (1) the map simulated by the TEM (Melillo et al., 1993) adapted from the maps of Matthews (1983), Olson et al. (1983),
Isachencko (1990) and Kuchler (1964) (Haxeltine and Prentice, 1996) and (2) the map of Matthews (1983).
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138126
with a mean of 2157 Gt (Table 5). The values
predicted by the PD280 simulation are slightly lower:
1150 Gt for the soil carbon, 761 Gt for the vegetation
carbon, and 1911 Gt for the total biospheric carbon.
These values are directly calculated from the model,
i.e., by multiplying the estimated carbon stocks (the
sum of all vegetation, litter, and soil carbon pools) for
each PFT by their cover fraction in each grid cell. This
estimation is thus made at the PFT level, and hence, is
totally independent of the biome assignment scheme.
Note that the IPCC values of Table 5 correspond to a
present-day climate, with an atmospheric CO2 level of
358 ppmv and the vegetation distribution strongly
disturbed by human activities. The difference between
the PD280 stocks and these values is probably mainly
due to the rise in the atmospheric CO2 level since pre-
industrial times.
This pre-industrial validation gives us confidence
in our biogeography model, since the precision of the
predicted vegetation distribution is comparable to that
of existing present potential natural vegetation maps.
Moreover, the predicted carbon stocks fall within the
uncertainty limits of previous reconstructions.
5. LGM results
5.1. Climatic data
The global continental mean of the climatic data
changes from present to LGM for the eight LGM
simulations are presented in Fig. 3. The general trends
which emerge from these data are that LGM climatic
conditions were colder, less rainy, and more windy.
Fig. 2. Comparison between present vegetation distribution simulated with the CARAIB model (PD280) and two maps of present potential
natural vegetation distribution: Melillo adapted and Matthews. Each pixel of the CARAIB map is compared with the corresponding pixel of the
natural vegetation map, center of a circular area of radius r. The two natural maps are also compared. The comparison is made for each biome;
‘‘global’’ refers to the comparison for all biomes together, i.e., for all pixels of the continents.
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138 127
The cloudiness data do not allow us to conclude on
any trend. These changes are parallel to the develop-
ment of extensive ice caps at high to mid-latitudes of
the Northern Hemisphere in response to a smaller
contrast between summer and winter insolations in
these regions. At lower latitudes, changes in precip-
itation and winds fields are linked to the strengthening
of the Hadley circulation and the reduced seasonal
shifts of the Inter-tropical Convergence Zone (Nich-
olson and Flohn, 1980).
The difference between fixed and calculated SST
vary from one GCM to another. Therefore, the effect
of SSTs on continental climatic data computed by
GCMs remains uncertain. The MRI2 and UGAMP
models present a colder continental temperature in the
calculated version with a smaller difference for
UGAMP. The LMD4 model shows the inverse trend
with a small difference and the GEN2 model presents
no difference. Following the UGAMP, GEN2, and
LMD4 models, SSTs have little to no influence on the
continental temperatures and have thus not affected
the variations of the biospheric carbon stocks since the
LGM. By contrast, MRI2 predicted a marked decline
in continental temperatures in response to lower SSTs
as obtained in the calculated scheme. These reduced
continental temperatures could have a substantial
effect on the estimation of continental carbon storage
at the LGM. Although it is not proven at this point
that MRI2 is the GCM that most accurately predicts
continental temperature response to SSTs, we decided
to further investigate the effects on LGM carbon
stocks of SST-induced changes in continental temper-
atures, as predicted by the MRI2 model.
5.2. Comparison with pollen data
The LGM simulation has been compared with 245
pollen data from Crowley (1995) and Elenga et al.
(2000). The comparison is made at the data point
level. The interpolated map of Crowley, the validity of
which is highly speculative, has not been used. The
comparison technique is the same as for the present
(Fig. 2), i.e., for each of the 245 pollen data, we verify
whether the biome type inferred from the pollen
record is present or not in the model simulation within
a circle of 0 (same 0.5� 0.5j pixel), 50, and 100 km.
The extension of the comparison to a circle of 100 km
around the pollen data is particularly justified here,
since (1) pollens can be transported relatively far from
their production site and (2) GCMs actually provide
climatic fields at much coarser spatial resolutions than
the 0.5� 0.5j one to which all inputs of the biospheremodel have been interpolated.
The overall agreement obtained with the 245
pollen data points (i.e., without distinction of biome
type) is presented for all models in Table 6. The pixel-
by-pixel agreement (0 km) varies from 17% to 28%
depending on the GCM simulation considered. This
agreement is strongly improved if the comparison is
extended to a circle of 100 km radius, since the scores
exhibited by the models then reach 36–53% of
agreement with the data. For the pixel-by-pixel com-
parison, the best agreement is obtained for the MRI2C
simulation, while in the 100 km case, LMD4F obtains
the highest score. For the MRI2 GCM, the calculated
SST version exhibits a better agreement with the
pollen data than the fixed SST one, while it is the
reverse for the other three GCMs. The agreement is
on average inferior by 15–20% with respect to the
comparison between PD280 and the Melillo adapted
map.
The percentages of agreement per biome for the
MRI2 model are presented in Fig. 4. The fixed and
calculated SST versions exhibit a lack of agreement
for the savanna biome. From a general point of view,
the agreement is good for desertic and semi-desertic
regions, the tundra and the broadleaved mixed forest
Table 5
Global values of continental biospheric carbon stocks (soil and
vegetation) for the pre-industrial simulation PD280
C soil
(Gt C)
C veg
(Gt C)
C tot
(Gt C)
PD280 1150 761 1911
Franc�ois et al. (1999) 1334–1379 646–650 1984–2025
Franc�ois et al. (1998) 1322 622 1943
Crowley (1995) 2167–2422
Prentice et al. (1993) 2122
Van Campo et al. (1993) 2116
Prentice and Fung (1990) 1143–1313 748–834 1891–2147
Adams et al. (1990) 1115 924 2039
IPCC values
(Climate Change, 1995)
1580 610 2190
Some references of pre-industrial estimations are given for com-
parison. The IPCC values are present-day values (atmospheric CO2
level of 358 ppmv and vegetation distribution disturbed by human
activities).
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138128
biomes, and poor for the conifers and the grasslands.
The mismatch concerning conifers could be due to the
fact that the pollen of the conifers are among those
who spread far from the originating trees and that they
are produced with high amounts which could bias the
estimates for the conifer biome. The agreement for the
tropical seasonal forest, which is based on only two
data points, is not very relevant.
The fact that the scores reached by the LGM
simulations are relatively high (especially for the
100 km radius comparison) and only slightly lower
than those obtained for the present-day biome com-
parison is quite encouraging. However, refinements
are still needed in the comparison methodology: (1)
more pollen data are needed to get a better coverage of
the LGM continents; (2) the biome classification and
the biomisation scheme of the model should be
identical to those used for the pollen data; and (3)
possible transport of pollens should be taken into
account.
Fig. 3. Change from present to LGM of the continental mean of climatic variables for the eight sets of GCM results used as inputs to the
CARAIB biospheric model. Note that wind speed was not available for the LMD4C simulation and was taken from LMD4F simulation.
Table 6
Overall agreement of the eight LGM vegetation distributions with
245 pollen data from Crowley (1995) and Elenga et al. (2000)
Simulations Overall agreement for all biomes (%)
r = 0 km r = 50 km r= 100 km
MRI2F 21.6 26.5 42.8
MRI2C 28.2 37.1 51.0
UGAMPF 17.2 23.7 40.8
UGAMPC 16.8 20.8 36.3
LMD4F 25.3 35.1 53.5
LMD4C 22.9 31.8 47.3
GEN2F 25.3 33.1 51.4
GEN2C 22.9 32.2 48.2
The comparison is made all biomes together.
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138 129
5.3. Simulation of the biome distribution and
vegetation area changes from the present
The vegetation distributions reconstructed from the
MRI2C and MRI2F simulations are displayed in Fig.
5. Globally, the fixed SST simulation produces a more
desertic landscape than the calculated SST one,
despite the higher precipitation rate in the former.
The reason is that the fixed SST version is also
globally warmer by f 2 jC, and hence, evapotrans-
piration can be expected to be higher. This effect is
reinforced by the substantially stronger winds of the
fixed SST version. Stronger winds indeed tend to
decrease the aerodynamic resistance, and hence, stim-
ulate evapotranspiration. Thus, wind speed appears to
be a critical factor for vegetation distribution. This
factor has been disregarded in all earlier reconstruc-
tions of LGM vegetation distribution. Locally, Central
America, Northern South America, Somalia, Pakistan,
and Northwestern India are more desertic in MRI2C
than in MRI2F, again at least partly in response to
stronger winds in these regions. For the tropical rain-
forest of South America, two opposite effects are
observed: for MRI2F, tropical rainforest area de-
creases with respect to the present, while for MRI2C,
it increases, which is contrary to all previous LGM
vegetation distribution reconstructions (Van Campo et
al., 1993; Crowley, 1995; Franc�ois et al., 1999). Thisis presumably associated with the colder temperatures
prevailing over South America in the MRI2C simu-
lation, since colder temperatures tend to reduce evap-
otranspiration. It would be interesting to have a more
complete set of pollen data for this region.
The global trends of vegetation changes in MRI2
simulations are summarized in Fig. 6 and compared to
those of other GCMs. This figure has been constructed
on the basis of CARAIB results at the PFT level, and
hence, it does not rest on the biome assignment
scheme. All models predict essentially the same gen-
eral trend: an increase of the proportion of ice sheets
Fig. 4. Comparison between the MRI2C and MRI2F simulations and the pollen data from Crowley (1995) and Elenga et al. (2000). Each of the
245 pollen data is compared with the corresponding pixel of the CARAIB map, center of a circle of radius r. The biome classification is from
Crowley (1995). The numbers between brackets represent the numbers of pollen data for each biome.
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138130
and deserts at the LGM compared to the present,
mostly at the expense of trees. Grasses show only
small to moderate reductions. These general trends are
largely consistent with those obtained from previous
reconstructions based on palynological and sedimen-
tological data (Adams et al., 1990; Van Campo et al.,
1993; Crowley, 1995; Adams and Faure, 1998), except
that these studies tend to predict an increase of the
proportion of grass ecosystems. In general, our recon-
structions appear more forested in the tropics and
slightly more desertic in the extra-tropics. These pro-
portions of ice sheets, deserts, grasses and trees are
changed by less than 2–3% in the fixed SST version of
a given GCM compared to the calculated SST results.
As seen before for the MRI2 model, the fixed SST
version generally tends to be more desertic, except in
the case of the UGAMP model.
PFT and biome areas reconstructed with the MRI2
model for present-day and LGM times are presented
in Fig. 7. As mentioned, the area of grasses was
reduced at the LGM compared to the present in these
simulations. C3 grasses are slightly more affected than
Fig. 5. LGM vegetation distribution simulated by the CARAIB model with the two versions of the MRI2 GCM: calculated (MRI2C) and fixed
(MRI2F) SSTs.
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138 131
C4, since the CO2 reduction from 280 to 200 ppmv
tends to favor C4 species. Tree areas also decrease,
except that of broad-leaved evergreen tropical trees
which increases. Needle-leaved trees are more
affected in MRI2F and broad-leaved trees are more
affected in MRI2C. At the biome level, the areas of
ice sheets, deserts, semi-deserts and tundras are
strongly increased at the LGM compared to the
present. Tropical rainforests and temperate broad-
leaved evergreen forests show a moderate increase.
Temperate conifer forests and savannas exhibit essen-
tially no change. A moderate decrease is observed for
boreal deciduous and temperate broad-leaved decid-
uous forests. Boreal evergreen forests, temperate
mixed forests, tropical seasonal forests, and grass-
lands show strong decreases.
5.4. Carbon stocks and fluxes
Global values of the model biospheric carbon
stocks and the corresponding changes from present
are presented in Table 7. These simulated changes are
larger than those obtained from previous vegetation
reconstructions by the way of biospheric models,
which range from 0 to � 700 Gt C (Prentice and
Fung, 1990; Friedlingstein et al., 1992, 1995; Prentice
et al., 1993; Esser and Lautenschlager, 1994; Franc�oiset al., 1998, 1999), and than those suggested by
foraminifera isotopic data, which range from � 300
to � 1000 Gt C (Shackleton, 1977; Duplessy et al.,
1988; Curry et al., 1988; Bird et al., 1994). They are
in better agreement with interpretations from palyno-
logical and sedimentological proxy data, which range
Fig. 6. Proportion of the total continental area of ice sheet, desert, grasses, and trees for the eight LGM simulations, changes of these proportions
from the present to the LGM, and differences of these proportions between fixed and calculated SSTs for each of the four GCMs. Ice sheets are
regions with GDD5 < 50 jC day, deserts are pixels devoid of vegetation, while grass and tree proportions are obtained as the global cover
fraction of the corresponding PFTs predicted by CARAIB.
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138132
Fig. 7. PFT and biome areas of 5 simulations: MRI2C, MRI2F, PD200� , PD200+ and PD280.
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138 133
from � 700 to � 1600 Gt C (Adams et al., 1990; Van
Campo et al., 1993; Crowley, 1995; Adams and Faure,
1998). For all GCMs, the total biospheric carbon
stock is larger in the calculated SST version than in
the fixed one. MRI2 shows the largest difference with
209 Gt C, while UGAMP exhibits almost no differ-
ence with 10.7 Gt C. The reason why carbon storage
is larger in the calculated SST experiments is possibly
linked to the systematically smaller wind speed which
reduces evapotranspiration and hence leads to higher
soil water contents and vegetation productivity.
Nevertheless, these changes in carbon storage remain
very difficult to interpret, in view of the complex
dependence of vegetation net primary productivity
and microbial respiration on surface air temperature
and soil moisture.
5.5. Sensitivity experiments
The individual effects of atmospheric CO2 level
increase, sea level drop, and climate variation on the
total biospheric carbon stock since the LGM have
been analysed by comparing the PD280, PD200+ and
PD200� simulations (defined in Table 4) with
MRI2C. The atmospheric CO2 level effect has been
separated in two parts, i.e., the vegetation shift due to
the increase of this level and the pure fertilization
effect without vegetation migration, by comparing the
PD280 simulation with the PD200+ interm, in which
carbon stocks are calculated with the PFT distribution
of PD280 and the carbon distribution of PD200+
(Table 8). The CO2 fertilization effect is clearly
dominant and almost solely responsible for the total
biospheric carbon stock increase since the LGM. The
sea level drop, which decreases the continental area,
tends to decrease the carbon stocks, while the combi-
nation of the modification of the climatic variables,
i.e., the increase of temperature, precipitation and the
decrease of wind speed (Fig. 3), tends to increase the
carbon stocks and fluxes. The vegetation shift asso-
ciated with the atmospheric CO2 level increase has
rather weak impact on the carbon stocks.
The effect of the CO2 level on vegetation is
obtained by comparing the PD280 and PD200+ sim-
ulations (Fig. 7). The lower CO2 level appears as a
major contributor to the desertification during the
LGM. Our simulations indicate that this lower CO2
level is sufficient to explain the transformation of
present-day arid regions into desert during the LGM,
i.e., western North America, southern South America,
Australia, South Africa, and Somalia. It must be
pointed out that these regions received approximately
the same precipitation rate at the LGM than at present
according to the MRI2C simulations. On the other
hand, desertification of Europe and most of Asia
cannot be explained only by the CO2 level decrease,
but is associated with a substantial reduction of
precipitations. The C3 grass area decreases by
2.1*106 km2 while that of C4 grass increases by
2.2*106 km2. The ratio of C3/C4 grass areas is 0.66
for PD200+ and 0.80 for PD280, corresponding to an
increase of 0.14 (Fig. 7). As expected, C4 grasses are
thus dominant with respect to C3 in low atmospheric
CO2 level conditions. Globally, the productivity of all
Table 8
Effect of climate change, sea level increase, and atmospheric CO2
level increase (fertilization + PFT shift effects) on the increase of the
total biospheric carbon stock since the LGM to present-day
Simulations C total
(Gt)
Effect Change from
LGM (MRI2C)
(Gt)
MRI2C 1083.1 0
PD200� 1505.4 climate:
LGM! present
+ 422.3
PD200 + 1161.4 sea level rise � 344.0
PD200 + interm 1071.4 PFT shift
due to CO2
level increase
� 90.0
PD280 1910.9 CO2 level
increase without
PFT shift
+ 839.5
The total carbon stock of the PD200+ interm simulation is cal-
culated with the distribution of vegetation of PD280 and the carbon
distribution of PD200+.
Table 7
LGM global values of continental biospheric carbon stocks (soil and
vegetation), with the change from present in parentheses
Simulation C soil (Gt C) C veg (Gt C) C tot (Gt C)
MRI2C 605.9 (� 544.1) 477.2 (� 283.7) 1083.1 (� 827.8)
MRI2F 461.8 (� 688.2) 412.4 (� 348.5) 874.1 (� 1036.8)
UGAMPC 416.8 (� 733.2) 398.7 (� 362.2) 815.5 (� 1095.4)
UGAMPF 422.9 (� 727.1) 382.0 (� 378.9) 804.8 (� 1106.1)
LMD4C 532.9 (� 617.1) 450.8 (� 310.1) 983.7 (� 927.2)
LMD4F 502.8 (� 647.2) 419.0 (� 341.9) 921.8 (� 989.1)
GEN2C 575.1 (� 574.9) 493.8 (� 267.1) 1068.9 (� 842.0)
GEN2F 495.8 (� 654.2) 426.2 (� 334.7) 922.0 (� 988.9)
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138134
PFTs decreases in response to the CO2 drop. Biomes
like grasslands or savanna become desertic and closed
forests become woodlands. Tropical rainforest is
maintained because it contains PFTs of a sufficiently
high productivity to remain dominant, even if their
productivity decreases.
The effect of the continental area change associated
with the lower sea level at the LGM can be observed
by comparing the PD200+ and PD200� simulations.
This sensitivity experiment corresponds to a sea level
drop under current climatic conditions. The biomes
which tend to colonize the emerged shelf in this
experiment are ice sheets, tundra, tropical rainforest,
temperate mixed forest, and boreal evergreen forest/
woodland. In the case of the tropical rainforest, the
corresponding area increase is larger than the decrease
induced by the climate change. Consequently, the
expansion of the tropical rainforest biome at the
LGM in these simulations is associated with the
colonization of the emerged shelf (mostly in Indonesia
as obvious from Fig. 5), while on the present-day
continents, the area of the tropical rainforest actually
decreases which is more consistent with the trend
indicated by the data.
In view of the apparently important role of wind
speed in the LGM simulations, an additional sensi-
tivity test has been performed with the MRI2 model,
in which all inputs were set to the standard input fields
of the MRI2C simulation, except wind speed which
was set to the present-day value (eventually extrapo-
lated to the emerged shelf). In view of the globally
smaller wind speed (see Fig. 3), this new simulation
resulted in a reduction of the desert area by 0.7*106
km2 compared to MRI2C, at the benefit of grass and
tree areas which respectively increased by 0.4*106
km2 and 0.3*106 km2. These changes in the areas of
the PFTs remain relatively limited. However, the
global biospheric carbon stock increased by 50.5 Gt
C with respect to MRI2C, which is not negligible in
the global carbon budget. Wind speed thus occurs as a
significant factor which should taken into account in
future reconstructions of the LGM carbon stocks.
6. Conclusion
CARAIB simulates the vegetation distribution in
equilibrium with a given climate and atmospheric
CO2 concentration. This distribution is determined
by simulated NPP, resulting from a direct response
of photosynthesis and stomatal conductance to climate
and CO2. The model successfully reproduces the
broad-scale patterns in potential natural vegetation
for the current climate. It also produces estimates of
the biospheric carbon stocks, which fall in the range
of previous estimates for pre-industrial times. The
competition for light between grass and woody plant
types was modeled by introducing two vegetation
storeys, i.e., ground-level grasses and tree canopies.
This competition for light tends to favor trees at the
expense of grasses. An important application of CAR-
AIB is the simulation of the equilibrium response of
vegetation to changes in climate and atmospheric CO2
concentration.
A validation of simulated vegetation responses to
climate changes is only possible with past data.
CARAIB was forced with LGM climatic data from
four GCMs using fixed and calculated SST fields, i.e.,
eight scenarios. The simulated carbon stocks are in
reasonable agreement with reconstructions based on
palynological and sedimentological proxy data. These
values are larger than those obtained from previous
vegetation reconstructions by the way of biospheric
models, and from foraminifera data. The main char-
acteristic common to all vegetation reconstructions
performed with CARAIB for the LGM is the global
expansion of deserts and ice and a global reduction of
forests consistent with previous reconstructions (Van
Campo et al., 1993; Crowley, 1995; Franc�ois et al.,
1999). However, the model predicts a decrease of
grasslands and a slight expansion of the tropical
rainforest, in conflict with these previous reconstruc-
tions. Note, nevertheless, that the expansion of the
tropical rainforest is linked to the colonization of the
emerged shelf, while on the present-day continents the
tropical rainforest tends to be reduced, except region-
ally such as in South America for the MRI2C simu-
lation.
The four GCMs are not consistent with one another
concerning the effect of SSTs on the continental
climate, and therefore on the vegetation distribution
in equilibrium with this climate. However, it appears
that the use of calculated SSTs in the GCMs system-
atically leads to higher biospheric carbon stock pre-
dicted by the model. A more detailed study was
performed with the MRI2 model. The choice of this
D. Otto et al. / Global and Planetary Change 33 (2002) 117–138 135
model was motivated by the fact that the predicted
continental temperature is substantially colder than the
one calculated by its fixed SST version, which is
consistent with the recent studies indicating that the
tropics were colder than suggested by the CLIMAP
database. Moreover, the calculated SST version of this
model was among those which led to the best scores
in the comparison of the predicted biomes with pollen
data. There are two main differences between the
results obtained with the fixed and the calculated
SST versions of this GCM. Firstly, the fixed SST
version predicts a reduction of the tropical rainforest
of South America from the present to the LGM, while
the calculated one predicts its expansion, which is in
total disagreement with previous reconstructions. This
is probably associated with the colder tropical temper-
atures in the calculated SST version, which decreases
the continental surface evaporation and therefore
increases the soil water content in this region. Sec-
ondly, from a more global perspective, the fixed SST
version is more desertic than the calculated one. This
is probably due to increased wind speed. Therefore,
the calculated SST version of MRI2 leads to larger
biospheric carbon stocks than its fixed SST version.
Sensitivity tests suggest that CO2 fertilization
effect is among the most important factors responsible
for the changes in carbon stocks and vegetation
patterns between LGM and present times. C4 grasses
are less affected by the decrease in the atmospheric
CO2 concentration during glaciation than C3.
A series of model refinements may improve the
accuracy of the vegetation reconstructions. Firstly, the
vegetation distribution seems to be very sensitive to
wind speed data. All the vegetation distributions
reconstructed with CARAIB, for current or LGM
climate, are too desertic and present weaker carbon
stocks. The expression of the aerodynamic resistance,
i.e., the only way by which wind speed influences
vegetation, should be improved. Secondly, relative air
humidity data used for the LGM simulations are
present-day data. This approximation may alter the
simulated NPP, and therefore vegetation response.
Thirdly, a more accurate determination of the dynamic
equilibrium between grass and tree types would
require the implementation into the model of natural
disturbances, such as fires, which is known as an
important factor in determining this equilibrium (Hop-
kins, 1992) and which tends to favor grasses with
respect to trees. This would lead to a better represen-
tation of the transition between grasslands and closed
forests, which is too abrupt in the current vegetation
reconstructions with the CARAIB model.
Acknowledgements
The authors wish to thank S. Jousseaume and Gilles
Ramstein who made the PMIP database available for
this study. Funding for this research was provided by
the Communaute Franc�aise de Belgique-Direction de laRecherche Scientifique-Actions de Recherches Con-
certees (Contract No. ARC 98/03-219) and by ‘‘The
global carbon cycle and future atmospheric CO2
levels’’ (First Multiannual Scientific Support Plan for
a Sustainable Development Policy (SPSD I) of the
OSTC, Contract CG/DD/11A, 12/1996–11/2000). L.
Franc�ois is supported by the Belgian National
Foundation for Scientific Research (FNRS).
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