ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 27, NO. 5, 2010, 977–991
Evaluating the Dependence of Vegetation on Climate
in an Improved Dynamic Global Vegetation Model
ZENG Xiaodong∗ (曾晓东)
International Center for Climate and Environmental Sciences, Institute of Atmospheric Physics,
Chinese Academy of Sciences, Beijing 100029
(Received 1 November 2009；revised 29 December 2009)
ABSTRACT
The capability of an improved Dynamic Global Vegetation Model (DGVM) in reproducing the impact of
climate on the terrestrial ecosystem is evaluated. The new model incorporates the Community Land ModelDGVM (CLM3.0-DGVM) with a submodel for temperate and boreal shrubs, as well as other revisions such
as the “two-leaf” scheme for photosynthesis and the definition of fractional coverage of plant functional types
(PFTs). Results show that the revised model may correctly reproduce the global distribution of temperate
and boreal shrubs, and improves the model performance with more realistic distribution of different vegetation types. The revised model also correctly reproduces the zonal distributions of vegetation types. In
reproducing the dependence of the vegetation distribution on climate conditions, the model shows that the
dominant regions for trees, grasses, shrubs, and bare soil are clearly separated by a climate index derived
from mean annual precipitation and temperature, in good agreement with the CLM4 surface data. The
dominant plant functional type mapping to a two dimensional parameter space of mean annual temperature
and precipitation also qualitatively agrees with the results from observations and theoretical ecology studies.
Key words: dynamic global vegetation model, community land model, climate impact, vegetation response
Citation: Zeng, X. D., 2010: Evaluating the dependence of vegetation on climate in an improved dynamic
global vegetation model. Adv. Atmos. Sci., 27(5), 977–991, doi: 10.1007/s00376-009-9186-0.
1.
Introduction
The Dynamic Global Vegetation Model (DGVM)
is a group of models that simulate the distribution
and structure of natural vegetation dynamically, using
mostly mechanistic parameterizations of large-scale
vegetation processes (Foley et al., 1996; Friend et al.,
1997; Potter and Klooster, 1999; Woodward et al.,
2000; Cox, 2001; Sitch et al., 2003; Levis et al., 2004).
These models were designed to fit in the framework of
existing land surface models in order to facilitate the
coupling to global climate models (GCMs) or further
become a component of a fully coupled climate system
model (CSM) or dynamical earth system model (ESM)
(Zeng et al., 2008a) which are the major approaches
of current global change studies.
Before DGVMs emerged, land surface models and
climate models used prescribed vegetation distributions (percent of coverage) and traits (e.g., monthly
leaf area index) derived from observations. Such
∗ Corresponding
methodology is successful for simulations from a few
days up to several years. However, as the issue of
global change arouses increasing attention from both
political and scientific quarters, the capacity to practically simulate and predict changes 50 to 100 years in
the future are required. Because terrestrial ecosystems
may change according to both the climatic impact and
directly due to human activities, and in return influence the regional and global climate and environment,
it is crucial that the model includes a component which
can simulate the global vegetation dynamically and
predict the essential changes in the terrestrial ecosystem. The DGVMs are developed under such considerations.
The purposes of the various DGVMs can be summarized as the following:
(1) reproducing the current global distribution of
natural vegetation;
(2) reproducing the relationship between vegetation distribution and climate conditions;
author: ZENG Xiaodong, [email protected]
© China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS), Institute of Atmospheric
Physics (IAP) and Science Press and Springer-Verlag Berlin Heidelberg 2010
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DEPENDENCE OF VEGETATION ON CLIMATE IN CLM-DGVM
(3) reproducing and predicting the response of vegetation to interannual climate variability;
(4) predicting the changes in ecosystem distribution and structure, especially the transition between
different ecosystems, under future global change.
The first two purposes can be considered as the
validation of DGVMs. The vegetation-climate relationship has been sufficiently investigated by observational, theoretical, and ecological modeling researches. For example, the Moderate Resolution
Imaging Spectroradiometer (MODIS) has provided
products of global vegetation distribution with different classification schemes (e.g., IGBP, BGC, UMD)
and plant functional types (PFTs) (for more details, see the webpage of “MOD12C1 Land Cover
Product Binary Data from Boston University” at
http://duckwater.bu.edu/duckwater1/mod12c1/index.
html). Ramankutty and Foley (1999) derived the
global potential vegetation data from EROS land cover
classification data. Recognizing the close relationship
between vegetation and climate, the classification of
climate and vegetation are often combined with each
other. For example, the Köppen climate classification
and its modifications, which have been most widely
used over a century, adopt sub-categories of biome
forms such as rainforest, savannah, steppe, and desert
(Peel et al., 2007). On the other hand, Bailey (1998)
used the climatic concepts of tropical/temporal as well
as humid/dry for the major categories in the “Ecoregions of the Continents”, while Chapin et al. (2002)
further combined the “Walter climate diagram” into
the global distribution map of major biomes.
Other research, mostly from theoretical and modeling studies, has directly investigated the relation between vegetation and climate conditions. The well
known Holdridge life zones system (Lugo et al., 1999),
a global bioclimatic scheme for the classification of
land areas, incorporates mean annual biotemperature,
annual precipitation, and the ratio of annual potential
evapotranspiration to mean annual precipitation as indicators. Whittaker has also developed a classification of biomes according to mean annual temperature
and precipitation (Whittaker, 1975; Ricklefs, 2008).On
the other hand, the various biogeography models, e.g.,
BIOME-BGC (White et al., 2000), apply similar criteria to predict the equilibrium vegetation.
Simulations of the current global vegetation distribution have been carried out by some DGVMs, together with some statistics such as the percent of vegetation coverage (e.g., Sitch et al., 2003; Bonan and
Levis, 2006; Zeng et al., 2008b). Results show that
DGVMs can roughly reproduce the regimes of forest,
grassland, and desert, despite various biases and even
the lack of some important plant functional types such
VOL. 27
as shrubs in some of the models. Direct comparison
of model vegetation distribution with global observations might be difficult, because there can be biases in
the atmospheric data used to drive DGVM simulation.
Hence, it is better to evaluate a DGVM according to
its performance in reproducing the vegetation-climate
relationship revealed by previous ecological studies.
However, the direct analyses of vegetation-climate relationships in DGVMs have been mainly ignored, with
only a few preliminary studies on the dependence of
vegetation forms on precipitation (Zeng et al., 2008b).
The third purpose demonstrates the advantage of
the DGVMs over statistical biogeography models. Climate conditions such as the mean and interannual variability of precipitation and temperature are spatially
inhomogeneous. Observational studies have investigated the interannual variability of vegetation in response to precipitation (Knapp and Smith, 2001, Fang
et al., 2001), temperature and precipitation (Zhou et
al., 2007), and the El Niño/Southern Oscillation (Li
and Kafatos, 2000). It is well known that vegetation
may not be able to respond to high frequency of climate variations but rather acts as a climate integrator and smoothes the effects of climate (Hughes et al.,
2004). While equilibrium biogeography models cannot represent the transient responses of vegetation to
climate change because the ecological time scales of
vegetation dynamics are neglected (Levis et al., 2004),
DGVMs may be able to capture such vegetation variability as well as any feedbacks on vegetation-landatmosphere interactions and climate change. Furthermore, validating the vegetation response to climate variability is a very important step to take before a model can be used to predict ecosystem transitions, which is the next purpose potentially served by
DGVMs. Notaro (2008) showed the impact of interannual variability on the mean global vegetation distribution in LPJ-DGVM, but modeling studies of the
interannual variability of vegetation have not yet been
reported in the literature.
The last purpose on the list is obviously the most
important and essential task for DGVMs, and has been
investigated and reported in a number of research papers. However, due to the lack of model validation
related to the first three purposes (at least within
the literature), the capability of these models to predict future change in ecosystems is questionable. It is
not surprising that different DGVMs have predicted
very different and even contradictory future changes
in ecosystem structure.
This paper investigates the first two thrusts in the
previous list in detail with an improved dynamic global
vegetation model (CLM3.0-DGVM). The paper is arranged as follows: section 2 describes the improvement
NO. 5
of the CLM3.0-DGVM, with focus on the development
of a submodel for temperate and boreal shrubs. Section 3 shows the results of simulation of the global vegetation distribution, details of the vegetation-climate
relationship, and comparisons with observation and
theoretical ecological results. Finally, section 4 provides a summary and further discussion.
2.
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ZENG
The improved CLM3.0-DGVM
The Community Land Model 3.0 (CLM 3.0) (Oleson et al., 2004) is the land surface model for the Community Climate System Model (CCSM3.0) (Vertenstein et al., 2004). It is the successor to CLM2.0 and
CLM2.1 (Vertenstein et al., 2003), which is further
development of the Common Land Model (Dai et al.,
2003). CLM3.0 is released together with a DGVM
(Levis et al., 2004) developed from the Lund-PotsdamJena model (LPJ) (Sitch et al., 2003) following the
IBIS (Integrated Biosphere Simulator) approach. The
coupled model, denoted as CLM-DGVM hereafter, can
be either run offline or coupled with the Community
Atmosphere Model (CAM) (Collins et al., 2004) or run
as a component of CCSM. It is able to simulate the
global biogeography, but underestimates global tree
coverage and overestimates grass coverage in the offline simulation (Levis et al., 2004; Bonan and Levis,
2006), as well as incorrectly predicting Amazonia as
a deciduous tropical forest in the offline simulation or
as tropical grassland in the coupled run with CAM3
(Bonan and Levis, 2006). Additionally, shrubs are excluded from the list of PFTs in CLM-DGVM, despite
the fact that shrubs are widely spread over the world
and are vulnerable and sensitive to the changes in climate and environment. All these deficiencies imply
that the DGVM needs further improvements.
Recognizing the importance of shrubs in global
change, Zeng et al. (2008b) developed a temperate
shrub submodel for CLM-DGVM together with other
modifications and successfully reproduced the global
distribution of temperate shrubs in agreement with observations.
This paper expands Zeng et al. (2008b) by further
including the boreal shrubs. Boreal shrubs are mainly
distributed in the northern part of Asia and North
America, as well as in high elevation areas such as
the Tibetan Plateau. These areas feature very low
temperatures, with mean annual temperature lower
than −10◦ C, and are usually infertile. In relatively
warmer regions, boreal shrubs are replaced by boreal
trees and arctic grasses. Boreal shrubs can be up to 3m high, usually taller than temperate shrubs, and with
larger leaf area index (LAI). Actually, it is not easy to
distinguish boreal shrubs from boreal trees, especially
from satellite observations. For example, in previous
work that estimated global potential vegetation (Ramankutty and Foley, 1999), boreal shrubs in northern
Asia and America are classified as Boreal Evergreen or
Deciduous Forest/Woodland, while the open or closed
shrublands mainly refer to temperate shrubs.
The temperate shrub submodel of Zeng et al.
(2008b), as well as the newly developed boreal shrub
submodel, are summarized as follows:
(1) Explicit consideration of shrubs’ drought tolerance in the photosynthesis computation.
In the CLM, the impact of soil moisture on photosynthesis is represented by β t which is the soil moisture limitation function [see Eq. (8.8) of Oleson et al.,
2004]. The drought tolerance capability of temperate
shrubs suggests that shrubs should be able to maintain
a relatively high level of photosynthesis even under relatively low β t , which may limit the photosynthesis for
other non-drought tolerant PFTs. This implies that
the impact of β t on photosynthesis should be PFT dependent. Therefore, Eq. (8.8) of Oleson et al. (2004)
is rewritten as
Vmax = Vmax 25 (av max )
Tv −25
10
f (Tv )g(βt ) ,
(1)
where Vmax is the maximum rate of carboxylation,
Vmax 25 is the prescribed value at 25◦ C, αvmax =2.4 is
the Q10 parameter, Tv is the leaf temperature, and βt
is the soil moisture limitation function that is independent of PFT, and function
g(βt ) = min(βt /βt0 , 1.0) ,
(2)
where βt0 = 0.3 for temperate shrubs. For simplicity, βt0 = 1.0 for tree and grass PFTs to preserve the
calculation of photosynthesis for these species. Equation (2) allows shrubs to achieve higher photosynthesis than trees and grasses when βt is small. However,
shrubs still can not compete with trees and shrubs
in the region where water is not the limiting factor
for growth because of its relatively low value of Vmax
(Oleson et al., 2004).
Although water is not the potential climatic constraint to the growth of boreal shrubs (Nemani et
al., 2003), the drought tolerant feature of Eq. (2) is
adopted for boreal shrubs with the same βt0 as for
temperate shrubs.
(2) Phenology type and morphology parameters for
shrubs.
In CLM-DGVM, each wooded PFT is assigned
with a phenology type and a set of morphological parameters. There are three existing phenology types for
woody vegetation, i.e., evergreen, summergreen, and
raingreen. Because temperate shrubs mostly grow in
the arid to semiarid regions where water is the key
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DEPENDENCE OF VEGETATION ON CLIMATE IN CLM-DGVM
VOL. 27
Table 1. Parameters for shrub morphology. kla:sa is the proportionality coefficient between sapwood area and leaf area,
CAmax denotes the maximum crown area, and kallom1 and kallom2 are used to link canopy height and crown area to
stem diameter, respectively, in Levis et al. (2004). The new parameter values are used in the present paper, and the old
parameters are from Zeng et al. (2008b).
New
Old
kla:sa (m2 m−2 )
kallom1
kallom2
CAmax (m2 )
4000
4000
200
250
10
8
5
5
climate factor that most limits plant growth, its phenology type is set to raingreen in Zeng et al. (2008b).
However, it is found that the criteria for leaf emergence
in CLM-DGVM, i.e., that the 10-day running mean of
photosynthesis is higher than leaf maintenance respiration [Eq. (69) of Levis et al., 2004], is often satisfied
for temperate shrubs when Eqs. (1) and (2) are introduced. Thus temperate shrubs may grow leaves even
when water availability is relatively low, and shrubs
are forced to drop leaves 6 months later due to an
imposed drought phenology restriction (Levis et al.,
2004) even if water availability may be relatively high
at that time. To avoid this unrealistic behavior, the
LPJ-DGVM’s raingreen phenology scheme is used for
temperate shrubs. The requirement of dropping leaves
after a leaf-on period of 6 months is also removed, implying that temperate shrubs can become evergreen
under specific climate conditions. Additionally, the
warmest minimum monthly air temperature for establishment (cf. Table 2 of Levis et al., 2004) is set to “no
limit”, so that shrubs can grow in very hot regions.
On the other hand, the phenology type of boreal
shrubs is set to summergreen, and the coldest minimum monthly air temperature for establishment is set
to “no limit”, which is the same as boreal deciduous
trees and C3 arctic grasses.
Plant morphology determines the allocation of
yearly net primary productivity (NPP) to leaves,
roots, and stems. In CLM-DGVM all woody PFTs
share the same morphological parameters. Considering the significant differences of morphology between
shrubs and trees, the morphological parameters of
shrubs should be different from trees (Zeng et al.,
2008b). In Zeng et al. (2008b), the LAI of temperate shrubs is underestimated and is mostly less than
1. Considering that boreal shrubs usually have LAI
higher than 1, the morphological parameters of shrubs
are changed slightly (see Table 1) so that shrubs have
a relatively smaller crown area and hence higher LAI
than in Zeng et al. (2008b).
(3) Tree/grass/shrub hierarchy for light competition.
In the CLM-DGVM, different PFTs compete for
light and space when a gridcell is fully covered by vegetation. The hierarchy of trees and grasses for light
competition in the model roughly represents the advantage of trees in capturing incoming solar radiation
due to their taller status. This competition strategy is further expanded into the hierarchy of treesgrasses-shrubs, despite the fact that shrubs are a kind
of woody plant and are generally taller than grasses.
Such a hierarchy captures the essence of the fact that
ecosystems change from shrubland to grassland to forest as the precipitation or temperature increases, but
Table 2. Nitrogen limitation factor f (N ) for different plant functional types (PFTs).
PFT
Trees
Broadleaf Evergreen Tropical
Broadleaf Deciduous Tropical
Broadleaf Evergreen Temperate
Needleleaf Evergreen Temperate
Broadleaf Deciduous Temperate
Needleleaf Evergreen Boreal
Broadleaf Deciduous Boreal
Shrubs
Broadleaf Deciduous Temperate
Broadleaf Deciduous Boreal
Grasses
C4
C3 Non-arctic
C3 Arctic
Abbreviation
f (N )
BET Tr
BDT Tr
BEM Tr
NEM Tr
BDM Tr
NEB Tr
BDB Tr
0.72
0.66
0.50
0.63
0.50
0.45
0.38
BDM Sh
BDB Sh
0.60
0.55
C4
C3 NA
C3 AR
0.38
0.45
0.40
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the details for this treatment is different in some other
DGVMs (e.g., Cox, 2001).
(4) “Two-leaf” scheme of photosynthesis.
It is known that CLM3-DGVM underestimates the
gross and net primary production, and hence underestimates global tree coverages in favor of grass. Bonan
and Levis (2006) attribute this to the dry bias of soil
moisture in CLM3. However, Zeng et al. (2008b) found
that replacing the photosynthesis scheme may also resolve such deficiencies. Although CLM3 considers the
photosynthesis from both sunlit and shaded leaves, solar absorption (both direct and diffuse radiation) is
partitioned to sunlit leaves only (Oleson et al., 2004).
Hence, the model can be seen as single-leaf model.
Other land surface models such as LSM1.0 (Bonan,
1996) and the Common Land Model (Dai et al., 2003,
2004) apply two-leaf schemes with the consideration
of solar absorption by both sunlit and shaded leaves.
Thornton and Zimmermann (2007) show that a oneleaf scheme underestimates the photosynthesis level.
Hence, the two-leaf scheme for photosynthesis was applied in the shrub submodel in Zeng et al. (2008b).
However, the “two-leaf” treatment of photosynthesis
in Zeng et al. (2008b) tends to overestimate the photosynthesis level and results in a relative large LAI especially for tropical trees. For example, the broadleaf
evergreen tropical trees in central Amazonia may have
LAI larger than 8. This scheme also results in the
overestimate of coverage of trees and grasses in the
boreal region, in that the gridcells are fully occupied
without any space for further growth of boreal shrubs.
It has been reported that CLM3.5-DGVM is also overproductive due to the switch from the CLM3 “singleleaf” method to the new “two-leaf” parameterization,
overestimating tree cover at the expense of grasses and
grasses at the expense of bare ground (Oleson et al.,
2007). Following the treatment in CLM3.5, an additional term for the nitrogen limitation factor f (N ) is
introduced into Eq. (1) as:
Vmax = Vmax 25 (av max )
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ZENG
Tv −25
10
f (Tv )g(βt )f (N ) .
(3)
Despite the fact that f (N ) should be dependent on
both PFT characteristics and the soil nitrogen supply,
this is set to be a function of PFT only and is spatial
invariant for simplicity. See Table 2 for the values of
f (N ) used in the revised model.
There are also several other minor modifications,
including the definition of fractional coverages of PFTs
for the consistent treatment of the calculation of photosynthesis, respiration, and leaf area index, as well
as the improvement of the new allocation scheme to
avoid unrealistic behavior during the allocation computation.
The work of Zeng et al. (2008b) is developed in par-
allel to the next generation of the Community Land
Model, i.e., CLM3.5-DGVM. The CLM3.5 mainly improves simulation of the hydrologic cycle, and also provides a better surface dataset. However, the DGVM
remains largely unchanged. Because the corresponding atmospheric model as well as the climate system
model is not released, and it is not clear whether
CLM3.5-DGVM can be coupled to the CAM3.0 and
CCSM3.0, the revised model in this work sticks to old
CLM3.0-DGVM, preserving the possibility that it can
be coupled to existing atmospheric and/or climate system models. Actually, the shrub submodel based on
Zeng et al. (2008b) has been tested with an interim version of CLM3.5-DGVM, which has not been released
publicly, and some preliminary results show that the
model can reproduce the global shrubland distribution
(Samuel Levis, personal communication). Hence, the
major conclusion from this paper may be reproduced
by CLM3.5-DGVM with some of the parameters being
adjusted.
3.
3.1
Numerical simulations
Simulation design and analysis of results
Global offline simulations for 600 years at T-62
resolution (192×79 grid cells covering 60◦ S–90◦ N) using the revised model is performed to evaluate the
vegetation-climate relationship and is called “new simulation”. This is compared with a “control simulation”
(with the default CLM3.0-DGVM) to evaluate of the
impact of the model revision, especially the shrub submodel, on the CLM-DGVM simulations. The region
south of 60◦ S is not simulated because is hardly any
vegetation there. The near-surface atmospheric forcing data (of temperature, humidity, wind, precipitation, downward solar radiation, and surface pressure)
are generated by cycling the data of Qian et al. (2006)
of the years 1950 to 1999.
The new simulation has total of 12 PFTs, including
7 tree, 2 shrub, and 3 grass types, plus bare soil. The
control simulation does not have the 2 shrub PFTs.
Table 2 lists the abbreviation of these PFTs. The
CLM-DGVM simulation starts with all gridcells as
bare ground, i.e., there is no pre-established vegetation (Levis et al., 2004). Grass grows quickly, and
the global distribution of the 3 grass PFTs reach their
maxima in the first 20 to 60 years, and then decrease
as grass is gradually replaced by trees. On the contrary, the boreal vegetations grow most slowly among
all PFTs, and the needleleaf evergreen boreal trees
may need more than 500 years to approach their equilibrium states. Hence, the first 550 years of the simulation is treated as spin-up, and the results from the
last 50 years of simulation are averaged over each grid-
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DEPENDENCE OF VEGETATION ON CLIMATE IN CLM-DGVM
cell. This paper focuses on the most important index
of vegetation, i.e., the percent coverage of each PFT
within gridcells.
High quality observational data of the global vegetation distribution are important in the evaluation
of the simulation results. Among the various available datasets, the CLM surface dataset, which is released together with the model, is a good option, because it uses the same classification of PFTs as the
simulation and hence is more convenient for comparison. However, the surface dataset of CLM3.0 has been
found to be significantly different from the products
from MODIS (Oleson et al., 2003; Tian et al., 2004).
Lawrence and Chase (2007) developed new land surface parameters, which were adopted by CLM3.5, with
higher percentages of bare soil and boreal shrubs, and
lower percentages of temperate shrubs. CLM4, which
is still under development, will use a newer surface
dataset with two major differences from the CLM3.5
dataset, by using the new version of cropping from
Ramankutty et al. (2008), and removing some of the
previously observed bias in forest areas by replacing
the herbaceous vegetation in the previous data with
low trees (Peter Lawrence, personal communication).
In the following analysis, this dataset is used as the
observations and referred to as CLM4 surface data.
In order to avoid biases in the atmospheric forcing
data of Qian et al. (2006), other observational climate
datasets, i.e., the Global Preciptiation Climatology
Centre (GPCC) monthly precipitation data (available
at http://www.esrl.noaa.gov/psd/data/gridded/data.
gpcc.html), and the Climate Research Unit (CRU)
climatology of global temperature (available at
http://www.ipcc-data.org/obs/cru ts2 1.html),
are
used in the calculation of the relationship between
the observed vegetation distribution and climate conditions.
3.2
Simulation of the global vegetation distribution
Figure 1 shows the global distribution of evergreen
trees (sum of 4 PFTs), deciduous trees (sum of 3
PFTs), grasses (sum of 3 PFTs), temperate shrubs,
boreal shrubs, and bare soil averaged over the last
50 years of the new simulation. The global tree distribution mainly includes the tropical forest over the
Amazon, central Africa, and southern Asia (e.g., Indonesia), the temperate forest over southeast China,
the southeastern United States, and most of Europe,
and the boreal forest over the northern part of Asia
and North America. Grasses are mainly distributed
over southern Africa, the central United States, and
northeast Asia (i.e., east of Siberia). Shrubs grow in
the large areas in the sub-tropical arid to semiarid
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regions and in the high latitude heath regions, with
the temperate shrubs dominating southwestern North
America, southern Africa, the Middle East, northern
China, and Australia, with boreal shrubs found in the
far north regions of Asia and North America as well
as on the Tibetan Plateau. These distributions are
in good agreement with observations (e.g., Bonan and
Levis, 2006). The simulated global vegetation coverages (60◦ S–90◦ N) are: trees 38%, grasses 21%, shrubs
9%, and bare soil 32%.
Figure 2 shows the fractional coverage differences
in evergreen and deciduous trees, grasses, and bare
soil between the new and control simulations. It has
been known that the standard CLM3.0-DGVM underestimates global forest cover and overestimates grasslands, as well as underestimates evergreen trees in favor of deciduous trees (Bonan and Levis, 2006). In
the new simulation the global coverage of evergreen
trees (60◦ S–90◦ N) increases by 11.4%, whereas deciduous trees decrease by 3.4%. For example, the new
and control simulations both predict maximum total
tree cover (about 95%) over Amazonia, but the new
simulation correctly grows broadleaf evergreen tropical
trees instead of deciduous ones in the control simulation. As a result, the total tree coverage increases by
7.9%, with most of the difference (new – control) being
positive, especially in northwest and southeast North
America, southeast South America, and east Europe,
where the differences are higher than 20%. Because
grasses are lower in the hierarchy of light competition
than trees, grass coverage in these areas decrease by
about the same amount. These changes are mostly
caused by the introduction of the “two-leaf” scheme in
the new simulation, increasing the photosynthesis especially for the vegetation with higher LAI. The new
scheme favors the growth of evergreen trees because
they usually have higher LAI and a longer period of
photosynthesis than the deciduous ones. An exception
occurs in the region near (105◦ –135◦ E, 60◦ N) where
the new simulation has lower tree coverage and higher
grass coverage than the control simulation. Grasses
are also replaced by shrubs in the new simulation in
the arid to semiarid regions and infertile boreal regions. Hence, the global grass coverage decreases by
14.4% in the new simulation, which is larger than the
increase in tree coverage. The difference in bare soil is
relatively smaller than for the trees and grasses, and
the new simulation has 2.5% less bare soil than the
control simulation.
Table 3 shows the total area of major vegetation
categories (bare soil, trees, shrubs, grasses, and crops)
and various PFTs calculated from the control and
new simulations and comparisons with results from
CLM3.0, CLM3.5, and CLM4 surface data. The new
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ZENG
Fig. 1. Global distribution of the percent coverages of (a) evergreen trees, (b) deciduous trees, (c) grasses,
(d) bare soil, (e) temperate shrubs, and (f) boreal shrubs averaged over the last 50 years of the simulation.
The observed distribution of (g) temperate shrubs and (h) boreal shrubs from the CLM4 surface dataset
are also shown for comparison. The number on the top right corner shows the globally averaged coverage
(over 60◦ S–90◦ N) of the corresponding category.
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DEPENDENCE OF VEGETATION ON CLIMATE IN CLM-DGVM
simulation has higher coverage of evergreens than deciduous trees in both tropical and boreal regions, in
agreement with the observations, but contradicts the
control simulation. Both simulations have a much
higher percentage of tree coverage in the temperate
regions where the major agricultural areas are located
and the natural vegetation is most affected by human
activities. Additionally, the new simulation predicts
an area of 6.1×106 km2 covered by temperate shrubs,
which agrees with CLM3.5 and CLM4 surface data
(whereas the CLM3.0 surface data overestimates this
area as 15.0×106 km2 by including many areas of bare
soil within the open shrubland), but it somehow underestimates the boreal shrubs in favor of needleleaf
evergreen boreal trees.
In order to better evaluate the geographic distribution of the simulated vegetation, the zonal mean of
vegetation coverage of trees (sum of 7 PFTs), shrubs
(sum of 2 PFTs), grasses, and bare soil are calculated
from the new simulation and compared with the results
from the CLM4 surface dataset. Figure 3 shows that
the new simulation correctly reproduces the location of
zonal peaks of vegetation distributions. For example,
the peaks of the zonal distribution of trees are around
the equator, 60◦ N, and 50◦ S, for grasses at 15◦ and
30◦ –50◦ at both hemispheres, for shrubs at subtropical
VOL. 27
regions and 70◦ N, and for bare soil at 25◦ N, 25◦ S, and
80◦ N, and all were consistent with the CLM4 surface
dataset. But in magnitude, the simulation overestimates tree coverage and underestimates grass coverage, as mentioned earlier, which may partly be due to
the absence of crops in the model as well as the land
cover changes caused by human activities. The simulation also overestimates temperate shrub coverage
in the north hemisphere and underestimates it in the
south hemisphere. Additionally, the simulation misses
the peak for boreal shrubs and underestimates the coverage of bare soil between 60◦ –45◦ S. Such a bias, however, may be statistically insignificant, because both
the number of gridcells as well as the total land area
within this region is small.
3.3
Dependence of vegetation on the mean annual precipitation and temperature
The geographic distribution of vegetation is mainly
determined by climate conditions. The mean annual
precipitation and temperature are the two most crucial
climate factors that influence the terrestrial ecosystem.
Figure 4a shows the dependence of the average coverage of vegetation category (60◦ S–90◦ N) calculated
from the new simulation, as a function of mean annual
precipitation (MAP). The ecosystem is pure desert
Fig. 2. Global distribution of (a) evergreen trees, (b) deciduous trees, (c) grasses, and (d) bare soil percent
coverage differences (new−control). The number on the top right corner shows the global average (over
60◦ S–90◦ N) of the differences.
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ZENG
Table 3. Total area (in 106 km2 ) by major vegetation class (bare soil, tree, shrub, grass, and crop) and for various PFTs
calculated from the new simulation, control simulation, CLM3.0, CLM3.5, and CLM4 surface data.
Total area
Bare
Trees
BET Tr
BDT Tr
BEM Tr
NEM Tr
BDM Tr
NEB Tr
BDB Tr∗
Shrubs
BDM Sh
BDB Sh
BE Sh
Grasses
C4
C3 NA
C3 AR
Crops
new
ctrl
CLM3.0
CLM3.5
CLM4
55.0
50.7
13.3
8.1
3.5
2.8
11.1
9.9
2.0
11.9
6.1
5.8
0.0
27.5
9.6
12.2
5.7
0.0
58.3
40.2
5.9
11.9
2.7
1.0
8.0
4.9
5.8
0.0
0.0
0.0
0.0
46.6
14.8
19.8
12.0
0.0
38.6
28.5
8.5
5.7
0.9
3.4
3.5
4.5
2.0
19.9
15.0
4.6
0.2
36.9
11.3
19.2
6.4
21.2
54.9
26.3
8.2
4.4
1.1
2.1
3.2
5.3
2.0
13.6
5.7
7.9
0.1
35.1
14.3
14.2
6.6
15.2
54.7
37.5
11.0
5.8
1.6
3.3
4.4
8.5
2.9
13.1
5.6
7.4
0.1
26.0
10.5
11.7
3.9
13.7
∗ CLM-DGVM
merges NDB and BDB trees into one PFT. Hence, the results from the CLM surface data are treated in the same
way for comparison.
with no vegetation in the very arid region with MAP <
100 mm. Shrubs, as drought-tolerant vegetation, are
the dominant vegetation in the arid to semiarid regions, reaching peak coverage around MAP=300 mm,
Fig. 3. The zonal mean of vegetation coverage calculated
from (a) the new simulation and (b) the CLM4 surface
dataset.
and the shrubs cannot compete with grasses and trees
in the humid regions with MAP larger than 500 mm.
Trees and grasses coexist over a wide range of MAP
between 400 to 1200 mm, but then trees become dominant as the grass cover decreases sharply to below 20%
as MAP > 1500 mm. All these features are in agreement with the results from CLM4 surface data (Fig.
4b). Figure 4a differs from Fig. 4b in that it predicts
tree coverage is higher than that of grasses for MAP
between 700 to 1400 mm, but this region is heavily
disturbed by human activities, as shown by the curve
for crops in Fig. 4b.
Figures 4c and 4d show the dependence of the
average vegetation coverage as a function of mean
annual temperature (MAT). Both figures show that
the hottest (MAT >30◦ C) and the coldest (MAT
6−20◦ C) regions are mostly desert. The cold-tolerant
boreal shrubs are the dominant vegetation in the regions with MAT between −20◦ C to −10◦ C. There is
another peak of shrub coverage around MAT=15◦ C–
20◦ C which belong to the temperate shrubs. Grasses
can grow under all temperature conditions, and have
no preferred temperature. Both Figs. 4a and 4b show
two peaks of preferred temperature ’ for trees, centered on −10◦ C–0◦ C and 25◦ C of MAT respectively.
Again, Fig. 4c shows higher tree coverage than Fig. 4d
in the temperate regions, inconsistent with the major
agricultural regions (Fig. 4d).
Inspired by the Walter climate diagram, a new cli-
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DEPENDENCE OF VEGETATION ON CLIMATE IN CLM-DGVM
VOL. 27
Fig. 4. Dependence of vegetation coverage as a function of (a) mean annual precipitation (MAP, in mm), (c)
mean annual temperature (MAT, in ◦ C), and (e) PT=MAP－36MAT from CLM-DGVM simulation. (b), (d),
and (f) are the results from CLM4 surface data.
mate index PT is defined as
PT = MAP − kMAT ,
(4)
with MAP in mm and MAT in ◦ C. The factor k=36
mm (◦ C)−1 converts 10◦ C of monthly average temperature into 30 mm of monthly precipitation, similar to the concept of humid and arid periods in the
Walter climate diagram (Wu et al., 2004). Figure 4e
shows that the coverages of trees, grasses, shrubs, and
desert are very well separated as a function of PT;
desert dominates for PT< −200, trees dominate for
PT> 400, grasses are distributed around PT=200, and
shrubs prefer PT=−400 (for temperate shrubs) and
PT=800 (for boreal shrubs), in excellent agreement
with the results from CLM4 surface data (Fig. 4f).
Because annual precipitation and temperature are
usually correlated with each other, it is better to evaluate the dependence of vegetation on the combination
of MAP and MAT. Figure 5 shows that, the most suitable climate condition (i.e., ranges for MAP and MAT)
for different vegetation categories are totally different.
Trees dominate in the humid regions with MAT higher
than −10◦ C, and their required precipitation increases
as MAT increases. The favored condition for grasses
is with MAP within 400–900 mm and MAT within
10◦ C-25◦ C. Shrubs survive over arid to semiarid regions with MAP less than 300 mm and MAT around
15◦ C (temperate shrubs), and cold regions with MAT
between −20◦ C and −10◦ C (boreal shrubs). The most
arid/coldest regions are bare soil. Similar plots for
PFTs further show that each PFT has its own maximum distribution center (figure not shown), implying
the strong correlation between vegetation distribution
and the climate regime (combination of precipitation
and temperature) in the model. This feature, however, is different from the result of CLM4 surface data,
which shows that PFTs, and even the vegetation categories, are more widely distributed over the parameter space of MAT and MAP, although the center of
the distributions may be the same as indicated by the
new simulation.
The dominant PFT in relation to MAP and MAT
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ZENG
987
Fig. 5. The distribution of the simulated vegetation coverage in relation to mean annual temperature and
precipitation. The color shows the average percentage of vegetation coverage, the x-axis is the MAP, and
y-axis is the MAT.
is also calculated (Figs. 6a and 6b). For comparison,
for the CLM4 surface data the dominant PFT is calculated without considering the distribution of crops,
so that the set of dominant PFTs is the same as in
Fig. 6a. The region that is actually dominated by
crops is then marked by dots. As MAT and MAP
increase, both figures show that the dominant forest
ecosystem changes from boreal to temperate to tropical forest trees, accompanied by changes in leaf trait
and pheonology type. For example, in the tropical
region, deciduous trees grow with less precipitation
compared to evergreen trees when MAT is roughly the
same. The temperate forest changes from deciduous
to evergreen trees as MAP>1200 mm or MAT> 15◦ C.
One major difference between Figs. 6a and 6b is that
the large area dominated by temperate forest in Fig.
6a is replaced by C3 non-arctic grass in Fig. 6b, as
mentioned earlier in this paper. Actually, much of
these areas are indeed dominated by crops (Fig. 6b).
In the agricultural regions, trees are chopped down to
create farmland, and the remaining vegetation is usu-
ally grass rather than trees.
Figure 6a qualitatively agrees with the results from
theoretical ecology studies, e.g., Fig. 2.21 of Chapin et
al. (2002) or Fig. 5.5 of Ricklefs (2008). However, Fig.
2.21 of Chapin et al. (2002) suggests the minimum
requirement of MAP for the establishment of forest
is about 500 mm when MAT = 0◦ C, and 1000 mm
when MAT=10◦ C, both higher than indicated by Fig.
6a. This implies that the new simulation may overestimate the coverage of temperate forests by expanding
them to somewhat drier regions than in reality. Additionally, both Chapin et al. (2002) and Ricklefs (2008)
show that the area of tropical dry forest is much larger
than that of broadleaf deciduous tropical forests shown
in both Figs. 6a and 6b. In CLM-DGVM (both the
standard and the revised versions), the phenology of
broadleaf deciduous tropical trees is raingreen, which
is allowed to keep its full leaves on for up to 6 months,
and then is forced to drop leaves and remain at a
minimum level of leaves for another 6 months. On
the other hand, the broadleaf evergreen tropical trees
988
DEPENDENCE OF VEGETATION ON CLIMATE IN CLM-DGVM
VOL. 27
Fig. 6. The dependence of the dominant PFT on mean annual temperature (y-axis) and precipitation
(x-axis), obtained from (a) DGVM simulation and (b) CLM4 surface dataset. The dots show the areas
that are actually dominated by crops.
do not have a seasonal cycle of leaf area, and always
keep their leaves at the maximum level (Levis et al.,
2004). In reality, however, the phenology of tropical
trees changes from deciduous (dry forest) to semievergreen to evergreen as MAP increases and the length of
the dry season decreases; furthermore, the deciduous
trees may behave like evergreens in the wetter years
(Wu et al., 2004), while the evergreen trees also have
slight seasonal variations in leaf area, with less leaves
during the relatively drier periods (Archibold, 1995).
The lack of gradual changes between raingreen and evergreen phenologies in CLM-DGVM may account for
the inaccuracy in the prediction of the tropical ecosystem in Fig. 6a.
4.
Conclusions and discussion
A revised model of CLM3.0-DGVM with a submodel for temperate and boreal shrubs is used to
evaluate the impact of climate on terrestrial ecosystem. Results show that the revised model can correctly reproduce the global distribution of temperate
and boreal shrubs that are absent in the original CLMDGVM. The revision also improves the model performance, addresses the original model bias by the increasing tree coverage and decreasing grass coverage,
as well as resulting in a more realistic ratio between
evergreen to deciduous trees in the tropical and boreal
regions. Additionally, the new simulation correctly reproduces the zonal distributions of vegetation types.
Both the original and revised model overestimate the
tree coverage in the temperate regions, which may be
partly due to the heavily developed human activities
in these regions.
Results also show that the revised model correctly
reproduces the dependence of vegetation distribution
on climate conditions. Ecosystems change from desert
to shrubland to grassland and finally to forest as mean
annual precipitation increases. On the other hand,
ecosystems types progress from cold desert to boreal
shrubland to coexistence of grassland/forest and finally to hot desert as mean annual temperature increases. In relation to the combined influence of precipitation and temperature, trees, grasses, shrubs, and
bare soil have their own regions of dominance that are
clearly distinguished from each other. The dominant
changes for boreal, temperate, and tropical PFTs as
annual temperature and precipitation increases, accompanied by changes in leaf trait and pheonology
type, qualitatively agree with the results from observations and theoretical ecology studies.
One of the major contributions of this paper as
well as Zeng et al. (2008b) to CLM3.0-DGVM is the
development of a submodel for temperate and boreal
shrubs. Although shrubs cover about 10% of global
land surface (much less than trees, grasses, or bare soil)
they mostly dominate at arid to semiarid regions and
in infertile boreal regions where ecosystems are fragile
and sensitive to climate change. In the arid to semiarid
regions, precipitation is rare and usually irregular with
large interannual variability. Once destroyed, these
ecosystems are hard to restore, and large areas of preexisting grassland have been converted into desert or
invaded by shrubland (Bahre, 1995; van Auken, 2000).
The boreal regions, on the other hand, are expected
to undergo the largest warming effect within the forth-
NO. 5
989
ZENG
coming global climate change (IPCC, 2007).
While this study focuses on the equilibrium state of
the global vegetation distribution, there is also interannual variability in the simulation. The global distributions of annual precipitation and temperate interannual variability are spatially inhomogeneous and different from the global patterns of MAP and MAT. The
new simulation agrees with Notaro (2008), in that interannual climate variability favors grasses over trees.
Besides, the result also shows that the zonal means of
vegetative interannual variabilities are largest in the
sub-tropical regions in both hemispheres, and these
patterns roughly follow the coefficient of variation of
precipitation. Different PFTs have different sensitivities to the climate variability. Grasses, being annual
vegetation, can response to the high frequency of climate variability (at the yearly time scale) to some extent; whereas trees, being perennial vegetation, usually cannot. The interannual variability of vegetation
has impacts on vegetation-land-atmosphere interactions and may further influence the climate variability
when the vegetative model is coupled to the climate
model (Zhi et al., 2009). These results will be reported
in the next paper.
As mentioned in the first section, the essential task
of a DGVM is to predict the changes in ecosystem distribution and structure, especially at hot spots of transition between different ecosystems, such as from the
collapse of forests, the degradation of grasslands, and
the invasion of shrublands, which are often irreversible
(Scheffer et al., 2001) and have great impacts on local
climate and environment (Zhang et al., 2009) as well
as human activities. In DGVMs, vegetative species
are classified into several plant functional types to reduce the complexities in simulation, and usually the
sets of parameters for a particular PFT are optimized
for growth under the most favorable climate conditions and are invariant with space and time. However, the transition between ecosystems usually occurs
in the region where the climate is far from optimal
for the degrading vegetations as well as their replacements. Hence, the method of setting parameters in
current DGVMs may be insufficient to accurately predict the transition zones, even though the model may
be capable of reproducing the regimes of major terrestrial ecosystems. The next generation of DGVMs
may involve schemed allowing the parameters to be
adjustable both in space and time during the simulation in order to represent the capacity of vegetation to
adapt to climate, thereby better capturing the essential changes of ecosystems.
Acknowledgements. This work was supported by
Chinese Academy of Sciences (KZCX2-YW-219, 100 Tal-
ents Program) and Ministry of Science and Technology of
China (2009CB421406). The author is grateful to Dr. Xubin Zeng of University of Arizona, Dr. Peter Lawrence
and Dr. David Lawrence from University of Colorado for
valuable discussions. Two anonymous reviewers are appreciated for helpful comments.
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