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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, G03003, doi:10.1029/2009JG001062, 2010
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Dynamic responses of terrestrial ecosystems structure and
function to climate change in China
Fulu Tao1 and Zhao Zhang2
Received 1 June 2009; revised 3 February 2010; accepted 24 February 2010; published 16 July 2010.
[1] Our understanding of the structure and function of China’s terrestrial ecosystems,
particularly their responses to transient climate change on timescales of decades to
centuries, can be further improved by dynamic vegetation models that include vegetation
dynamics as well as biogeochemical processes. Here, the Lund‐Potsdam‐Jena dynamic
global vegetation model was calibrated to reasonably capture the general patterns of
vegetation distribution across China’s terrestrial ecosystems. New parameter values were
used for bioclimatic limits, and the model was validated against satellite, in situ, and
inventory data for net primary production (NPP), leaf area index, and carbon storage
simulations. Dynamic responses of China’s terrestrial ecosystems to rising atmospheric
CO2 concentration ([CO2]) and climate change in the 20th and 21st centuries were then
simulated. Simulations were driven by eight climate scenarios consisting of combinations
of two general circulation models and four emission scenarios from Intergovernmental
Panel on Climate Change Special Report Emission Scenarios ranging from low emission
to fossil intensive emission. We derived possible temporal and spatial change patterns
of vegetation distribution, carbon flux, and carbon pools across China. Simulations
showed that regional changes in temperature and precipitation would cause substantial
vegetation changes in northern, northeastern, and western China. Such vegetation
dynamics, and associated mechanisms such as responses of NPP and heterotrophic
respiration to rising [CO2] and regional climate change, would lead to corresponding
changes in temporal and spatial patterns of carbon flux and carbon pools. As a result, some
regional ecosystems could switch from carbon sinks to carbon sources or vice versa by the
end of the 21st century. China’s terrestrial ecosystems have an average carbon uptake of
0.12 to 0.20 Gt C yr−1 over the period of 1981–2100; however, the rate of increase in
carbon sinks is projected to decrease after about 2040, particularly under the fossil
intensive emission scenario.
Citation: Tao, F., and Z. Zhang (2010), Dynamic responses of terrestrial ecosystems structure and function to climate change in
China, J. Geophys. Res., 115, G03003, doi:10.1029/2009JG001062.
1. Introduction
[2] Terrestrial ecosystems interact closely with climate
systems through exchange of heat, moisture, trace gases,
aerosol and momentum between the land surface and the
atmosphere [Pielke et al., 1998]. Terrestrial ecosystems and
climate influence each other on timescales ranging from
seconds to millions of years [Sellers et al., 1995]. Ecosystem
structure and function are strongly determined on timescales
of decades to centuries by climate, primarily through temperature changes and water availability [Woodward and
McKee, 1991; Henderson‐Sellers and McGuffie, 1995]. Climate change, driven by rising atmospheric concentrations of
1
Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing, China.
2
State Key Laboratory of Earth Surface Processes and Resource
Ecology, Beijing Normal University, Beijing, China.
Copyright 2010 by the American Geophysical Union.
0148‐0227/10/2009JG001062
greenhouse gases, is projected to result in substantial
changes in vegetation patterns and, consequently, carbon
fluxes and storages [Cramer et al., 2001; Schaphoff et al.,
2006; Alo and Wang, 2008; Sitch et al., 2008]. These findings raise concern about alterations in ecosystems structure
and function and associated changes in ecosystems services.
However, the potential trajectories of change, especially at
the regional scale, remain uncertain [Reid et al., 2005;
Schröter et al., 2005; Morales et al., 2007; Schaphoff et al.,
2006; Alo and Wang, 2008].
[3] A number of process‐based models, including terrestrial biogeochemical models and dynamic global vegetation
models (DGVMs), have been developed as tools to project
transient terrestrial ecosystems responses to climate change
at the continental to global scales [e.g., Foley et al., 1996;
Haxeltine and Prentice, 1996; Friend et al., 1997;
Woodward et al., 1998; Kucharik et al., 2000; Cramer et al.,
2001; Sitch et al., 2003]. Terrestrial biogeochemical models,
which simulate fluxes of carbon, water and nitrogen coupled
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within terrestrial ecosystems by assuming a constant distribution of vegetation, have indicated that global Net Ecosystem Production (NEP) of ecosystems significantly
increases in response to transient changes in atmospheric
CO2 concentration ([CO2]) and climate, but this response
declines as the CO2 fertilization effect becomes saturated
and is diminished by further changes in climate factors [e.g.,
Cao and Woodward, 1998]. Vegetation dynamics are,
however, important when assessing changes in biogeochemical fluxes and carbon storage. This is especially
important under rapid climate change where species’ bioclimatic thresholds may be exceeded, which leads to significant changes in vegetation types over large regions [e.g.,
Kirilenko and Solomon, 1998]. Therefore, projecting transient terrestrial ecosystem responses under rapid climate
change requires comprehensive models that include vegetation dynamics as well as biogeochemical processes
[Cramer et al., 2001]. Cramer et al. [2001] used six
DGVMs, forced by the Intergovernmental Panel on Climate
Change (IPCC) IS92 scenario of rising [CO2], to quantify
possible responses of ecosystems processes to rising [CO2]
and climate change. They found a progressive erosion of the
global carbon sink by climatic change after the year 2050 as
a result of increased heterotrophic respiration and reductions
in regional precipitation. Using the Lund‐Potsdam‐Jena
DGVM (LPJ‐DGVM), Schaphoff et al. [2006] investigated
the commonalities and differences in projected land biosphere carbon storage among climate change projections
derived from one emission scenario by five different general
circulation models (GCMs). They found uncertainty in
future terrestrial carbon storage because climate projections
varied greatly. The largest uncertainty was in the response of
tropical rain forests of South America and Central Africa.
Scholze et al. [2006] quantified the risks of climate‐induced
changes in key ecosystem processes during the 21st century
by forcing the LPJ‐DGVM with multiple scenarios from 16
climate models and mapping the proportions of model runs
showing forest/nonforest shifts or exceedance of natural
variability in wildfire frequency and freshwater supply. A
land carbon sink of ≈ 1 Pg C yr−1 was simulated for the late
20th century, but for global warming over 3°C, this sink
converted to a carbon source during the 21st century in 44%
scenario simulations. More recently, Alo and Wang [2008]
examined the response of global potential natural vegetation distribution, net primary production (NPP) and fire
emissions to future changes in [CO2] and climate using the
National Center for Atmospheric Research Community
Land Model’s DGVM and climate projections from eight
GCMs. They found a considerable poleward spread or shift
of temperate or boreal forests in the North Hemisphere high
latitudes and a substantial degradation of vegetation type
(e.g., increase of drought deciduous tree cover at the
expense of evergreen trees) in the tropics. Using five
DGVMs coupled to a fast ‘climate analog model’ based on
Hadley Center GCM, Sitch et al. [2008] showed that all
DGVMs simulate global sinks over the 21st century for the
four Special Report Emission Scenarios (SRES): A1FI, A2,
B1 and B2. However, for the most extreme A1FI scenario,
three out of five DGVMs simulate global sources in the final
decades of the 21st century.
[4] Responses of terrestrial ecosystems to climate change
or variability at regional or national scales have recently
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been of key concern [e.g., Pacala et al., 2001; Bachelet et
al., 2003; Cao et al., 2003; Tian et al., 2003; Schröter et
al., 2005; Morales et al., 2007; Doherty et al., 2010], both
to address uncertainties and to inform policies to mitigate
anthropogenic CO2 emissions. With increasing scientific
and political interest in the global carbon cycle at regional
levels, there is strong impetus to better understand the carbon balance of China [Schimel et al., 2001; IPCC, 2007;
Houghton, 2007; Piao et al., 2009]. However, the influence
of increased [CO2] and climate change on the carbon cycle
of terrestrial ecosystems in China is still unclear (Mu et al.,
2008). NPP and/or carbon storage in vegetation and soils of
terrestrial ecosystems in China have been extensively
investigated by carbon density methods combined with
inventory data [Fang et al., 2001; Ni, 2001; Wang et al.,
2001; Piao et al., 2009], by equilibrium models [Peng
and Apps, 1997; Xiao et al., 1998; Gao et al., 2000; Ni et
al., 2000; Pan et al., 2000; Ni, 2001; Tian et al., 2003]
and by terrestrial biogeochemical models [Cao et al., 2003;
Mu et al., 2008]. For example, Fang et al. [2001] used an
improved estimation method of forest biomass and a 50‐year
national forest resource inventory in China to estimate
changes in the storage of living biomass between 1949 and
1998. They found that carbon storage of China’s forests
increased significantly after the late 1970s mainly due to
forest expansion and regrowth. Ni [2001] estimated the
carbon storage of terrestrial ecosystems in China using a
common carbon density method at fine (10′), median (20′)
and coarse (30′) spatial resolutions. Using an equilibrium
biogeography model, BIOME3, driven by equilibrium climate change scenarios, he also showed that climate change
would produce an increase in carbon stored by vegetation
and particularly soils. Cao et al. [2003] used a process‐
based biogeochemical model and a remote sensing‐based
production efficiency model to investigate the response of
terrestrial carbon uptake to interannual variability and [CO2]
increases in China during 1981–2000. They indicated that
China’s terrestrial ecosystems were sequestering carbon but
the capacity was undermined by ongoing climate change.
Therefore, the responses of China’s terrestrial ecosystems to
ongoing warming trends are of concern [Cao et al., 2003].
Piao et al. [2009] analyzed the current terrestrial carbon
balance of China during the 1980s and 1990s using three
different methods: biomass and soil carbon inventories
extrapolated by satellite greenness measurements, ecosystems models and atmospheric inversions. The three methods
produced similar estimates of regional sinks in the range of
0.19–0.26 Pg C yr−1.
[5] However, little is known about long‐term trends and
future projected changes of vegetation dynamics, carbon
fluxes and carbon storage in China. Our understanding of
China’s terrestrial ecosystems carbon sinks, particularly
their responses to rising [CO2] and transient climate change
on timescales of decades to centuries, can be further
improved by dynamic vegetation models that explicitly
represent the interactions of ecosystem carbon and water
exchanges with vegetation dynamics. In the present study,
the Lund‐Potsdam‐Jena dynamic global vegetation model
(LPJ‐DGVM) [Sitch et al., 2003], updated with hydrology
of Gerten et al. [2004], is calibrated and validated against
various observational and inventory data, and is forced with
eight climate scenarios to simulate the responses of China’s
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Table 1. PFT Bioclimatic Limits Used by Sitch et al. [2003]a
PFT
Tc, min
(°C)
Tc, max
(°C)
GDDmin
(°C)
Tw‐c, min
(°C)
Tw, Up‐Limit
(°C)
Tropical broad‐leaved evergreen (TrBE)
Tropical broad‐leaved raingreen (TrBR)
Temperate needle‐leaved evergreen (TeNE)
Temperate broad‐leaved evergreen (TeBE)
Temperate broad‐leaved summergreen (TeBS)
Boreal needle‐leaved evergreen (BoNE)
Boreal needle‐leaved summergreen (BoNS)
Boreal broad‐leaved summergreen (BoBS)
Temperate herbaceous (TeH)
Tropical herbaceous (TrH)
15.5
15.5
−2.0
3.0
−17.0
−32.5 [−28.0]
‐
‐
‐
15.5 [15.0]
‐
‐
22.0
18.8
15.5
−2.0 [−15.0]
−2.0 [−24.0]
−2.0 [−15.0]
15.5
‐
‐
‐
900
1200
1200
600
350
350
‐
‐
‐
‐
‐
‐
‐
‐
43
‐
‐
‐
‐
‐
‐
‐
‐
23.0 [21.0]
23.0 [21.0]
23.0 [24.0]
‐
‐
a
The parameter values in italics are replaced by the values in square brackets in this study. Tc, min = minimum coldest‐month temperature for survival;
Tc, max = maximum coldest‐month temperature for establishment; GDDmin = minimum degree‐day sum (2°C base for BoNS and 5°C base for all other
PFTs) for establishment; Tw‐c, min = minimum warmest minus coldest month temperature range; Tw, up‐limit = upper limit of temperature of the warmest
month.
terrestrial ecosystems structure and function to
changes in [CO2] and climate. We investigate the
responses of the structure, the carbon fluxes and
China’s terrestrial ecosystems to rising [CO2] and
climate change in the 20th and 21st centuries.
transient
dynamic
pools of
transient
2. Methods and Data
2.1. LPJ‐DGVM
[6] The LPJ‐DGVM [Sitch et al., 2003; Gerten et al.,
2004] simulates photosynthesis, plant distribution and
competition of 10 plant functional types (PFTs), including
tropical broad‐leaved evergreen (TrBE), tropical broad‐
leaved raingreen (TrBR), temperate needle‐leaved evergreen
(TeNE), temperate broad‐leaved evergreen (TeBE), temperate broad‐leaved summergreen (TeBS), boreal needle‐leaved
evergreen (BoNE), boreal needle‐leaved summergreen
(BoNS), boreal broad‐leaved summergreen (BoBS), temperate herbaceous (TeH) and tropical herbaceous (TrH)
(Table 1). Plant distribution is based on bioclimatic limits
for plant growth and regeneration. PFT‐specific parameters
govern competition for light and water among the PFTs.
Dispersal processes are not explicitly modeled, and an
individual PFT can invade new regions if its bioclimatic
limits and competition with other PFTs allow establishment.
Carbon is stored in seven PFT‐associated pools, representing leaves, sapwood, heartwood, fine roots, a fast and a slow
decomposing aboveground litter pool and a below‐ground
litter pool. Two soil carbon pools for each grid cell receive
input from the litter pools of all PFTs present. Photosynthesis is a function of absorbed photosynthetically active
radiation, temperature, [CO2], day length, canopy conductance and biochemical pathway (C3, C4). A form of the
Farquhar scheme [Farquhar et al., 1980; Collatz et al.,
1992], with leaf‐level optimized nitrogen allocation
[Haxeltine and Prentice, 1996] and an empirical convective
boundary layer parameterization [Monteith, 1995], is used to
couple carbon and water cycles. The carbon flux by fire
disturbance is calculated based on litter density, litter
moisture content, a fuel‐load threshold and PFT‐specific fire
resistances [Thonicke et al., 2001]. Decomposition rates of
soil and litter organic carbon depend on soil temperature
[Lloyd and Taylor, 1994] and moisture [Foley, 1995]. Soil
temperature is calculated from surface air temperature and
follows the surface temperature annual cycle with a damped
oscillation and a temporal lag [Sitch et al., 2003]. Temperature diffusivity depends on soil texture and soil moisture.
The model has been extensively used to study past, present
and future terrestrial ecosystems dynamics and biochemical
and biophysical interactions between ecosystems and the
atmosphere at global, regional and site scales [e.g., Cramer
et al., 2001; Bachelet et al., 2003; Sitch et al., 2003;
Schröter et al., 2005; Morales et al., 2007; Scholze et al.,
2006; Sitch et al., 2008; Doherty et al., 2010].
2.2. Model Input Data and Modeling Protocol
2.2.1. Model Input Data
[7] Model input parameters are monthly mean air temperature, total precipitation, the number of wet days and
percentage of full sunshine, annual [CO2] and soil texture
class. Monthly fields of mean temperature, precipitation,
cloud cover and the number of wet days on a 0.5° × 0.5°
grid from 1901 to 2000 were taken from the Climatic
Research Unit’s (CRU) data set called CRU TS 2.1
[Mitchell and Jones, 2005]. The historical global [CO2] data
set was from the Carbon Cycle Model Linkage Project
[Kicklighter et al., 1999; McGuire et al., 2001]. The data set
was extended to 2000 according to atmospheric observations [Keeling and Whorf, 2005]. Soil texture data were
based on the FAO soil data set [Zobler, 1986; Food and
Agriculture Organization, 1991].
[8] Eight future climate scenarios consisting of the combinations of two GCMs and four IPCC SRES emission
scenarios (A1FI, A2, B1, B2) were used in this study. The
two GCMs were the UK Hadley Center for Climate Prediction and Research UKMO‐HadCM3 (hereafter referred
to as HadCM3) and the Canadian Centre for Climate
Modeling and Analysis CCCma‐CGCM2 (hereafter referred
to as CGCM2). CGCM2 predicts warmer and drier conditions, while HadCM3 predicts wetter conditions over China.
The scenarios on monthly fields of mean temperature, precipitation and cloud cover on a 0.5° × 0.5° grid from 2001 to
2100 were taken from the CRU, University of East Anglia
[Mitchell et al., 2004]. The monthly wet days in the 21st
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Figure 1. Time series of (a) temperature, (b) precipitation
and (c) atmospheric CO2 concentration scenarios in China
based on IPCC SRES emission scenarios A1FI, A2, B1
and B2 and HadCM3 and CGCM2 GCMs. All values represent the proportional changes relative to the averages during
1961–1990.
century were assumed to replicate the observed values in the
20th century because this variable is not available in future
climate change scenarios. The outputs of GCMs were
interpolated to 0.5° resolution using a Delaunay triangulation of a planar set of points. The climate scenarios of the
21st century replicate observed month‐to‐month, inter‐
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annual and multidecadal climate variability of the detrended
20th century climate. The full method for its construction is
described by Mitchell et al. [2004]. The data set of global
[CO2] scenario under A1FI, A2, B1 and B2 emission scenarios, respectively, was taken from the IPCC third assessment report [IPCC, 2001]. For each year, the average of the
reference estimate from the ISAM model and the reference
estimate from the Bern‐CC model was used. The climate
and CO2 scenarios used in this study are shown in Figure 1.
According to HadCM3 GCM, compared with the baseline
level (1961–1990), annual mean temperature across China is
projected to increase by a factor of 1.58 (B1) to 2.12 (A1FI)
(Figure 1a), and total annual precipitation across China is
projected to increase by a factor of 1.30 (B1) to 1.51 (A1FI)
by 2100 (Figure 1b). In contrast, according to CGCM2
GCM, mean annual temperature across China is projected to
increase by a factor of 1.58 (B1) to 2.23 (A1FI) (Figure 1a),
and total annual precipitation across China is projected to
increase by a factor of 1.19 (B1) to 1.30 (A1FI) by 2100
(Figure 1b). Compared with the baseline level (1961–1990),
[CO2] is projected to increase by a factor of 1.64 (B1) to
2.90 (A1FI) by 2100 (Figure 1c). Spatially, compared with
the period from 1961 to 1990, using HadCM3 GCM driven
by the A1FI emission scenario, precipitation is projected to
increase during 2071–2100 in most of China, with the
highest increase occurring in the south. Annual mean temperature is projected to increase from 3.2 to 7.3°C across
China, with the lowest increase occurring in the southeast
and the highest increases in the northeast and northwest. In
contrast, using CGCM2 GCM driven by the A1FI scenario,
precipitation is projected to decrease in southern China, and
mean annual temperature is projected to increase from 2.9 to
9.1°C across China, with the lowest increase in the southwest and the highest increase in the northwest.
2.2.2. Modeling Protocol
[9] The simulation for each grid cell began from “bare
ground” and was “spun up” (under non‐transient climate)
for 1000 model years to develop equilibrium carbon pools
and vegetation cover following the standard simulation by
Sitch et al. [2003]. The model was then driven with transient
historical climate and [CO2] from 1901 to 2000. After
comparing simulated spatial distribution of PFTs in 1980
with Hou et al.’s [1982] potential vegetation map of China
(Scale: 1: 4, 000, 000), we revised the PFTs bioclimatic
limits parameters and again conducted the “spin up” for
1000 model years and the simulations from 1901 to 2000.
The procedures were repeated until LPJ‐DGVM‐simulated
PFTs spatial distribution in 1980 was in general agreement
with Hou et al.’s [1982] potential vegetation map of China.
[10] Finally, the calibrated model was applied to simulate
the dynamic responses of the terrestrial ecosystems to climate change and rising [CO2] across China from 1901 to
2100 at the grid cell resolution of 0.5° by 0.5°. It was driven
with transient historical climate (1901–2000) and each of
the eight future climate scenarios (2001–2100) from 1901 to
2100 together with transient historical [CO2] (1901–2000)
and future [CO2] under the corresponding emission scenario
(2001–2100). We further investigated the temporal and
spatial patterns and mechanisms in changes of PFT distribution and foliar projective cover, carbon pools and carbon
fluxes under eight different climate scenarios.
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2.3. NPP and LAI Data Set for Model Validation
[11] NPP data from Chinese forests and grassland, which
were compiled by the Global Primary Production Data Initiative (GPPDI) at the point scale [Olson et al., 2001], were
used to validate model estimates of NPP for Chinese forests
and grassland. In the data set, point data for biomass and NPP
of major Chinese forest types were compiled based on
inventories of the Forestry Ministry of China between 1989
and 1993. Additional data were obtained from published
forest reports, as well as from more than 60 Chinese journals
and some unpublished literature up to 1994 [Luo, 1996; Ni et
al., 2001]. The data covered six major forest biomes,
including 690 study sites from 17 forest types across China.
The data set included latitude, longitude, elevation, leaf area
index (LAI), total biomass and total NPP for each site. The
data on grassland NPP was based on the work by Xiao and
Ojima [1996, 1999]. LAI in the data set for major forest
types in China, which was constructed from 1266 site‐
specific plots with closed‐mature ages stand [Luo, 1996],
was used to validate the LAI estimated by the model. In
addition, a global satellite‐derived LAI product based on
PATHFINDER Version 3 AVHRR NDVI, half‐degree
monthly fields from 1982 to 2000 [Myneni and Song, 2001],
was also used to validate the model‐estimated LAI. The
product was derived from an improved version of the
AVHRR Pathfinder Land NDVI Data set using model‐
derived LAI versus NDVI relations [Myneni et al., 1997].
3. Results
3.1. Calibration and Validation of LPJ‐DGVM for
China’s Terrestrial Ecosystems
[12] Model validation is difficult for global or continental
models simulating potential vegetation in the absence of
limiting nutrients, pests and pathogens and most of all, any
human impacts [Bachelet et al., 2003]. In this study, we
first calibrated the bioclimatic limits parameters of PFTs in
LPJ‐DGVM against Hou et al.’s [1982] potential vegetation map of China (Scale: 1: 4, 000, 000). Then new
parameter values were used for bioclimatic limits, and the
model was validated against satellite, in situ and inventory
data of China’s terrestrial ecosystems for NPP, LAI and
carbon storage simulations.
3.1.1. Potential Vegetation Distribution
[13] LPJ‐DGVM‐simulated distribution of PFTs with
maximum fractional coverage in 1980 is compared numerically with Hou et al.’s [1982] potential vegetation map of
China (Scale: 1: 4, 000, 000) (Figure 2) using kappa statistics. The 110 vegetation types in Hou et al.’s vegetation
map were grouped into 10 PFTs defined by LPJ‐DGVM.
According to Hou et al.’s potential vegetation map of China,
few grids have TrBR and TrH as dominant fractional coverage in China. Comparing the simulated patterns of dominant PFTs in 1980 (not shown) with the potential
vegetation map of China [Hou et al., 1982] using the original set of PFT bioclimatic limits parameters for the global
scale [Sitch et al., 2003], we found that LPJ‐DGVM did
not capture the general patterns of PFTs well. Particularly,
the BoBS in northeastern China (mainly mixed forests) and
the TeH in northern and northwestern China (mainly
shrubby, sub‐shrubby and semi‐arboreal deserts) were mis-
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represented by BoNE. We believe that parameters related to
vegetation growth and competition in LPJ‐DGVM, which
were mostly based on European forests and especially boreal
forests, should be calibrated for regional use.
[14] We therefore revised the bioclimatic limits parameters, especially for BoNE, BoNS and BoBS, based on
China’s vegetation distribution and regional climate statistics. We again conducted the “spin up” for 1000 model years
and simulations from 1901 to 2000. The procedures were
repeated until LPJ‐DGVM‐simulated PFTs spatial distribution in 1980 was in general agreement with Hou et al.’s
[1982] potential vegetation map of China. Using the new
parameter values for bioclimatic limits (Table 1), the model
generally captured the broad patterns of distribution of
dominant vegetation types across China reasonably well,
particularly for BoNS in northeastern China, TeBE in
southern China, BoBS in southwestern China, barren or
desert vegetation in northwestern China and TeBS in
northern China (Figure 2). Their kappa statistics are 0.78,
0.72, 0.54, 0.48 and 0.42, with agreement being 0.99, 0.93,
0.93, 0.82 and 0.82, respectively (Table 2). The greatest lack
of agreement between the model simulation and Hou et al.’s
map occurs in BoNE and TeNE. The dominance of TeH in
northern and northwestern China in Hou et al.’s map is
misrepresented by BoNE using LPJ‐DGVM. The dominance of TeBS in eastern China in Hou et al.’s map is
misrepresented by TeNE using LPJ‐DGVM. Note that the
lack of dominance does not mean a complete absence. In
fact, some coverage of TeBS in eastern China in Hou et al.’s
map is simulated by LPJ‐DGVM.
3.1.2. NPP
[15] Model estimates of NPP during the period 1985–
1994 by PFTs are compared with GPPDI point NPP data.
The comparison shows that the model underestimates NPP
for TrBR, TeBE and TeBS and overestimates it for BoNS
and BoBS (Figure 3). Compared with estimates by other
models [e.g., Cramer et al., 1999; Cao et al., 2003], LPJ‐
DGVM simulates somewhat higher NPP. Nevertheless, the
range of uncertainty in NPP is of a similar magnitude to
ranges observed between different DGVMs and other
model‐based estimates of global NPP [Cramer et al., 1999,
2001; Sitch et al., 2003; Zaehle et al., 2005]. The uncertainty in NPP simulation is due to the uncertainty in parameters controlling assimilation rate, plant respiration and
plant water balance [Zaehle et al., 2005]. Despite considerable uncertainty in simulated global NPP, present‐day
global land‐atmosphere carbon fluxes are relatively well
constrained [Zaehle et al., 2005].
3.1.3. LAI
[16] Model estimates of LAI are compared with satellite‐
derived LAI to validate its spatial and temporal patterns.
PFTs fractional coverage weighted mean LAI is calculated
for each grid cell and compared with satellite‐derived mean
LAI during summer (including June, July and August) from
1982 to 2000, which is calculated from the LAI product
based on PATHFINDER Version 3 AVHRR NDVI, half‐
degree monthly fields [Myneni and Song, 2001]. The
comparison shows that the model captures the spatial and
temporal patterns of the satellite‐derived LAI reasonably
well (Figures 4 and 5). Spatially, both estimates show that
relatively higher LAI was located in areas around Sichuan
Basin, southwestern China, northeastern China and portions
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Figure 2. (a) Potential vegetation distribution mapped by Hou et al. [1982] , simulated by LPJ‐DGVM
(b) in 1980, in 2080 according to (c) HadCM3 GCM A1FI and (d) B1 emission scenarios and in 2080
according to (e) CGCM2 GCM A1FI and (f) B1 emission scenarios. Full names of PFTs are as in Table 1.
of southeastern China (Figure 4); relatively lower LAI was
located in the North China Plain and the Northeast China
Plain. The spatial patterns are also generally consistent with
the LAI patterns of major forest types in China based on the
1266 site‐specific plots with closed‐mature ages stand [Luo,
1996] (Figure 6). Temporally, the model estimates of mean
LAI across China’s terrestrial ecosystems correlated significantly (r = 0.573, p = 0.010) with satellite‐derived mean
LAI during summers from 1982 to 2000 (Figure 5). Comparing the spatial patterns of LAI among years (Figure 4), we
also find that model estimates of LAI can generally capture
the dynamic changes of LAI derived from satellites. In terms
of the absolute value of LAI, model estimates of LAI are
generally higher than satellite‐derived LAI but are lower
than plot‐level observations (Figure 6). LAI values based on
AVHRR NDVI at large spatial resolutions are generally
lower than actual values for a number of reasons [Ganguly,
2008], including scaling problems in which LAI values
tend to be lower at large spatial scales [Tian et al., 2002], the
saturation problem of the main algorithm, particularly for
forest stands with large LAI values [Wang et al., 2005], and
problems with land cover heterogeneity [Tian et al., 2002].
In addition, the model estimates do not account for land use,
and the plot‐level observations are from plots with closed‐
mature ages stand [Luo, 1996], both tend to be higher than
actual LAI at 0.5° resolution.
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Table 2. PFTs Defined by LPG‐DGVM, Kappa Statistics and
Agreement for a Comparison of Simulated Vegetation Patterns
With Hou et al.’s [1982] Potential Vegetation Map of China
PFT
Kappa Statistics
Agreement
Tropical broad‐leaved evergreen
(TrBE)
Tropical broad‐leaved raingreen
(TrBR)
Temperate needle‐leaved evergreen
(TeNE)
Temperate broad‐leaved evergreen
(TeBE)
Temperate broad‐leaved
summergreen (TeBS)
Boreal needle‐leaved evergreen
(BoNE)
Boreal needle‐leaved summergreen
(BoNS)
Boreal broad‐leaved summergreen
(BoBS)
Temperate herbaceous (TeH)
Tropical herbaceous (TrH)
Barren or desert
0.11
0.99
0.00
1.00
0.14
0.88
0.72
0.93
0.42
0.82
0.04
0.92
0.78
0.99
0.54
0.93
0.35
‐
0.48
0.77
‐
0.82
3.1.4. Carbon Pools
[17] Compared with other studies, LPJ‐DGVM‐simulated
carbon storages in vegetation and soils of terrestrial ecosystems of China are within ranges of other estimates (Table 3).
They agree particularly well with estimates by Ni [2001]
using the carbon density method, which were based on estimates by Olson et al. [1983], Zinke et al. [1984] and Prentice
et al. [1993] together with potential vegetation maps.
3.2. Changes in Potential Vegetation Distribution
[18] Changes in vegetation distribution and structure
would occur in response to climate warming and regional
precipitation change (Figure 2). For example, according to
both GCMs by 2080 under the A1FI scenario, LPJ‐DGVM
simulates the expansion of grassland to present forests areas
in northern and northeastern China and to present barren
areas in western China and the Tibetan Plateau. It also
simulates the expansion of TeBS to northern and northeastern China. However, BoBS and BoNS in northeastern
China are projected to shrink substantially due to rising
temperatures. In contrast, TeBE and TeNE in southern and
southeastern China are projected to be relatively stable.
[19] At high altitude regions of the Tibetan Plateau and
western China, with a cold and dry climate, vegetation
would colonize present barren areas rendered accessible by
climatic amelioration, while trees would advance onto
alpine grassland or tundra. As a result, barren areas in
western China and the Tibetan Plateau would be greatly
reduced. Under the B1 scenario by both GCMs, we also find
patterns in shifting vegetation similar to those under the
A1FI scenario, although less extensive.
3.3. Changes in PFTs Fractional Coverage
[20] PFTs fractional coverage within a grid cell is represented by foliar projective cover. As an example, Figure 7
depicts the geographic distribution of changes in fractional
coverage of major PFTs in China from 1990 to 2050 and
2080 under the HadCM3 B1 scenario. Generally, large
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magnitudes of fractional coverage changes tend to coincide
with changes in the dominant vegetation type (Figure 2). For
example, the simulations show large increases in fractional
coverage of TeH in the Tibetan Plateau and northeastern
China. TeBS fractional coverage increases in North China
Plain and expands to northern and northeastern China. In
contrast, the fractional coverage of BoNE, BoNS and BoBS
decreases notably in northeastern China. TeNE and TeBE
fractional coverage in southern China is relatively stable
because the upper limit of temperature for these PFTs was
not set. Compared with the geographic shifts in dominant
vegetation types, more widespread changes in fractional
coverage of the different vegetation types occur (Figure 7)
as some changes in fractional coverage are not enough to
change the dominant vegetation type in large areas.
[21] In previous studies at the global scale using the
National Center for Atmospheric Research Community
Land Model’s DGVM and climate projections from eight
GCMs, Alo and Wang [2008] showed that the coverage of
deciduous trees could decrease in portions of eastern China
in almost all scenarios, but the magnitude is not large
enough to cause changes in dominant vegetation types.
Scholze et al. [2006] showed a high risk of forest loss in
eastern China, with forest extending into Arctic and semiarid savannas. Sitch et al. [2008] showed that tree coverage
would increase substantially in the Tibetan Plateau at the
expense of herbaceous coverage. These results at the global
scale are, however, not observed in our study (Figure 7).
Our results do support the findings of Schaphoff et al.
[2006] that the improved conditions make possible the
growth of herbaceous vegetation, while tree establishment
remains difficult in the Tibetan Plateau.
3.4. Changes in Carbon Fluxes
[22] Simulations are shown for historical and projected
changes in terrestrial NPP, heterotrophic respiration (RH),
NEP and carbon stock under the eight climate scenarios
(Figure 8). Model estimates of terrestrial NPP in China have
increased since the 1920s. Under all the eight climate scenarios, terrestrial NPP in China is projected to increase and
reach a peak around 2090 and decline thereafter likely
because of drought stress (Figure 8). Increases in NPP since
Figure 3. Comparisons between observed and simulated
NPP by PFT.
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Figure 4. Comparisons of LAI simulated by LPJ‐DGVM and derived from PATHFINDER Version 3
AVHRR NDVI during summer in 1982, 1990 and 2000, as well as the average during 1982–2000, across
China’s terrestrial ecosystems.
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Figure 5. Time series of mean LAI across China’s terrestrial ecosystems simulated by LPJ‐DGVM and derived from
PATHFINDER Version 3 AVHRR NDVI during summer
from 1982 to 2000.
the 1920s were primarily the result of fertilization effects of
increased CO2 levels and the physiological and phenological
effects of temperature increase [Ainsworth and Long, 2005;
Piao et al., 2005]. In addition, an increase in precipitation
also contributed to the increase in NPP, particularly in arid
and semi‐arid regions of China [Cao et al., 2003]. A1FI and
A2 scenarios, however, produce larger changes in NPP than
do B1 and B2 scenarios while having a similar trend. This is
because A1FI and A2 scenarios will result in higher atmospheric CO2 levels and greater climate changes (Figure 1).
Spatially, the largest increase in NPP is located in southwestern and northeastern China, generally reflecting the
spatial patterns of increases in temperature and precipitation
during 2071–2100 (Figure 9). Our study shows that NPP in
northwestern China will increase only slightly (Figure 9),
which is consistent with results of Alo and Wang [2008].
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[23] Model estimates of RH showed an increasing trend
during the 20th century that will continue to increase
through the 21st century with climate warming under all
eight climate scenarios (Figure 8). RH depends directly on
temperature and the amount of litter, which is controlled by
NPP. Patterns of temporal and spatial changes in RH follow
changes in NPP (Figures 8 and 9) but with an approximate
10‐year lag because of carbon turnover. RH is also affected
by soil moisture. Again, A1FI and A2 scenarios produce
larger increases in RH than do B1 and B2 scenarios.
[24] NEP, defined as the difference between NPP and RH
(i.e., NEP = NPP − RH), represents the net carbon flux from
the atmosphere to ecosystems. Model estimates of NEP in
China have shown a general increasing trend since about
1920. However, under all eight climate scenarios, the rates
of increases are projected to decrease, and NEP will start to
decline after reaching an upper limit around 2040 (Figure 8).
The result suggests a carbon sink ‘saturation’ for terrestrial
ecosystems in response to rising [CO2] and climatic change.
Terrestrial ecosystems in some regions of China would
switch from sinks to sources of carbon after around 2040. A
combination of mechanisms, such as saturation of atmospheric CO2 fertilization effects, increasing RH in response
to climate warming and drought‐induced NPP depression
[Cramer et al., 2001], may be acting to bring about these
changes. Changes in total NEP of China are quite consistent
among the eight climate scenarios.
[25] Over the period of 1981–2100, the modeled carbon
budget of China’s terrestrial ecosystems ranged from a sink
of 14.78 (14.10) Gt C under the A1FI emission scenario to
24.12 (15.83) Gt C under the B1 scenario by CGCM2
(HadCM3), representing an average accumulative carbon
sink of 0.123 (0.118) to 0.20 (0.13) Gt C yr−1, respectively
(Figure 8). The two climate scenarios respectively represent
the lower and upper limits of projected changes in [CO2]
and temperature data used in this study. However, the rate of
increasing carbon sink is projected to decrease after around
2040 under all climate scenarios, particularly those driven
by the A1FI emission scenario.
Figure 6. LAI of major forest types in China from the 1266 site‐specific plots with closed‐mature stand
ages based on Luo [1996].
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Table 3. Comparisons of Simulated Carbon Storage (Gt) in Terrestrial Ecosystems of China to Other Estimates in Chinaa
Model or Vegetation Classification
Vegetation C
Soil C
Baseline vegetation
Baseline biome
BIOME3 model
Osnabrück model
Data‐based estimates
Terrestrial ecosystems
LPJ‐DGVM model
35.23
34.31–76.17 (53.96)b
35.20–79.88 (57.74)b
57.9
47.1
6.1c
55.61d
119.76
101.40–134.26 (117.84)b
102.09–134.24 (118.54)b
100.0
101.1
185.7
104.01d
Spatial Resolution
10′
10′
0.5°
0.5°
×
×
×
×
10′
10′
0.5°
0.5°
0.5° × 0.5°
Source
Ni [2001]
Ni [2001]
Ni [2001]
Peng and Apps [1997]
Peng and Apps [1997]
Fang et al. [1996a, 1996b]
This study
a
Revised from Ni [2001] .
Values are low–high (median).
c
With extensive and sparse agricultural vegetation; others only with natural vegetation. Based on field estimates from various ecosystem studies; others
based on estimates from Olson et al. [1983], Post et al. [1982], Prentice et al. [1993] , and Zinke et al. [1984] combined with potential vegetation maps
from biogeographical model or Hou et al.’s [1982] potential vegetation map.
d
Average from 1981 to 1990.
b
[26] The latitudinal distribution of NEP shows that the
largest carbon sink was located in the belt between latitude
32° and 46° during the period of 1961–1990 (Figure 10).
NEP is projected to increase substantially in the belt
between latitude 27.5° and 39° during 2011–2040, 2041–
2070 and 2071–2100 under most of the climate scenarios
and by both GCMs, suggesting the middle latitude regions
have the potential to become carbon sinks (Figure 10). In
contrast, NEP is projected to decrease substantially in the
regions above a latitude around 39°, suggesting that high
latitude regions would become potential sources of carbon.
Compared with middle and low latitude regions, high latitude
regions are projected to become warmer, leading to larger
increases in RH. Alternatively, grasslands are projected to
replace forests in some areas of high latitude regions (for
example in northern and northeastern China), which would
reduce the carbon sequestration potential of ecosystems there.
[27] Spatial patterns of NEP show that terrestrial ecosystems across China were mainly carbon sinks during 1961–
1990 (Figure 11), except the Tibetan Plateau, which exhibited
an approximate equilibrium between carbon uptake and
release to the atmosphere, or a small amount of carbon
release. The strongest carbon sink was located in northern
China with the TeBS PFT. During 2071–2100, under the
A1FI emission scenario, large areas in northeastern and
southwestern China are projected to switch from carbon
sinks to sources according to both CGCM2 and HadCM3
GCMs. In contrast, large areas in western China and the
Tibetan Plateau are projected to switch from sources to sinks
of carbon or to increasing sink strength (Figure 11). Vegetation dynamics in the region mentioned above could induce
a transient accumulation of carbon. In addition, the relative
changes in NPP and RH in response to rising [CO2] and
regional temperature and precipitation changes affect NEP
dynamics. The same change patterns are projected under the
B1 emission scenario by both GCMs, although moderately.
Note that here we show the mean patterns of NEP during a
30‐year period. The spatial patterns of NEP are, however,
subject to large interannual variability.
3.5. Changes in Carbon Pools
[28] Model estimates of China’s vegetation carbon storage
had decreasing trends from the beginning of the 20th century to around 1970 (Figure 12). They have increased
sharply since the 1970s, coinciding with rising [CO2] and
climate warming. The increasing trend is projected to continue until around 2040, coinciding with the transition in
NEP changes. After that, all climate scenarios but the most
modest produce largely fluctuating vegetation carbon
storages, mainly because of ‘saturation’ of carbon sinks in
ecosystems, regional drought and vegetation dynamics resulting from climate change. Spatial patterns show that
vegetation carbon storage will increase most in the North
China Plain during 2071–2100 due to increasing precipitation, vegetation expansion and an increase in vegetation
fractional coverage. In contrast, vegetation carbon storage is
projected to decrease most in northeastern China due to
forest dieback (Figures 7 and 9).
[29] Model estimates of soil carbon in China have
increased steadily since around 1930 (Figure 12). This
increasing trend is projected to accelerate in the 21st century
in most climate scenarios except those driven by the A1FI
emission scenario. Under the A1FI scenario, soil carbon is
projected to peak around 2070 and decline thereafter according to both GCMs (Figure 12), suggesting that higher
temperatures should undermine soil carbon. Spatial patterns
show that soil carbon storage will increase most in the
Tibetan Plateau and portions of western China during 2071–
2100 under the HadCM3 B1 scenario (Figure 9), which is
consistent with the pattern of change in NPP. However, it is
projected to decrease somewhat in southeastern China.
[30] Model estimates of total carbon storage in China’s
terrestrial ecosystems have increased since the 1930s and
will increase under all climate scenarios. However, the rate
of increase would decrease after about 2040 under two climate scenarios driven by the A1FI (Figure 12). The total
carbon storage was 158.9 Gt C by 1980, and by 2100, it is
projected to range from 173.7 (173.0) Gt C under the A1FI
emission scenario to 183.2 (174.6) Gt C under the B1 scenario by CGCM2 (HadCM3). Spatial patterns show that the
total carbon storage will increase most in the Tibetan Plateau
and portions of northern China during 2071–2100 under the
HadCM3 B1 scenario. In contrast, it is projected to decrease
most in northeastern China (Figure 9).
[31] Model estimates of carbon lost by fire in China
showed an increasing trend in the 20th century but with
substantial interannual and decadal variations, which is
consistent with the investigation by Lü et al. [2006]. In the
LPJ‐DGVM model, the occurrence of fire is taken to be
dependent on the amount of dry combustible material and
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Figure 7. Geographic distribution of changes in fractional coverage of major PFTs in China from 1990
to 2050 and 2080, simulated by LPJ‐DGVM under the HadCM3 B1 scenario.
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Figure 8. Model estimates of NPP, RH, NEP and cumulative change in carbon stocks in China’s terrestrial ecosystems in the 20th and 21st centuries.
litter moisture. It is projected to increase in the 21st century
using CGCM2, which is a warmer and drier GCM. In
contrast, the occurrence of fire is projected to decrease in the
21st century using HadCM3, a wetter GCM (Figure 12).
Spatial patterns show that carbon lost by fire will increase
most in portions of western China during 2071–2100 under
the HadCM3 B1 scenario. In contrast, it is projected to
decrease most in portions of northeastern China (Figure 9).
[32] Consistent with our results, a previous study at the
global scale [Schaphoff et al., 2006] shows that the Tibetan
Plateau is characterized by an increase in soil carbon storage
mainly from increased litter fall due to improved growth
conditions for herbaceous vegetation, while tree establishment remains slow. However, using LPJ‐DGVM, Sitch et
al. [2008] showed that vegetation carbon stocks could
increase substantially in the Tibetan Plateau due to a large
increase in tree coverage. Our study also shows that soil
carbon stocks could decrease somewhat in southeastern
China during 2071–2100, which is consistent with results of
Sitch et al. [2008].
3.6. Response Mechanisms of NEP in Subregions
Across China
[33] It has been well established that rising [CO2] and
climate change influence NEP in various ways. CO2 fertilization generally enhances leaf photosynthesis and, therefore, gross primary production (GPP). Increased temperature
extends the growing period and increases photosynthesis
rates, both allowing an increase in annual GPP. Conversely,
high temperatures may increase autotrophic and heterotrophic respiration and consequently decrease NEP. Changes in
rainfall amount and distribution could increase both drought
and evapotranspiration in some regions, leading to decreases
in photosynthesis and soil respiration. Climate change and
variability could also cause changes in vegetation dynamics
and consequently in NPP, RH and NEP. NEP dynamics
depend on these processes and others, which could vary
across time and space depending on climate and ecosystem
properties. To quantify the changes in terrestrial carbon
sinks, an accurate description of NEP dynamics is required
at the global or regional scale. It is difficult, however, to
analyze these complex interactions directly at such scales
[Davi et al., 2006]. To gain further insight into the possible
response mechanisms in different subregions, we plotted
simulated time series of NPP, RH, NEP and a simplified
drought index at four representative grids across China
(Figure 13). The simplified drought index is calculated as
the differences between precipitation (P) and water loss
through evapotranspiration (E) and runoff (R), roughly representing the amount of water remaining in the soil in a
year. The four grids include: a grid centered on longitude
123.25 and latitude 49.75 in northeastern China (NE grid
hereafter), which is projected to transition from a carbon
sink to a source of carbon; a grid centered on longitude
90.75 and latitude 33.75 in the Tibetan Plateau (TP grid
hereafter), which is projected to transition from barren
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Figure 9. Spatial patterns of average NPP, RH, carbon lost by fire (CFire), vegetation carbon storage (CVeg), soil carbon
storage (CSoil) and total carbon storage (CTotal) across China’s terrestrial ecosystems during 1961–1990 and their changes
during 2071–2100, simulated by LPJ‐DGVM under the HadCM3 B1 scenario.
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Figure 10. Latitudinal distributions of China’s terrestrial NEP during the periods of 1961–1990, 2020s
(2011–2040), 2050s (2041–2070) and 2080s (2071–2100) by (top) HadCM3 and (bottom) CGCM2
GCMs. All values represent those for lands in 0.5° latitudinal bands.
ground to a carbon sink; a grid centered on longitude 106.25
and latitude 29.25 in southwestern China (SW gird hereafter),
which is projected to transition from a carbon sink to a carbon
source; and a grid centered on longitude 115.25 and latitude
25.25 in southeastern China (SE grid hereafter), which is
projected to transition from a carbon sink to a weak source of
carbon. Locations of the grids are shown in Figure 11a.
Specific mechanisms act on NEP dynamics for each grid.
For example, in the NE grid, dramatic increases in NPP, and
especially RH, are projected to occur after around 2050
when BoBS and grasslands would replace BoNS as the
dominant vegetation types as a result of climate warming
and an increase in regional precipitation. The peak in RH
after 2050 could be due to vegetation dynamics and associated changes in NPP, soil temperature and water conditions.
RH is significantly correlated with NPP (r = 0.819, n = 200,
p < 0.01) and with the drought index (r = 0.278, n = 200, p <
0.01). In the TP grid, a dramatic increase in RH, and espe-
cially NPP, is projected to occur after around 2050 when
grasslands would colonize present barren areas due to
increases in temperature and precipitation. RH is significantly correlated with NPP (r = 0.930, n = 200, p < 0.01);
however, correlations between NPP, RH and the drought
index are not significant. In the SW grid, the increasing
trend in photosynthesis is projected to level off after around
2040 due to saturation of photosynthesis to [CO2] and water
stress, and RH will be depressed by the amount of available
litter controlled by NPP and water stress. RH is significantly
correlated with NPP (r = 0.566, n = 200, p < 0.01) and the
drought index (r = 0.496, n = 200, p < 0.01). In the SE grid,
the trend of increasing photosynthesis is projected to level
off after around 2050 due to saturation of photosynthesis to
[CO2], and RH will be depressed by the amount of available
litter and water stress. RH is significantly correlated with
NPP (r = 0.400, n = 200, p < 0.01) and the drought index (r =
0.576, n = 200, p < 0.01). Therefore, the relative influences of
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Figure 11. Spatial patterns of mean NEP (g C m−2 yr−1) during (a) 1961–1990, 2071–2100 according to
(b) HadCM3 GCM A1FI and (c) B1 emission scenarios and 2071–2100 according to (d) CGCM2 GCM
A1FI and (e) B1 emission scenarios.
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Figure 12. Model estimates of vegetation carbon storage, soil carbon storage, total carbon storage and
carbon lost by fire in China’s terrestrial ecosystems in the 20th and 21st centuries.
different factors on NEP dynamics will depend on regional
climate, vegetation dynamics and other ecosystem properties
[Davi et al., 2006].
4. Discussion
4.1. Regional Responses of Vegetation Dynamics,
Carbon Flux and Pools
[34] We found that regional changes in temperature and
precipitation would cause substantial vegetation dynamics
throughout China. In northern and northeastern China,
grassland would colonize present forested areas, and areas
of BoBS and BoNS would shrink. In western China,
grassland would colonize previously barren areas, while
trees would advance onto alpine grassland or tundra. The
vegetation dynamics, and the associated mechanisms (for
example, the response of NPP and RH to regional climate
change and rising [CO2]), would substantially change the
spatial and temporal patterns of carbon flux and carbon
pools. For example, portions of northeastern and southwestern China would switch from sinks to sources of carbon. This suggests that the enhanced productivity might in
part become offset by increasing carbon losses through
respiration resulting from a combined effect of high temperature and increased precipitation. In contrast, areas in
western China would switch from sources to sinks of carbon,
or the strength of existing sinks would increase. We found
that China’s terrestrial NPP would reach an upper limit
around 2090 and decline thereafter, perhaps due to drought
stress; NEP would reach to an upper limit around 2040 and
decline thereafter, one‐decade in advance of responses of
global vegetation [Cramer et al., 2001]. We also found that
vegetation carbon stocks would fluctuate dramatically after
about 2040. Findings in the current study have some consistencies and discrepancies with previous studies at the
global scale [e.g., Cramer et al., 2001; Cao and Woodward,
1998; Scholze et al., 2006; Sitch et al., 2008] or those that
used terrestrial biogeochemical models and equilibrium
models [e.g., Peng and Apps, 1997; Xiao et al., 1998; Gao
et al., 2000; Ni et al., 2000; Pan et al., 2000; Ni, 2001; Tian
et al., 2003; Cao et al., 2003; Mu et al., 2008]. For example,
using the original set of PFTs bioclimatic limits parameters
for the global scale [Sitch et al., 2003], LPJ‐DGVM failed to
capture the general patterns of PFTs well in China. Ignoring
potential shifts in biome distribution under projected
change, simulations using the equilibrium model Biome‐
BGC [Mu et al., 2008] resulted in substantially different
patterns of carbon fluxes and storages throughout China.
[35] The dynamic responses of China’s terrestrial ecosystems to climate change differ from ecosystems in other
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Figure 13. Model estimates of NPP (g C m−2 yr−1), RH (g C m−2 yr−1), P (precipitation) ‐ E (evapotranspiration) ‐ R (runoff) (mm yr−1) and NEP (g C m−2 yr−1) for four representative grids: NE (longitude
123.25, latitude 49.75), TP (longitude 90.75, latitude 33.75), SW (longitude 106.25, latitude 29.25) and
SE (longitude 115.25, latitude 25.25), under the HadCM3 A1FI climate scenario. Locations of the grids
are shown in Figure 11a.
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regions, warranting the importance of regional studies. For
example, in Europe, studies project ecosystems to exhibit a
small net carbon sequestration until 2070 followed by a
carbon‐neutral behavior or a net release until 2100 [Morales
et al., 2007]. This ‘saturation’ signal of NEP has not been
detected in the conterminous United States throughout the
21st century, where ecosystems would alternate between
carbon sinks and sources with drought playing an important
role [Bachelet et al., 2003]. In East Africa, despite decreases
in soil carbon after 2050, seven out of nine model simulations continued to show an annual net land carbon sink in
the final decades of the 21st century because vegetation
biomass continued to increase [Doherty et al., 2010].
Regional studies and comparisons between the responses of
regional ecosystems to rising [CO2] and regional climate
change are important for an improved understanding of
underlying mechanisms.
4.2. Uncertainties of the Study
[36] After calibrating the bioclimatic limits parameters of
major PFTs, LPJ‐DGVM, as a state‐of‐the‐art DGVM,
reasonably captures the broad patterns of China’s terrestrial
ecosystems and the temporal and spatial patterns of vegetation LAI. Nevertheless, further works on parameters calibration and model validation are necessary. Alternatively,
more models with different structures should be used to
represent the uncertainties from models [Cramer et al.,
2001] and parameters [Zaehle et al., 2005].
[37] Validating the model with current conditions is no
guarantee it will work properly in the future. For example,
Free‐Air CO2 enrichment experiments have provided ample
evidence that photosynthetic capacity acclimates to elevated
[CO2] in C3 plants, and the scale of down‐regulation varies
with genetic and environmental factors [Leakey et al., 2009].
However, this issue is not considered in the current study.
Another unknown is temperature acclimation. The response
of photosynthesis to temperature is plastic, and how these
functions are represented may have an overall effect on
whether the system becomes a net carbon source or a sink.
[38] In addition, the uncertainty in climate change scenarios by GCMs could lead to considerable ranges in simulated ecosystem carbon cycle responses [Morales et al.,
2007]. LPJ‐DGVM, like most other DGVMs, assumes
that there are no limitations of nutrients such as nitrogen,
potassium and phosphorus. However, ecosystem carbon
accumulation may be constrained by nutrients, particularly
nitrogen [Hungate et al., 2003]. As a result, these models
probably exaggerate the potential of the terrestrial biosphere
to sequestrate carbon.
[39] Finally, this study deals with potential natural vegetation, which is free from human disturbance. However,
human activities, such as land change, can have major influences on ecosystem dynamics and consequently carbon
cycles [e.g., Wang et al., 2001; Bondeau et al., 2007; Piao et
al., 2009]. For example, Fang et al. [2001] found that forest
expansion and regrowth significantly increased carbon
storage in China’s forests after the late 1970s. Wang et al.
[2001] found that carbon densities in forests have a significant negative relationship with population densities in
China. Tian et al. [2003] found that land‐use change appeared to be the most important factor controlling carbon
dynamics in monsoon Asia at long time scales. Therefore,
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this study could be further improved by accounting for such
uncertainties.
5. Conclusions
[40] This study simulated the temporal and spatial responses
of vegetation dynamics, carbon flux and carbon pools across
China’s terrestrial ecosystems to rising [CO2] and climate
change in the 20th and 21st century. Our results suggest that
rising [CO2] and regional climate change would lead to
dramatic changes in regional vegetation distribution and
consequently changes in the spatial and temporal patterns of
carbon flux and carbon pools in China. Regional ecosystems
could switch from sinks to sources of carbon or vice versa
by the end of the 21st century. As a whole, temporal patterns
of NEP showed that NEP of China’s ecosystems would
reach an upper limit around 2040 and decline thereafter. Our
results show that China’s ecosystems generally have been
and will continue to sequestrate carbon by an average of
0.12 to 0.20 Gt C yr−1 over the period of 1981–2100. However, the rate of increase in the carbon sink would decrease
after about 2040, and vegetation carbon stocks would also
start to fluctuate dramatically at that time.
[41] We also identified uncertainties in the estimations of
GCMs and emission scenarios, suggesting the importance of
using a suite of emission scenarios and different GCMs
when assessing potential future changes in ecosystem
structure and function. Uncertainties in studies could stem
from several sources including land use change.
[42] The dynamic responses of ecosystems to global
change at the regional scale, as well comparisons between
the responses of certain major regions, will contribute to our
understanding of the vulnerability of regional ecosystems to
global change, as well as their relative potential to mitigate
greenhouse forcing in the long‐term. We believe that this
work presents improved understanding of transient responses of structure and function of China’s terrestrial
ecosystems to global change. The overall temporal and
spatial patterns derived in this study can be used with
confidence as a basis for policy‐making.
[43] Acknowledgments. This study was supported by the National
Key Program for Developing Basic Science (project 2009CB421105) and
the ‘Hundred Talents‘ Program from the Chinese Academy of Sciences,
China. We thank X. Wang of the Research Center for Eco‐Environmental
Sciences, Chinese Academy of Sciences, for providing digital maps of vegetation of China. We are grateful to anonymous reviewers and editors for
their insightful comments on an earlier version of this manuscript.
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