Documenting year-to-year variations in carbon storage potential in
terrestrial ecosystems is crucial for the determination of carbon dioxide
(CO2) emissions. However, the magnitude, pattern, and inner biomass
partitioning of carbon storage potential and the effect of the changes in
climate and CO2 on inner carbon stocks remain poorly quantified.
Herein, we use a spatially explicit individual-based dynamic global
vegetation model to investigate the influences of the changes in climate and
CO2 on the enhanced carbon storage potential of vegetation. The
modelling included a series of factorial simulations using the Climatic Research Unit (CRU) dataset
from 1916 to 2015. The results show that CO2 predominantly leads to a
persistent and widespread increase in light-gathering vegetation biomass
carbon stocks (LVBC) and water-gathering vegetation biomass carbon stocks
(WVBC). Climate change appears to play a secondary role in carbon storage
potential. Importantly, with the intensification of water stress, the
magnitude of the light- and water-gathering responses in vegetation carbon
stocks gradually decreases. Plants adjust carbon allocation to decrease the
ratio between LVBC and WVBC for capturing more water. Changes in the pattern
of vegetation carbon storage were linked to zonal limitations in water, which
directly weaken and indirectly regulate the response of potential
vegetation carbon stocks to a changing environment. Our findings differ from
previous modelling evaluations of vegetation that ignored inner carbon
dynamics and demonstrate that the long-term trend in increased vegetation
biomass carbon stocks is driven by CO2 fertilization and temperature
effects that are controlled by water limitations.
Introduction
As a result of the changes in climate and atmospheric carbon dioxide
(CO2), the terrestrial ecosystem carbon cycle exhibits remarkable
trends in interannual variations, which induce uncertainty in estimated
carbon budgets (Cheng et al., 2017; Erb et al., 2018; Fan et al., 2019; Keenan et al., 2016). Recent studies
assessing interannual fluctuations in terrestrial carbon sinks have shown
that the land carbon cycle is the most uncertain component of the global
carbon budget (Ahlstrom et al., 2015; Piao et al., 2020; Jung et al., 2017;
Humphrey et al., 2018; Gentine et al., 2019; Humphrey et al., 2021). These
uncertainties result from an incomplete understanding of vegetation biomass
carbon production, allocation, storage, loss, and turnover time (Bloom et
al., 2016). The extent and distribution of vegetation carbon storage is
central to our understanding of how to maintain a balanced land carbon
cycle. Changes in terrestrial vegetation carbon storage have a significant
effect on atmospheric CO2 concentrations and determine whether biomes
become a source or sink of carbon (Erb et al., 2018; Humphrey et al., 2018;
Terrer et al., 2021). Therefore, investigating the processes producing
changes in carbon storage is key to improving the accuracy of estimated
terrestrial carbon budgets and to tapping the greenhouse gas moderation
potentials of vegetation (IPCC, 2007; Saugier et al., 2001).
The atmospheric CO2 concentration is affected by the vegetation carbon
stock, while the long-term trend of vegetation carbon storage capacity is
also affected by the changes in climate and CO2. Since the beginning of
industrialization, there has been a noticeable enhancement in the plant
capacity of storing and sequestering carbon, which is needed for stabilizing
greenhouse gas concentrations and mitigating global warming (Chen et al.,
2019; Pan et al., 2011; Piao et al., 2006; Le Noë et al., 2020; Magerl et al., 2019; Bayer
et al., 2015; Harper et al., 2018). Due to the interaction between
terrestrial vegetation and a changing environment, both photosynthesis and
respiration of the vegetation also changed. To better absorb CO2 and
sunlight required for photosynthesis, vegetated zones are gradually covered
by vegetation with higher plant height and wider leaf area (Erb et al.,
2008). This change has coincided with a widespread change in other
vegetation features, including a positive increase in annual gross primary
productivity and a greening of the biosphere (Madani et al., 2020; Zhu et
al., 2016). The spatiotemporal distribution and environmental drivers in
total carbon storage potential have been well documented on the basis of
model estimates and satellite-based assessments (Erb et al., 2007, 2018; Bazilevich et al., 1971; Saugier et al., 2001; Bartholome and
Belward, 2005; Olson et al., 1983; Pan et al., 2013; Ajtay et al., 1979;
Ruesch and Gibbs, 2008; Kaplan et al., 2011; Shevliakova et al., 2009;
Prentice et al., 2011; West et al., 2010; Hurtt et al., 2011). In contrast,
the variability in inner components of carbon storage potential has not been
extensively studied. Without an accurate assessment of the dynamics of each
fraction, attribution of carbon storage potential to environmental drivers
is highly uncertain. Consequently, partitioning potential vegetation carbon
storage and revealing its inner processes are essential to accurately
comprehend the current state of carbon storage capacity and reveal the
influence of various drivers on the long-term trend of carbon storage
potential.
The change in carbon storages in vegetation inner components is not only
affected by environmental factors but also controlled by the allocation scheme
of assimilated carbon (Friedlingstein et al., 1999). Fractional dynamics of the carbon stock are widely
used as a key indicator to investigate the responses of vegetation to
environmental drivers, which also reflect the response strategies of
vegetation in environments with different water limitations (Yang et al.,
2010). In arid regions, vegetation utilizes a tolerance strategy to allocate
biomass, storing more biomass carbon in roots to resist enhanced water
stress (Chen et al., 2013). Conforming to the optimal partitioning
hypothesis, plants store more carbon in shoots and leaves in environments
where water is more available and shift more carbon to roots when water is
more limited (Yang et al., 2010; McConnaughay and Coleman, 1999). Water
availability controls both carbon allocation and storage and can potentially
transform zones characterized by a positive response to changes in climate
and CO2 to zones exhibiting a negative response. For example, global
warming positively stimulates plant productivity (Keenan et al., 2016), while
Madani et al. (2020) found that productivity showed a negative response to
temperature in tropical zones due to increasing water stress. With increased
warming, water limitations are predicted to increasingly reduce the
proportion of leaves' biomass and decrease plant photosynthesis (Ma et al.,
2021). Water limitations have a strong regulating effect on the spatial
pattern of change in vegetation carbon storage, demonstrating that the effects of
the changes in climate and CO2 on the dynamics of the plant organs are
affected by the terrestrial water gradient. Thus, it is important to
systematically investigate the distinct responses of carbon storage
potential to changes in climate and CO2 under differing conditions of
water stress.
As documented above, many studies have investigated the total changes in
zonal and global terrestrial storage of carbon, while few studies have
examined trends in the component partitioning of vegetation carbon storage.
Large gaps in our knowledge of the effects of various drivers on the
partitioning of carbon stocks in vegetation biomass remain. Meanwhile,
plants adjust their carbon allocation scheme to adapt to environmental change.
With increased warming, an increase in the magnitude of water stress may
dramatically change or even reverse the impact of these drivers on inner
components of carbon storage (Ma et al., 2021). Evaluating the response
pattern of carbon stocks to various drivers under conditions of limited
water is elemental for clearly documenting the response mechanism of
vegetation carbon storage potential.
Here, we use a spatially explicit, individual-based dynamic global vegetation
model (SEIB-DGVM), along with the component partitioning method to (1) systematically determine the long-term variability in carbon storage
potential and understand its response mechanisms and (2) estimate trends in
partitioning of potential biomass carbon stocks of vegetation biomass.
Throughout this study, the potential biomass carbon stock, biomass carbon
stored in vegetation without anthropogenic disturbance, is recognized as an
indicator of the potential of carbon storage by natural vegetation. Using a
set of factorial simulations to isolate responses to environmental change,
we analyse the contributions of multiple driving factors to the trends of
two fractions of carbon stock at large scales individually. We then
conceptualize the role of water availability through an aridity index (AI),
in which hydrological zones are subdivided by their degree of aridity. By
comparing the differences in the magnitude of response between the fractions
of light- and water-gathering carbon stocks for varying degrees of water
availability, we assess the effect of water limitations on the response
pattern of potential carbon stocks to changes in climate and CO2.
Model description, experimental design, observational data, and evaluation
metrics
In this section, we provide a list of data sources (Sect. 2.1), an overview
of the modelling concept (Sect. 2.2), the representation of biomass carbon
stock partitioning in the SEIB-DGVM (Sect. 2.3), an overview of the
experimental scheme used in the model simulations (Sect. 2.4), and an
overview about data sources and pre-processing of the observation dataset for
model evaluation (Sect. 2.5).
Forcing data
Long-term daily meteorological time series data are required to run model
simulations, including precipitation, daily range of air temperature, mean
daily air temperature, downward shortwave radiation at midday, downward
longwave radiation at midday, wind velocity, and relative humidity. These
data were obtained from the Climatic Research Unit (CRU) time series 4.00
gridded dataset (degree 0.5∘) for the period 1901–2015 (Harris
et al., 2020). Because the CRU dataset is a monthly based dataset, the
monthly meteorological data were converted into daily climatic variables by
supplementing daily climatic variability within each month using the
National Centers for Environmental Prediction (NCEP) daily climate dataset.
The NCEP data, displayed using the T62 Gaussian grid with 192 × 94
points, were interpolated into a 0.5∘ grid (which corresponds to
the CRU dataset) using a linear interpolation method. By combining the CRU
data with the interpolated NCEP dataset, we were able to directly obtain most of the driving meteorological data (details in Sato et al., 2020).
Neither the CRU nor NCEP datasets included downward shortwave and longwave
radiation at midday. Thus, daily cloudiness values in the NCEP were used to
calculate radiation values using empirical functions (Sato et al., 2007).
These data were all aggregated to a daily timescale with 0.5∘
resolution to run SEIB-DGVM.
Atmospheric CO2 concentrations were collected from Sato et al. (2020),
who provide reconstructed CO2 concentrations between 1901 and 2015.
The statistical reconstruction of global atmospheric CO2 was used in
this analysis. These reconstructions were based on present annual CO2
concentrations recorded from the Mauna Loa monitoring station. These data
assume atmospheric CO2 concentration was 284 ppm in 1750 and
statistically interpolate atmospheric CO2 concentrations to fill the
gap from 1750 to 2015.
The physical parameters of the soil used in the model include soil moisture
at the saturation point, field capacity, matrix potential, wilting point, and
albedo. These data were obtained from the Global Soil Wetness Project 2.
Overview of modelling concept in SEIB-DGVM
Model SEIB-DGVM version 3.02 (Sato et al., 2020) was employed in this study.
This is a process-based dynamic global vegetation model driven by
meteorological and soil data. It is an explicit and computationally
efficient carbon cycle model designed to simulate transient effects of
environmental change on terrestrial ecosystems and land–atmosphere
interactions. It describes three groups of processes: land-based physical
processes (e.g. hydrology, radiation, aridity), plant physiological
processes (e.g. photosynthesis, respiration, litter), and plant dynamic
processes (e.g. establishment, growth, mortality). Twelve plant functional
types (PFTs) were classified. During the simulation, a sample plot was
established at each grid cell, and then the growth, competition, and
mortality of each of the individual PFTs within each plot were modelled by
considering the specific conditions for that individual as it relates to
other individuals that surround it (Sato et al., 2007).
SEIB-DGVM treats the relationships between soil, atmosphere, and terrestrial
biomes in a consistent manner, including the fluxes of energy, water, and
carbon. Based on specified climatic conditions and soil properties,
SEIB-DGVM simulates the carbon cycle, energy balance, and hydrological
processes. SEIB-DGVM utilizes three computational time steps. (1) During the
growth phase, the metabolic procedures including photosynthesis,
respiration, and carbon allocation are executed for each individual tree
every simulation day. (2) The monthly process of tree growth including
reproduction, trunk growth, and expansion of a cross-sectional area of the
crown are executed. (3) On the last day of each year, the height of the
lowest branch increases as a result of purging crown disks, or self-pruning
of branches, at the bottom of the crown layer. The simulated unit of the
model is a 30 m × 30 m spatially explicit “virtual forest”. A grass
layer was placed under the woody layer and provides for a comprehensive,
spatially explicit quantification of terrestrial carbon sinks and sources.
The soil depth was set at 2 m and was divided into 20 layers, each with a
thickness of 0.1 m. The photosynthetic rate of a single leaf was simulated
following a Michaelis-type function (Ryan, 1991). Respiration was divided
into two types: growth respiration and maintenance respiration. Growth
respiration is defined as a construction cost for plant biosynthesis, which
is quantified by the chemical composition of each organ (Poorter, 1994).
Maintenance respiration of live plants occurs every day regardless of the
phenological phase and is controlled by the temperature and nitrate content
of each organ (Ryan, 1991). For a wide variety of plant organs, the
maintenance respiration rate is linearly related to the nitrogen content of
living tissue. The relative proportions of nitrogen in each organ for any
PFT are linearly correlated. Nitrogen deposition is not included in SEIB-DGVM.
Atmospheric CO2 was envisioned to be absorbed by photosynthesis of
woody PFTs and grass PFTs. This assimilated carbon flux was then allocated
into all the plant organs (leaf, trunk, root, and stock) where maintenance
respiration and growth respiration occur. The hydrology module treats
precipitation, canopy interception, transpiration, evaporation, meltwater,
and penetration.
Carbon stock of vegetation biomass partitioningParameterization of daily allocation
Flexible allocation schemes about resources and biomass are set up in the
framework of the SEIB-DGVM biogeochemical model. Based on the updated
observation data, the allocation schemes of boreal needle-leaved
summer-green trees and tropical broad-leaved evergreen trees are improved in
SEIB-DGVM V3.02. Allocation schemes of other PFTs are the same as the
original version. Atmospheric CO2 is assimilated by the photosynthesis
of both woody and grass foliage and then is added into the non-structural
carbon of the plant. This non-structural carbon of photosynthetic production
is allocated to all the plant organs (foliage, trunk, root, and stock),
supplying what is needed for the maintenance and growth of each organ. When
the non-structural carbon is greater than 0 during the growth phase, the
following dynamic carbon allocation is executed for each individual plant at
the daily timescale, such that the following can be said.
When the fine root biomass (massroot) of wood or grass does not
satisfy minimum requirements for fulfilling functional balance
(massleaf/ FRratio), the mass of non-structural carbon is allocated
to the root biomass to supplement the deficit. Here, massleaf is the
leaf biomass, and FRratio is the ratio of massleaf to
massroot satisfying the functional balance.
The stock biomass is supplemented until it is equal to leaf biomass.
This scheme is active after the first 30 d of the growing phase.
Woody leaf biomass is constrained by three limitations of the maximum
leaf biomass, which are calculated as follows:max1=crownarea+πcrowndiametercrowndepth1LAmaxSLAmax2=ALM12πdbhheartwood/2+dbhsapwood/22-πdbgheartwood/22SLA3max3=massavailableRGf4massleaf=min(max1,max2,max3),where max1, max2, and max3 are, respectively, maximum
leaf biomass for a given crown surface area, cross-sectional area of
sapwood, and non-structural carbon; the constant of specific leaf area (SLA) is the PFT-specific leaf area per unit biomass
(m2 g-1); LAmax is the plant-functional-type-specific
maximum leaf area per unit crown surface area excluding the bottom layer
(m2 m-2); ALM1 represents the area of transport tissue per
unit biomass and is a constant (dimensionless). If the massleaf is
less than the minimum (max1,max2,max3), the mass of
non-structural carbon is allocated into leaf biomass to supplement the
deficit.
When the leaf area index of grass equals the optimal leaf area index, it
stops allocating non-structural carbon to grass leaf, which is calculated
aslaiopt=lnpargrass-lnpsatlue1-cost/SLA0.09093×dlen×psat-2-1eK,where laiopt is the optimal leaf area index (m2 m-2),
pargrass is the grass photosynthetically active radiation (µmol photon m-2 s-1), psat is the light-saturated photosynthetic
rate (µCO2 m-2 s-1), lue is the light use efficiency
of photosynthesis (mol CO2 mol photon-1), cost is the cost of
maintaining leaves per unit dry leaf mass (DM) per day (g DM g DM-1 d-1),
dlen is day length (hour), and eK is light attenuation coefficient at
midday.
When non-structural carbon is less than 10 g DM PFT-1, or
annual net primary production (NPP) is less than 10 g DM PFT-1 in the previous year, the
following daily simulation processes (5–6) will be skipped.
When total woody biomass is more than 10 kg DM, which defines the
minimum tree size for reproduction, 10 % of non-structural carbon is used
for every daily process of reproduction, including having flowers, pollen,
nectar, fruits, and seeds. These organs are not explicitly modelled in
SEIB-DGVM.
During the simulation of trunk growth, the remaining non-structural
carbon is allocated to sapwood biomass. There is no direct allocation to
heartwood, which is transformed slowly from sapwood biomass. For grass PFTs
biomass, the densities of all organs comprising the biomass never decline
below 0.1 g DM m-2 even if the environment is deteriorated for grass
survival. A more detailed description of SEIB-DGVM is given by Sato et al. (2007).
To control plant phenology and the rate of photosynthesis as a function of
the limitation in terrestrial water, the physiological status of the
limitation of terrestrial water is calculated as6psat=PMAXcetmpceCO2cewater7cewater=statwaterstatwater=8maxpoolw(1)/Depth(1),poolw(2)/Depth(2)-WwiltWfi-Wwilt,where psat is the single-leaf photosynthetic rate of tree PFTs and
grass PFTs (µmol CO2 m-2 s-1); PMAX is the potential
maximum of photosynthetic rate (µmol mol-1 CO2 m-2 s-1); cetmp and ceCO2 are the temperature and
CO2 concentration effect coefficient (dimensionless), separately;
cewater is the water effect coefficient (dimensionless);
statwater is the physiological status of the terrestrial water
limitation, which ranges between 0.0–1.0 (dimensionless); poolw(n) is the water content in soil layer n (mm); Depth(n) is the depth of
the soil layer n (mm); Wwilt is soil moisture at the wilting point (m m-1); and Wfi is soil moisture at field capacity (m m-1). When
the temperature of all soil layers is less than 0 ∘C,
statwater equals 0.
Carbon stock partitioning method
SEIB-DGVM allocates and stores the biomass carbon in four pools of woody PFT
(foliage, trunk, root, and stock) and three pools of grass PFT (foliage,
root, and stock). To investigate the fractional variability in carbon
sequestration potential between the pools, we partitioned potential
vegetation carbon stocks based on the physiological function of the plant
(Fig. A1). The root–shoot ratio (R / S) has been used to distinguish and
investigate the ratio of belowground biomass (root biomass) and
aboveground biomass (shoot biomass) (Zhang et al., 2016). In this study, we
adjusted the method of calculating the R / S ratio by distinguishing between
the light-gathering vegetation biomass carbon stock (LVBC) and the
water-gathering vegetation biomass carbon stock (WVBC). LVBC represents the
biomass carbon invested by a plant that is used to gather sunlight, including
biomass carbon from woody foliage, woody trunk, and grass foliage. WVBC
represents biomass carbon used to gather water, including biomass carbon
from woody fine roots and grass fine roots, excluding the stock pool. Stock
biomass is used for foliation after the dormant phase and after fires and is
a reserve resource in each individual tree. Fine root biomass is just a tiny
fraction of the total biomass but has a very high turnover rate and
determines the capacity of vegetation to absorb soil water. Thus,
LVBCWVBC=Tmassleaf+Tmasstrunk+GmassleafTmassroot+Gmassroot×100%,
where LVBC is light-gathering vegetation biomass carbon stock (kg C m-2);
WVBC is water-gathering vegetation biomass carbon stock (kg C m-2);
Tmassleaf is the leaf biomass carbon stock of woody vegetation (kg C m-2); and Tmasstrunk is the trunk biomass carbon stock of trees
(kg C m-2), including both branch and structural roots. This biomass is
simplistically attributed to light-gathering vegetation organs and is used
primarily to support the plant. Gmassleaf is the leaf biomass carbon
stock of grass (kg C m-2), whereas Tmassroot and
Gmassroot are functional root (fine roots) biomass carbon stocks of
trees and grass, separately (kg C m-2), which absorb water and
nutrition from soil.
Experimental designSetup of model runs
SEIB-DGVM simulations begin with seeds of selected PFTs planted in bare
ground. The establishment of PFT seeds is determined by the climatic
conditions in each grid cell. We inputted the transient climate data from
1901 to 1915 to spin up the model in a repetitive loop. No obvious trend in
climatic factors was observed during this period (Tei et al., 2017). A
spin-up period of 1050 years was necessary to bring the terrestrial
vegetation carbon cycle into a dynamic equilibrium. To reach
quasi-equilibrium in the vegetation biomass, about 1000 years of simulation
was required as a spin-up procedure.
Factorial simulation scheme
In order to further quantify the relative contributions of varying
atmospheric CO2 concentrations, precipitation, temperature, radiation,
and other factors (wind velocity and relative humidity), we performed six
factorial simulations. In simulation S1, atmospheric CO2 concentration
and all climate variables were varied. In simulation S2, only atmospheric
CO2 concentration was varied, and climate variables were held constant
(climate variables of the transient period, 1901–1915 were repeatedly
inputted). In simulation S3 (or S4 and S5), atmospheric CO2 and
precipitation (or temperature and radiation) were varied, and other climate
variables were held constant. In simulation S6, atmospheric CO2, wind
velocity, and relative humidity were varied, and other climate variables
were held constant. Finally, S2 was used to evaluate the effects of CO2
fertilization on carbon stock variation. The differences in S2–S3, S2–S4,
S2–S5, and S2–S6 were used to evaluate the response of carbon stock growth
to precipitation, temperature, radiation, and other drivers, respectively.
Note: in factorial simulation S1, historical atmospheric CO2 concentration and historical climate fields from the CRU dataset were used. In simulation S2, only historical atmospheric CO2 concentration was used, and climate variables of the transient period (1901–1915) were repeatedly input. In simulation S3 (or S4 or S5), only historical atmospheric CO2 concentrations and precipitation (or temperature or radiation) were input, and climate variables of the transient period (1901–1915) were repeatedly input. In the last simulation, S6, only historical atmospheric CO2 concentrations and other climate variables were input, including wind velocity and relative humidity.
Non-parametric test methods
Each driving factor (atmosphere CO2, precipitation, temperature, and
radiation) has a different influence on the carbon stock, so it is difficult
to make a simple pre-assumption about the population distribution pattern
for factorial simulations. We used the non-parametric Mann–Kendall and Sen
slope estimator statistical tests (Gocic and Trajkovic, 2013) to assess the
ability of SEIB-DGVM to simulate the response patterns of carbon storage
potential to a change in climate and CO2 concentrations. We regressed
the simulated 100-year mean global average carbon stock time series to
reveal the accumulative influences of the single variables based on the
factorial simulations where only one or two drivers were varied. As shown in
Figs. A2 and A3, detection trends of LVBC and WVBC for all driving factors
performed statistically well (in agreement at the 95 % confidence
intervals), indicating that this analytical method was suitable for trend
attribution at the global scale.
Distinguishing hydrological regions
Locally available water strongly regulates and limits the response of carbon
stocks to changes in climate and CO2. We used aridity index (AI) to
distinguish between the global hydrological regions for comparing the
long-term trend in carbon stocks over different hydrological environments
and for quantifying the influences of each hydrological environment on the
variations in the trends. The AI was defined as
AI=P‾ETp‾,
where P‾ is the multiyear mean precipitation (mm yr-1), and
ETp‾ is the multiyear mean potential evapotranspiration (mm yr-1), which was calculated by the Penman–Monteith model (Monteith
and Unsworth, 1990). As in a previous study (Chen et al., 2019), five
hydrological regions were categorized based on AI value. Under the
influences of climate change, the hydrological condition was changed in some
grid cells (Fig. A4). For example, the grid cell classified as sub-humid
zone in the period of 1916–1945 was redefined as semi-arid zone in the
period of 1986–2015. In this study, grid cells with consistent hydrological
condition between the period of 1916–1945 and the period of 1986–2015 were
selected and classified (Fig. 1).
Global spatial patterns of water availability. Spatial variations in water availability were categorized based on the multiyear average aridity index (AI), defined as the ratio of the multiyear mean precipitation to the potential evapotranspiration. Categories include hyper-arid (AI ≤ 0.05), arid (0.05 < AI ≤ 0.2), semi-arid (0.2 < AI ≤ 0.5), sub-humid (0.5 < AI ≤ 0.65), and humid (AI > 0.65). The white grid cells were not assigned a hydrological category.
Observation dataset for model evaluation
A global time series of potential vegetation carbon was modelled by the
SEIB-DGVM between 1916–2015. In terrestrial vegetation biomes, there is a
high correlation between biomass carbon stock density and NPP per unit (Erb
et al., 2016; Kindermann et al., 2008) (Fig. A1). Thus, we collected the NPP
observation dataset and used NPP as a proxy for the carbon stock to assess
model accuracy. Ecosystem Model–Data Intercomparison (EMDI) builds upon the
accomplishments of the original worldwide synthesis of NPP measurements and
associated model driver data prepared by the Global Primary Production Data
Initiative. We obtained the monitoring station data from the EMDI working
group and then compared their data with modelled multiyear average NPP in
the period of 1916–1999 (Fig. 2).
Multiyear average NPP simulated by SEIB-DGVM and EMDI global site distribution. Yellow rhombuses indicate the monitoring stations of the EMDI.
However, in situ observations are sparse for global spatiotemporal validation.
Therefore, we used the MOD17A3 products to further verify the simulated
potential NPP in the 21st century. These data were collected by the
Moderate Resolution Imaging Spectroradiometer and are some of the most
widely used data to assess the accuracy of global model simulations
(Gulbeyaz et al., 2018). The natural vegetation zones refer to the
hypothetical condition that would prevail in an assumed absence of
anthropogenic activity, but under historical climate fields (Erb et al.,
2018; Haberl et al., 2014). The potential NPP is defined as the assimilated
carbon stored in natural vegetation without the disturbance of anthropogenic
activities (Erb et al., 2018).
In order to distinguish the distribution of vegetation grid cells without
anthropogenic disturbance, we obtained global land cover types in the period
2001–2015 from MCD12C1 (Table A1). We included grid cells whose largest
vegetation component was evergreen needleleaf forest, evergreen broadleaf
forest, deciduous needleleaf forest, deciduous broadleaf forest, mixed
forest, closed shrublands, open shrublands, woody savannas, savannas, or
grasslands. Other grid cells were excluded from our analysis.
Some of the grid cells covered by grassland were grazed by livestock, leading to
the decrease in NPP of grass PFTs. There is a weak anthropogenic disturbance
in rangeland, while managed pasture is intensely grazed by livestock. To
remove pasture area with strong anthropogenic disturbance, we obtained
land use forcing data from Land-Use Harmonization (LUH2) to map the
distribution of managed pasture data from 2001 to 2015 (Hurtt et al., 2020).
As shown in Fig. A5, grassland in eastern Asia, western Europe, south-central Africa, and western South America was severely affected by grazing.
To exhibit the disturbance of managed pasture, we calculated the mean
fraction of managed pasture within the corresponding 0.5∘ grid
unit. When the fraction of managed pasture is over 10 %, the grid cell was
considered to be affected by managed pasture. To reduce the interference
effects of livestock grazing, we first removed the grid cells affected by
managed pasture. Then, we map the distribution of natural vegetation grid
cells without anthropogenic disturbance (Fig. A6). This exclusion method
is only used for potential NPP comparison.
Results and discussionEvaluation of SEIB-DGVM
Figure 3 illustrates the comparison between model-simulated and observed
multiyear mean NPP during 1916–1999. The determined coefficient (R2)
between EMDI, observed and estimated multiyear average NPP of 669 in situ observations
is 0.54, which is significant at the p=0.01 level. The slope of the
regressed line is 0.70 during the 20th century.
Comparison of multiyear average NPP calculated by SEIB-DGVM and EMDI for the 20th century. The solid line is the best-fit curve, and the dashed line represents a perfect correspondence in the results of the two.
Based on the land cover type dataset from 2001 to 2015, we obtained NPP-MOD17A3
data in natural vegetation zones without anthropogenic disturbance in the
same period. Figure 4 shows that the modelled NPP from the SEIB-DGVM
exhibited a high degree of consistency with the NPP-MOD17A3 data in natural
vegetation zones over the period (R2=0.63, p<0.05). The
general spatiotemporal agreement between the simulated NPP derived from
SEIB-DGVM with in situ observations and derived from satellites reveals that it is
reasonable to use the SEIB-DGVM simulations to evaluate the same mechanisms
controlling global potential biomass carbon stocks of vegetation.
Spatial patterns in the potential NPP correlation coefficients (P<0.05) between SEIB-DGVM and MODIS between 2001–2015. These data were used to validate SEIB-DGVM.
Finally, the modelled result of potential vegetation biomass carbon stock
was compared with current existing data from the literature and
state-of-the-art datasets. Figure 5 shows that the modelled results are
within the range of potential carbon stocks, which indicate that the
SEIB-DGVM reliably simulated the carbon stock dynamics.
Estimates of the potential vegetation biomass carbon stock from the literature (blue plot), state-of-the-art datasets (red plot), and this study (black line). Datasets are from the following studies: (1) Erb et al. (2018, 2007), (2) Bazilevich et al. (1971), (3) Saugier et al. (2001), (4) Erb et al. (2018) and Bartholome and Belward (2005), (5) Olson et al. (1983), (6) Erb et al. (2018) and Pan et al. (2011), (7) Ajtay et al. (1979), (8) Erb et al. (2018) and Ruesch and Gibbs (2008), (9) Kaplan et al. (2011), (10) Shevliakova et al. (2009), (11) Kaplan et al. (2011), (12) Pan et al. (2013), (13) Prentice et al. (2011), (14) Erb et al. (2018, 2007), (15) Erb et al. (2018) and West et al. (2010), (16) Hurtt et al. (2011).
Enhanced carbon stocks and their fractions
We distinguished the changes in LVBC and WVBC from total vegetation carbon
stocks. The historical temporal trends over the period are shown in Fig. 6a. The potential vegetation carbon stock exhibits a net increase of 119.26 ± 2.44 Pg C in the last century (±2.44 represents intra-annual
fluctuation in carbon stock, which is the difference between the maximum value
and minimum value of the carbon stock within the year). Based on Pearson
correlation analysis, this increasing trend of annual average carbon stock
exhibits a robust agreement with the dramatic increase in atmospheric
CO2 concentration (R2=0.9677, p<0.001), suggesting that
the carbon stock is strongly affected by CO2 fertilization. Meanwhile,
the positive correlation between the carbon stock and CO2 generally
extends across LVBC (R2=0.9669) and WVBC (R2=0.9622). After
the value of the global terrestrial carbon stock and trends were partitioned
among the vegetation functional classes, we see that LVBC increases by 116.18 ± 2.34 Pg C (or ∼15.60 %), which explains 97.42 %
of the total carbon stock increasing trend and dominates the positive global
carbon stock trend; WVBC also increases by 3.08 × 0.14 Pg C (or
∼18.03 %) over the past century.
Global potential biomass carbon stocks of vegetation during the past 100 years. (a) The evolution of global potential biomass stocks (LVBC+WVBC), along with changes in biomass stocks that can be attributed to the variability and trend of LVBC and WVBC through the 20th century. The red line represents the monthly value of LVBC, the blue line represents the monthly value of WVBC, and the black line represents the annual value of CO2 concentration. (b, c) Zonally averaged sums of the annual LVBC and WVBC for latitudinal bands during the first decade (1916–1925, red line) and the last decade (2006–2015, blue line) show the increased carbon stock capacity.
The global distributions of the decadal-average change in LVBC and WVBC are
shown in Fig. 6b and c, respectively. The significant historical changes
in climate and CO2 enhance the carbon stock of the terrestrial
ecosystem, and their positive influences are broadly distributed across a
latitudinal north–south gradient. The latitudinal bands of increasing
annual LVBC are mainly distributed at the tropical and boreal latitudes,
which is consistent with Fig. 7b. The decadal and inter-annual
variabilities in LVBC are dominated by the tropical and boreal zones, where
large portions of which are highly productive (Ahlstrom et al., 2015;
Poulter et al., 2014). Tropical LVBC dominates the long-term trend of global
LVBC in the last 100 years. Compared with LVBC, the increase in tropical
WVBC is light. There is a single peak in the spatial variation in annual
WVBC (Figs. 6c and 7c). WVBC exhibits robust growth at most
latitudes and increases mainly at boreal latitudes.
Spatial variability in estimated LVBC and WVBC trends
In Fig. 7a and b, total carbon stock and LVBC exhibited a
significantly increasing trend in eastern South America, southern Africa,
and northern Asia, while it declined in central North America, north-western
South America, and central Africa. WVBC showed a more widely increasing
tendency in North America, south-eastern South America, and Europe, while it had
a decreasing trend in some zones of Asia. We find that the total carbon
stock as well as the light- and water-gathering vegetation biomass carbon
stocks over the period of 1916–2015 exhibited a remarkable spatial
heterogeneity. Figure 7a shows that an increase in vegetation carbon stocks
occurred over zones and global aggregate levels during the entire study
period. About 57.39 % of the terrestrial grid cells exhibited an increase
with a noticeable trend (p<0.05) in biomass carbon stock; 53.82 %
of global grid cells possessed increases that were statistically significant
at the p=0.01 level. To determine the contributions of each fraction
(LVBC, WVBC) to the total change in the potential vegetation carbon stock,
we partitioned and present the historical spatial and temporal patterns for
each fraction separately (Fig. 7b, c). LVBC contributes 97.33 % of the
incremental change in total carbon stock (116.18 × 2.34 Pg C), with
about 51.32 % of the grid cells possessing a noticeable positive trend
(p=0.01). Generally, spatial patterns of LVBC and the total carbon stock
are consistent (Fig. 7a, b), which further supports the argument that
LVBC dominates the trend in carbon stocks in most zones. Although the
proportion of the total change in carbon stocks is small (2.58 % of total
carbon stock increase), about 61.00 % of the land surface shows an
increase in WVBC; of these terrestrial grid cells, 55.81 % was
characterized by a significant p=0.01 increase.
Spatial patterns in the trends of potential vegetation carbon stocks and their fractions from 1916 to 2015. Difference induced by changes in climate and CO2 in terrestrial biomass carbon stock (a), LVBC (b), and WVBC (c) during the historic period 1916–2015. The blue bar indicates the significantly increasing trends, and the red bar indicates the significantly decreasing trends in carbon stocks. (d) Trend in the LVBC / WVBC ratio from 1916 to 2015. The blue bar indicates significantly increasing trends in the ratio, and vice versa. The grey bar indicates that the trend is statistically insignificant (P>0.05). The sub-graphs show the significant test results. A “+” symbol indicates a positive trend, and vice versa.
Under the influence of a changing climate and CO2 concentrations,
there is a slight increase in the ratio of global LVBC / WVBC; the rate of
increase is 0.0171 yr-1 in the last 100 years, which is significant
at the 0.01 level (Fig. 7d). About 42.08 % of the terrestrial grid cells
exhibit an increase with a noticeable trend (p<0.05) in the ratio
of LVBC and WVBC; 37.95 % of global grid cells possessed increases that
are statistically significant at the p=0.01 level. Meanwhile, 33.32 % of
the land surface shows a significant decrease in LVBC / WVBC; of these
terrestrial grid cells, 30.06 % are characterized by a significant p=0.01
decrease. Grid cells with noticeable increases in the ratio of LVBC to WVBC
are mainly located in southern Africa, central South America, and northern
Eurasia. Negative trends in LVBC / WVBC ratios are found in northern America,
southern Europe, and tropical Africa.
Responses of LVBC and WVBC to environmental drivers
The responses of LVBC and WVBC to changes in climate and CO2 are both
positive at the global level (Fig. 8a, c), although zonally, they exhibit
both negative and positive responses (Fig. 8b, d). Based on the results
of factorial simulations and Mann–Kendall and Sen tests, CO2
fertilization explains the largest proportion of the change in the carbon
stock; about 82.45 % of the change in LVBC was positive (Fig. 8a), whereas
89.28 % of the change in WVBC was positive (Fig. 8c). In factorial
simulation S2, the long-term trend of LVBC was 15.521 g C m-2 yr-1,
and that of WVBC was 0.435 g C m-2 yr-1 in the period from 1916
to 2015 (Figs. A2a and A3a). The separately simulated LVBC and WVBC
increased by 80.98 and 2.66 Pg C with increasing atmospheric CO2
concentrations (from 301.73 ppm in 1916 to 400.83 ppm in 2015). The other
climatic drivers (precipitation, temperature, radiation, humidity, and wind
speed) remained at baseline values. While the increase or decrease in the
carbon stock may be attributed to more than one driving factor, within any
specified grid cell, the one with the highest positive or negative
contribution is the dominant driver that consistently resulted in the
highest increase or decrease in the carbon stock for that grid cell. The
spatial pattern illustrates that CO2 dominates the variability in LVBC
in 7.28 % of the grid cells, including 1.21 % of the grid cells that
exhibited a negative change and 6.07 % that exhibited a positive change
(Fig. 8b). CO2 dominates the variability in WVBC in 27.60 % of the
grid cells, including 1.73 % of the grid cells that exhibited a negative
change and 25.87 % of grid cells with a positive change (Fig. 8d). Under
the effect of CO2 fertilization, grid cells with increased trend in
WVBC are mainly distributed at boreal latitudes (Fig. 6c). These trends are
consistent with previous studies (Tharammal et al., 2019; Zhu et al., 2016;
Keenan et al., 2016) in which positive trends occurred, especially for WVBC.
The proportion of changes in vegetation biomass carbon stocks attributed to driving factors. Ratios of the driving factors of CO2 fertilization effects (CO2), climate change effects (CLI), precipitation (Pre), temperature (Tem), and radiation (Rad) for LVBC (a) and WVBC (c) are calculated by the Mann–Kendall and Sen slope estimator statistical tests. Attribution of LVBC (b) and WVBC (d) dynamics to driving factors calculated as averages along 15∘ latitude bands. At the local scale, the driving factors include CO2, Pre, Tem, Rad, and other climate factors (OF). The fraction of global grid cells (%) that are predominantly influenced by the driving factors is shown at the bottom of the bar. The “–” symbol before the fraction indicates a negative effect of the driving factor on carbon stock, and vice versa.
Climate change induced by the greenhouse effect explains part of the
increase in carbon stocks, but unlike CO2 fertilization, climate has
dramatic negative effects on some vegetated zones. Figure 8a illustrates
that temperature is the largest climatic contributor to the change in LVBC
(13.83 %, 2.572 g m-2 yr-1), followed by precipitation
(8.51 %, 1.572 g m-2 yr-1) and radiation (-3.19 %, -0.649 g m-2 yr-1). The spatial distribution shows that temperature
predominantly influences the change in LVBC (Fig. 8b), influencing over
27.56 % of the global vegetated grid cells, followed by precipitation
(21.88 %) and radiation (20.67 %). Figure 8c shows there are negative contributions of precipitation to the change in WVBC at the global level (-2.76 %, -0.013 g m-2 yr-1) by precipitation. Temperature is the
largest climatic contributor to the change in WVBC (15.36 %, 0.075 g m-2 yr-1), followed by radiation (-5.63 %, -0.027 g m-2 yr-1). Modelled WVBC trends based on the factorial simulations have
similar spatiotemporal patterns to LVBC (Figs. A2 and A3), and the spatial
patterns of light- and water-gathering carbon stocks show a significantly
increasing trend in most of the boreal zones. In the Southern Hemisphere,
the trends of WVBC are extensively statistically insignificant in all
factorial simulations, and only a small proportion of grid cells show a
significantly increasing trend. There is a significantly increasing trend in
LVBC in south-central Africa and northern South America. The effects of
temperature on WVBC are stronger than LVBC because temperature has a
stronger effect on the metabolism process of root growth, dominating the
turnover rate and the costs of maintenance respiration in root growth
processes (Gill and Jackson, 2000). It should be noted that trends in the
global carbon stock can be largely attributed to the influences of CO2,
precipitation, temperature, and radiation (Fig. 8). Nonetheless, at the
zonal scale, the contributions of other factors should be considered, such
as humidity and wind speed. The effects of these other factors dominate
trends in LVBC in over 16.05 % of the grid cells that increased and
6.57 % of the grid cells that decreased. In the case of changes in WVBC,
other factors were dominant drivers in over 14.75 % of the grid cells that
increased and 3.57 % of the grid cells that decreased. Under the effect of
climate, the variability in LVBC and WVBC is positive in most grid cells,
promoting the noticeable increase in carbon stocks at boreal latitudes.
Constraints imposed by water limitations
Terrestrial water availability emerged as a key regulator of terrestrial
carbon storage, by affecting the response mechanism of the vegetation carbon
stock to changes in driving factors. As shown in Figs. 9 and 10, with the
accumulated change in LVBC and WVBC in the period of 1916 to 2015 across the
aridity index (i.e. an increase in available water), the magnitude and
range in responses of LVBC density and WVBC density gradually increase.
Based on the results of the historical simulation (Fig. 9), we find a
positive relationship between LVBC and aridity index. In extreme water
stress, the increase in LVBC tends to zero, and plants stop increasing their
carbon storage. There is no obvious difference in the slopes of fitting
curves between factorial simulations, which shows the robustness in the
response of LVBC to the change in water stress. The pattern of the enhanced
magnitude and range of variation in the WVBC density is unimodal with water
stress gradient in all factorial simulations. With the increase in AI, the
magnitude of change in WVBC increases at first and then decreases finally.
The mitigation of water stress promotes WVBC increase, while excess surface
water limits the response of WVBC to changes in climate and CO2. These
results reveal that the carbon stock increases stimulated by changes in
climate and CO2 are constrained by water availability. With increased
warming, water limitations are expected to increasingly limit the carbon
stock increase, especially in arid regions. To further reveal the controls of
water limitation on the responses of inner carbon storages to each driver,
we analyse the long-term variability in potential vegetation carbon stocks
by means of factorial simulations for each hydrological region (Fig. 1).
Figure A7b shows that the fluctuation range (the difference between maximum
value and minimum value in each factorial simulation) of LVBC density across
all factorial simulations is 1.202 kg C m-2 in the hyper-arid regions
for the 1916–2015 period. As shown in Fig. A7f, the fluctuation range of
LVBC density in humid regions is 6.068 kg C m-2 during the same period.
In Fig. A8b, the maximum change in magnitude of WVBC density across all
factorial simulations is 0.011 kg C m-2 in the hyper-arid regions during
the time of 1916–2015. In Fig. A8f, the maximum change in magnitude of WVBC
density is 0.046 kg C m-2 in humid regions during the same period.
Compared with plants in arid regions, plants in humid regions show more
dramatic responses to the stimulation from drivers' change. With a lessening
of water stress (from hyper-arid to humid regions), the response magnitudes
of the carbon stock to the changes in climate and CO2 gradually become
more noticeable. The robust pattern in the zonal average density of the
carbon stock shows that terrestrial water limitations strongly regulate the
enhanced magnitude of the carbon stock.
Relationships of the incremental change between AI and LVBC. Magnitude of change in LVBC in the historical scenario S1 (a), CO2 in scenario S2 (b), CO2+ precipitation in scenario S3 (c), CO2+ temperature in scenario S4 (d), and CO2+ radiation in scenario S5 (e). The range of the box is 25 %–75 % of values, the range of the whiskers is 10 %–90 % of values, the small red square is average value, the red line is the median line, and the black line is the fitted curve. Positive value of the y axis represents the magnitude of increased LVBC from 1916 to 2015 under water-limited conditions, and vice versa. AI of grid cells is calculated by multiyear average precipitation and multiyear average potential evapotranspiration in the period of 1916–2015. Categories of hydrological zones include hyper-arid (AI ≤ 0.05), arid (0.05 < AI ≤ 0.2), semi-arid (0.2 < AI ≤ 0.5), sub-humid (0.5 < AI ≤ 0.65), and humid (AI > 0.65).
Relationships of the incremental change in AI and WVBC. Magnitude of change in WVBC in the historical scenario S1 (a), CO2 in scenario S2 (b), CO2+ precipitation in scenario S3 (c), CO2+ temperature in scenario S4 (d), and CO2+ radiation in scenario S5 (e). The range of the box is 25 %–75 % of values, the range of the whiskers is 10 %–90 % of values, the small red square is average value, the red line is the median line, and the black line is the fitted curve. Positive value of the y axis represents the magnitude of increased WVBC from 1916 to 2015 under water-limited conditions, and vice versa. AI of grid cells is calculated by multiyear average precipitation and multiyear average potential evapotranspiration in the period of 1916–2015. Categories of hydrological zones include hyper-arid (AI ≤ 0.05), arid (0.05 < AI ≤ 0.2), semi-arid (0.2 < AI ≤ 0.5), sub-humid (0.5 < AI ≤ 0.65), and humid (AI > 0.65).
Water limitations not only directly reduced the magnitude of the response in
the two fractions' carbon stock (LVBC and WVBC) to changes in climate and
CO2 but also indirectly confined the response direction of each
fraction's carbon stock by transforming vegetation structure and function.
Figure 11 illustrates temporal variations in the carbon stock ratio within
and between hydrological regions. From hyper-arid regions to humid regions,
the fluctuation range of LVBC / WVBC ratio significantly changes. The
fluctuation magnitudes of LVBC / WVBC in humid and hyper-arid regions are
greater than those in other hydrological regions. Compared with plants in
hyper-arid regions, plants in humid regions exhibit more significant
responses to changes in climate and CO2. Meanwhile, the long-term
effects of driver changes have a remarkable influence on this carbon
allocation pattern at the global level (Fig. 7d). Under the synergistic effect
of drivers and water stress, the trends of light- and water-gathering
vegetation carbon stock are upward in the past 100 years (Fig. 6).
However, there is a difference in the increasing rate between LVBC and WVBC,
resulting in a dramatic and complicated fluctuation in global LVBC / WVBC
ratio (Fig. 11a). Whereas LVBC decreases and WVBC increases in hyper-arid
and arid regions (Figs. A7 and A8), causing a downward trend in the LVBC / WVBC
ratio, semi-arid regions see an increase in LVBC. So, the ratio of LVBC and
WVBC shows a downward trend in these regions. LVBC in semi-arid regions
shows upward tendency in the past years (Fig. A7d) because of the aridity
mitigation. There is an upward trend in WVBC in semi-arid regions (Fig. A8d). Plants in semi-arid regions still utilize a tolerance strategy and
allocate more non-structural carbon to water-gathering vegetation organs to
resist water stress, resulting in the decline in the LVBC / WVBC ratio. In humid
regions, light- and water-gathering biomass carbon stocks both increased
(Figs. A7 and A8). The proportion of LVBC increases more than that of WVBC
for capturing more resources like CO2 and radiation energy, leading to
an increase in the LVBC / WVBC ratio. The value of LVBC / WVBC in S3 is higher
than that in S4 and S5, which shows that precipitation makes a greater
contribution to the change in LVBC / WVBC ratio among meteorological factors.
Temporal fluctuations in carbon stock dynamics in vegetation biomass in different factorial simulations. Black indicates historical factorial simulation from 1901–2015, green indicates the CO2-driven factorial simulation, blue indicates the precipitation-driven factorial simulation, red indicates the temperature-driven factorial simulation, and yellow indicates radiation-driven factorial simulation. Uncertainty bounds are provided as shaded areas and reflect the intra-annual fluctuation (±1 s.d.). (a) Modelled trend of LVBC / WVBC ratio in global area. (b–f) Modelled trend of the LVBC / WVBC ratio in different hydrological regions (Fig. 1).
Discussions and conclusion
To understand the response of carbon storage potential and its inner biomass
carbon stocks to environmental change, we conducted a series of factorial
simulations using SEIB-DGVM V3.02. More importantly, we investigated the
extent of the responses of carbon stocks to water limitations.
Over the past 100 years, there has been an ongoing increase in the carbon
storage capacity of the terrestrial ecosystem from 735 Pg C in 1916 to 855 Pg C in 2015 (Fig. 6), which has slowed the rate at which atmospheric
CO2 has increased and may have mitigated global warming. These findings
are consistent with the conclusions of research conducted at the local
scale. For example, based on carbon flux data, Erb et al. (2008) suggested
that the vegetation carbon stock in Austria increased from 1043 to 1249 Mt C (aboveground carbon stock growth was 1.059 Mt C yr-1, and
belowground carbon stock growth was 0.2 Mt C yr-1) since
industrialization. Le Noë et al. (2020) showed that increases in the
carbon stocks and carbon density were the predominant drivers in the forest
terrestrial carbon sequestration capacity in France from 1850 to 2015. Tong
et al. (2020) also found a substantial increase in aboveground carbon stocks
in southern China (0.11 Pg C yr-1) during the period 2002–2017.
However, these studies focused on zonal trends in total vegetation carbon
stocks and did not investigate the extent of the response in vegetation
carbon stocks partitioned between light- and water-gathering biomass. Our
results show that the increase in carbon stock in light-gathering vegetation
organs was much larger than that in water-gathering vegetation organs, and
light-gathering biomass carbon stock dominates the historical trend of the
terrestrial carbon stock. During the past decades, the global land surface
has been greening because of the flux and storage of more carbon into plant
trunks and foliage (Zhu et al., 2016). LVBC increases by 116.18 × 2.34 Pg C from 1916 to 2015, accounting for 97.42 % of the total carbon stock
increase (119.26 × 2.44 Pg C). The long-term trends and spatial
pattern of vegetation carbon stock predominated the variability
characteristic of LVBC. The latitudinal bands of increasing annual change in
LVBC are mainly distributed at tropical latitudes, a conclusion consistent
with prior knowledge that tropical zones dominate carbon uptake and storage
(Erb et al., 2018; Schimel et al., 2015). Biomass carbon allocation between
light- and water-gathering vegetation organs reflects the changes in
individual growth, community structure, and ecosystem function, which are
important attributes in the investigation of carbon stocks and carbon
cycling within the terrestrial biosphere (Hovenden et al., 2014; Fang et
al., 2010; Ma et al., 2021). During the past 100 years, the ratio of
LVBC / WVBC showed a slight upward trend since LVBC increased relatively more
than WVBC. The rate of increase is 0.0171 yr-1, which is significant at
the 0.01 level. To better absorb CO2 and sunlight required for
photosynthesis, vegetated regions are gradually covered by vegetation with
higher plant height and wider leaf area, thereby adjusting their
characteristic ecosystem functions (Erb et al., 2008).
Based on our factorial simulations (Fig. 8), the influences of CO2
fertilization induce the most significant variation in the vegetation carbon
stock. In addition, the responses of carbon stocks to the changes in
climatic factors are obvious, particularly at the grid cell scale. Previous
studies have pointed out that the variation in the terrestrial carbon stock
caused by releasing or sequestering carbon is sensitive to anomalous changes
in water availability and light use efficiency (Madani et al., 2020;
Humphrey et al., 2018). At the grid cell scale, as shown in Fig. 8b and
d, temperature, radiation, precipitation, and other climate factors
(humidity and wind speed) dominate the long-term trend of carbon stocks over
two-thirds of the global grid cells. At the global scale, climate factors
explain 17.55 % and 10.72 % of the long-term trend in LVBC and WVBC,
respectively (Fig. 8a and c). LVBC and WVBC variations driven by climate
factors are ultimately offset by spatially compensatory effects, which
dampens the response of the carbon stock to these factors at the global
scale (Jung et al., 2017). Thus, contributions of precipitation and
radiation to the variability in LVBC and WVBC are relatively low at the
global scale, and the effects of humidity and wind speed on global carbon
stock are minor. This spatially compensatory effect of climate change is
consistent with a previous analysis (Zhu et al., 2016) which found that
climate change explains only 8 % of the increasing trend in foliage carbon
storage at the global level but that it dominates the trend over 28.4 %
of the global land area. Results show that trends in temperature drive
historical long-term trends in the potential carbon stocks, with faster
increases and considerable variation occurring by grid cell. Thus, our
results reveal that temperature dominates the long-term trends of carbon
stock among climatic drivers, while a relatively strong compensatory effect
exists in the global change in the carbon stock induced by precipitation,
radiation, humidity, and wind speed.
By partitioning the trends of LVBC and WVBC into five hydrological regions
(Fig. 1), we found that the long-term change in carbon stocks is tightly
coupled to terrestrial water availability. These results indicate that
vegetation in humid regions is responsible for most of the trend in global
LVBC, while plants in semi-arid regions play a dominant global role in
controlling the long-term trend in WVBC (Figs. 9 and 10). As water stress
decreases, the magnitude and range in variation in LVBC gradually increase
(Fig. 9), which suggests that limited water availability constrains the
response magnitude of the changes in LVBC to changes in CO2 and
climate. The response pattern of WVBC growth to the increasing water
availability is different from that of LVBC. Drought mitigation promotes the
growth of WVBC. In sub-humid and humid regions, plants face low water
limitations and intensified light competition and have to invest as much
non-structural carbon as possible into leaf and trunk. This allocation
scheme leads to the decreased investment of ΔWVBC in wet regions.
The result is consistent with previous finding that plants reduce investment
to roots in dense forests where aboveground competition for light is high
(Ma et al., 2021). Moreover, we found that indirect effects of water
limitation regulate increasing rate of each carbon pool. Although vegetation
carbon stocks dramatically increase under the effects of climate and
CO2 changes, the increasing rate of LVBC is faster than WVBC in humid
regions. Vegetation stores more biomass in aboveground plant organs (trunk
and foliage) to gather light. Dryland plants decrease the LVBC / WVBC ratios
and store more biomass below ground to enhance the capture of water
resources. Based on these results, we demonstrate that water limitations
controlled the variable response of terrestrial vegetation carbon stocks.
Our findings are consistent with other reports about the impact of
increasing water limitations on the terrestrial ecosystem. Based on observation
from satellite remote sensing, Madani et al. (2020) found that the
constraining impact of water limitation determines whether global ecosystem
productivity responds positively or negatively to the changes in climate
factors. Humphrey et al. (2021) found that increasing water stress limits
the response magnitude of carbon uptake rates through a down-regulation of
stomatal conductance and suggested that land carbon uptake is driven by
temperature and vapour pressure deficit effects that are controlled by
terrestrial water availability. Ma et al. (2021) found that plants increase
investment into building roots in arid regions because the extent of water
limitation there is exacerbated by global warming. Terrestrial hydrological
conditions significantly affect the carbon cycle of terrestrial ecosystems,
including carbon uptake, allocation, and stock. Terrestrial ecosystems
utilize sensitive strategies to allocate and store biomass to adjust to
local hydrological conditions. A significant conclusion is that water
constraints not only confine the responses of vegetation carbon stocks to
drivers but also constrain the proportion of biomass carbon stocks in
light- and water-gathering fractions.
Distinguishing the response of carbon stock fractions estimated by SEIB-DGVM
improves the understanding of the interactive impacts of terrestrial carbon
and water dynamics. However, uncertainty still exists because of the
limitations in the processes of modelling vegetation metabolism with
SEIB-DGVM. Trunk biomass contains tree branches and structural roots (coarse
roots and tap roots) (Sato et al., 2007), so the R / S ratio of potential
vegetation in factorial simulations is smaller than the R / S of actual
vegetation in observation stations. Root biomass only contains the fine root
biomass, leading to an apparent underestimation in belowground organ biomass
of trees and grasses compared with previous conclusions (Ma et al., 2021; Yang
et al., 2010). Availability of nitrogen is a key limiting factor for
vegetation growth, especially when higher CO2 fertilization effects
exist (Tharammal et al., 2019). The limitation could be alleviated by
nitrogen deposition in most temperate and boreal ecosystems. The SEIB-DGVM
experiments were conducted with a focus on documenting CO2fertilization and climate change interactions; these experiments did not
consider the influences of nitrogen deposition, which should cause an
underestimation of the contributions of CO2 fertilization to biomass
production.
In summary, we evaluated SEIB-DGVM V3.02 and used this model to offer new
perspectives on the response of vegetation carbon storage potential to
changes in climate and CO2. Our simulation results show that changes in
CO2, rather than climate, dominate the light- and water-gathering
partitioning of the carbon storage potential. More importantly, we suggest
that the impact of CO2 fertilization and temperature effects on
vegetation carbon sequestration potential depends on water availability and
its impacts on plant stress. With increased global warming, water
limitations are expected to increasingly confine global carbon sequestration
and storage. Our findings highlight the need to account for terrestrial
water limitation effects when estimating the response of the terrestrial
carbon storage capacity to global climate change and the need for stronger
interactions between those involved in vegetation model development and
those in between the hydrological and ecological research communities.
MCD12C1 legend and class descriptions.
NameValueDescriptionEvergreen needleleaf forests1Dominated by evergreen conifer trees (canopy > 2 m). Tree cover > 60 %.Evergreen broadleaf forests2Dominated by evergreen broadleaf and palmate trees (canopy > 2 m). Tree cover > 60 %.Deciduous needleleaf forests3Dominated by deciduous needleleaf (larch) trees (canopy > 2 m). Tree cover > 60 %.Deciduous broadleaf forests4Dominated by deciduous broadleaf trees (canopy > 2 m). Tree cover > 60 %.Mixed forests5Dominated by neither deciduous nor evergreen (40 %–60 % of each) tree type (canopy > 2 m). Tree cover > 60 %.Closed shrublands6Dominated by woody perennials (1–2 m height) > 60 % cover.Open shrublands7Dominated by woody perennials (1–2 m height) 10 %–60 % cover.Woody savannas8Tree cover 30 %–60 % (canopy > 2 m).Savannas9Tree cover 10 %–30 % (canopy > 2 m).Grasslands10Dominated by herbaceous annuals (< 2 m).Permanent wetlands11Permanently inundated lands with 30 %–60 % water cover and > 10 % vegetated cover.Croplands12At least 60 % of area is cultivated cropland.Urban and built-up lands13At least 30 % impervious surface area including building materials, asphalt, and vehicles.Cropland/natural vegetation mosaics14Mosaics of small-scale cultivation 40 %–60 % with natural tree, shrub, or herbaceous vegetation.Permanent snow and ice15At least 60 % of area is covered by snow and ice for at least 10 months of the year.Barren16At least 60 % of area is non-vegetated barren (sand, rock, soil) areas with less than 10 % vegetation.Water bodies17At least 60 % of area is covered by permanent water bodies.Unclassified255Has not received a map label because of missing inputs
Schematic of ecosystem carbon cycle. Yellow arrow indicates carbon flux. Atmospheric CO2 transitions into gross primary production (GPP) by photosynthesis. GPP is partitioned into respiration and net primary production (NPP). NPP is partitioned into three biomass carbon pools (foliage, trunk, and root).
Potential LVBC trend maps during the period of 1916 to 2015 under different factorial simulations. (a) CO2 driving factorial simulation (S2), (b) CO2+precipitation driving factorial simulation (S3), (c) CO2+temperature driving factorial simulation (S4), and (d) CO2+ radiation driving factorial simulation (S5). Positive values indicate increasing trends in the ratio, and vice versa. All results from the Mann–Kendall and Sen slope statistical tests correspond to the 95 % confidence interval.
Potential WVBC variation trend maps during the period of 1916 to 2015 under different factorial simulations. (a) CO2 driving factorial simulation (S2), (b) CO2+precipitation driving factorial simulation (S3), (c) CO2+temperature driving factorial simulation (S4), and (d) CO2+radiation driving factorial simulation (S5). Positive values indicate increasing trends in the ratio, and vice versa. All results from the Mann–Kendall and Sen slope statistical tests correspond to the 95 % confidence interval.
The shift in hydrological regions defined by the multiyear average AI index from the period of 1916–1945 to the period of 1986–2015. The outermost numbers represent the percentage of hydrological regions in 1916–1945.
Spatial distribution of multiyear average fraction of managed pasture from 2001–2015 at 0.5 × 0.5 arcdeg resolution.
Map showing the largest vegetation component of each grid cell without anthropogenic disturbance from MCD12C1 and LUH2. ENF: evergreen needleleaf forest; EBF: evergreen broadleaf forest; DNF: deciduous needleleaf forest; DBF: deciduous broadleaf forest; MF: mixed forest; CS: closed shrublands; OS: open shrublands; WS: woody savannas; SA: savannas; GL: grasslands; NI: not included, which means the zone is not covered by vegetation without anthropogenic disturbance.
Trends in average density of potential LVBC. (a) Modelled trend of annually averaged LVBC globally. Modelled trends in annually averaged LVBC in hyper-arid regions (b), arid regions (c), semi-arid regions (d), sub-humid regions (e), and humid regions (f).
Trends in average density of potential WVBC. (a) Modelled trend of annually averaged WVBC globally. Modelled trends in annually averaged WVBC in hyper-arid regions (b), arid regions (c), semi-arid regions (d), sub-humid regions (e), and humid regions (f).
Code and data availability
The code of SEIB-DGVM version 3.02 can be download from http://seib-dgvm.com/ (Sato et al., 2020). Climatic Research Unit data can be downloaded from
https://crudata.uea.ac.uk/cru/data/hrg/ (Harris et al., 2020). The soil physical
parameters can be downloaded from http://cola.gmu.edu/gswp/ (last access: 7 September 2022, Dirmeyer et al., 1999). The reconstructed CO2 concentration dataset can be downloaded from http://seib-dgvm.com/. In model validation, Ecosystem
Model–Data Intercomparison (multiyear average NPP product) data were
collected from 10.3334/ORNLDAAC/615 (Olson et al., 2013). Remote
sensing product MOD17A3 data were obtained from https://lpdaac.usgs.gov/products/mod17a3hgfv006/ (Running et al., 1999), MCD12C1 data were
obtained from https://ladsweb.modaps.eosdis.nasa.gov/search/order (Sulla-Menasha and Friedl, 2018), and LUH2 data were
obtained from https://luh.umd.edu/ (Hurtt et al., 2020). All data required to reproduce the analyses described herein are publicly available at the following DOI 10.5281/zenodo.5811832 (Tong, 2021).
Author contributions
ST designed the research. ST and HS performed the research and developed the
methodology. ST analysed data and produced the outputs. ST, HS, JC,
and CYX wrote the first manuscript draft. WW and GW supervised the
study. All the authors discussed the methodology and commented on various
versions of the manuscript.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank Zefeng Chen for technical support. We gratefully thank the following data providers and model developers for their continuous efforts and for sharing their data: the University of East Anglia, the National Centers for Environmental Prediction (NCEP), the National Oceanic and Atmospheric Administration (NOAA), the University of Maryland, and the Center for Ocean–Land–Atmosphere Studies (COLA).
Financial support
This research has been jointly supported by the National Natural Science Foundation of China (grant nos. 51979071, U2240218, 91547205), the National Key Research and Development Program of China (2018YFA0605402, 2021YFC3201100), the QingLan Project of Jiangsu Province, the National “Ten Thousand Program” Youth Talent, and the “333 project” of Jiangsu Province.
Review statement
This paper was edited by Hans Verbeeck and reviewed by two anonymous referees.
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