Multiple lines of evidence have demonstrated the persistence of global land
carbon (C) sink during the past several decades. However, both annual net
ecosystem productivity (NEP) and its inter-annual variation (IAVNEP)
keep varying over space. Thus, identifying local indicators for the
spatially varying NEP and IAVNEP is critical for locating the major and
sustainable C sinks on land. Here, based on daily NEP observations from
FLUXNET sites and large-scale estimates from an atmospheric-inversion
product, we found a robust logarithmic correlation between annual NEP and
seasonal carbon uptake–release ratio (i.e. U/R). The cross-site variation in
mean annual NEP could be logarithmically indicated by U/R, while the spatial
distribution of IAVNEP was associated with the slope (i.e. β)
of the logarithmic correlation between annual NEP and U/R. Among biomes, for
example, forests and croplands had the largest U/R ratio (1.06 ± 0.83)
and β (473 ± 112 g C m-2 yr-1), indicating the
highest NEP and IAVNEP in forests and croplands, respectively. We
further showed that these two simple indicators could directly infer the
spatial variations in NEP and IAVNEP in global gridded NEP products.
Overall, this study provides two simple local indicators for the intricate
spatial variations in the strength and stability of land C sinks. These
indicators could be helpful for locating the persistent terrestrial C sinks
and provide valuable constraints for improving the simulation of
land–atmospheric C exchanges.
Introduction
Terrestrial ecosystems reabsorb about one-quarter of anthropogenic CO2
emission (Ciais et al., 2019) and are primarily responsible for the recent
temporal fluctuations in the measured atmospheric-CO2 growth rate
(Randerson, 2013; Le Quéré et al., 2018). In addition, evidence
based on eddy-flux measurements (Baldocchi et al., 2018; Rödenbeck et
al., 2018), aircraft atmospheric budgets (Peylin et al., 2013) and
process-based model simulations (Poulter et al., 2014; Ahlström et al.,
2015) has shown a large spatial variability in net ecosystem productivity
(NEP) on land. The elusive variation in terrestrial NEP over space
refers to both of the substantial varying mean annual NEP and the divergent
inter-annual variability (IAV) in NEP (i.e. IAVNEP; usually quantified
as the standard deviation of annual NEP) across space (Baldocchi et al.,
2018; Marcolla et al., 2017). The mean annual NEP is related to the strength
of carbon exchange of a specific ecosystem (Randerson et al., 2002; Luo and
Weng, 2011; Jung et al., 2017), while IAVNEP characterizes the
stability of such a carbon exchange (Musavi et al., 2017). Thus, whether and
how NEP and IAVNEP change over space is important for predicting
the future locations of carbon sinks on land (Yu et al., 2014; Niu et
al., 2017).
Large spatial difference in terrestrial NEP has been reported from eddy-flux
measurements, model outputs and atmospheric-inversion products. In addition,
the global average IAV of NEP is large relative to global annual mean NEP
(Baldocchi et al., 2018). More importantly, the spatial variations in NEP
and IAVNEP have been typically underestimated by the global flux-tower-based product and the process-based global models (Jung et al., 2020;
Fu et al., 2019). These discrepancies have further revealed the necessity of
identifying local indicators for the spatially varying NEP and IAVNEP
separately. The NEP in terrestrial ecosystems is determined by two
components, including vegetation photosynthesis and ecosystem respiration
(Reichstein et al., 2005), and their relative difference could determine the
spatial variation in NEP (Baldocchi et al., 2015; Biederman et al., 2016).
Many previous analyses have attributed the IAVNEP at the site level to
the different sensitivities of ecosystem photosynthesis and respiration to
environmental drivers (Gilmanov et al., 2005; Reichstein et al., 2005) and
biotic controls (Besnard et al., 2018; Musavi et al., 2017). For example,
some studies have reported that IAVNEP is more associated with
variations in photosynthesis than carbon release (Ahlström et al., 2015;
Novick et al., 2015; Li et al., 2017), whereas others have indicated that
respiration is more sensitive to anomalous climate variability (Valentini et
al., 2000; von Buttlar et al., 2018). However, despite previous efforts
in a predictive understanding of the land–atmospheric C exchanges, the
multi-model spread has not reduced over time (Arora et al., 2020).
Therefore, it is imperative to explore the potential indicators for the
spatially varying NEP, which could help attribute the spatial variation in
NEP and IAVNEP into different processes and provide valuable
constraints for the global C cycle. Alternatively, the annual NEP of a given
ecosystem can be also directly decomposed into net CO2 uptake flux and
CO2 release flux (Gray et al., 2014), which are more direct components
for NEP (Fu et al., 2019). It is still unclear how the ecosystem net
CO2 uptake and release fluxes would control the spatially varying NEP.
Conceptually, the total net CO2 uptake flux (U) is determined by the
length of the CO2 uptake period (CUP) and the CO2 uptake rate, while the
total net CO2 release flux (R) depends on the length of the CO2 release
period (CRP) and the CO2 release rate (Fig. 1b). The variations in NEP
thus could be attributed to these decomposed components. A strong spatial
correlation between mean annual NEP and length of the CO2 uptake period has
been reported in evergreen needle- and broad-leaved forests (Churkina et
al., 2005; Richardson et al., 2013; Keenan et al., 2014), whereas
atmospheric-inversion data and vegetation photosynthesis models indicated a
dominant role of the maximal carbon uptake rate (Fu et al., 2017; Zhou et
al., 2017). However, the relative importance of these phenological and
physiological indicators for the spatially varying NEP remains unclear.
Relationship between annual NEP and U/R for 72
FLUXNET sites (of the form NEP=β⋅ln(UR)). (a) Dependence of annual NEP on the ratio
between total CO2 exchanges during net uptake (U) and release (R) periods
(i.e. U/R). Each line represents one flux site with at least 5
years of observations. (b) Conceptual figure for the decomposition framework
introduced in this study. Annual NEP can be quantitatively decomposed into
the following indicators: NEP=U-R. (c) Distribution of the explanation of
U/R on temporal variability in NEP (R2) for FLUXNET sites.
In this study, we decomposed annual NEP into U and R and explored the local
indicators for spatially varying NEP. Based on the eddy-covariance fluxes
from the FLUXNET2015 dataset (Pastorello et al., 2017) and the atmospheric-inversion product (Rödenbeck et al., 2018), we examined the relationship
between NEP and its direct components. In addition, we used the observations
to evaluate the spatial variations in NEP and IAVNEP in the FLUXCOM
product and a process-based model (CLM4.5; Oleson et al., 2013). The major
aim of this study is to explore whether there are useful local indicators
for the spatially varying NEP and IAVNEP in terrestrial ecosystems.
Materials and methodsDatasets
Daily NEP observations of eddy-covariance sites are obtained from the
FLUXNET2015 Tier 1 dataset. The FLUXNET2015 dataset provides half-hourly
data of carbon, water and energy fluxes at over 210 sites that are
standardized and gap-filled (Pastorello et al., 2017). However, time series
of most sites are still too short for the analysis of inter-annual variation
in NEP. So only the sites that provided the availability of eddy-covariance
flux measurements for at least 5 years are selected. This leads to a global
dataset of 72 sites with different biomes across different climatic regions.
Based on the biome classification from the International Geosphere–Biosphere
Programme (IGBP) provided for the FLUXNET2015 sites, the selected sites
include 35 forests (FORs), 15 grasslands (GRAs), 11 croplands (CROs), 4
wetlands (WETs), 2 shrublands (SHR) and 5 savannas (SAVs; Fig. S1 and Table S1 in the Supplement).
The Jena CarboScope inversion product combines high-precision measurements
of atmospheric-CO2 concentration with simulated atmospheric transport
to infer the net CO2 exchanges between land, ocean and atmosphere at
large scales (Rödenbeck et al., 2018). Here, we used the daily
land–atmosphere CO2 fluxes from the s85_v4.1 version at
a spatial resolution of 5∘×3.75∘.
Considering the relatively low spatial resolution of the Jena CarboScope inversion product, the daily fluxes were only used to calculate the local indicators
for the spatially varying NEP at the global scale.
Daily NEP simulations from the Community Land Model version 4.5 (CLM4.5) were
also used to calculate the local indicators for the spatially varying NEP at
the corresponding flux tower sites. We ran the CLM4.5 model from 1985 to
2010 at a spatial resolution of 1∘ with meteorological forcing from the Climate Research Unit and National Centers for Environmental Prediction (CRUNCEP). Here, NEP was derived as the difference between gross primary productivity (GPP) and total ecosystem respiration (TER), and
TER was calculated as the sum of simulated autotrophic and heterotrophic
respiration. The daily outputs from CLM4.5 were used to calculate the local
indicators for the spatially varying NEP at both the global scale and the
FLUXNET site level.
The FLUXCOM product presents an upscaling of carbon flux estimates from 224
flux tower sites based on multiple machine-learning algorithms and
meteorological drivers (Jung et al., 2017). To be consistent with the
meteorological forcing of the Jena CarboScope inversion product and the CLM4.5 model, we
used the FLUXCOM CRUNCEPv6 products. In addition, in order to reduce the
uncertainty caused by machine-learning methods, we averaged all the FLUXCOM
CRUNCEPv6 products with different machine-learning methods. It should be
noted that the inter-annual variability in the FLUXCOM product is driven by
meteorological measurements and satellite data, which partially include
information on vegetation state and other land surface properties. Daily
outputs from FLUXCOM for the period 1985–2010 at 0.5∘ spatial
resolution were used to calculate the local indicators for the spatially
varying NEP at both the global scale and the FLUXNET site level.
Decomposition of NEP and the calculations for its local
indicators
The annual NEP of a given ecosystem can be defined numerically as the
difference between the net CO2 uptake and release (Fig. 1b). These
components of NEP contain both photosynthesis and respiration flux, which
directly indicate the net CO2 exchange of an ecosystem. The total net
CO2 uptake flux (U) and the total net CO2 release flux (R) can be
further decomposed as
1U=U‾×CUP2R=R‾×CRP,
where CUP (d yr-1) is the length of the CO2 uptake period, and CRP (d yr-1) is the length of the CO2 release period; U‾ (g C m-2 d-1) is the mean daily net CO2 uptake over CUP, and R‾ (g C m-2 d-1) represents the mean daily net CO2 release over
CRP. Many studies have reported that the vegetation net CO2 uptake during
the growing season and the non-growing season soil net CO2 release are
tightly correlated (Luo and Zhou, 2006; Zhao et al., 2016). Therefore, we
further tested the relationship between annual NEP and U/R (i.e.
NEP∝UR), which reflects the seasonal carbon
uptake–release ratio. Consequently, NEP in any given ecosystem can be
expressed as (Fig. S2)
NEP=β⋅lnUR,
where the parameter β represents the slope of the linear relationship
of NEP∝ln(UR), indicating the
site-level carbon uptake sensitivity. Based on the definitions of U and R, the
ratio U/R can be further written as
UR=U‾R‾⋅CUPCRP.
The ratio of U‾R‾ reflects the relative physiological
difference between ecosystem CO2 uptake and release strength, while the
ratio of CUPCRP is an indicator of net ecosystem CO2
exchange phenology. Environmental changes may regulate these ecological
processes and ultimately affect the ecosystem NEP. The slope β
indicates the response sensitivity of NEP to the changes in phenology and
physiological processes. All of β, CUPCRP and
U‾R‾ were then calculated from the selected eddy-covariance sites and the corresponding pixels of these sites in models.
These derived indicators from eddy-covariance sites were then used to
benchmark the results extracted from the same locations in models.
Relationship between annual NEP and U/R for the Jena CarboScope inversion product (of the form NEP=β⋅ln(UR)). The black box indicates the location of the
sample.
Calculation of the relative contributions
We further quantified the relative contributions of
U‾R‾ and CUPCRP in driving the spatial
variations in NEP:
NEP=β⋅lnU‾R‾+lnCUPCRP.
For each eddy-covariance site, the parameter β was constant. Then, we
used a relative-importance analysis method to quantify the relative
contributions of these two ratios to the spatial variations in
NEP. The algorithm was performed with the “relaimpo” package
in R (R Development Core Team, 2011). The relaimpo package is based on
variance decomposition for multiple-linear-regression models. We chose the
most commonly used method named “Lindeman–Merenda–Gold” (LMG; Grömping, 2007) from the methods provided by the relaimpo package.
This method allows us to quantify the contributions of explanatory variables
in a multiple-linear-regression model. Across the 72 FLUXNET sites, we
quantified the relative importance of U‾R‾ and
CUPCRP to cross-site changes in NEP.
ResultsThe relationship between NEP and its direct components
To find local indicators for the spatially varying NEP in terrestrial
ecosystems, we tested the relationship between NEP and its direct
components (U and R) across the 72
flux tower sites. The results showed that annual NEP was closely related to
the ratio U/R (Fig. S2). The logarithmic correlations between
annual NEP and U/R were significant at all sites (Fig. 1a), and
∼90 % of R2 values fall within a range of 0.7 to 1 (Fig. 1c).
In addition, the relationship between NEP and U/R was also
confirmed by the atmospheric-inversion product (i.e. Jena CarboScope inversion). The control of U/R on annual NEP was robust in most
global grid cells (i.e. 0.6<R2<1). The coefficient
of determination for this relationship was higher in 80 % of the regions
but lower in North America (Fig. 2). These two datasets both showed that the
indicator U/R could successfully capture the variability in annual
NEP.
Local indicators for spatially varying NEP
Across the 72 flux tower sites, the across-site variation in mean annual NEP
was significantly correlated to mean annual
ln(UR) of each site (R2=0.65, P<0.01; Fig. 3a). In this network, the mean annual ratio
ln(UR) was a good indicator for cross-site variation in
NEP. By contrast, the spatial variation in IAVNEP was moderately
explained by the slope (i.e. β) of the temporal correlation between
NEP and ln(UR) at each site (R2=0.39,
P<0.01; Fig. 3b) rather than ln(UR)
(Fig. S3). The wide range of the ratio β reveals a large divergence in NEP
sensitivity across biomes, ranging from 121 ± 118 g C m-2 yr-1 in shrubland to 473 ± 112 g C m-2 yr-1 in
cropland.
Contributions of the two indicators in explaining the
spatial patterns of mean annual NEP and IAVNEP. (a) The relationship
between annual mean NEP and ln(UR) across
FLUXNET sites (R2=0.65, P<0.01). The insets show the
magnitude of ln(UR) for different terrestrial
biomes. (b) The explanation of β in IAVNEP (R2=0.39, P<0.01). The insets show the distribution of parameter β for
different terrestrial biomes. The number of years at each site is
indicated with the size of the point.
The decomposition of indicator U/R into U‾R‾
and CUPCRP allowed us to quantify the relative importance of
these two ratios in driving NEP variability. The linear regression and
relative-importance analysis showed a more important role of
CUPCRP (58 %) than U‾R‾ (42 %) in
explaining the cross-site variation in NEP (Fig. 4). Therefore, the spatial
distribution of mean annual NEP was more strongly driven by the phenological
changes.
The relative contributions of the local indicators in explaining the spatial patterns of mean annual NEP. (a) The linear regression between mean annual NEP with
CUPCRP (R2=0.33, P<0.01) and
U‾R‾ (R2=0.25, P<0.01) across sites. (b) The relative contributions of each indicator to the spatial variation in mean annual NEP. The number of years at each site is indicated with the size of the point.
Simulated spatial variations in NEP by models
We further used these two simple indicators (i.e. UR and
β) to evaluate the simulated spatial variations in NEP by the global
flux-tower-based product (i.e. FLUXCOM) and a widely used process-based
model at the FLUXNET site level (i.e. CLM4.5). We found that the low
spatial variation in mean annual NEP in FLUXCOM and CLM4.5 could be inferred
from their more converging ln(UR) than
flux tower measurements (Fig. 5). The underestimated variation in
IAVNEP in these modelling results was also clearly shown by the smaller
β values (268.22, 126.00 and 145.08 for FLUXNET, FLUXCOM and CLM4.5,
respectively; Fig. 5b).
Representations of the spatially varying NEP and its local
indicators in the FLUXCOM product and the Community Land Model (CLM4.5) at the
FLUXNET site level. (a) The variation in mean annual NEP and IAVNEP
is derived from FLUXNET, FLUXCOM and CLM4.5. Variation in mean annual NEP: the
standard deviation of mean annual NEP across sites; variation in
IAVNEP: the standard deviation of IAVNEP across sites. (b) Representations of the local indicators for NEP in FLUXNET, FLUXCOM and
CLM4.5. The corresponding distributions of ln(UR) and β are shown at the top and right. Significance of the
relationship between annual NEP and ln(UR) for
each site is indicated by the circles. Closed circles: P<0.05; open
circles: P>0.05. Note that the modelled results are from the
pixels extracted from the same locations of the flux tower sites.
In addition, the spatial variations in NEP and IAVNEP were associated
with the spatial resolution of the product (Marcolla et al., 2017).
Considering the scale mismatch between FLUXNET sites and the gridded
product, we run the same analysis at the global scale based on the Jena CarboScope inversion product. At the global scale, the spatial variation in mean annual
NEP can also be well indicated by ln(UR) (Fig. 6). The larger net C uptake in FLUXCOM resulted from its higher simulations
for ln(UR). Furthermore, the larger spatial
variation in IAVNEP in CLM4.5 could be inferred from the indicator
β.
Representations of the spatially varying NEP and its local
indicators in the FLUXCOM product and the Community Land Model (CLM4.5) at the
global scale. (a) The variation in mean annual NEP and IAVNEP is derived
from Jena CarboScope inversion, FLUXCOM and CLM4.5. Variation in mean annual NEP: the
spatial variation in mean annual NEP; variation in IAVNEP: the spatial
variation in standard deviation in IAVNEP. (b) Representations of the
local indicators for NEP in Jena CarboScope inversion, FLUXCOM and CLM4.5.
DiscussionNew perspective for locating the major and
sustainable land C sinks
Large spatial differences in mean annual NEP and IAVNEP have been
well documented in previous studies (Jung et al., 2017; Marcolla et al.,
2017; Fu et al., 2019). Here we provide a new perspective for quantifying
the spatially varying NEP by tracing annual NEP into several local
indicators. Therefore, these traceable indicators could provide useful
constraints for predicting annual NEP, especially in areas without
eddy-covariance towers.
Typically, the C sink capacity and its stability in a specific ecosystem are
characterized separately (Keenan et al., 2014; Ahlström et al., 2015; Jung
et al., 2017). Here we integrated NEP into two simple indicators that could
directly locate the major and sustainable land C sink. Among biomes, forests
and croplands had the largest ln(UR) and β,
indicating the strongest and the most unstable C sink in forests and
croplands, respectively. However, the relatively lower β in
shrublands and savannas should be interpreted cautiously. There are very few
semi-arid ecosystems in the FLUXNET sites, while they represent a large
portion of land at the global scale and have been shown to substantially
control the inter-annual variability in NEP (Ahlström et al., 2015). The
highest β implies that the land covered by cropland has the largest
IAVNEP. Therefore, the reported rapid global expansion of cropland may
enlarge the fluctuations in land–atmosphere CO2 exchange. In fact, the
cropland expansion has been confirmed as one important driver of the recent
increasing global vegetation growth peak (Huang et al., 2018) and
atmospheric-CO2 seasonal amplitude (Gray et al., 2014; Zeng et al.,
2014).
Joint control of plant phenology and physiology on mean annual
NEP
Recent studies have demonstrated that the spatio-temporal variations in
terrestrial gross primary productivity are jointly controlled by plant
phenology and physiology (Xia et al., 2015; Zhou et al., 2017). Here we
demonstrated that the spatial difference in mean annual NEP was determined
by both the phenology indicator CUPCRP (58 %) and the
physiological indicator U‾R‾ (42 %). In addition, the
lower contribution of the physiological indicator could partly be attributed
to the convergence of U‾R‾ across FLUXNET sites (Fig. S4).
The convergent U‾R‾ across sites was first discovered
by Churkina et al. (2005) as 2.73 ± 1.08 across 28 sites, which
included deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), and cropland and grassland. In this study, we found that the
U‾R‾ across the 72 sites is 2.71 ± 1.61, which
confirms the findings of Churkina et al. (2005). This spatial convergence of
U‾R‾ at the site level provides important constraints for
global models that simulate large spatial variation in physiological
processes (Peng et al., 2015; Xia et al., 2017). These findings imply that
the phenology changes will greatly affect the locations of the terrestrial
carbon sink by modifying the length of the carbon uptake period (Richardson et
al., 2013; Keenan et al., 2014).
The simulated local indicators from gridded products
This study showed that the considerable spatial variations in mean annual
NEP and IAVNEP from global gridded products could also be inferred from
their local indicators. The low variations in the U/R ratio in CLM4.5
could be largely due to their simple representations of the diverse
terrestrial plant communities in a few functional plant types with
parameterized properties (Cui et al., 2019; Sakschewski et al., 2015). In
addition, the higher U/R ratio from the FLUXCOM product indicated its
widely reported larger net C uptake (Fig. 6; Jung et al., 2020). Meanwhile,
the ignorance of fire, land-use change and other disturbances could lead to
the smaller β by allowing for only limited variations in phenological
and physiological dynamics (Reichstein et al., 2014; Kunstler et al., 2016).
Although the magnitude of IAVNEP depends on the spatial resolution
(Marcolla et al., 2017), we recommend that future model-benchmarking analyses
use not only the global product compiled from the machine-learning method (Bonan
et al., 2018) but also the site-level measurements or indicators (Xia et
al., 2020).
Conclusions and further implications
In summary, this study highlights the changes in NEP and IAVNEP over
space on land and provides the U/R ratio and β as two
simple local indicators for their spatial variations. These indicators could
be helpful for locating the persistent terrestrial C sinks where the
ln(UR) ratio is high but the β is low. Their
estimates based on observations are also valuable for benchmarking and
improving the simulation of land–atmospheric C exchanges in Earth system
models. The findings in this study have some important implications for
understanding the variation in NEP on land. First, forest ecosystems
have the largest annual NEP due to the largest ln(UR),
while croplands show the highest IAVNEP because of the highest β. Second, the spatial convergence of U‾R‾ suggests a
tight linkage between plant growth and soil microbial
activities in the non-growing season (Xia et al., 2014; Zhao et al., 2016). However, it remains
unclear whether the inter-biome variation in U‾R‾ is
due to different plant–microbe interactions between biomes. Third, the
within-site convergent but spatially varying β needs better
understanding. Previous studies have shown that a rising standard deviation
of ecosystem functions could indicate an impending ecological state
transition (Carpenter and Brock, 2006; Scheffer et al., 2009). Thus, a
sudden shift in the β value may be an important early-warning signal
for the critical transition of carbon uptake sensitivity of an ecosystem. In
this study, the atmospheric-inversion product shows low correlation between
NEP and ln(UR) in some boreal ecosystems, which might
be due to the fact that the terrestrial NEP is not well constrained for these regions or because these boreal ecosystems are experiencing state transition. Therefore, the
robustness in the relationship between annual NEP and
ln(UR) depends on the temporal stability of carbon
uptake sensitivity for an ecosystem. In addition, the spatial variation in
β reveals the differences in carbon uptake sensitivity across
ecosystems. Furthermore, considering the limited eddy-covariance sites with
long-term observations, these findings need further validation once the
longer time series of measurements from more sites and vegetation types
become available.
Data availability
The FLUXNET2015 dataset is available online at https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/ (Lawrence Berkeley National Laboratory, 2019), and the data
supporting the findings of this study are available within the article and
the Supplement. The FLUXCOM NEP product can be downloaded
from the data portal of the Max Planck Institute for Biochemistry
(https://www.bgc-jena.mpg.de/geodb/projects/Home.php; Jung et al., 2017). The Jena CarboScope
inversion product is available at
http://www.bgc-jena.mpg.de/CarboScope/?ID=s (Rödenbeck et al., 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-6237-2020-supplement.
Author contributions
EC and JX devised and conducted the analysis. YL, SN, YW and CB provided critical feedback on the method and results. All
authors contributed to discussion of results and writing the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work
used eddy-covariance datasets acquired and shared by the FLUXNET community,
including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica,
CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass,
ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia and USCCC. The
ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The
FLUXNET eddy-covariance data processing and harmonization were carried out by
the European Fluxes Database Cluster, AmeriFlux Management Project and
Fluxdata project of FLUXNET with the support of the CDIAC and ICOS Ecosystem
Thematic Center as well as the OzFlux, ChinaFlux and AsiaFlux offices.
Financial support
This research has been supported by the National Key R&D Program of China (grant no. 2017YFA0604600), the National Natural Science Foundation of China (grant no. 31722009), National 1000 Young Talents Program of China, Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration (grant no. SHUES2020B01), and the Fundamental Research Funds for the Central Universities.
Review statement
This paper was edited by Sönke Zaehle and reviewed by three anonymous referees.
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