Effects of atmospheric aerosols on terrestrial carbon fluxes and CO2 concentrations in China

Journal Pre-proof Effects of atmospheric aerosols on terrestrial carbon fluxes and CO2 concentrations in China

Xiaodong Xie, Tijian Wang, Xu Yue, Shu Li, Bingliang Zhuang, Minghuai Wang PII:

S0169-8095(19)31111-1

DOI:

https://doi.org/10.1016/j.atmosres.2020.104859

Reference:

ATMOS 104859

To appear in:

Atmospheric Research

Received date:

26 August 2019

Revised date:

21 December 2019

Accepted date:

15 January 2020

Please cite this article as: X. Xie, T. Wang, X. Yue, et al., Effects of atmospheric aerosols on terrestrial carbon fluxes and CO2 concentrations in China, Atmospheric Research(2019), https://doi.org/10.1016/j.atmosres.2020.104859

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier.

Journal Pre-proof

Effects of atmospheric aerosols on terrestrial carbon fluxes and CO2 concentrations in China Xiaodong Xie1 Tijian Wang1,*, Xu Yue2, Shu Li1, Bingliang Zhuang1, and Minghuai Wang1 1 School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China 2 Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China * Corresponding author: [email protected]. Tel: +862589683797

Abstract

of

Atmospheric aerosols have contributed to the terrestrial carbon cycle through diffuse

ro

radiation fertilization effect and hydrometeorological feedbacks. In turn, perturbation of the terrestrial carbon sink alters atmospheric carbon dioxide (CO2) concentrations and

-p

influences future climate change. Here, we use a regional climate model, RegCM4,

re

coupled with the Yale Interactive terrestrial Biosphere model (YIBs) to assess the effects of the current aerosol loading on terrestrial carbon fluxes and atmospheric CO2

lP

concentrations during 2006–2015 over China. We found that aerosols enhance gross primary production (GPP) by 0.36 Pg C yr−1 (5%), which primarily stems from

na

Southwest and Southeast China. Meanwhile, the aerosol-induced diffuse fraction (DF) increase, surface cooling and vapor pressure deficit (VPD) decrease together lead to a

Jo ur

−0.06 C yr−1 (21%) reduction in the net ecosystem exchange (NEE). Among them, aerosol-induced DF increment is found to be the dominant contributor, which covers ~ 59–62% of China’s land area. The effects of aerosols on GPP and NEE are more evident in the growing season, with maximum effects occurring in July and August, respectively. Moreover, the terrestrial carbon sink enhancement due to aerosols further results in a significant decline in CO2 concentrations, with a large reduction (>2 ppm) found in southern and eastern parts of China during the summer. Our results highlight the importance of understanding the interactions among aerosol pollution, climate change, and the global carbon cycle. Keywords: Aerosols, Diffuse radiation fertilization, Terrestrial carbon fluxes, Carbon dioxide, China

–1–

Journal Pre-proof

1. Introduction Carbon dioxide (CO2) is an important greenhouse gas (GHG) and contributes ~ 65% to the total radiative forcing of long-lived GHGs [IPCC, 2013]. Atmospheric CO2 concentrations have been steadily increasing since the industrial revolution. The 14th World Meteorological Organization (WMO) Greenhouse Gas Bulletin reports that the global annual surface mean CO2 reached 405 ppm in 2017, with a mean annual growth rate of 2.24 ppm yr−1 over the last 10 years [WMO, 2018]. China has experienced a

of

relatively higher CO2 concentration (407 ppm) and growth rate (2.28 ppm yr−1) due to large emissions from human activities [CMA, 2018]. Anthropogenic CO2 emissions in

-p

carbon emitter in the world [Shan et al., 2018].

ro

China account for approximately 30% of global emissions, making China the largest

Apart from the steadily increasing CO2 levels, China has experienced frequent

re

episodes of severe haze pollution over the last decade due to rapid industrialization and

lP

urbanization [Yang et al., 2018]. However, it remains unclear how this high aerosol pollution over China may affect the atmospheric CO2 concentration through their

na

influences on ecosystem. Atmospheric aerosols have been known to affect the Earth’s radiation budget through the scattering and absorption of solar radiation; this has been

Jo ur

termed as the aerosol direct effect [Charlson et al., 1992]. This aerosol-induced radiation perturbation can further alter the underlying surface air temperature and the hydrological cycle, which is essential to plant growth and carbon assimilation [Law et al., 2002]. Thus, aerosols may exert a complex influence on terrestrial ecosystems through a disturbance in solar radiation and the related meteorological factors [Gu et al., 1999]. Terrestrial biosphere is an essential carbon sink, and absorbs ~ 30% of the anthropogenic CO2 emission [Quéré et al., 2018]. Therefore, aerosols can further affect CO2 concentrations through their effects on terrestrial carbon uptake. Previous studies have found that aerosols can enhance plant productivity and carbon sequestration by increasing diffuse radiation, which is known as diffuse radiation fertilization effect [Kanniah et al., 2012]. An earlier study by Gu et al. [2003] observed an enhancement in the terrestrial carbon sink at a deciduous forest following an eruption of Mount Pinatubo. However, several studies have suggested that aerosols could exert –2–

Journal Pre-proof both positive and negative impacts on ecosystem carbon uptake subject to the aerosol loading and sky conditions [Oliphant et al., 2011; Mercado et al., 2009]. For example, Cirino et al. [2014] assessed that the ecosystem carbon uptake increased by 20–29% in Amazonia when the AOD ranged from 0.1 to 1.5. Whereas Cohan et al. [2002] quantified that the net effects of aerosols on net primary production (NPP) could be positive, neutral, or negative depending on the AOD and cloud cover fractions. A recent study based on combined vegetation and radiation modelling has reported a 20–60% increase in NPP under clear-sky conditions over China. However, this benefit of diffuse radiation fertilization effect for shaded leaves can be offset or even reversed by the

of

inhibition due to a reduction in total radiation [Yue and Unger, 2017]. The effects of

ro

current aerosol loading on NPP are found to have significant regional difference in China. In the Northeast China, observed AOD is lower than the AOD threshold for

-p

maximum NPP, leading to a strong increase in NPP. Limited diffuse radiation

re

fertilization effect is found in the south-eastern coastal region and the North China, while negative effect occurs in Southwest China.

lP

Correspondingly, environmental variables such as temperature and vapor pressure deficit (VPD) can also strongly affect terrestrial carbon fluxes [Zhang et al., 2010].

na

However, the possible mechanisms of aerosol-related temperature depression in altering ecosystem carbon uptake can be more complicated. The net effect on the terrestrial

Jo ur

carbon sink depends not only on whether the resulting temperature is higher or lower than the photosynthetic optimum value [Cohan et al., 2002], but also on the relationship between temperature and the sensitivity of carbon assimilation and decomposition rate [Zhang et al., 2019]. Moreover, temperature controls the length of the growing season, especially in boreal regions, which is essential for terrestrial carbon uptake even at the ecosystem scale [Richardson et al., 2010]. Nevertheless, several studies have suggested that a rising temperature can enhance current vegetation growth in boreal regions due to an extension of the growing season [Wang et al., 2011], but vegetation growth in the tropics would be inhibited since the high temperature would exceed the thermal optimum of photosynthesis [Jung et al., 2017]. Globally, surface cooling from anthropogenic aerosols is estimated to have increased terrestrial carbon storage by 11.6– 41.8 Pg C between 1860 and 2005 [Zhang et al., 2019]. –3–

Journal Pre-proof The VPD is the driving force for transpiration in plants, and can strongly affect stomatal conductance and thus modulate the exchange of water, carbon, and energy between the canopy and the atmosphere [Damour et al., 2010]. A high aerosol loading could decrease the VPD through a reduction in air temperature and an increase in relative humidity, thus stimulating stomatal conductance and enhancing carbon uptake [Min and Wang, 2008]. The sensitivity of carbon fluxes to the VPD depends on plant functional type and ambient meteorological conditions. The effect of VPD on terrestrial carbon exchanges can be as important as that of diffuse radiation fertilization effect [Steiner and Chameides, 2005; Bai et al., 2012]. It is found to contribute ~ 58% of the

ro

plays a minor role for croplands [Strada et al., 2015].

of

total effects of aerosol loading for a typical temperate forest [Wang et al., 2018], but

Although many studies have reported the importance of aerosols to terrestrial carbon

-p

uptake, the combined effects of aerosols on plant growth and carbon assimilation still

re

remain unclear. Previous observation and modelling studies mainly devote to the diffuse radiation fertilization effect, but ignore the meteorological and hydrological feedbacks

lP

[Yue and Unger, 2017]. And most modelling studies are based on global model with a coarse horizontal resolution, which is hard to capture the regional climate signals by

na

local forcings and feedbacks. Moreover, most of the existing studies only focus on the effects of aerosols on plant growth and terrestrial carbon exchange, and few studies have

Jo ur

investigated the potential effects of aerosols on atmospheric CO2 concentrations. Thus, the main objectives of this study are to estimate the effects of the current aerosol loading in China on the terrestrial carbon fluxes and surface CO2 concentrations, and to investigate the relative contribution of different aerosol-related meteorological factors such as radiation, temperature and VPD to these effects. A new version of the regional climate model, RegCM4 [Shalaby et al., 2012], is used to simulate atmospheric aerosols and CO2. Additionally, the Yale Interactive terrestrial Biosphere (YIBs) model [Yue and Unger, 2015] is coupled to the RegCM4 model to explore the response of carbon dynamics to aerosols and the covarying meteorological factors. Compared with the global model, regional model can provide more information (such as land cover type and topography data) at fine grid scale, which helps to better simulate the biophysical process of terrestrial ecosystems and the spatiotemporal distribution of atmospheric –4–

Journal Pre-proof aerosols and CO2 concentrations. The methods and input data are described in Section 2. The results are presented and discussed in Section 3. A summary and conclusions are provided in Section 4.

2. Data and methods 2.1 Description of RegCM4 The RegCM4 was developed by the International Centre for Theoretical Physics (ICTP) and has been widely used to research the interactions between aerosols, climate, and biogeochemical cycles [Zhou et al., 2014]. A detailed description of the model

of

framework can be found in Giorgi et al. [2012]. The basic aerosol module in RegCM4 calculates sulfate, black carbon, organic carbon, dust, and sea salt aerosols [Solmon et

ro

al., 2006], and includes advection, diffusion, and dry and wet removal processes. The

-p

latest version of RegCM4 includes an online gas phase chemistry module, Carbon Bond Mechanism (CBMZ, version Z) [Shalaby et al., 2012], which is coupled to a

re

thermodynamic equilibrium model, ISORROPIA II, to describe inorganic aerosols such

lP

as nitrate and sulfate [Li et al., 2016]. Secondary organic aerosols (SOA) are calculated based on a volatility basis set (VBS) model proposed by Yin et al. [2015]. The radiative

na

transfer scheme derived from the Community Climate Model (CCM3, version 3), developed by the National Center for Atmospheric Research (NCAR), is implemented in RegCM4 [Kiehl et al., 1996]. For shortwave radiation, the effects of atmospheric water

Jo ur

vapor and greenhouse gases are included in RegCM4 following the δ-Eddington approximation. Scattering and absorption of aerosols are also accounted for, based on the aerosol optical properties (e.g. absorption coefficient and single scattering albedo) [Giorgi et al., 2012].

To investigate aerosol direct radiative effects on carbon dynamics, the RegCM4 is revised to include CO2 simulations [Xie et al., 2018]. CO2 is added to the model as an inert tracer species, whose concentrations are modulated by atmospheric transport, and source and sink processes. Four types of surface fluxes are implemented, including anthropogenic CO2 emissions, biomass burning emissions, air-sea CO2 exchanges, and terrestrial biosphere CO2 fluxes. The first three are prescribed (detailed in Section 2.3) whereas the biospheric fluxes are calculated using the YIBs model (detailed in Section 2.2). Detailed model setup and input data used in this study can be found in Section 2.3. –5–

Journal Pre-proof

2.2 Description of RegCM4-YIBs coupled model The terrestrial biosphere model YIBs was coupled to the RegCM4 to simulate terrestrial carbon cycles. The YIBs model is a process-based vegetation model that describes plant photosynthesis, respiration, and carbon allocation [Yue and Unger, 2015]. The Farquhar and Ball-Berry model is used to calculate leaf photosynthesis and stomatal conductance [Ball et al., 1987; Farquhar et al., 1980]. The canopy radiation scheme proposed by Spitters [1986] is adopted and each canopy layer is split into sunlit and shaded leaves. The soil respiration scheme derived from the Carnegie-Ames-

of

Stanford Approach (CASA) biosphere model [Potter et al., 1993] is implemented. Leaf

ro

area index (LAI) and tree height are updated in the model based on carbon allocation and prognostic phenology [Yue and Unger, 2015]. Thus, the YIBs model can simulate

-p

the responses of the terrestrial ecosystem to perturbations in meteorology, CO2

re

concentration, and land use [Yue et al., 2017]. A detailed description and evaluation of the YIBs model can be found in Yue and Unger [2015].

lP

One of the major parts of our work on model development is the implementation of the YIBs model to the RegCM4. In our model, the meteorological fields such as

na

radiation, temperature, and humidity that are calculated by the RegCM4, are used to drive the YIBs model every 6 minutes. As mentioned in Section 2.1, the RegCM4

Jo ur

simulates nearly all natural and anthropogenic aerosols, and includes the direct radiative effects of aerosols. The YIBs model uses the same domain as the RegCM4 and calculates ecosystem photosynthesis and respiration. The net ecosystem exchange (NEE) is calculated by subtracting GPP from the terrestrial ecosystem respiration (TER) and is provided to the RegCM4 as the biospheric flux mentioned in Section 2.1. Thus, the coupled model can investigate the interactions between climate, aerosol, and the terrestrial ecosystem.

2.3 Experimental design and input data The simulation domain used in this study is centred at 36°N, 107°E and covers most parts of East Asia (Figure 1). The horizontal resolution is 60 km × 60 km and the vertical resolution is set at 18 levels up to 50 hPa. The ECMWF’s (European Centre for –6–

Journal Pre-proof Medium-Range Weather Forecasts) Interim reanalysis (ERA-Interim) data at a grid resolution of 1.5° × 1.5° is interpolated for dynamic initial and boundary conditions at 6hour intervals to drive the model. The climatological chemical data from the global chemical transport model (MOZART) is used to provide the initial and boundary conditions of aerosols [Horowitz et al., 2003; Emmons et al., 2010]. Three dimensional CO2 concentrations obtained from National Oceanic and Atmospheric Administration’s (NOAA) CarbonTracker (CT) reanalysis model is used as initial and boundary conditions for background CO2 fields. The CT model is a data assimilation system that uses atmospheric CO2 observations from a global observational network and simulates

of

atmospheric transport to provide optimized CO2 mole-fractions and fluxes [Peters et al.,

ro

2007]. The CT2016 version used in this study provides CO2 mole-fractions data every 3hours for the global atmosphere at 25 pressure levels in a 3° × 2° grid resolution (data

-p

available at ftp://aftp.cmdl.noaa.gov/products/carbontracker). The RegCM4 provides

re

several parameterization schemes. The major physics options in our simulations are the cumulus convection scheme of Grell [1993], the boundary layer scheme of Holtslag et

lP

al. [1990], and the moisture scheme of Pal et al. [2000]. We use present-day equilibrium tree height and soil carbon pool derived from a 30-year spin-up procedure as the initial

na

condition in the YIBs model. Land cover data are derived based on retrievals from both MODIS [Hansen et al., 2003] and the Advanced Very High-Resolution Radiometer

Jo ur

(AVHRR) [Defries et al., 2001]. The original 16 plant functional types (PFTs) in this dataset is aggregated into 8 PFTs as required by the YIBs model: evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), C4 grassland (GRA4), C3 grassland (GRA3), shrubland (SHR), tundra (TDA), and cropland (CROP) (Figure 1). The MIX Asian emission inventory is applied to provide anthropogenic aerosol and CO2 emissions. This inventory was developed for the Model Inter-Comparison Study for Asia (MICS-ASIA III) and has a spatial resolution of 0.25° × 0.25° (data available at http://www.meicmodel.org/) [Li et al., 2017]. Biomass-burning aerosol and CO2 emissions are obtained from the Fire Inventory from the National Center for Atmospheric

Research

(NCAR)

(FINN)

(data

available

at

https://www2.acom.ucar.edu/modeling/finn-fire-inventory-ncar/) with a daily 1 km –7–

Journal Pre-proof resolution [Wiedinmyer et al., 2011]. Ocean CO2 fluxes are collected from CT2016 as mentioned previously. Two sets of numerical experiments are carried out to estimate aerosol direct radiative effects on carbon dynamics using the coupled model. In the first experiment (named RealCase), aerosol loading is fully coupled to the meteorology. The other is a control run (named CtrlCase) with the aerosol radiative effects switched off. All the cases are performed for 15 years (from 2001 to 2015), and the last 10 years are used for analyses. The aerosol direct radiative effects can therefore be evaluated by calculating the differences between the two experiments. Climate models always generate internal

of

variability, which can be considered as noise. Thus, a two-sided Student’s t-test is

ro

applied to assess the statistical significance of the difference between two experiments. We establish the null hypothesis that the difference in the means is zero:

-p

H0: 𝜇1 = 𝜇2

(1)

re

where “μ1” and “μ2” represent the averages of the RealCase and the CtrlCase. The test statistic is given as Equation 2:

𝜇1 −𝜇2

lP

t=

√2(𝑠12 +𝑠22 )/(𝑛1 +𝑛2 −2)

(2)

na

where “s1” and “s2” are the variances of the two experiments. And n = n1 = n2 is total

2.4 Analyses

Jo ur

data size. The null hypothesis will be rejected at the confidence level of 99%.

The terrestrial ecosystem is a complex system that is closely related to the surrounding environment, such as radiation, temperature, and water availability. Changes in meteorological factors due to aerosols are fully coupled with each other, thus making it hard to determine out how aerosols can affect carbon dynamics. We use a multiple regression approach to decompose the difference in terrestrial carbon fluxes and CO2 concentrations between the two experiments (i.e., RealCase − CtrlCase, see experimental design in Section 2.3) into the effects of different meteorological factors, as expressed by Equation 3: ∆Y = 𝑎 × ∆𝐷𝐹 + 𝑏 × ∆𝑇𝑎𝑖𝑟 + 𝑐 × ∆𝑉𝑃𝐷 + 𝜀

(3)

where ∆Y is the difference in terrestrial carbon fluxes (GPP and NEE) and CO2 –8–

Journal Pre-proof concentrations between two experiments (i.e., RealCase − CtrlCase). “a”, “b”, and “c” are the regression coefficients calculated using maximum likelihood estimates (MLE), which represent the estimated sensitivities of each ∆Y to DF, near surface air temperature (Tair), and VPD, respectively. ∆DF, ∆Tair, and ∆VPD are differences of corresponding meteorological factors between the two experiments (i.e., RealCase − CtrlCase). ε is a residual error term. Thus, the contributions of the changed meteorological factors caused by aerosols to terrestrial carbon fluxes and CO2 concentrations are estimated using Equations 4–6: (4)

∆𝑌𝑇𝑎𝑖𝑟 = 𝑏 × ∆𝑇𝑎𝑖𝑟

(5)

ro

∆𝑌𝑉𝑃𝐷 = 𝑐 × ∆𝑉𝑃𝐷

of

∆𝑌𝐷𝐹 = 𝑎 × ∆𝐷𝐹

(6)

-p

This decomposition method ascribes the changes in terrestrial carbon fluxes and CO2 concentrations to what are considered as the domain drivers, based on several field

re

experiments and model researches [Cirino et al., 2014; Wang et al., 2018]. However,

lP

other meteorological forcings might also affect terrestrial carbon fluxes and CO2. These omitted variables can generate some errors in our estimate, which is included in the

na

residual term. Additionally, the responses of the terrestrial carbon fluxes and CO2 to meteorological variables are assumed to be linear, which might differ from that in natural environments. Thus, the sensitivities estimated here denote the contributive

Jo ur

effect rather than the “true” value [Piao et al., 2013]. Nevertheless, previous studies have evaluated this simple approach and found that it is effective for estimating the effects of different meteorological forcings, especially for long time scale [Jung et al., 2017; Zhang et al., 2019].

3. Results 3.1 Model evaluation Simulated AOD is evaluated against Terra/MODIS AOD dataset (Figure 2). MODIS Collection 6 (C6) Level-3 Monthly Products (MOD08_M3; data available at https://modis-atmos.gsfc. nasa.gov/products/monthly) with a spatial resolution of 1° × 1° is applied. Here, we use the Deep Blue (DB) algorithm to derive aerosols over China, –9–

Journal Pre-proof which shows a better performance than other algorithms [Wei et al., 2019]. As can be seen from Figure 2, the spatial distribution and seasonal variations of the AOD are well captured by the model, with a significant correlation coefficient of 0.8. Generally, the AOD is high over most parts of eastern China with an annual mean of > 0.5. Two high AOD centres are observed in the North China Plain and the Sichuan Basin, and are attributed to the sustained anthropogenic emissions in these regions [Luo et al., 2014]. Monthly mean AODs in spring and summer are relatively larger than those in other seasons; maximum AODs are often observed in April (Figure S1). Previous studies show that the frequent dust storm during March to May is the dominant contributor to

of

the high AODs noted in spring [Xu et al., 2015]. Compared with monthly MODIS

ro

AODs, however, the model underestimates the mean AOD by 6.1% over China, especially in spring and summer.

-p

In addition, we compare the simulated AOD with measurements from six

re

AERONET stations over China: Beijing, Taihu, Xianghe, SACOL, Lulin and Hong_Kong_PolyU. The selection of these stations is determined by data availability,

lP

and long-term monitoring has been carried out at these six stations. Their location and surface type are listed in Table 1. Here we use AERONET level 2 version 3 data

na

products (data available at https://aeronet.gsfc.nasa.gov/), which is cloud-screened and quality assured. Since there is no common wavelength for simulation, MODIS, and

Jo ur

AERONET, we interpolate the AERONET AOD values at 440 and 675 to 550 nm using the Ångström empirical formula:

𝐴𝑂𝐷𝜆 = 𝛽 × 𝜆–𝛼

(7)

Where AODλ is the AOD at a given wavelength λ (unit: μm), β is the turbidity coefficient which is equal to the AOD at 1 μm, and α is the Ångström exponent. Figure 2c shows the validation of simulated AOD in terms of the monthly means using AERONET observations from 2006 to 2015. As it can be seen from Figure 2c, the correlation coefficients between simulation and observation range from 0.75 to 0.81, indicating that RegCM4-YIBs can reasonably reproduce the observed AOD. However the predicted AOD is generally overestimated by 5.81% (ranging from −4.94% to 13.71%) except for Beijing and SACOL sites. Such discrepancy is in part attributed to the uncertainty of emission sources as well as the biases in aerosol optical parameters. –10–

Journal Pre-proof To validate the simulated GPP, we use a global benchmark product derived by empirically upscaling observations from the current global network of eddy-covariance towers

(FLUXNET)

(data

available

at

https://www.bgc-

jena.mpg.de/geodb/projects/Home.php) [Jung et al., 2009]. This dataset is not a direct measurement of GPP, but a data-driven model using a model tree ensemble (MTE) approach [Jung et al., 2011]. GPP estimated in the CtrlCase is presented in Figure 3a, and FLUXNET GPP is given in Figure 3b. Note that all the results are averaged for the period 2006–2015. As can be seen, the spatial distribution of GPP is well captured by the coupled model. High GPP is simulated in Southwest, Central, and Southeast China,

of

where ENF and DBF are the dominant land cover types (Figure 1). The model

ro

overestimates annual GPP over Northeast (11.8%) and Southwest (9.7%) China but underestimates it by 12.4% over Southeast China. Overall, the simulated annual GPP on

-p

a national scale is 6.57 Pg C yr−1, which is slightly higher than the benchmark product (~

re

4.7%). Our result is comparable to previous estimates, such as Li et al. [2013] and Zhu et al. [2007], who estimated China’s annual GPP to be 6.04 and 6.24 Pg C yr−1,

lP

respectively. Compared with the results based on a global ModelE2–YIBs model [Yue et al., 2017], our estimate of GPP is closer to the benchmark product. This is probably

na

related to the higher spatial resolution when using the regional model. Additionally, the global MODIS satellite based NPP products (MOD17A3 Collection 5.5, here after

Jo ur

denoted as MOD17) [Zhao et al., 2005] are applied to evaluate simulated NPP in the CtrlCase. The MOD17 uses an empirical light use efficiency (LUE) approach to provide indirect estimates of global NPP. Large uncertainties remain due to limitations in the driver data and algorithm parameters, especially at a regional scale [Liu et al., 2013]. However, the spatial pattern of MOD17 is generally reasonable and has been widely used for model evaluations [Pavlick et al., 2013]. From Figures 3c and 3d, the simulated annual mean NPP for the period 2006–2015 is 9.8% higher than from the MOD17, mainly because the model overestimates values over Central (13.9%) and Northeast (19.7%) China. At the national scale, however, the estimated annual NPP (2.95 Pg C yr−1) is close to the average value (2.92 ± 0.12 Pg C yr−1) from 37 existing NPP data sets [Wang et al., 2017]. What’s more, the simulated NEE derived from the CtrlCase is shown in Figure 4. –11–

Journal Pre-proof High NEE is found in the Southwest, the Southeast and some parts of Northeast China, where forests dominate. Compared to that from CT model, the RegCM4-YIBs model reproduced reasonable seasonal variations of NEE with a high correlation coefficient (R = 0.8, confidence level above 99%) and a relatively low bias (NMB = −25% on national scale). The differences between the simulated NEE and the CT2016 NEE show that the model underestimates NEE (−0.21 ± 0.005 Pg C yr−1) in winter and spring, but overestimate NEE (0.4 ± 0.13 Pg C yr−1) in summer and autumn. Furthermore, national NEE is estimated as −0.29 Pg C yr−1, which can offset 17.6% of the carbon emissions from fossil fuel consumption in China from 2001 to 2010 [Zheng et al., 2016]. This

of

value is comparable to previous studies using different methods (Table 2). However, the

ro

NEE estimates still have large uncertainties among different studies with a wide range from −0.17 Pg C yr−1 to −0.39 Pg C yr−1, probably due to the limitations in observations,

-p

model structure and algorithm parameters [Jiang et al., 2013].

re

For surface CO2 mixing ratios, we use ground-based in-situ measurements obtained from the World Data Centre for Greenhouse Gases (WDCGG, data available at

lP

https://gaw.kishou.go. jp/). The locations of the six stations used in this study are shown in Figure 1, and their geographic information is listed in Table 1. Simulated monthly

na

CO2 mixing ratios from 2006 to 2015 with standard deviations are compared with monthly observations at the six WDCGG sites (Figure 5). Generally, the simulated CO2

Jo ur

concentrations are in good agreement with observations, with high correlation coefficients ranging from 0.84 (SDZ) to 0.98 (YON). The annual trends and seasonal variabilities are well captured by the coupled model. The CO2 concentrations in a remote ocean station (YON), and inland background stations (WLG and UUM) show a lower seasonal variation due to less influence from surface fluxes. A higher CO2 concentration is found in the urban station (SDZ), which suffers from strong anthropogenic emissions. The surface CO2 mixing ratios at the six stations all showed positive trends during 2006 to 2015, and the annual growth rates ranged from 2.11 (UUM) to 2.75 (LLN) ppm yr−1. The model shows best performance in the remote ocean site, YON, due to the limited influence of biogenic fluxes and anthropogenic emissions. However, the model performs less well for the coastal (TAP) and mountain (LLN) sites. The averaged CO2 mixing ratios simulated by the model during the study period are 1.2 (TAP) and 1.6 (LLN) ppm –12–

Journal Pre-proof higher than ground measurements. Possible reasons are the influence of complex local topography, the uncertainties in predicting mesoscale systems such as land-sea breeze, as well as the effect of ocean flux. For the inland urban site, SDZ, the model overestimates the overall CO2 concentration by 4.9 ppm, especially during the summer. In urban regions, it is difficult for the model to capture the impacts of local emission perturbation because the emission inventories used here are based on monthly means [Kou et al., 2013]. Additionally, the model performs better in winter and spring, but worse in summer for all sites. One of the possible reasons for this is the uncertainty in the simulated biospheric flux, which has been found to have significant impacts on

of

surface CO2 concentrations [Ahmadov et al., 2007]. From Figure 4b, the difference

ro

between the simulated NEE with that from CT model is 0.53 Pg C yr−1 in summer, much higher than that value in winter (−0.22 Pg C yr−1), spring (−0.21 Pg C yr−1) and autumn

-p

(0.28 Pg C yr−1). This indicates that the uncertainty in the simulated NEE is relatively

re

larger in summer, which will further lead to worse model performance in this season.

lP

3.2 Aerosol-induced changes in meteorology

Figures 6a and 6b show the simulated effects of aerosols on surface direct and

na

diffuse radiation (to be clear, all the radiation mentioned later is the one at surface) over China, respectively. Between 2006 and 2015, aerosol loading in China caused a 9.26 W

Jo ur

m−2 (8.03%, Figure S2) decrease in the direct downward solar radiation, and a 3.72 W m−2 (4.47%) increase in the diffuse component, leading to a total 5.54 W m−2 (2.79%) decrease in the total downward solar radiation. In general, the radiation change is larger in regions with a high AOD, such as the North China Plain, the Yangtze River Delta, and the Sichuan Basin. The largest increase in diffuse radiation can reach up to 30 W m−2 (20%). For Northwest China, radiation changes are relatively lower (within ± 2 W m−2), which is related to the carbonaceous and dust aerosols in this region. Previous studies have found that the carbonaceous aerosols emitted from agricultural and forest fires in northern India and Nepal and dust storms originated in Taklimakan Desert could be transported to the Himalayas and Tibetan Plateau, especially in spring [Cong et al., 2015; Xia et al., 2008]. Thus, the accumulated carbonaceous and dust aerosols further lead to a higher AOD in this region during spring (Figure S3), which can partly explain –13–

Journal Pre-proof the changes in the direct and diffuse radiation in this region. Changes in the radiation balance affect surface temperatures and the hydrological cycle, which further influence plant photosynthesis and respiration. The simulated differences in Tair due to the aerosol direct effect are shown in Figure 6c. The existence of atmospheric aerosols leads to a cooler atmosphere near the surface over China, with the maximum cooling exceeding 1.3 °C in the Sichuan Basin. The cooling effect is mostly found in south and east, whereas it is weaker in west and northeast. At the national scale, the annual Tair decreased by 0.26 °C. Our result is slightly lower than that (0.31 °C) calculated by Wang et al. [2015], who estimate both aerosol direct and indirect

of

effects.

ro

The VPD is the difference (deficit) between the amount of moisture in the air and how much moisture the air can hold when it is saturated. Previous studies have found

-p

that the VPD can profoundly affect plant productivity through reducing the stomatal

re

conductance [Novick et al., 2016]. Aerosol-induced changes in VPD are presented in Figure 6d. Generally, the VPD shows reductions over most of China. Large reductions

lP

are found in south and east, with a maximum of up to 1.2 hPa. On an annual mean and national mean basis, aerosols induce a reduction in the VPD of 0.24 hPa. The relationship

na

of ∆VPD against AOD is shown in Figure S4a. The correlation coefficient between ∆VPD and AOD is −0.64, indicating that VPD decreases significantly with AOD (confidence

Jo ur

level above 99%). One of the possible reason is that high AOD could decrease VPD through the cooling effect [Cirino et al., 2014]. This is coincident with the high correlation coefficient (R = 0.88, confidence level above 99%) between ∆VPD and ∆Tair (Figure S4b). Additionally, our results are consistent with previous studies based on field experiments [Gu et al., 2002; Wang et al., 2018] and model simulations [Wu et al., 2017].

3.3 Responses of terrestrial carbon fluxes Aerosol-induced

changes

in

surface

radiation

and

in

the

correlated

hydrometeorology affect the physiological processes of vegetation, thereby altering the carbon fluxes of terrestrial ecosystems. Figure 7 shows the simulated impacts on GPP, the TER, and the NEE over China. We estimate that aerosols lead to an increase in –14–

Journal Pre-proof ecosystem GPP over most of China, with regional enhancements of up to 0.94 g C m−2 day−1 in parts of Southwest, Southeast, and North China. The annual cycle of aerosolinduced GPP enhancement presents a single peak on the national scale (Figure S5). The largest change in GPP is found during July, when there is a high level of radiation and a moderate aerosol loading. This summer peak mainly originates from North, Northeast, and Central China, where crops are the dominant type of land cover (Figure 1). However, at lower latitudes, such as Southwest and Southeast China, the impact of aerosols on GPP is notable throughout the year. On a national basis, the aerosol direct effect is estimated to increase the total GPP by 0.36 Pg C yr−1, which is equivalent to 5%

of

of the simulated annual GPP (as mentioned in Section 3.1). A recent study has reported

ro

a similar GPP enhancement of 5–8% in eastern North America and Eurasia [Strada and Unger, 2016], which is consistent with our estimate. Compared with that at global scale,

-p

the estimated GPP enhancement in China is relatively higher. For example, Chen and

re

Zhuang [2014] found that aerosol enhanced the terrestrial GPP by 4.9 Pg C yr−1 (4%), while Strada and Unger [2016] showed a weak GPP sensitivity to aerosols (∼ 1–2%).

lP

This is probably related to the severe haze pollution in China [Zhai et al., 2019]. Additionally, aerosol is predicted to increase total NPP in China by 0.22 Pg C yr−1,

na

which is close to that (0.2 Pg C yr−1) from Yue et al. [2017]. Changes in the TER exhibit the similar spatial and temporal pattern with that of GPP

Jo ur

(Figure 7b), because they are coupled through the distribution of carbon assimilated in ecosystem carbon pools [Liu et al., 2018]. We estimate that current aerosol loading leads to a total TER increase of 0.30 Pg C yr−1 in China, with the maximum enhancement occurring in Sichuan Basin. The increase in the TER is smaller than that of GPP, leading to a negative NEE change (−0.06 Pg C yr−1), i.e., a larger terrestrial carbon sink due to the aerosol direct effect (Figure 7c). The estimated NEE change is equivalent to 21% of the total national NEE simulated in our coupled model (−0.29 Pg C yr−1), and 23%–31% of that estimated by Piao et al. [2009] (−0.19 to −0.26 Pg C yr−1). Our estimates agree well with that from a previous modelling study by Mercado et al. [2009], who reported an enhancement of 23.7% in global terrestrial carbon sink between 1960 and 1999. Additionally, the simulated NEE changes are consistent with that from several measurement-based studies, which found terrestrial carbon uptake increased by 10.3– –15–

Journal Pre-proof 34.9% due to aerosol effect [Bai et al., 2012; Cirino et al., 2014]. On an annual basis, most of the NEE decreases stem from Southwest and Central China, where EBF and DEF trees are dominant. The total NEE reductions in these regions contribute to ~ 50% of the total NEE change. In summer, however, the aerosol-induced NEE decreases are more significant in North and Northeast China, that account for 25% and 21% of the total NEE change, respectively. Interannual variations of ∆GPP and ∆NEE are shown in Figure 8. Generally, the interannual variations (defined as one standard deviation for 2006–2015) of ΔGPP (0.04 Pg C yr−1) and ΔNEE (5.5 Tg C yr−1) are relatively smaller than the mean ΔGPP (0.36

of

Pg C yr−1) and the mean ΔNEE (−0.06 Pg C yr−1) due to the aerosol direct effect. The

ro

changes in GPP show decrease trends (confidence level above 90%) from 2006 to 2010, but increase (confidence level above 95%) from 2010 to 2014. Such trends are similar

-p

with that of ΔDF, indicating that aerosol-induced DF change might be a dominant

re

contributor to the GPP change. The largest change in GPP (0.43 Pg C yr−1) occurs in the year 2014, while the smallest change is found in the year 2010 (0.32 Pg C yr−1). The

na

during 2010–2013.

lP

interannual variation of ∆NEE is similar with that of ∆GPP, with smaller NEE changes

3.4 Dominant aerosol-induced meteorological factors

Jo ur

To determine how aerosols affect terrestrial carbon fluxes, we observe the changes in GPP and NEE with respect to different meteorological factors using the method mentioned in Section 2.4. The main meteorological factors contributing to the variation in the terrestrial carbon flux are presented in Figure 9. Generally, the impacts of the aerosol direct effect on ecosystem carbon dynamics are complex and cannot be attributed to a single meteorological factor. However, the increase in the aerosol-induced DF is found to be the main factor causing GPP and NEE changes over most of China. The areas dominated by the DF cover approximately 59% and 62% of China’s land area for GPP and NEE, respectively, especially in northern and south-eastern regions. This finding is in general agreement with Knohl and Baldocchi [2008], who found that the enhanced photosynthesis of shaded leaves under conditions of diffuse radiation is sufficient to explain the increase in ecosystem carbon uptake efficiency. Similar results –16–

Journal Pre-proof are also found in previous studies based on observations [Cheng et al., 2015; Wang et al., 2018]. Meanwhile, for changes in GPP, the proportion of Tair-dominated areas (22%) is almost the same as that of the VPD-dominated areas (19%). However, for NEE change, Tair is found to contribute much more than VPD, especially in Southwest China, which is dominated by forests. This difference is mainly due to the strong sensitivity of respiration to temperature in regions with large soil carbon pools [Cox et al., 2013; Strada et al., 2015; Zhang et al., 2019]. Figure S6 shows the contributions of different aerosol-related meteorological factors to annual mean (averaged for 2006–2015) terrestrial carbon fluxes. We estimate that

of

changes in the DF due to the aerosol direct effect promote ecosystem productivity. As

ro

the DF increases due to aerosol scattering, shaded leaves receive more diffuse radiation while sunlit leaves are typically near light saturation, thus promoting the photosynthesis

-p

rate of the entire canopy [Rap et al., 2015; Yue and Unger, 2017]. The contributions of

re

the DF to GPP in North (44.3 Tg C yr−1), Central (46.5 Tg C yr−1), and Southeast (42.8 Tg C yr−1) China are nearly twice than that of other regions (≤ 25 Tg C yr−1), thereby

lP

resulting in an integrated, large GPP enhancement in China (192.4 Tg C yr−1) due to the DF. However, the impact of the DF increase on GPP is relatively limited in Southwest

na

China (13.6 Tg C yr−1), mainly due to the high DF from existing cloud cover in this region [Yue et al., 2017]. Surface cooling due to the aerosol direct effect is found to

Jo ur

inhibit plant productivity. A decreased Tair tends to suppress the rate of photosynthesis and shorten the length of the growing season, resulting in a reduction of GPP [Niu et al., 2008]. Overall, the change in GPP that is caused by Tair is relatively lower than of that related to the DF. A strong impact from aerosols as induced by a decrease in Tair on GPP is found in Southwest and Central China, where warmer temperatures are experienced. The aerosol-related VPD change is found to enhance GPP over China, and shows strong regional differences. A lower VPD stimulates stomatal opening and enhances water use efficiency, which results in an increase in GPP [Wu et al., 2017]. In Southwest China, the VPD contributes ~ 50 Tg C yr−1 change in GPP, whereas it exhibits a minor impact over other regions. This result is consistent with a recent data-based research by Li et al. [2018] that found that GPP was most sensitive to the VPD in Southwest China. Aerosol-induced DF increase is the main contributor to changes in the NEE over –17–

Journal Pre-proof most of China (Figure S6). The largest impact of the DF on the NEE is found in Central China, followed by Southeast and North China. These regions suffer from a high aerosol loading, thus leading to a substantial increase in the DF (Figure 6). From Figure S6, an aerosol-induced decrease in Tair exhibits a different signal for GPP to that of NEE, indicating that terrestrial carbon uptake is stimulated by Tair. A possible reason for this is that the carbon decomposition rate increases with temperature exponentially, thereby resulting in a stronger decrease in the TER than in GPP [Zhao et al., 2006]. A large impact of a decreased Tair on NEE is observed for Southwest and Central China, which are characterised by a warm and humid climate. Additionally, changes in the NEE due to

of

the VPD are similar to those of GPP, and the maximum VPD-related NEE change

ro

occurs in Southwest China.

-p

3.5 Aerosol-induced changes of surface CO2 concentration

re

Enhanced terrestrial carbon uptake due to the aerosol direct effect causes a further decline in atmospheric CO2 levels. Aerosol-induced seasonal changes in surface CO2

lP

concentrations between 2006 and 2015 over China are presented in Figure 10. Overall, CO2 reductions due to aerosols are particularly high during the summer (−0.62 ppm),

na

followed by spring (−0.31 ppm), and autumn (−0.21 ppm). A large CO2 reduction (>2 ppm) is observed in southern and eastern parts of China during the summer, which is

Jo ur

consistent with the strong terrestrial carbon sink enhancement in these regions (Figure 7). During the spring, CO2 reduction is mainly observed in Southwest China and some areas of Southeast and Central China, with a maximum reduction of 4 ppm. During the autumn, a moderate CO2 reduction can be seen in the Sichuan Basin and some areas of Central China. Although aerosol loading during the winter is relatively high over China, the impact on CO2 concentration is weak (−0.18 ppm). The seasonal differences in the variation of CO2 can be explained by the associated change in the NEE, which is caused by the aerosol direct effect and atmospheric transport. Our results are comparable with that of Kou et al. [2015], who found a remarkable CO2 reduction due to the biospheric flux over China. Moreover, changes in CO2 concentrations show a strong seasonal variation in the northern part of China, with a large CO2 reduction in the summer but minor changes in other seasons. This is mainly related to plant phenology and the fact –18–

Journal Pre-proof that the growing season is usually during the summer. The monthly mean CO2 changes between 2006 and 2015 over different regions are presented in Figure S7. Generally, surface CO2 concentrations exhibit a large reduction between June and October over most regions, when the maximum regional decrease of 1.19 ± 0.21 ppm occurs during August in Central China. As expected, the aerosol effect on surface CO2 is stronger during the summer on a national scale, with the maximum (~ 0.57 ± 0.19 ppm) occurring in August. A minor effect on CO2 concentrations is observed in Northwest China, which is a region dominated by bare soil and grassland (Figure 1) and experiences low plant productivity (Figure 3). In contrast, Southwest China is found

of

to have a large CO2 reduction throughout the year, which can be explained by the

ro

substantial decline in CO2 levels over the Sichuan Basin (Figure 10). To further investigate the impacts of aerosols on CO2, we estimate the sensitivities

-p

of CO2 to the DF, Tair, and the VPD based on the method mentioned in Section 2.4

re

(Figure S8). Overall, CO2 concentrations exhibit a strong negative sensitivity to the DF over most of China, with a magnitude of ~ 0.2 ppm/(W/m2). Whereas a modest positive

lP

sensitivity to the DF is estimated in a few regions over North China, where an aerosolinduced decrease in direct radiation inhibits the photosynthesis rate and leads to a

na

reduction in the terrestrial carbon sink. In contrast to the DF, the CO2 sensitivity to Tair is generally positive, especially in Southwest and North China, hence the aerosol-induced

Jo ur

surface cooling can reduce surface CO2 concentrations in these regions. The strongest positive sensitivity to Tair is found in the Hebei and Yunnan provinces, with values of up to 4 ppm/°C. Over Central and Northeast China, however, surface CO2 concentrations present a weak positive sensitivity to Tair. Moreover, positive CO2 sensitivity to the VPD is found over most of China with a maximum of 20 ppm/kPa observed for Northeast China. This positive sensitivity indicates that the decreased VPD due to the aerosol direct effect can lead to a reduction in CO2 concentrations. However, negative sensitivity can be found in a few parts of Southwest and Southeast China, which are regions that experience sufficient water availability. Overall, an increased DF together with a decreased Tair and VPD due to the aerosol direct effect can be beneficial for ecosystem carbon uptake, and lead to a substantial surface CO2 reduction.

–19–

Journal Pre-proof 3.6 Uncertainties We recognize a few caveats in this study that may cause uncertainty in this regional assessment. The limitations in input data and model parameters may generate uncertainties in simulating terrestrial carbon sink. Although our model reproduces reasonable seasonal variations of NEE when compared with CT2016 results (Figure 4b), the simulated NEE is much lower (~ 40%) than that derived from inventory-based method (Table 2). A possible explanation of this discrepancy is the increasing trend of carbon sink over China in recent years. Based on satellite data, Chen et al. [2019] found a greening pattern in China with plant leaf area increased by 17.8% for the period 2000–

of

2017, especially in forests and croplands. However, the estimated NEEs from different

ro

process-based vegetation models show a wide range, indicating that large uncertainties still exist in current model structure.

-p

Another uncertainty may come from the linear assumption in the multiple regression

re

approach (section 2.4). The response of plant biophysical process to meteorological and hydrological factors can be much more complex. Although this decomposition approach

lP

has been used and evaluated in previous studies [Piao et al., 2013; Strada et al., 2015; Jung et al., 2017; Zhang et al., 2019], it may fail when factors are strongly correlated or

na

terrestrial carbon fluxes are affected by other factors such as soil moisture and precipitation. To test the reasonability of our assessment, the frequency distribution

Jo ur

histogram of regression standardized residuals of GPP at four sites are given in Figure S9. Generally, the residual term belongs to normal distribution and the mean of the residual term is close to zero, indicating that the regression model is reasonable. What’s more, aerosol first and second indirect effects are not included due to their large uncertainties [IPCC, 2013]. Previous observation-based studies have found the response of terrestrial carbon fluxes to aerosol radiation effects is sensitive to sky conditions [Jing et al., 2010; Cirino et al., 2014]. Thus, the aerosol-cloud interactions may play an important role on land carbon fluxes, which should be further evaluated in future work. Despite these uncertainties, our results reveals that current aerosol loading in China enhances ecosystem production and carbon uptake mainly through the diffuse radiation fertilization effect, which is consistent with previous studies based on field experiments –20–

Journal Pre-proof [Gu et al., 2003; Strada et al., 2015; Cheng et al., 2015] and model simulations [Knohl and Baldocchi, 2008; Mercado et al., 2009; Yue and Urger, 2017]. The enhanced ecosystem carbon sink will further decrease atmospheric CO2 concentrations and mitigate the global warming tendency, which has not yet been considered in current generation of climate models. Our results highlight that more effort is need to investigate the interactions between aerosol pollution and carbon dynamics.

4. Summary and conclusions

of

In this study, we estimate the effects of aerosol loading on terrestrial carbon fluxes and atmospheric CO2 concentrations over China using the regional climate model

ro

RegCM4 coupled with the terrestrial biosphere model YIBs. The coupled model can

-p

reasonably capture the basic characteristics of the AOD, terrestrial carbon fluxes, and surface CO2 concentrations for the period from 2006 to 2015.

re

Our results indicate that aerosols stimulate plant growth as well as terrestrial carbon uptake in China. At the national scale, we estimate a 0.36 Pg C yr−1 (5%) increase in

lP

GPP and a decrease of −0.06 Pg C yr−1 (21%) in the NEE due to aerosols during 2006–

na

2015. We found that enhanced GPP occurred mainly in Southwest, Southeast, and North China, whereas the reduced NEE mainly stemmed from Southwest and Central China. In

Jo ur

addition, both GPP and the NEE exhibit a strong change that is related to aerosols during the summer. By analysing the changes of GPP and the NEE and assigning them to different meteorological factors, we found that a change in the aerosol-induced DF is the main contributor to both GPP and the NEE. The DF-dominated areas cover approximately 59% and 62% of China for GPP and the NEE, respectively. Moreover, surface cooling due to an aerosol direct effect is found to inhibit plant productivity but promote terrestrial carbon uptake. We also quantify the effects of aerosols on surface CO2 concentrations over China. A significant decline in CO2 is observed over most of China, with a maximum reduction of 4 ppm during the summer. On a national basis, the annual cycle of CO2 reduction exhibits a maximum of ~ 0.57 ± 0.19 ppm during August. A large CO2 reduction is observed in Southwest China throughout the year. Additionally, the sensitivities of CO2 are positive with respect to the DF, but negative with respect to Tair and the VPD, thus –21–

Journal Pre-proof indicating that the combined effects of increased aerosol-induced DF, surface cooling, and decreased VPD lead to the decline in surface CO2 concentration. Our study highlights the complex interaction between short-lived aerosols and long-lived CO2. Moreover, our results suggest that aerosol loading may play an important role in the global carbon cycle and future climate change.

Acknowledgements This work is supported by the National Natural Science Foundation of China (Grant

of

China

(Grant

Nos.

2016YFC0203303,

2017YFC0210106,

ro

Program

of

Nos. 41575145, 41621005, 91544230), National Key Basic Research & Development

2018YFC0213503). We wish to thank all research teams for providing their CO2

-p

observations on the WDCGG website. In addition, we would like to acknowledge

re

Tsinghua University for the availability of anthropogenic emissions and the National Center for Atmospheric Research (NCAR) for the free use of fire emissions. We also

References

na

CT2016 results.

lP

thank NOAA, Earth System Research Laboratory (ESRL) for providing CarbonTracker

Jo ur

Ahmadov, R., C. Gerbig, R. Kretschmer, S. Koerner, B. Neininger, A. J. Dolman, and C. Sarrat (2007), Mesoscale covariance of transport and CO2 fluxes: Evidence from observations and simulations using the WRF-VPRM coupled atmosphere-biosphere model, Journal of Geophysical Research: Atmospheres, 112(D22), D22,107, doi:10.1029/2007JD008552. Bai, Y., J. Wang, B. Zhang, Z. Zhang, and J. Liang (2012), Comparing the impact of cloudiness on carbon dioxide exchange in a grassland and a maize cropland in northwestern China, Ecological Research, 27(3), 615–623, doi:10.1007/s11284-012-0930-z. Ball, J. T., I. E. Woodrow, and J. A. Berry (1987), A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions, pp. 221–224, Springer Netherlands, Dordrecht, doi:10.1007/978-94-017-05196_48. Charlson, R. J., Schwartz, S. E., Hales, J. M., Cess, R. D., Coakley, J. A., Hansen, J. E., and –22–

Journal Pre-proof Hofmann, D. J. (1992), Climate Forcing by Anthropogenic Aerosols, Science, 255(5043), 423–430, doi:10.1126/science.255.5043.423. Chen, M., and Q. Zhuang (2014), Evaluating aerosol direct radiative effects on global terrestrial ecosystem

carbon

dynamics

from

2003

to

2010,

Tellus

B,

66,

21808,

doi:10.3402/tellusb.v3466.21808. Chen, C., T. Park, X. Wang, S. Piao, B. Xu, R. Chaturvedi, R. Fuchs, V. Brovkin, P. Ciais, R. Fensholt, H. Tømmervik, G. Bala, Z. Zhu, R. Nemani, and R. Myneni (2019), China and India lead in greening of the world through land-use management, Nature Sustainability, 2, 122–129, doi:10.1038/s41893-019-0220-7.

of

Cheng, S., G. Bohrer, A. Steiner, D. Hollinger, A. Suyker, R. Philips, and K. Nadelhoffer

ecosystems,

Agricultural

doi:10.1016/j.agrformet.2014.11.002.

and

Forest

Meteorology,

201,

98–110,

-p

temperate

ro

(2015), Variations in the influence of diffuse light on gross primary productivity in

re

Cirino, G. G., R. A. F. Souza, D. K. Adams, and P. Artaxo (2014), The effect of atmospheric aerosol particles and clouds on net ecosystem exchange in the Amazon, Atmospheric

lP

Chemistry and Physics, 14(13), 6523–6543, doi:10.5194/acp-14-6523-2014. CMA (2018), China Greenhouse Gas Bulletin: The State of Greenhouse Gases in the

na

Atmosphere Based on Chinese and Global Observations before 2017, Tech. rep., China Meteorological Administration.

Jo ur

Cohan, D. S., J. Xu, R. Greenwald, M. H. Bergin, and W. L. Chameides (2002), Impact of atmospheric aerosol light scattering and absorption on terrestrial net primary productivity, Global Biogeochemical Cycles, 16(4), 37–1–37–12, doi:10.1029/2001GB001441. Cong, Z., S. Kang, K. Kawamura, B. Liu, X. Wan, Z. Wang, S. Gao, and P. Fu (2015), Carbonaceous aerosols on the south edge of the Tibetan Plateau: concentrations, seasonality and sources. Atmospheric Chemistry and Physics, 15(3), 1573–1584, doi:10.5194/acp-151573-2015. Cox, P. M., D. Pearson, B. B. Booth, P. Friedlingstein, C. Huntingford, C. D. Jones, and C. M. Luke (2013), Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability, Nature, 494, 341, doi:10.1038/nature11882. Damour, G., T. Simonmeau, H. Cochard, and L. Urban (2010), An overview of models of stomatal conductance at the leaf level, Plant, Cell & Environment, 33(9), 1419–1438, –23–

Journal Pre-proof doi:10.1111/j.1365-3040.2010.02181.x. Defries, R. S., M. C. Hansen, J. R. G. Townshend, A. C. Janetos, and T. R. Loveland (2001), A new global 1-km dataset of percentage tree cover derived from remote sensing, Global Change Biology, 6(2), 247–254, doi:10.1046/j.1365-2486.2000.00296.x. Emmons, L. K., S. Walters, P. G. Hess, J. F. Lamarque, G. G. Pfister, D. Fillmore, C. Granier, A. Guen- ther, D. Kinnison, T. Laepple, J. Orlando, X. Tie, G. Tyndall, C. Wiedinmyer, S. L. Baughcum, and S. Kloster (2010), Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3(1), 43–67, doi:10.5194/gmd-3-43-2010.

of

Fang, J., G. Yu, L. Liu, S. Hu, and F. Chapin (2018), Climate change, human impacts, and

ro

carbon sequestration in China. Proceedings of the National Academy of Sciences, 115, 4015-4020. doi: 10.1073/pnas.1700304115.

-p

Farquhar, G. D., S. von Caemmerer, and J. A. Berry (1980), A biochemical model of

re

photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149(1), 78–90, doi:10.1007/BF00386231.

lP

Fowler, D., K. Pilegaard, M. A. Sutton, P. Ambus, M. Raivonen, J. Duyzer, D. Simpson, H. Fagerli, S. Fuzzi, J. K. Schjoerring, C. Granier, A. Neftel, I. S. A. Isaksen, P. Laj, M.

na

Maione, P. S. Monks, J. Burkhardt, U. Daemmgen, J. Neirynck, E. Personne, R. WichinkKruit, K. Butterbach- Bahl, C. Flechard, J. P. Tuovinen, M. Coyle, G. Gerosa, B. Loubet,

Jo ur

N. Altimir, L. Gruenhage, C. Ammann, S. Cieslik, E. Paoletti, T. N. Mikkelsen, H. RoPoulsen, P. Cellier, J. N. Cape, L. Horváth, F. Loreto, U. Niinemets, P. I. Palmer, J. Rinne, P. Misztal, E. Nemitz, D. Nilsson, S. Pryor, M. W. Gallagher, T. Vesala, U. Skiba, N. Brüggemann, S. Zechmeister-Boltenstern, J. Williams, C. O’Dowd, M. C. Facchini, G. de Leeuw, A. Flossman, N. Chaumerliac, and J. W. composition change:

Erisman (2009), Atmospheric

Ecosystems-Atmosphere interactions, Atmospheric Environment,

43(33), 5193–5267, doi:10.1016/j.atmosenv.2009.07.068. Giorgi, F., E. Coppola, F. Solmon, L. Mariotti, M. Sylla, X. Bi, N. Elguindi, G. Diro, V. Nair, G. Giu- liani, et al. (2012), RegCM4: model description and preliminary tests over multiple CORDEX domains, Climate Research, 52, 7–29, doi:10.3354/cr01018. Grell, G. A. (1993), Prognostic evaluation of assumptions used by cumulus parameterizations, Monthly

Weather

Review,

121(3), –24–

764–787,

doi:10.1175/1520-

Journal Pre-proof 0493(1993)121<0764:PEOAUB> 2.0.CO;2. Gu, L., J. D. Fuentes, H. H. Shugart, R. M. Staebler, and T. A. Black (1999), Responses of net ecosystem exchanges of carbon dioxide to changes in cloudiness: Results from two North American deciduous forests, Journal of Geophysical Research: Atmospheres, 104(D24), 31,421– 31,434, doi:10.1029/1999JD901068. Gu, L., D. Baldocchi, S. Verma, T. Black, T. Vesala, E. Falge, and P. Dowty, (2002), Advantages of diffuse radiation for terrestrial ecosystem productivity, Journal of Geophysical Research: Atmospheres, 107(D6), 1–23, doi:10.1029/2001dj001242. Gu, L., D. D. Baldocchi, S. C. Wofsy, J. W. Munger, J. J. Michalsky, S. P. Urbanski, and T. A.

of

Boden (2003), Response of a deciduous forest to the Mount Pinatubo eruption: Enhanced

ro

photosynthesis, Science, 299(5615), 2035–2038, doi:10.1126/science.1078366. Hansen, M. C., R. S. DeFries, J. R. G. Townshend, M. Carroll, C. Dimiceli, and R. A. Sohlberg

-p

(2003), Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of

re

the MODIS Vegetation Continuous Fields Algorithm, Earth Interactions, 7(10), 1–15, doi:10.1175/ 1087-3562(2003)007<0001:GPTCAA>2.0.CO;2.

lP

Holtslag, A. A. M., E. I. F. De Bruijn, and H.-L. Pan (1990), A high resolution air mass transformation model for short-range weather forecasting, Monthly Weather Review,

na

118(8), 1561–1575, doi: 10.1175/1520-0493(1990)118<1561:AHRAMT>2.0.CO;2. Horowitz, L. W., S. Walters, D. L. Mauzerall, L. K. Emmons, P. J. Rasch, C. Granier, X. Tie, J.-

Jo ur

F. Lamarque, M. G. Schultz, G. S. Tyndall, J. J. Orlando, and G. P. Brasseur (2003), A global simu- lation of tropospheric ozone and related tracers: Description and evaluation of MOZART, version 2, Journal of Geophysical Research: Atmospheres, 108(D24), doi:10.1029/2002JD002853.

IPCC (2013), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, doi: 10.1017/CBO9781107415324. Jiang, F., H. Wang, J. Chen, L. Zhou, W. Ju, A. Ding, L. Liu, and W. Peters (2013), Nested atmospheric inversion for the terrestrial carbon sources and sinks in China. Biogeosciences, 10, 5311-5324. doi: 10.5194/bg-10-5311-2013. Jing, X., J. Huang, G. Wang, K. Higuchi, J. Bi, Y. Sun, H. Yu, and T. Wang (2010), The effects –25–

Journal Pre-proof of clouds and aerosols on net ecosystem CO2 exchange over semi-arid Loess Plateau of Northwest China, Atmospheric Chemistry and Physics, 10, 8205–8218, doi:10.5194/acp10-8205-2010. Jung, M., M. Reichstein, and A. Bondeau (2009), Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6(10), 2001–2013, doi:10.5194/bg-6-2001-2009. Jung, M., M. Reichstein, H. A. Margolis, A. Cescatti, A. D. Richardson, M. A. Arain, A. Arneth, C. Bernhofer, D. Bonal, J. Chen, D. Gianelle, N. Gobron, G. Kiely, W. Kutsch, G. Lasslop, B. E. Law, A. Lindroth, L. Merbold, L. Montagnani, E. J. Moors, D. Papale, M.

of

Sottocornola, F. Vaccari, and C. Williams (2011), Global patterns of land-atmosphere

satellite,

and

meteorological

observations,

ro

fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, Journal

of

Geophysical

Research:

-p

Biogeosciences, 116(G3), G00J07, doi:10.1029/2010JG001566.

re

Jung, M., M. Reichstein, C. R. Schwalm, C. Huntingford, S. Sitch, A. Ahlström, A. Arneth, G. Camps Valls, P. Ciais, P. Friedlingstein, F. Gans, K. Ichii, A. K. Jain, E. Kato, D. Papale,

lP

B. Poulter, B. Raduly, C. Rödenbeck, G. Tramontana, N. Viovy, Y.-P. Wang, U. Weber, S. Zaehle, and N. Zeng (2017), Compensatory water effects link yearly global land CO2 sink

na

changes to temperature, Nature, 541, 516, doi:10.1038/nature20780. Kanniah, K. D., J. Beringer, P. North, and L. Hutley (2012), Control of atmospheric particles on

Jo ur

diffuse radiation and terrestrial plant productivity: A review, Progress in Physical Geography: Earth and Environment, 36(2), 209–237, doi:10.1177/0309133311434244. Kiehl, J., J. Hack, G. B. Bonan, B. A. Boville, and B. P. Briegleb (1996), Description of the NCAR community climate model (CCM3), NCAR/TN-420+STR, doi:10.5065/D6FF3Q99. Knohl, A., and D. D. Baldocchi (2008), Effects of diffuse radiation on canopy gas exchange processes in a forest ecosystem, Journal of Geophysical Research: Biogeosciences, 113(G2), G02,023, doi: 10.1029/2007JG000663. Kou, X., M. Zhang, and Z. Peng (2013), Numerical Simulation of CO2 Concentrations in East Asia with RAMS-CMAQ, Atmospheric and Oceanic Science Letters, 6(4), 179–184, doi:10.3878/j.issn.1674-2834.13.0022. Kou, X., M. Zhang, Z. Peng, and Y. Wang (2015), Assessment of the biospheric contribution to surface atmospheric CO2 concentrations over East Asia with a regional chemical transport –26–

Journal Pre-proof model, Advances in Atmospheric Sciences, 32(3), 287–300, doi:10.1007/s00376-014-40596. Law, B., E. Falge, L. Gu, D. Baldocchi, P. Bakwin, P. Berbigier, K. Davis, A. Dolman, M. Falk, J. Fuentes, A. Goldstein, A. Granier, A. Grelle, D. Hollinger, I. Janssens, P. Jarvis, N. Jensen, G. Katul, Y. Mahli, G. Matteucci, T. Meyers, R. Monson, W. Munger, W. Oechel, R. Olson, K. Pilegaard, K. P. U, H. Thorgeirsson, R. Valentini, S. Verma, T. Vesala, K. Wilson, and S. Wofsy (2002), Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation, Agricultural and Forest Meteorology, 113(1), 97–120, doi:10.1016/S0168-1923(02)00104-1.

of

Li, M., Q. Zhang, J. I. Kurokawa, J. H. Woo, K. He, Z. Lu, T. Ohara, Y. Song, D. G. Streets, G.

ro

R. Carmichael, Y. Cheng, C. Hong, H. Huo, X. Jiang, S. Kang, F. Liu, H. Su, and B. Zheng (2017), MIX: a mosaic Asian anthropogenic emission inventory under the international

re

963, doi:10.5194/ acp-17-935-2017.

-p

collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17(2), 935–

Li, S., T. Wang, F. Solmon, B. Zhuang, H. Wu, M. Xie, Y. Han, and X. Wang (2016), Impact of

lP

aerosols on regional climate in southern and northern China during strong/weak East Asian summer monsoon years, Journal of Geophysical Research: Atmospheres, 121(8), 4069–

na

4081, doi:10.1002/2015JD023892.

Li, Y., H. Shi, L. Zhou, D. Eamus, A. Huete, L. Li, J. Cleverly, Z. Hu, M. Harahap, Q. Yu, L.

Jo ur

He, and S. Wang (2018), Disentangling climate and LAI effects on seasonal variability in water use efficiency across terrestrial ecosystems in China, Journal of Geophysical Research: Biogeosciences, 123(8), 2429–2443, doi:10.1029/2018JG004482. Liu, Y., W. Ju, H. He, S. Wang, R. Sun, and Y. Zhang (2013), Changes of net primary productivity in China during recent 11 years detected using an ecological model driven by MODIS data, Frontiers of Earth Science, 7(1), 112–127, doi:10.1007/s11707-012-0348-5. Liu, Z., A. P. Ballantyne, B. Poulter, W. R. L. Anderegg, W. Li, A. Bastos, and P. Ciais (2018), Precipitation thresholds regulate net carbon exchange at the continental scale, Nature Communications, 9(1), 3596, doi:10.1038/s41467-018-05948-1. Luo, Y., X. Zheng, T. Zhao, and J. Chen (2014), A climatology of aerosol optical depth over China from recent 10 years of MODIS remote sensing data, International Journal of Climatology, 34(3), 863–870, doi:10.1002/joc.3728. –27–

Journal Pre-proof Mercado, L. M., N. Bellouin, S. Sitch, O. Boucher, C. Huntingford, M. Wild, and P. M. Cox (2009), Impact of changes in diffuse radiation on the global land carbon sink, Nature, 458, 1014, doi:10.1038/nature07949. Min, Q., and S. Wang (2008), Clouds modulate terrestrial carbon uptake in a midlatitude hardwood

forest,

Geophysical

Research

Letters,

35(2),

L02,406,

doi:10.1029/2007GL032398. Niu, S., Z. Li, J. Xia, Y. Han, M. Wu, and S. Wan (2008), Climatic warming changes plant photosynthesis and its temperature dependence in a temperate steppe of northern China, Environmental

and

Experimental

Botany,

91–101,

of

doi:10.1016/j.envexpbot.2007.10.016.

63(1),

ro

Novick, K. A., D. L. Ficklin, P. C. Stoy, C. A. Williams, G. Bohrer, A. C. Oishi, S. A. Papuga, P. D. Blanken, A. Noormets, B. N. Sulman, R. L. Scott, L. Wang, and R. P. Phillips

-p

(2016), The increasing importance of atmospheric demand for ecosystem water and carbon

re

fluxes, Nature Climate Change, 6, 1023, doi:10.1038/nclimate3114. Oliphant, A., D. Dragoni, B. Deng, C. Grimmond, H.-P. Schmid, and S. Scott (2011), The role

lP

of sky conditions on gross primary production in a mixed deciduous forest, Agricultural and Forest Meteorology, 151(7), 781–791, doi:10.1016/j.agrformet.2011.01.005.

na

Pal, J. S., E. E. Small, and E. A. B. Eltahir (2000), Simulation of regional-scale water and energy budgets: Representation of subgrid cloud and precipitation processes within RegCM, of

Geophysical

Jo ur

Journal

Research:

Atmospheres,

105(D24),

29,579–29,594,

doi:10.1029/2000JD900415.

Pavlick, R., D. T. Drewry, K. Bohn, B. Reu, and A. Kleidon (2013), The Jena DiversityDynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs, Biogeosciences, 10(6), 4137–4177, doi:10.5194/bg-10-4137-2013. Peters, W., A. R. Jacobson, C. Sweeney, A. E. Andrews, T. J. Conway, K. Masarie, J. B. Miller, L. M. P. Bruhwiler, G. Pétron, A. I. Hirsch, D. E. J. Worthy, G. R. van der Werf, J. T. Randerson, P. O. Wennberg, M. C. Krol, and P. P. Tans (2007), An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker, Proceedings of the National Academy of Sciences, 104(48), 18,925, doi:10.1073/pnas.0708986104. Piao, S., J. Fang, P. Ciais, P. Peylin, Y. Huang, S. Sitch, and T. Wang (2009), The carbon –28–

Journal Pre-proof balance of terrestrial ecosystems in China, Nature, 458, 1009, doi:10.1038/nature07944. Piao, S., S. Sitch, P. Ciais, P. Friedlingstein, P. Peylin, X. Wang, A. Ahlström, A. Anav, J. G. Canadell, N. Cong, C. Huntingford, M. Jung, S. Levis, P. E. Levy, J. Li, X. Lin, M. R. Lomas, M. Lu, Y. Luo, Y. Ma, R. B. Myneni, B. Poulter, Z. Sun, T. Wang, N. Viovy, S. Zaehle, and N. Zeng (2013), Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends, Global Change Biology, 19(7), 2117–2132, doi:10.1111/gcb.12187. Potter, C. S., J. T. Randerson, C. B. Field, P. A. Matson, P. M. Vitousek, H. A. Mooney, and S. A. Klooster (1993), Terrestrial ecosystem production: A process model based on global and

surface

data,

Global

Biogeochemical

Cycles,

of

satellite

811–841,

ro

doi:10.1029/93GB02725.

7(4),

Quéré, C. L., R. M. Andrew, P. Friedlingstein, S. Sitch, J. Pongratz, A. C. Manning, et al.

-p

(2018), Global carbon budget 2017, Earth System Science Data, 10(1), 405–448,

re

doi:10.5194/essd-10-405-2018.

Rap, A., D. V. Spracklen, L. Mercado, C. L. Reddington, J. M. Haywood, R. J. Ellis, O. L.

lP

Phillips, P. Artaxo, D. Bonal, N. Restrepo Coupe, and N. Butt (2015), Fires increase Amazon forest productivity through increases in diffuse radiation, Geophysical Research

na

Letters, 42(11), 4654–4662, doi:10.1002/2015GL063719. Richardson, A. D., T. A. Black, P. Ciais, N. Delbart, M. A. Friedl, N. Gobron, D. Y. Hollinger,

Jo ur

W. L. Kutsch, B. Longdoz, S. Luyssaert, M. Migliavacca, L. Montagnani, J. W. Munger, E. Moors, S. Piao, C. Rebmann, M. Reichstein, N. Saigusa, E. Tomelleri, R. Vargas, and A. Varlagin (2010), Influence of spring and autumn phenological transitions on forest ecosystem productivity, Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1555), 3227–3246, doi:10.1098/rstb.2010.0102. Shalaby, A., A. S. Zakey, A. B. Tawfik, F. Solmon, F. Giorgi, F. Stordal, S. Sillman, R. A. Zaveri, and A. L. Steiner (2012), Implementation and evaluation of online gas-phase chemistry within a regional climate model (RegCM-CHEM4), Geosci. Model Dev., 5(3), 741–760, doi:10.5194/ gmd-5-741-2012. Shan, Y., D. Guan, H. Zheng, J. Ou, Y. Li, J. Meng, Z. Mi, Z. Liu, and Q. Zhang (2018), China CO2

emission

accounts

1997–2015,

doi:10.1038/sdata.2017.201. –29–

Scientific

Data,

5,

170201,

Journal Pre-proof Solmon, F., F. Giorgi, and C. Liousse (2006), Aerosol modelling for regional climate studies: application to anthropogenic particles and evaluation over a European/African domain, Tellus B, 58(1), 51–72, doi:10.1111/j.1600-0889.2005.00155.x. Spitters, C. (1986), Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis Part II. Calculation of canopy photosynthesis, Agricultural and Forest Meteorology, 38(1), 231–242, doi:10.1016/01681923(86)90061-4. Steiner, A. L., and W. L. Chameides (2005), Aerosol-induced thermal effects increase modelled

57(5), 404–411, doi:10.3402/tellusb.v57i5.16559.

of

terrestrial photosynthesis and transpiration, Tellus B: Chemical and Physical Meteorology,

ro

Strada, S., N. Unger, and X. Yue (2015), Observed aerosol-induced radiative effect on plant productivity in the eastern United States, Atmospheric Environment, 122, 463–476,

-p

doi:10.1016/j.atmosenv.2015.09.051.

re

Strada, S. and N. Unger (2016), Potential sensitivity of photosynthesis and isoprene emission to direct radiative effects of atmospheric aerosol pollution, Atmospheric Chemistry and

lP

Physics, 16, 4213–4234, doi:10.5194/acp-16-4213-2016. Tian, H., J. Melillo, C. Lu, D. Kicklighter, M. Liu, W. Ren, X. Xu, G. Chen, C. Zhang, S. Pan, J.

global

change

na

Liu, and S. Running (2011), China’s terrestrial carbon balance: Contributions from multiple factors,

Global

Biogeochem.

Cycles,

25,

GB1007,

Jo ur

doi:10.1029/2010GB003838.

Wang, J., J. Dong, Y. Yi, G. Lu, J. Oyler, W. K. Smith, M. Zhao, J. Liu, and S. Running (2017), Decreasing net primary production due to drought and slight decreases in solar radiation in China from 2000 to 2012, Journal of Geophysical Research: Biogeosciences, 122(1), 261– 278, doi:10.1002/2016JG003417. Wang, T. J., B. L. Zhuang, S. Li, J. Liu, M. Xie, C. Q. Yin, Y. Zhang, C. Yuan, J. L. Zhu, L. Q. Ji, and Y. Han (2015), The interactions between anthropogenic aerosols and the East Asian summer monsoon using RegCCMS, Journal of Geophysical Research: Atmospheres, 120(11), 5602–5621, doi:10.1002/2014JD022877. Wang, X., S. Piao, P. Ciais, J. Li, P. Friedlingstein, C. Koven, and A. Chen (2011), Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006, Proceedings of the National Academy of Sciences, 108(4), –30–

Journal Pre-proof 1240–1245, doi:10.1073/pnas.1014425108. Wang, X., J. Wu, M. Chen, X. Xu, Z. Wang, B. Wang, C. Wang, S. Piao, W. Lin, G. Miao, M. Deng, C. Qiao, J. Wang, S. Xu, and L. Liu (2018), Field evidences for the positive effects of

aerosols

on

tree

growth,

Global

Change

Biology,

24(10),

4983–4992,

doi:10.1111/gcb.14339. Wei, J., Z. Li, Y. Peng, and L. Sun (2019), MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison, Atmospheric Environment, 201, 428 – 440, doi:10.1016/j.atmosenv.2018.12.004. Wiedinmyer, C., S. K. Akagi, R. J. Yokelson, L. K. Emmons, J. A. Al-Saadi, J. J. Orlando, and

of

A. J. Soja (2011), The Fire INventory from NCAR (FINN): A high resolution global model

ro

to estimate the emissions from open burning, Geosci. Model Dev., 4(3), 625–641, doi:10.5194/gmd-4-625-2011.

-p

WMO (2018), WMO Greenhouse Gas Bulletin: The State of Greenhouse Gases in the

re

Atmosphere Based Global Observations Through 2017, Tech. rep., Switzerland, World Meteorological Organization.

lP

Wu, J., K. Guan, M. Hayek, N. Restrepo-Coupe, K. T. Wiedemann, X. Xu, R. Wehr, B. O. Christoffersen, G. Miao, R. da Silva, A. C. de Araujo, R. C. Oliviera, P. B. Camargo, R. K.

na

Monson, A. R. Huete, and S. R. Saleska (2017), Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual

Jo ur

timescales, Global Change Biology, 23(3), 1240–1257, doi:10.1111/gcb.13509. Xia, X., P. Wang, Y. Wang, Z. Li, J. Xin, J. Liu, and H. Chen (2008), Aerosol optical depth over the Tibetan Plateau and its relation to aerosols over the Taklimakan Desert, Geophysical Research Letters, 35, L16804, doi:10.1029/2008GL034981. Xie, X., X. Huang, T. Wang, M. Li, S. Li, and P. Chen (2018), Simulation of non-homogeneous CO2 and its impact on regional temperature in East Asia, Journal of Meteorological Research, 32(3), 456–468, doi:10.1007/s13351-018-7159-x. Xu, X., J. Qiu, X. Xia, L. Sun, and M. Min (2015), Characteristics of atmospheric aerosol optical depth variation in China during 1993-2012, Atmospheric Environment, 119, 82–94, doi:10.1016/ j.atmosenv.2015.08.042. Yang, Y., H. Wang, S. J. Smith, R. Zhang, S. Lou, Y. Qian, P.-L. Ma, and P. J. Rasch (2018), Recent intensification of winter haze in China linked to foreign emissions and meteorology, –31–

Journal Pre-proof Scientific Reports, 8(1), 2107, doi:10.1038/s41598-018-20437-7. Yin, C., T. Wang, F. Solmon, M. Mallet, F. Jiang, S. Li, and B. Zhuang (2015), Assessment of direct radiative forcing due to secondary organic aerosol over China with a regional climate model,

Tellus

B:

Chemical

and

Physical

Meteorology,

67(1),

24,634,

doi:10.3402/tellusb.v67.24634. Yue, X., and N. Unger (2015), The Yale Interactive terrestrial Biosphere model version 1.0: description, evaluation and implementation into NASA GISS ModelE2, Geosci. Model Dev., 8(8), 2399–2417, doi:10.5194/gmd-8-2399-2015. Yue, X., and N. Unger (2017), Aerosol optical depth thresholds as a tool to assess diffuse

of

radiation fertilization of the land carbon uptake in China, Atmospheric Chemistry and

ro

Physics, 17(2), 1329–1342, doi:10.5194/acp-17-1329-2017.

Yue, X., N. Unger, K. Harper, X. Xia, H. Liao, T. Zhu, J. Xiao, Z. Feng, and J. Li (2017), Ozone

-p

and haze pollution weakens net primary productivity in China, Atmospheric Chemistry and

re

Physics, 17(9), 6073–6089, doi:10.5194/acp-17-6073-2017. Zhai, S., D. Jacob, X. Wang, L. Shen, K. Li, Y. Zhang, K. Gui, T. Zhao, and H. Liao (2019),

lP

Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology, Atmospheric Chemistry and Physics, 19,

na

11031–11041, doi:10.5194/acp-19-11031-2019. Zhang, H., B. Chen, I. Laan‐ Luijkx, J. Chen, G. Xu, J. Yan, L. Zhou, Y. Fukuyama, P. Tans,

Jo ur

and W. Peters (2014), Net terrestrial CO2 exchange over China during 2001–2010 estimated with an ensemble data assimilation system for atmospheric CO2. J. Geophys. Res. Atmos. 119, 3500-3515. doi:10.1002/2013JD021297. Zhang, M., G.-R. Yu, L.-M. Zhang, X.-M. Sun, X.-F. Wen, S.-J. Han, and J.-H. Yan (2010), Impact of cloudiness on net ecosystem exchange of carbon dioxide in different types of forest ecosystems in China, Biogeosciences, 7(2), 711–722, doi:10.5194/bg-7-711-2010. Zhang, Y., D. Goll, A. Bastos, Y. Balkanski, O. Boucher, A. Cescatti, M. Collier, T. Gasser, J. Ghattas, L. Li, S. Piao, N. Viovy, D. Zhu, and P. Ciais (2019), Increased global land carbon sink due to aerosol-induced cooling, Global Biogeochemical Cycles, 33(3), 439–457, doi:10.1029/ 2018GB006051. Zhao, L., Y. Li, S. Xu, H. Zhou, S. Gu, G. Yu, and X. Zhao (2006), Diurnal, seasonal and annual variation in net ecosystem CO2 exchange of an alpine shrubland on qinghai-tibetan plateau, –32–

Journal Pre-proof Global Change Biology, 12(10), 1940–1953, doi:10.1111/j.1365-2486.2006.01197.x. Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running (2005), Improvements of the MODIS terrestrial gross and net primary production global data set, Remote Sensing of Environment, 95(2), 164–176, doi:10.1016/j.rse.2004.12.011. Zheng, T., J. Zhu, J., S. Wang, and J. Fang (2016), When will China achieve its carbon emission peak? Natl. Sci. Rev., 3, 8–12, doi:10.1093/nsr/nwv079. Zhou, Y., A. Huang, J. Jiang, and M. La (2014), Modeled interaction between the subseasonal evolving of the East Asian summer monsoon and the direct effect of anthropogenic sulfate, Journal

of

Geophysical

Research:

Atmospheres,

1993–2016,

of

doi:10.1002/2013JD020612.

119(5),

ro

Zhu, W. Q., Y. Z. Pan, and J. S. Zhang (2007), Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing, Chin J Plan Ecolo, 31(3), 413–424,

Jo ur

na

lP

re

-p

doi:10.17521/ cjpe.2007.0050.

–33–

Journal Pre-proof Figure captions: Figure 1: The land cover map over the modeling domain applied in this study. Six sub-regions in China are used for analysis (North, Northeast, Northwest, Southwest, Central, and Southeast China). The numbers in red circles show the locations of WDCGG CO2 measurement sites (see Table 1).

Figure 2: Evaluation of simulated aerosol optical depth (AOD) over China. (a) simulated AOD at 550 nm (averaged for 2006-2015), (b) the satellite retrieval of the MODIS AOD at 550 nm (averaged for 2006-2015), and (c) monthly simulated versus observed AOD values from 6

of

AERONET sites (see Table 1); lines of best fit (least square method), correlation coefficients

ro

(R), and Normal Mean Bias(NMB) are included.

-p

Figure 3: Comparisons of the observed (a, c) and the simulated (b, d) GPP (a, b) and NPP (c, d)

re

over China (All the results are the annual average for the period 2006–2015, simulations are

lP

derived from the CtrlCase). Units: g C m−2 day−1.

Figure 4: (a) The simulated NEE derived from the CtrlCase (averaged for 2006–2015); Units: g

na

C m−2 day−1. (b) Comparison of monthly-mean simulated NEE derived from the CtrlCase (black line) against CT2016 NEE (red line), and their difference (blue histogram) over China;

Jo ur

correlation coefficients (R), and Normal Mean Bias (NMB) are included; Units: Pg C yr −1.

Figure 5: Comparisons between the simulated (red) and the observed (black) monthly mean CO2 mixing ratios from 2006 to 2015 (Simulations are derived from the CtrlCase). The shaded areas refer to standard deviation of the simulation, and the correlation coefficients (R) are presented in each plot. Sites information is listed in Table 1. Units: ppm.

Figure 6: Aerosol-induced changes in meteorology over China (averaged for 2006–2015). (a) direct radiation (RadD, W m−2), (b) diffuse radiation (RadF, W m−2), (c) near surface air temperature (Tair, ℃), (d) vapor pressure deficit (VPD, hPa). The average values over the whole China are shown in the title brackets of each subpanel. Significant changes (confidence level above 99%) are marked with black dots. –34–

Journal Pre-proof

Figure 7: Variations in (a) GPP, (b) TER, (c) NEE due to aerosol direct effect over China (averaged for 2006–2015). Note that negative values of NEE denote more CO2 absorbed by terrestrial ecosystem due to aerosols. Significant changes (confidence level above 99%) are marked with black dots. Units: g C m−2 day−1.

Figure 8: Interannual variations over China from 2006 to 2015 in (a) ∆GPP and ∆NEE (Pg C yr−1), (b) ∆Tair (℃), ∆DF (%) and ∆VPD (hPa). Error bars indicate the standard deviation.

of

Figure 9: The main meteorological factors contribute to the aerosol-induced (a) GPP, and (b)

ro

NEE change. Regions dominated by DF, Tair and VPD are presented in red, blue, and green, respectively. The inset pie charts show the proportion of areas dominated by the three

re

-p

meteorological factors.

Figure 10: Seasonal variations (averaged for 2006–2015) of CO2 concentrations over China due

lP

to aerosols: (a) spring, (b) summer, (c) autumn, and (d) winter. The average values over the whole China are shown in the title brackets of each subpanel. Significant changes (confidence

Jo ur

na

level above 99%) are marked with black dots. Units: ppm.

–35–

Journal Pre-proof

Tables: Table 1. Geographic information of the AERONET and WDCGG measurement sites Latitude(°N)

Longitude(°E)

Altitude(m)

Description

Beijing

39.98

116.38

92

City

Taihu

31.42

120.22

20

Lake

AERONET

XiangHe

39.75

116.96

36

Rural

stations

SACOL

35.95

104.14

1965

Mountain

Hong_Kong_PolyU

22.30

114.18

30

City

Lulin

23.47

120.87

2868

Mountain

Mt. Waliguan (WLG)

36.28

100.9

3815

Inland (plateau)

Tae-ahn Peninsula (TAP)

36.72

126.12

21

Coastal

WDCGG

Ulaan Uul (UUM)

44.45

111.08

1012

Inland (grassland)

stations

Lulin (LLN)

23.46

120.86

2867

Mountain

Yonagunijima (YON)

24.47

123.02

30

Remote Ocean

Shangdianzi (SDZ)

40.65

117.12

287

Inland (urban)

lP

re

-p

ro

of

Station

Table 2. Comparison of simulated NEE in China with previous studies. Period 2001–2010 1982-1999 2002-2008 1996-2005 2006-2015 1980-2002 1961-2005 2006-2015 2001-2010

na

Source Fang et al. (2018) Piao et al. (2009) Jiang et al. (2013) Piao et al. (2009) This study Piao et al. (2009) Tian et al. (2011) CT2016 Zhang et al. (2014)

Jo ur

Category

Method Inventory-based method Inventory-based method Inversion method Inversion method Process-based model Process-based model Process-based model Assimilation system Assimilation system

–36–

NEE (Pg C yr−1) −0.20 −0.18 ± 0.07 −0.31 −0.35 ± 0.33 -0.29 −0.17 ± 0.04 −0.21 ± 0.03 −0.39 −0.33

-p

ro

of

Journal Pre-proof

re

Figure 1: The land cover map over the modeling domain applied in this study. Six sub-regions in China are used for analysis (North, Northeast, Northwest, Southwest, Central, and Southeast

lP

China). The numbers in red circles show the locations of WDCGG CO2 measurement sites (see

Jo ur

na

Table 1).

Figure 2: Evaluation of simulated aerosol optical depth (AOD) over China. (a) simulated AOD at 550 nm (averaged for 2006-2015), (b) the satellite retrieval of the MODIS AOD at 550 nm (averaged for 2006-2015), and (c) monthly simulated versus observed AOD values from 6 AERONET sites (see Table 1); lines of best fit (least square method), correlation coefficients (R), and Normal Mean Bias(NMB) are included.

–37–

lP

re

-p

ro

of

Journal Pre-proof

na

Figure 3: Comparisons of the observed (a, c) and the simulated (b, d) GPP (a, b) and NPP (c, d) over China (All the results are the annual average for the period 2006–2015, simulations are

Jo ur

derived from the CtrlCase). Units: g C m−2 day−1.

Figure 4: (a) The simulated NEE derived from the CtrlCase (averaged for 2006–2015); Units: g C m−2 day−1. (b) Comparison of monthly-mean simulated NEE derived from the CtrlCase (black line) against CT2016 NEE (red line), and their difference (blue histogram) over China; correlation coefficients (R), and Normal Mean Bias (NMB) are included; Units: Pg C yr −1. –38–

Journal Pre-proof

of

Figure 5: Comparisons between the simulated (red) and the observed (black) monthly mean CO2

ro

mixing ratios from 2006 to 2015 (Simulations are derived from the CtrlCase). The shaded areas refer to standard deviation of the simulation, and the correlation coefficients (R) are presented in

Jo ur

na

lP

re

-p

each plot. Sites information is listed in Table 1. Units: ppm.

Figure 6: Aerosol-induced changes in meteorology over China (averaged for 2006–2015). (a) direct radiation (RadD, W m−2), (b) diffuse radiation (RadF, W m−2), (c) near surface air temperature (Tair, ℃), (d) vapor pressure deficit (VPD, hPa). The average values over the whole China are shown in the title brackets of each subpanel. Significant changes (confidence level above 99%) are marked with black dots. –39–

Journal Pre-proof

Figure 7: Variations in (a) GPP, (b) TER, (c) NEE due to aerosol direct effect over China

of

(averaged for 2006–2015). Note that negative values of NEE denote more CO2 absorbed by terrestrial ecosystem due to aerosols. Significant changes (confidence level above 99%) are

Jo ur

na

lP

re

-p

ro

marked with black dots. Units: g C m−2 day−1.

Figure 8: Interannual variations over China from 2006 to 2015 in (a) ∆GPP and ∆NEE (Pg C yr−1), (b) ∆Tair (℃), ∆DF (%) and ∆VPD (hPa). Error bars indicate the standard deviation. –40–

Journal Pre-proof

Figure 9: The main meteorological factors contribute to the aerosol-induced (a) GPP, and (b)

of

NEE change. Regions dominated by DF, Tair and VPD are presented in red, blue, and green, respectively. The inset pie charts show the proportion of areas dominated by the three

Jo ur

na

lP

re

-p

ro

meteorological factors.

Figure 10: Seasonal variations (averaged for 2006–2015) of CO2 concentrations over China due to aerosols: (a) spring, (b) summer, (c) autumn, and (d) winter. The average values over the whole China are shown in the title brackets of each subpanel. Significant changes (confidence level above 99%) are marked with black dots. Units: ppm. –41–

Jo ur

na

lP

re

-p

ro

of

Journal Pre-proof

–42–

Journal Pre-proof Author Statement

Xiaodong Xie and Tijian Wang contributed to the design of the research. Xiaodong Xie carried out the simulation and wrote the paper. Xu Yue provided the YIBs model and helped model set up. Shu Li, Bingliang Zhuang, and Minghuai Wang contributed to

Jo ur

na

lP

re

-p

ro

of

scientific discussions and commented on the manuscript.

–43–

Journal Pre-proof

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work

Jo ur

na

lP

re

-p

ro

of

reported in this paper.

–44–

Journal Pre-proof Highlights: 1. The effects of aerosol-induced radiation perturbation and hydrometeorological feedbacks on terrestrial carbon fluxes and CO2 concentrations has been investigated.

2. Current aerosol loading is estimated to enhance the annual terrestrial carbon sink by 0.06 Pg C yr−1 (21%) over China.

3. Aerosols reduce summer CO2 concentrations by 0.62 ppm through their effects on

of

vegetation, with the largest reduction being above 2 ppm.

ro

4. Aerosol-induced diffuse fraction increment is the dominant contributor to changes in

Jo ur

na

lP

re

-p

terrestrial carbon fluxes and CO2 concentrations over China.

–45–