Draft - TSpace Repository: Home...Draft 3 33 1. Introduction 34 The global dimming phenomenon, caused by the drastic increase in clouds and atmospheric 35 particulates produced by

Draft

Cloud regulations on the gross primary productivity of a

poplar plantation under different environmental conditions

Journal: Canadian Journal of Forest Research

Manuscript ID cjfr-2016-0413.R1

Manuscript Type: Article

Date Submitted by the Author: 16-Dec-2016

Complete List of Authors: Xu, Hang; Beijing Forestry University, institute of soil and water conservation Zhang, Zhiqiang; Beijing Forestry University, College of Soil and Water Conservation Chen, Jiquan; Michigan State University, Landscape Ecology & Ecosystem Science (LEES) Lab, Center for Global Change and Earth Observations

(CGCEO), and Department of Geography Zhu, Mengxun; Beijing Forestry University, College of Soil and Water Conservation Kang, Manchun; Beijing Forestry University, institute of soil and water conservation; China Three Gorges University, College of Hydraulic and Environmental Engineering

Keyword: cloudiness, gross primary productivity, poplar plantation, environmental conditions, path analysis

https://mc06.manuscriptcentral.com/cjfr-pubs

Canadian Journal of Forest Research

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1

Cloud regulations on the gross primary productivity of a poplar plantation under different 1

environmental conditions 2

3

4

Hang Xu1, Zhiqiang Zhang

1, Jiquan Chen

2, Mengxun Zhu

1, Manchun Kang

1,3 5

1. Key Laboratory of Soil and Water Conservation and Desertification Combating, Ministry 6

of Education, College of Soil and Water Conservation, Beijing Forestry University, 7

Beijing 100083, PR China 8

2. Landscape Ecology & Ecosystem Science (LEES) Lab, Center for Global Change and 9

Earth Observations (CGCEO), and Department of Geography, Michigan State University, 10

East Lansing, MI 48823, USA 11

3. College of Hydraulic and Environmental Engineering, China Three Gorges University, 12

Yichang 443002, PR China 13

Corresponding author: Dr. Zhiqiang Zhang (email: [email protected]). 14

Tel.: +86 10 62338097; fax: +86 10 62337873. 15

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Abstract: Cloud regulates the gross primary productivity (GPP) of forest ecosystems by 16

changing the radiation component and other environmental factors. In this study, we used an 17

open-path eddy covariance system and microclimate sensors installed over a poplar plantation in 18

northern China to measure the carbon exchange and climate variables during the mid-growing 19

seasons (June to August) in 2014 and 2015. The results indicated that the GPP of the plantation 20

peaked when the clearness index (CI) was between 0.45 and 0.65, at which point diffuse 21

photosynthetically active radiation (PARdif) had reached its maximum. Cloudy skies increased 22

the maximum ecosystem photosynthetic capacity (Pmax) by 28% compared with clear skies. 23

PARdif and soil moisture were the most and the least crucial driver for photosynthetic 24

productivity of the plantation under cloudy skies, respectively. The ecosystem photosynthetic 25

potential was higher under lower vapor pressure deficit (VPD < 1.5 kPa), lower air temperature 26

(Ta < 30°C), and non-stressed conditions (REW > 0.4) for cloudy skies due to effects of Ta and 27

VPD on stoma. Overall, our research highlighted the importance of cloud-induced radiation 28

component change and environmental variation in quantifying the GPP of forest ecosystems. 29

Key words: cloudiness, gross primary productivity, poplar plantation, environmental conditions, 30

path analysis 31

32

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1. Introduction 33

The global dimming phenomenon, caused by the drastic increase in clouds and atmospheric 34

particulates produced by fossil fuel combustion and other human activities, has continuously 35

decreased the amount of global solar radiation reaching the ground since the 1950s by 0.51 ± 36

0.05 W m-2

yr-1

(IPCC 2007; Wang et al. 2014). Despite this trend, there have been 37

improvements for most areas of Europe and North America (Barnes et al. 2014), yet a sustaining 38

decline in global radiation exists in the developing countries of the Northern Hemisphere (Streets 39

et al. 2006; Wang and Yang 2014). Clouds and particulates can directly decrease total radiation 40

while increasing diffuse radiation (Oliphant et al. 2011; Zhang et al. 2011), consequently 41

affecting the growth and development of terrestrial ecosystems (Bai et al. 2012; Cheng et al. 42

2015). 43

Numerous studies have indicated that increased diffuse radiation under cloudy sky 44

conditions can drastically enhance the gross primary productivity (GPP) of forest ecosystems and 45

light use efficiency (LUE; defined as GPP/PAR) in regions that are not saturated with light 46

(Kanniah et al. 2012, 2013; Cheng et al. 2015). On a global scale, the carbon sink of terrestrial 47

ecosystems between 1960 and 1999 were estimated to increase by ~25% due to the increase in 48

the diffuse fraction largely associated with the ‘global dimming phenomenon’ (Mercado et al., 49

2009). The underlying mechanisms for this increase are multi-fold; the increase in diffuse 50

radiation can 1) lead to the removal of canopy photoinhibition in sunlit leaves (i.e., leaves above 51

the canopy) at high levels of radiation under clear sky conditions (Gu et al. 2002; Knohl and 52

Baldocchi 2008), 2) uniformly distribute the radiation in the canopy columns (Greenwald et al. 53

2006; Alton et al. 2007), and 3) stimulate plant stomatal opening, which is sensitive to the ratio 54

of blue to red light (Dengel and Grace 2010). 55

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Cloudiness and its temporal changes are often coupled with different environmental 56

conditions, such as vapor pressure deficit (VPD), air temperature (Ta) and soil moisture (Kanniah 57

et al. 2012, 2013), all of which produce complex, coupled effects on ecosystem photosynthesis 58

and production (Cai et al. 2008). A few studies found that Ta and VPD had no significant effects 59

on controlling the ecosystem photosynthesis response to diffuse radiation (Jing et al. 2010; 60

Kanniah et al. 2011; Oliphant et al. 2011), while other studies found that these factors played a 61

crucial role (Yamasoe et al. 2005; Zhang et al. 2011). Yamasoe et al. (2005) reported that the 62

increase in VPD under clear sky conditions could constrain carbon exchange in a tropical forest. 63

Zhang et al. (2011) found that decreases in VPD and Ta could depress canopy photosynthesis 64

under cloudy skies in an irrigated maize cropland. Clearly, the significance of the radiation 65

component and other environmental factors on ecosystem GPP remain unclear within these 66

complex, interactive environmental conditions. Moreover, there are few studies that have 67

quantified the effects of cloud-induced radiation components and other environmental factor 68

changes on the ecosystem photosynthetic productivity of plantations. For example, recent studies 69

based on a path analysis of the direct correlation between environmental factors and ecosystem 70

GPP have demonstrated the complex nature of the above relationships (Shao et al. 2016). 71

Here, we hypothesized that the diffuse PAR (PARdif), mainly controlled by clouds, would be 72

the dominant factor regulating forest ecosystem GPP under cloudy skies and that clouds would 73

affect the GPP distinctively under different environmental conditions. To test the hypotheses, we 74

used diurnal 30-min data (PAR > 4 µmol m-2

s-1

) collected during the mid-growing season (June 75

to August) in combination with ancillary meteorological observations from an eddy covariance 76

tower over a poplar plantation in northern China, to 1) quantify the cloudiness effect on 77

ecosystem GPP and LUE, 2) explore GPP responses to cloudy skies under different 78

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environmental conditions, and 3) identify the direct and indirect effects of environmental 79

variables on ecosystem photosynthesis under various cloudy skies. 80

2. Materials and Methods 81

2.1 Site description 82

This study was conducted over a poplar plantation (Populus × euramericana) in Gongqing 83

Forest Farm (116°42′41″E, 40°06′27″N, 29 m a. s. l.), which is located on a fluvial plain near the 84

Chaobai River in Shunyi District, Beijing, China. The trees were planted in ~1996 at a density of 85

4 m × 3 m. The average height and diameter at breast height (DBH) of the plantation were 17.5 ± 86

1.6 m (mean ± SD) and 25.7 ± 1.6 cm, respectively, in 2014 and 2015. The leaf area index (LAI) 87

varied between 2.15 and 3.41 during the mid-growing season in 2014 and 2015. The understory 88

vegetation was dominated by Swida alba Opiz., Pinus bungeana Zucc., Caryophylli Flos., 89

Sabina vulgaris., and Gaillardia aristata Pursh. The region is typical of the sub-humid warm 90

temperate monsoon climate, with a mean annual temperature of 11.5°C and an average daily 91

temperature of 4.9°C in January and 25.7°C in July. According to the long-term (1990–2010) 92

meteorological Shunyi Weather Station (116°37′E, 40°08′N, 28.6 m a. s. l.), the mean annual 93

precipitation is ~576 mm, with 75% falling between July and September. The mean annual 94

relative humidity is 50% and the total hours of sunshine are ~690 h in the summer (i.e., June to 95

August). The soil texture is sandy with high permeability and a low water-holding capacity. The 96

ground water level during the study was ~2 m. 97

2.2 Fluxes and meteorological measurements 98

An eddy covariance (EC) system was installed on a 30 m tall tower in November 2013 to 99

continuously measure the exchange of carbon, water and energy between the plantation and the 100

atmosphere. An open-path gas analyzer (EC150, Campbell Scientific Inc., USA) and a 3-D sonic 101

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anemometer (CSAT3, Campbell Scientific Inc., USA) were installed at 26 m with a north 102

declination of 165° (i.e., the prevailing wind direction). 103

The global shortwave solar radiation (G), photosynthetically active radiation (PAR) and net 104

radiation (Rn) were measured at the top of the tower (30 m) by a pyranometer (LI-200x, LI-COR 105

Inc., USA), a light quantum sensor (LI-190SB, LI-COR Inc., USA), and a net radiometer (CNR1, 106

Kipp and Zonen., NL), respectively. Two tipping-bucket rain gauges (TE525-L, Campbell 107

Scientific Inc., USA) were mounted above the canopies to measure the cumulative precipitation 108

over 30 min periods. Air temperature and relative humidity were measured at 0.5, 1.5, 5, 15 and 109

30 m above the ground using temperature and humidity sensors (HC2S3, Campbell Scientific 110

Inc., USA), from which VPD was calculated. Soil moisture was measured by TDR (CS616, 111

Campbell Scientific Inc., USA) at 5, 20, 50, 100, 150 and 200 cm below the soil surface. Soil 112

temperature (Ts) and soil heat flux (G) were measured with two thermocouples (TCAV107, 113

Campbell Scientific Inc., USA) and two soil heat transducers (HFT3, Campbell Scientific Inc., 114

USA) installed at depths of 5 and 25 cm. All data were logged into CR3000/1000 dataloggers 115

(Campbell Scientific Inc., USA) at 2/10 Hz. The leaf area index was measured twice a month 116

using the plant canopy analyzer (LAI-2200, LI-COR Inc., USA). 117

2.3 Flux corrections and data processing 118

Eddypro (https://www.licor.com/env/support/) was used to process the 10 Hz raw data, 119

including spike filtering, rotating the coordinates’ axis using the planar fit method (Foken et al. 120

2012) and WPL corrections (Webb 1982). Stationary and integral turbulent tests were performed 121

(Foken 2006), splitting all data into 9 classes. The data of classes 9 and 8 with wind directions 122

between > 350° and < 45° (north declination) were discarded because of insufficient turbulence 123

and because they fell under the tower’s shade. The data with friction velocity below the threshold 124

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0.20 m s-1

for 2014 and 0.25 m s-1

for 2015 (Reichstein et al. 2005) were filtered out to eliminate 125

the underestimation under stable stratifications. Simultaneously, the KM model was applied to 126

remove data from beyond the study area (Kormann and Meixner 2001). With these quality 127

controls and other system malfunctions (e.g., power failure, instrumentation malfunctioning and 128

heavy rain conditions), 49% in 2014 and 52% in 2015 of data were finally reserved. Daytime 129

gaps less than < 7 days were filled by using the marginal distribution sampling (MDS) approach 130

(Reichstein et al. 2005). In addition, the larger gaps (i.e., more than 7 days) were filled by the 131

mean diurnal variation (MDV) method with consecutive 15-day windows because the largest gap 132

was less than 10 days (Krishnan et al. 2012). 133

The energy balance ratio (EBR), defined as effective energy/available energy, was 0.93 and 134

0.85 based on daytime 30-min fluxes in 2014 and 2015, respectively. Although there was energy 135

storage in the canopy and air, our results were similar to the average value (0.84) of 173 136

FLUXNET sites (Stoy et al. 2013). We applied a non-linear regression method to calculate the 137

ecosystem respiration (ER, i.e., nighttime NEE) and GPP (Lloyd and Taylor 1994) as follows: 138

(1)

)T)t(T

1

TT

1(E

fre0soil0ref

short,0

eR)t(ER−

−−

⋅

⋅= 139

(2) NEE-ERGPP = 140

where Rref is the ER under the reference temperature, E0,short denotes the activation energy 141

parameter that determines the short-term temperature sensitivity (4 days in this study), Tref is the 142

reference temperature (10°C), T0 is a constant (-46.02°C) and Tsoil is the soil temperature at 5 cm. 143

NEE is the net ecosystem exchange. 144

To compare the GPP responses to sky conditions under different environmental conditions, 145

we divided the VPD into VPD < 1.5 kPa and VPD ≥ 1.5 kPa (Zhou et al. 2013), Ta into Ta < 146

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30°C and Ta ≥ 30°C (Kanniah et al. 2013) and relative extractable soil water (REW) into REW < 147

0.4 and REW ≥ 0.4, representing soil water stress and non-stress, respectively (Granier et al. 148

1999). REW was calculated as follows: 149

(3) minmax

min

VWCVWC

VWCVWCREW

−−

= 150

where VWCmax and VWCmin are the maximum and minimum soil moisture content measured at 5 151

cm. 152

2.4 Defining sky conditions 153

The clearness index (CI) that is the ratio of solar radiation (G) observed above the canopies 154

to the extraterrestrial global horizontal solar radiance (G0) (Kanniah et al. 2013), was used to 155

represent the sky conditions (Gu et al. 2002) as follows: 156

(4) 0G

GCI =

157

(5) βsin365

d360cos033.01GG sc0

+= 158

where Gsc is the solar constant (1370 W m-2

), d is the day of the year, and β is the solar 159

elevation computed by using the Solar Position Online Calculator operated by the National 160

Renewable Energy Lab, USA (http://www.nrel.gov/midc/solpos/solpos.html). 161

A distinct CI threshold (CIt) to define the sky conditions cannot be found because CI 162

changes not only with clouds and aerosol concentration but also with the variation of solar 163

elevation (Gu et al. 1999). CIt at a given solar elevation must be smoothly increased with sinβ 164

and form an envelope curve in the lumped scatter plot of CIt against sinβ (Gu et al. 1999). Due to 165

the asymmetry between mornings and afternoons, we calculated the CIt and its relationship with 166

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sinβ separately (Fig. 1) with a cubic polynomial equation (Eq. 6) as follows: 167

(6) dβsincβsinbβsinaCI 23

t +++=

168

where a, b, c and d were estimated regression coefficients. Cloudy sky conditions were defined 169

when CI was less than CIt at a given solar elevation. To eliminate the effect of solar elevation on 170

the relationship between ecosystem photosynthesis and CI, we divided the 30-70° solar elevation 171

data (β) into 5° intervals (Zhang et al. 2010; Bai et al. 2012). 172

2.5 Diffuse PAR 173

PARdif was calculated by using CI and solar zenith angle as (Reindl et al. 1991) follows: 174

(7) fDPARPARPAR dif ×=

175

(8) ( )[ ]

( ) ( ) βcosβ90cosq11

qq13.01fDPAR

32

2

−−+−+

=

176

(9) CIG

Gq

0

f

⋅=

177

when 0 ≤ CI ≤ 0.3; restrain Gf/G0 ≤ CI, 178

(10)

( )βsin0123.0CI254.0020.1CIG

G

0

f +−= 179

when 0.3 < CI≤ 0.78; restrain 0.1CI ≤ Gf/G0 ≤ 0.97CI, 180

(11) )βsin177.0CI749.1400.1(CIG

G

0

f +−= 181

when 0.78 < CI; restrain Gf/G0 ≥CI, 182

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(12)

)βsin182.0CI486.0(CIG

G

0

f −=

183

where fDPAR is the fraction of diffuse PAR and Gf is the diffuse fraction of the global solar 184

radiation (W m-2

). 185

2.6 Light response model 186

The rectangle hyperbola light response model, known as the Michaelis-Menten equation, 187

which can simulate the process of leaf and canopy level photosynthesis (Michaelis and Menten 188

1913), is widely used for quantifying the effects of cloudiness on ecosystem photosynthesis 189

under different sky conditions. The formula is as follows: 190

(13) max

max

PαPAR

PARαPGPP

+= 191

where α is the ecosystem apparent quantum yield and Pmax is the maximum ecosystem 192

photosynthetic capacity (µmol m-2

s-1

). 193

2.7 Statistical analysis 194

We developed our path coefficient model by focusing on the interactive environmental 195

factors, including PARdif, direct PAR (PARdir), VPD, Ta and REW, to identify their direct and 196

indirect effects on the GPP of the plantation under different environmental conditions. In 197

simplifying the model structure, we only accepted the significant paths (p < 0.05) and 198

standardized path coefficients (ρ) that were out of the range of -0.1 to 0.1. All observed data were 199

standardized before putting into the path model, and the ρ and discrepancy were calculated based 200

on the maximum likelihood method (Shao et al. 2016). The final models were adopted when the 201

comparative fit index (CFI) was more than 0.9 and the root mean square error of approximation 202

(RMSEA) was less than 0.05 (Kline 2011). The path analysis and data conversion were applied 203

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within AMOS (version 24.0, Chicago, IL). All selected regression curves were statistically 204

significant (p < 0.05) and based on several related studies (Zhang et al. 2010, Oliphant et al. 2011, 205

Bai et al. 2012, Kanniah et al. 2013), which were conducted by SigmaPlot (version 12.5, 206

California, USA). 207

3. Results 208

3.1 Microclimate and CI 209

The variations in the mean daily CI, REW, Ta, VPD, and daily total global solar radiation 210

(G), daily total precipitation (P) during the mid-growing season of 2014 and 2015 were shown in 211

Fig. 2. The mean daily CI in 2014 was 0.41 ± 0.141 (mean ± SE), which was greater than that in 212

2015 (0.36 ± 0.133). Total daily G showed consistent changes, corresponding to the change in CI 213

(Fig. 2c/d). The mean daily G in 2015 (16.4 ± 0.59 MJ m-2

) was less than that in 2014 (17.0 ± 214

0.53 MJ m-2

). The total G values during the same period from 2014 and 2015 were 1561.9 MJ 215

m-2

and 1507.3 MJ m-2

, respectively. The total rainfall during the mid-growing season of 2014 216

was 200.6 mm, 54% less than the long term average (432.1 mm) (Fig. 2a/b). As a result, the 217

plantation experienced 68 days of soil water stress (REW < 0.4). In contrast, the rainfall in 2015 218

was 439.4 mm, resulting in 53 days of drought stress. The mean air temperature (Ta) during the 219

mid-growing season in 2015 (27.9 ± 0.07°C) was lower than that in 2014 (28.1 ± 0.09°C) (Fig. 220

2e/f). The maximum mean daily Ta of 2014 and 2015 was 30.8°C and 33.2°C, respectively, with 221

both occurring in July. During the study period, Ta under clear sky conditions (29.6 ± 0.23°C) 222

was 8% higher than that of cloudy sky conditions (27.1 ± 0.08°C). The mean daily VPD in 2015 223

was 1.6 ± 0.02 kPa, which was lower than that in 2014 (1.8 ± 0.03 kPa). The low Ta and high P in 224

2015 resulted in a lower VPD than that in 2014. 225

In addition, sky conditions indicated by CI during the mid-growing seasons of two years 226

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were related to the changes in VPD, Ta, PAR and PARdif (Fig. 3). VPD, Ta and PAR linearly 227

increased with increasing CI (p < 0.05), however, the variation in PARdif against CI presented a 228

downward-parabolic trend under selected solar elevations during the study period. Sensitivities 229

of environmental factors (i.e., the slopes of lines) to CI were higher under the solar elevations of 230

65-70° compared to the solar elevations of 45-50°, similarly, higher coefficients of determination 231

were also found at higher solar elevations. 232

3.2 GPP and LUE against CI 233

The variations in the GPP to CI were conic across all ranges of β in the study period (Fig. 4). 234

The GPP peaked when CI ranged between 0.45 and 0.65 and was reduced when the skies were 235

more cloudy or sunny. The mean diurnal GPP rate under cloudy sky conditions (21.93 ± 0.744 236

µmol m-2

s-1

) in two years was higher than that in clear skies (20.01 ± 1.386 µmol m-2

s-1

). More 237

pronounced changes in the GPP against CI were observed at relatively higher solar elevations, 238

demonstrating that ecosystem GPP enhanced more by cloudiness at higher solar elevations. In 239

addition, LUE linearly increased with cloudiness among the solar elevations (Fig. 5) and was 240

more efficient under cloudy skies (0.054 ± 0.001 µmol CO2 µmol-1

PAR) than under clear skies 241

(0.048 ± 0.006 µmol CO2 µmol-1

PAR). 242

3.3 Light response under different sky conditions 243

The canopy photosynthetic productivity responded differently to PAR for clear and cloudy 244

skies (Fig. 6). The apparent quantum efficiency (α) was ~9% lower under cloudy skies (0.106 ± 245

0.003 µmolCO2 µmol-1

PAR) than that under clear skies (0.117 ± 0.009 µmol CO2 µmol-1

PAR), while 246

ecosystem photosynthetic potential (Pmax) was elevated by 28% under cloudy sky conditions 247

(41.51 ± 0.684 µmol m-2

s-1

). Furthermore, with a lower Ta (Ta < 30°C) and VPD (VPD < 1.5 248

kPa), the Pmax of the plantation was increased by 30% and 33% due to the cloudiness (Fig. 7), 249

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respectively. These increase rates were higher than that under higher VPD and Ta groups. In 250

addition, Pmax was more sensitive to clouds under non-stressed conditions compared with water 251

stress conditions (Fig. 7). Under cloudy sky conditions, the Pmax value was 33% higher for 252

VPD < 1.5 than that for VPD ≥ 1.5, 12% higher for Ta < 30°C compared with that for Ta ≥ 30°C, 253

and 29% higher for non-stressed soil than that for water stress soil. Our results indicated that 254

lower VPD, lower Ta and non-stressed REW further promoted the ecosystem photosynthetic 255

potential under cloudy skies. 256

3.4 Direct and indirect influences of environmental factors on GPP 257

There were different standardized path coefficients from climate variables to ecosystem 258

photosynthetic productivity (Fig. 8). PARdif was the most important driver for ecosystem GPP 259

under all environmental conditions, with ρ hardly changing with the variation of environmental 260

groups (ρ = 0.54-0.65). Interestingly, soil REW was irrelevant to the GPP across all groups. Ta 261

and direct PAR (PARdir) played positive roles (i.e., ρ > 0) on the GPP, while VPD suppressed 262

ecosystem photosynthesis in most cases. 263

An environmental variable had different direct effects on GPP under different environmental 264

conditions. The variation of path coefficients between radiation (i.e., PARdif and PARdir) and GPP 265

was minimal while the variation between VPD and GPP was maxed (ρ = -0.52-0) among the 266

environmental groups. A non-correlation between VPD and GPP was found when VPD was 267

lower than 1.5 kPa. In addition, the influence of VPD on GPP under higher VPD (VPD ≥ 1.5 kPa) 268

and higher Ta (Ta ≥ 30°C) conditions was more negative than that under lower VPD and lower Ta. 269

VPD depressed photosynthesis more severely under soil water stress conditions than 270

non-stressed periods, and Ta was more relevant to GPP when it was lower compared to higher, 271

while there were no discrepancies within the other environmental groups. 272

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The indirect effects of Ta on GPP were negative, indicating that the increase in Ta would 273

indirectly lead to the decrease in GPP via increasing VPD. There were more significant indirect 274

effects under higher VPD (-0.32) and water-stress free conditions (-0.41) compared to lower 275

VPD and water stress conditions. Similarly, the indirect effects of Ta on GPP under high Ta 276

conditions (-0.26) were less than that (-0.14) under low Ta. Consequently, the total effects (i.e., 277

sum of direct and indirect effects) of Ta on GPP were positive only under lower VPD, lower Ta or 278

non-stressed conditions. 279

4. Discussion 280

4.1 Effects of cloudiness on GPP 281

Moderate cloud coverage (0.45-0.65) and corresponding diffuse radiation fractions between 282

0.50 and 0.80 presented optimal conditions that enhanced ecosystem productivity in the poplar 283

plantation. Similar results were also observed within a temperate broadleaf forest in North 284

America (Oliphant et al. 2011) and a temperate broadleaved pine forest in Northwestern China 285

(Zhang et al. 2010). However, Kanniah et al. (2013) found that GPP rate was highest under clear 286

sky conditions in a tropical savanna. Radiation component changes and light distribution within 287

an ecosystem influences the canopy’s photosynthetic productivity under cloudy sky conditions 288

(Knohl and Baldocchi 2008). Leaf area index and leaf orientation/arrangements within canopies 289

are the primary factors determining scattered radiation usage in an ecosystem (Greenwald et al. 290

2006; Kanniah et al. 2012). Leaf inclination angles of the sunlit leaves of the poplar plantation 291

were less than those of the sunshade leaves, which utilizes direct light and diffuse light 292

simultaneously (Dickmann et al. 1990). 293

Ecosystem LUE consistently increased with cloudiness without any discrepancy among the 294

solar elevations (p > 0.05) (Fig. 5), which further demonstrates the positive effects that 295

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cloudiness has on GPP. Cloudiness reduced the direct PAR for sunlit leaves and increased the 296

PARdif for shade leaves. These changes in PAR will reduce photosynthesis in the canopy’s sunlit 297

foliage (within the photo prohibition range) but elevate the GPP of the shade leaves. Shade 298

foliage and understory plants received more radiation to enhance their LUE and GPP (Alton et al. 299

2007; Kanniah et al. 2011). As total radiation continued to decline against cloudiness, ecosystem 300

GPP began to decrease because the radiation on the sunlit leaves was less than the light 301

saturation point (Fig. 6). The reduction in GPP could not be compensated by the enhanced 302

photosynthesis within the shade foliage or understory plants, which benefited from the diffuse 303

radiation (Misson et al. 2005). Our data showed that cloudiness had strong effects on the 304

plantation’s GPP and LUE, and that while LUE was enhanced by cloudiness, it could not lead to 305

an increase in ecosystem GPP under conditions dominated by scattered radiation (i.e., fDPAR > 306

0.5) in this plantation. 307

4.2 Effects of CI on environmental factors 308

Canopy GPP and other physiological processes were jointly influenced by multiple 309

environmental factors, such as radiation, VPD, air temperature and soil moisture status (Yuan et 310

al. 2009; Tian et al. 2011). Ta linearly decreased with CI (Fig. 3c) due to reduced incident 311

radiation (Gu et al. 1999). Likewise, VPD was lowered by cloudiness (Fig. 3a). Overall, clouds 312

lowered leaf temperature and increased air humidity, thus reducing VPD. Although VPD, Ta and 313

PAR decreased with cloudiness for selected solar elevations (p < 0.05), the changes in 314

environmental factors could not be well interpreted by CI at a lower solar elevation, indicating 315

that the decrease in VPD and Ta at a lower solar elevation was not solely attributed by cloudiness. 316

This result might be due to the strong positive correlations between VPD, Ta and solar elevations 317

(pVPD < 0.001, pTa < 0.001). Under clear sky conditions, CI increased with sunrise (Perez et al. 318

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1990; Misson et al. 2005). Thus, the decrease in VPD and Ta with decreasing CI was partly 319

induced by the decline in solar elevations at lower solar elevations. Unlike the linear relation of 320

PAR with CI, the PARdif was nonlinearly correlated with CI (Fig. 3d), as indicated by previous 321

studies (Alton et al. 2007; Zhang et al. 2011). PARdif initially increased with the increase in CI 322

and peaked until CI reached 0.40–0.60, and PARdif declined afterwards in accordance with GPP 323

(Fig. 4). Thus, PARdif played a crucial role in controlling and regulating the plantation’s GPP 324

under cloudy skies. 325

4.3 Environment regulations on GPP under different conditions of cloudiness 326

Cloudiness induced variations in radiation components and other environmental factors that 327

led to interactive effects on forest ecosystem productivity. Our path analysis results showed that 328

PARdif was the primary factor under cloudy skies controlling photosynthesis in this fast-growing 329

polar plantation, with no obvious discrepancy for the environmental groups (Fig. 8). This 330

indicated that PARdif did not cause the obvious difference in GPP between clear and cloudy skies 331

under lower VPD, lower Ta and non-stressed periods. The increase in VPD limited the poplar’s 332

photosynthesis under different environmental conditions because leaf guard cell behavior is 333

highly controlled by the balance of vapor pressure inside and outside of leaves (Farquhar and 334

Sharkey 1982) and tends to be closed (i.e., decrease stomatal conductance) when there is an 335

increase in VPD (Zhou et al. 2013; Goodrich et al. 2015). The sensitivity of ecosystem 336

photosynthesis to VPD varied by environmental conditions (Day 2000). The discrepancy is due 337

to the degree of deficit in CO2 and H2O for leaf photosynthesis. Ta directly played a positive role 338

in ecosystem photosynthesis because increased Ta enhanced enzyme activity and photosynthetic 339

electron transfer efficiency (Berry and Bjorkman 1980), which the poplar plantation could have 340

used to promote leaf stomatal conductance, transpiration rates, and higher photosynthetic rates 341

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(Song et al. 2014). Additionally, increases in Ta also resulted in increased VPD, leading to the 342

indirect depression of ecosystem photosynthesis except for lower VPD conditions. These results 343

confirmed that the regulation of ecosystem photosynthesis by environmental factors was 344

complicated and coupled. Moreover, poplar species consume large quantities of water and are 345

usually sensitive to soil water content (Kang et al. 2015). However, REW appeared to have been 346

the least important factor for GPP under any environmental conditions likely because the 347

taproots of the plants absorbed deep soil water in this plantation’s sandy soil, where sufficient 348

underground water exists during water stress periods (Comas et al. 2013; Su et al. 2014). 349

Furthermore, there might have been a lagged photosynthesis response to the changes in soil 350

water content, which lowers the correlation strength (Jia et al. 2014). 351

Correlation coefficients of the environmental factors among the different groups indicated 352

that the GPP increase in varying degrees by cloudiness was directly ascribed to the discrepancy 353

in the total effects of Ta (i.e., the balance between negative effects of VPD and positive effects of 354

Ta ) on leaf stoma rather than the effects of PARdif (Fig. 8). Clearly, the role of each 355

environmental factor in regulating GPP and photosynthesis needs to be understood in the context 356

of other variables, including cloudiness. 357

5. Conclusions 358

Cloudiness induced changes in radiation components while other environmental factors had 359

coupling effects on the GPP of a fast-growing poplar plantation in northern China. The GPP of 360

the plantation peaked with a CI of 0.45-0.65 when diffuse radiation was the largest component of 361

the total solar radiation. Clouds increased ecosystem Pmax and LUE, leading to the continuous 362

increase of canopy GPP until scattering radiation dominated. Our path analysis indicated that 363

PARdif was the most crucial driver while soil moisture was the least important factor for 364

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photosynthetic productivity of the poplar plantation under cloudy sky conditions. In addition, 365

lower VPD (VPD < 1.5 kPa), lower Ta (Ta < 30°C) and non-stressed conditions (REW > 0.4) 366

were more conducive to enhancing ecosystem photosynthesis under cloudy skies due to the 367

comprehensive effects of VPD and Ta to stomatal conductance. Therefore, evaluating the 368

cloudiness effects for quantifying the GPP of forest ecosystems is important. Although we 369

quantified the direct and indirect environmental effects on GPP under cloudy skies, the weakness 370

of path analysis was that empirical relationships between ecosystem GPP and environmental 371

factors were only considered. Thus further studies focusing on more biophysical processes 372

should be conducted to explore more accurately the cloud effect on forest ecosystem 373

productivity. 374

375

Acknowledgments 376

This study was financially supported by the Beijing Education Commission Grant to jointly 377

support the Universities of Ministry of Education in Beijing and the National Special Research 378

Program for Forestry, which was entitled “Forest Management Affecting the Coupling of 379

Ecosystem Carbon and Water Exchange with Atmosphere” (grant no. 201204102). The fourth 380

author also acknowledges the financial support of the Beijing Municipality Educational 381

Committee under the graduate student training program. Partial support by the US–China Carbon 382

Consortium (USCCC) is also acknowledged. The authors thank Gabriela Shirkey for editing the 383

manuscript for language.384

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References

Alton, P.B., North, P.R., and Los, S.O. 2007. The impact of diffuse sunlight on canopy light-use efficiency,

gross photosynthetic product and net ecosystem exchange in three forest biomes. Glob. Chang. Biol. 13(4):

776–787. doi:10.1111/j.1365-2486.2007.01316.x.

Bai, Y.F., Wang, J., Zhang, B.C., Zhang, Z.H., and Liang, J. 2012. Comparing the impact of cloudiness on

carbon dioxide exchange in a grassland and a maize cropland in northwestern China. Ecol. Res. 27(3):

615–623. doi:10.1007/S11284-012-0930-Z.

Barnes, E.A., Dunn-Sigouin, E., Masato, G., and Woollings, T. 2014. Exploring recent trends in Northern

Hemisphere blocking. Geophys. Res. Lett. 41(2): 638–644. doi:10.1002/2013GL058745.

Berry, J., and Bjorkman, O. 1980. Photosynthetic Response and Adaptation to Temperature in Higher Plants.

Annu. Rev. Plant Physiol. 31(1): 491–543. doi:10.1146/annurev.pp.31.060180.002423.

Cai, T., Flanagan, L.B., Jassal, R.S., and Black, T.A. 2008. Modelling environmental controls on ecosystem

photosynthesis and the carbon isotope composition of ecosystem-respired CO2 in a coastal Douglas-fir

forest. Plant, Cell Environ. 31(4): 435–453. doi:10.1111/j.1365-3040.2008.01773.x.

Cheng, S.J., Bohrer, G., Steiner, A.L., Hollinger, D.Y., Suyker, A., Phillips, R.P., and Nadelhoffer, K.J. 2015.

Variations in the influence of diffuse light on gross primary productivity in temperate ecosystems. Agric.

For. Meteorol. 201: 98–110. doi:10.1016/j.agrformet.2014.11.002.

Comas, L.H., Becker, S.R., Cruz, V.M. V, Byrne, P.F., and Dierig, D. a. 2013. Root traits contributing to plant

productivity under drought. Front. Plant Sci. 4(11): 442. doi:10.3389/fpls.2013.00442.

Day, M.E. 2000. Influence of temperature and leaf-to-air vapor pressure deficit on net photosynthesis and

stomatal conductance in red spruce (Picea rubens). Tree Physiol. 20: 57–63. doi:10.1093/treephys/20.1.57.

Dengel, S., and Grace, J. 2010. Carbon dioxide exchange and canopy conductance of two coniferous forests

under various sky conditions. Oecologia 164(3): 797–808. doi:10.1007/s00442-010-1687-0.

Dickmann, D.I., Michael, D. a., Isebrands, J.G., and Westin, S. 1990. Effects of leaf display on light

interception and apparent photosynthesis in two contrasting Populus cultivars during their second growing

season. Tree Physiol. 7(1-2-3-4): 7–20. doi:10.1093/treephys/7.1-2-3-4.7

Farquhar, G.D., and Sharkey, T.D. 1982. Stomatal Conductance and Photosynthesis. Annu. Rev. Plant Physiol.

33(1): 317–345. doi:10.1146/annurev.pp.33.060182.001533.

Foken, T. 2006. Angewandte Meteorologie. Springer: 325. doi:10.1007/978-3-540-38204-1.

Foken, T., Aubinet, M., and Leuning, R. 2012. The Eddy Covariance Method. In Eddy Covariance. pp. 1–19.

doi:10.1007/978-94-007-2351-1.

Goodrich, J.P., Campbell, D.I., Clearwater, M.J., Rutledge, S., and Schipper, L.A. 2015. High vapor pressure

deficit constrains GPP and the light response of NEE at a Southern Hemisphere bog. Agric. For. Meteorol.

203: 112–123. doi:10.1016/j.agrformet.2015.01.001.

Granier, A., Bréda, N., Biron, P., and Villette, S. 1999. A lumped water balance model to evaluate duration and

Page 19 of 30



Draft

20

intensity of drought constraints in forest stands. Ecol. Modell. 116(2–3): 269–283.

doi:10.1016/S0304-3800(98)00205-1.

Greenwald, R., Bergin, M.H., Xu, J., Cohan, D., Hoogenboom, G., and Chameides, W.L. 2006. The influence

of aerosols on crop production: A study using the CERES crop model. Agric. Syst. 89(2–3): 390–413.

doi:10.1016/j.agsy.2005.10.004.

Gu, L.H., Baldocchi, D., Verma, S.B., Black, T.A., Vesala, T., Falge, E.M., and Dowty, P.R. 2002. Advantages

of diffuse radiation for terrestrial ecosystem productivity. J. Geophys. Res. Atmos. 107(D6): ACL 2-1.

doi:10.1029/2001JD001242.

Gu, L.H., Fuentes, J.D., Shugart, H.H., Staebler, R.M., and Black, T. a. 1999. Responses of net ecosystem

exchanges of carbon dioxide to changes in cloudiness: Results from two North American deciduous

forests. J. Geophys. Res. 104(D24): 31421. doi:10.1029/1999JD901068.

IPCC. 2007. IPCC Fourth Assessment Report (AR4). IPCC 1: 976. doi:ISSN: 02767783.

Jia, X., Zha, T.S., Wu, B., Zhang, Y.Q., Gong, J.N., Qin, S.G., Chen, G.P., Qian, D., Kellomäki, S., and Peltola,

H. 2014. Biophysical controls on net ecosystem CO2 exchange over a semiarid shrubland in northwest

China. Biogeosciences 11(17): 4679–4693. doi:10.5194/bg-11-4679-2014.

Jing, X., Huang, J., Wang, G., Higuchi, K., Bi, J., Sun, Y., Yu, H., and Wang, T. 2010. The effects of clouds and

aerosols on net ecosystem CO2 exchange over semi-arid Loess Plateau of Northwest China. Atmos. Chem.

Phys. 10(17): 8205–8218. doi:10.5194/acp-10-8205-2010.

Kang, M.C., Zhang, Z.Z., Noormets, A., Fang, X.R., Zha, T.G., Zhou, J., Sun, G., McNulty, S.G., and Chen, J.Q.

2015. Energy partitioning and surface resistance of a poplar plantation in northern China. Biogeosciences

12(14): 4245–4259. doi:10.5194/bg-12-4245-2015.

Kanniah, K.D., Beringer, J., and Hutley, L. 2013. Exploring the link between clouds, radiation, and canopy

productivity of tropical savannas. Agric. For. Meteorol. 182–183: 304–313. Elsevier B.V.

doi:10.1016/j.agrformet.2013.06.010.

Kanniah, K.D., Beringer, J., and Hutley, L.B. 2011. Environmental controls on the spatial variability of

savanna productivity in the Northern Territory, Australia. Agric. For. Meteorol. 151(11): 1429–1439.

doi:10.1016/j.agrformet.2011.06.009.

Kanniah, K.D., Beringer, J., North, P., and Hutley, L. 2012. Control of atmospheric particles on diffuse

radiation and terrestrial plant productivity: A review. Prog. Phys. Geogr. 36(2): 209–237.

doi:10.1177/0309133311434244.

Kline, R.B. 2011. Principles and practice of structural equation modeling. In Structural Equation Modeling.

doi:10.1038/156278a0.

Knohl, A., and Baldocchi, D.D. 2008. Effects of diffuse radiation on canopy gas exchange processes in a forest

ecosystem. J. Geophys. Res. Biogeosciences 113(2): 1–17. doi:10.1029/2007JG000663.

Kormann, R., and Meixner, F.X. 2001. An analytical footprint model for non-neutral stratification.

Boundary-Layer Meteorol. 99(2): 207–224. doi:10.1023/A:1018991015119.

Page 20 of 30



Draft

21

Krishnan, P., Meyers, T.P., Scott, R.L., Kennedy, L., and Heuer, M. 2012. Energy exchange and

evapotranspiration over two temperate semi-arid grasslands in North America. Agric. For. Meteorol. 153:

31–44. doi:10.1016/j.agrformet.2011.09.017.

Lloyd, J., and Taylor, J.A. 1994. On the temperature dependence of soil respiration. Funct. Ecol. 8: 315–323.

doi:10.2307/2389824.

Michaelis, L., and Menten, M.L. 1913. Die Kinetik der Invertinwirkung. Biochem Z 49(2): 333–369.

doi:10.1021/bi201284u.

Misson, L., Lunden, M., McKay, M., and Goldstein, A.H. 2005. Atmospheric aerosol light scattering and

surface wetness influence the diurnal pattern of net ecosystem exchange in a semi-arid ponderosa pine

plantation. Agric. For. Meteorol. 129(1–2): 69–83. doi:10.1016/j.agrformet.2004.11.008.

Oliphant, A.J., Dragoni, D., Deng, B., Grimmond, C.S.B., Schmid, H.P., and Scott, S.L. 2011. The role of sky

conditions on gross primary production in a mixed deciduous forest. Agric. For. Meteorol. 151(7):

781–791. Elsevier B.V. doi:10.1016/j.agrformet.2011.01.005.

Perez, R., Ineichen, P., Seals, R., and Zelenka, A. 1990. Making full use of the clearness index for

parameterizing hourly insolation conditions. Sol. Energy 45(2): 111–114.

doi:10.1016/0038-092X(90)90036-C.

Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N.,

Gilmanov, T., Granier, A., Grünwald, T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T.,

Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.M., Pumpanen, J., Rambal, S.,

Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R. 2005.

On the separation of net ecosystem exchange into assimilation and ecosystem respiration: Review and

improved algorithm. Glob. Chang. Biol. 11(9): 1424–1439. doi:10.1111/j.1365-2486.2005.001002.x.

Reindl, D.T., Beckman, W.A., and Duffie, J.A. 1991. Diffuse fraction correlations. Sol. Energy 45(1): 1–7.

doi:10.1016/0038-092X(91)90123-E.

Shao, J.J., Zhou, X.H., Luo, Y.Q., Li, B., Aurela, M., Billesbach, D., Blanken, P.D., Bracho, R., Chen, J.Q.,

Fischer, M., Fu, Y.L., Gu, L.H., Han, S.J., He, Y.T., Kolb, T., Li, Y.N., Nagy, Z., Niu, S.L., Oechel, W.C.,

Pinter, K., Shi, P.L., Suyker, A., Torn, M., Varlagin, A., Wang, H.M., Yan, J.H., Yu, G.R., and Zhang, J.H.

2016. Direct and indirect effects of climatic variations on the interannual variability in net ecosystem

exchange across terrestrial ecosystems. Tellus B 68(0). doi:10.3402/tellusb.v68.30575.

Song, Y., Chen, Q.Q., Ci, D., Shao, X.N., and Zhang, D.Q. 2014. Effects of high temperature on

photosynthesis and related gene expression in poplar. BMC Plant Biol. 14(1): 111.

doi:10.1186/1471-2229-14-111.

Stoy, P.C., Mauder, M., Foken, T., Marcolla, B., Boegh, E., Ibrom, A., Arain, M.A., Arneth, A., Aurela, M.,

Bernhofer, C., Cescatti, A., Dellwik, E., Duce, P., Gianelle, D., van Gorsel, E., Kiely, G., Knohl, A.,

Margolis, H., Mccaughey, H., Merbold, L., Montagnani, L., Papale, D., Reichstein, M., Saunders, M.,

Serrano-Ortiz, P., Sottocornola, M., Spano, D., Vaccari, F., and Varlagin, A. 2013. A data-driven analysis

Page 21 of 30



Draft

22

of energy balance closure across FLUXNET research sites: The role of landscape scale heterogeneity.

Agric. For. Meteorol. 171–172: 137–152. doi:10.1016/j.agrformet.2012.11.004.

Streets, D.G., Wu, Y., and Chin, M. 2006. Two-decadal aerosol trends as a likely explanation of the global

dimming/brightening transition. Geophys. Res. Lett. 33(15). doi:10.1029/2006GL026471.

Su, H., Li, Y.G., Liu, W., Xu, H., and Sun, O.J. 2014. Changes in water use with growth in Ulmus pumila in

semiarid sandy land of northern China. Trees-Struct. Funct. 28(1): 41–52.

doi:10.1007/s00468-013-0928-3.

Tian, H.Q., Melillo, J., Lu, C.Q., Kicklighter, D., Liu, M.L., Ren, W., Xu, X.F., Chen, G.S., Zhang, C., Pan,

S.F., Liu, J.Y., and Running, S. 2011. China’s terrestrial carbon balance: Contributions from multiple

global change factors. Global Biogeochem. Cycles 25(1). doi:10.1029/2010GB003838.

Wang, R., Balkanski, Y., Boucher, O., Ciais, P., Peñuelas, J., and Tao, S. 2014. Significant contribution of

combustion-related emissions to the atmospheric phosphorus budget. Nat. Geosci. 8(1): 48–54.

doi:10.1038/ngeo2324.

Wang, Y.W., and Yang, Y.H. 2014. China’s dimming and brightening: Evidence, causes and hydrological

implications. Ann. Geophys. 32(1): 41–55. doi:10.5194/angeo-32-41-2014.

Webb, E.K. 1982. On the correction of flux measurements for effects of heat and water vapour transfer.

Boundary-Layer Meteorol. 23: 251-254. doi:10.1007/BF00123301.

Yamasoe, M., von Randow, C., Manzi, A., Schafer, J., Eck, T., and Holben, B. 2005. Effect of smoke on the

transmissivity of photosynthetically active radiation inside the canopy. Atmos. Chem. Phys. 5(4),

pp.5909-5934. doi:10.5194/acpd-5-5909-2005.

Yuan, W.P., Luo, Y.Q., Richardson, A.D., Oren, R., Luyssaert, S., Janssens, I.A., Ceulemans, R., Zhou, X.H.,

Grünwald, T., Aubinet, M., Berhofer, C., Baldocchi, D.D., Chen, J.Q., Dunn, A.L., Deforest, J.L., Dragoni,

D., Goldstein, A.H., Moors, E., Munger, J.W., Monson, R.K., Suyker, A.E., Starr, G., Scott, R.L.,

Tenhunen, J., Verma, S.B., Vesala, T., and Wofsy, S.T.E. 2009. Latitudinal patterns of magnitude and

interannual variability in net ecosystem exchange regulated by biological and environmental variables.

Glob. Chang. Biol. 15(12): 2905–2920. doi:10.1111/j.1365-2486.2009.01870.x.

Zhang, B.C., Cao, J.J., Bai, Y.F., Yang, S.J., Hu, L., and Ning, Z.G. 2011. Effects of cloudiness on carbon

dioxide exchange over an irrigated maize cropland in northwestern China. Biogeosciences Discuss. 8(1):

1669–1691. doi:10.5194/bgd-8-1669-2011.

Zhang, M., Yu, G.R., Zhang, L.M., Sun, X.M., Wen, X.F., Han, S.J., and Yan, J.H. 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.

Zhou, J., Zhang, Z.Z., Sun, G., Fang, X.R., Zha, T., McNulty, S., Chen, J.Q., Jin, Y., and Noormets, A. 2013.

Response of ecosystem carbon fluxes to drought events in a poplar plantation in Northern China. For. Ecol.

Manage. 300: 33–42. Elsevier B.V. doi:10.1016/j.foreco.2013.01.007.

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Fig. 1. The change of the clearness index under clear skies (CIt) with the sin of solar elevation (sinβ) from June to August in 2015.

Fig. 1 127x82mm (300 x 300 DPI)

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Fig. 2. Variation of environmental factors during the mid-growing season in 2014 and 2015. Daily precipitation (P) and relative extractable water (REW) (a/b); mean daily clearness index (CI) and daily total

global solar radiation (G) (c/d); mean daily air temperature (Ta) and vapor pressure deficit (VPD) (e/f).

Fig. 2 143x187mm (300 x 300 DPI)

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Fig. 3. Changes of vapor pressure deficit (VPD) (a), air temperature (Ta) (b), photosynthetically active radiation (PAR) (c) and diffuse photosynthetically active radiation (PARdif) (d) with clearness index (CI) for

selected intervals of solar elevation during June to August in 2014 and 2015.

Fig. 3 169x145mm (300 x 300 DPI)

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Fig. 4. Relationship between gross primary productivity (GPP) and the clearness index (CI) at different selected solar elevations from June to August in 2014 and 2015.

Fig. 4

169x145mm (300 x 300 DPI)

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Fig. 5. Relationship between light use efficiency (LUE) and the clearness index (CI) at different selected solar elevation from June to August in 2014 and 2015.

Fig. 5

169x145mm (300 x 300 DPI)

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Fig. 6. Ecosystem photosynthesis-radiation relationship of the plantation under different sky conditions from June to August in 2014 and 2015.

Fig. 6 127x84mm (300 x 300 DPI)

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Fig. 7. The growth rate of canopy photosynthetic potential capability (Pmax) (µmol m-2 s-1) under cloudy sky conditions versus clear sky conditions. Numbers above bars show the growth percentage of Pmax by

cloudy skies compared to the clear skies under different environmental classifications. Fig. 7

127x79mm (300 x 300 DPI)

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Fig. 8. The direct and indirect effects of environmental factors on ecosystem photosynthetic productivity (GPP) for different levels of vapor pressure deficit (VPD) (a/b), air temperature (Ta) (c/d) and relative

extractable water (REW) (e/f) under cloudy skies during the study period. The values beside the paths are

standardized path coefficients (ρ: -1-1), ‘ρ < 0’ denotes negative correlation and ‘ρ > 0’ denotes positive correlation.

Fig. 8 170x219mm (300 x 300 DPI)

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