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Journal of Resources and Ecology >

2019 , Vol. 10 >Issue 2: 137 - 146

DOI: https://doi.org/10.5814/j.issn.1674-764X.2019.02.004

Forest Ecosystem

Cloudy Sky Conditions Promote Net Ecosystem CO2 Exchange in a Subtropical Coniferous Plantation across Seasons

  • HAN Jiayin 1, 2 ,
  • YE Shu 1, 2 ,
  • GUO Chuying 1, 2 ,
  • ZHANG Leiming , 1, 2, * ,
  • LI Shenggong 1, 2 ,
  • WANG Huimin 1, 2
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  • 1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
*Corresponding author: ZHANG Leiming, E-mail:

Received date: 2018-11-28

  Accepted date: 2019-01-05

  Online published: 2019-03-30

Supported by

National Key Research and Development Program of China (2017YFC0503801;2016YFA0600104).

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Abstract

Dynamic changes in solar radiation have an important influence on ecosystem carbon sequestration, but the effects of changes caused by sky conditions on net ecosystem CO2 exchange (NEE) are unclear. This study analyzed the effects of sunny, cloudy, and overcast sky conditions on NEE using carbon flux and meteorological data for a subtropical coniferous plantation in 2012. Based on one-year data, we found no seasonal variation in the light response curve under various sky conditions. Compared with sunny sky conditions, the apparent quantum yield (α) and potential photosynthetic rate at a light intensity of 150 and 750 W m-2 (P150 and P750) under cloudy sky conditions increased by an average of 82.3%, 217.7%, and 22.5%; α and P150 under overcast sky conditions increased by 118.5% and 301% on average. Moderate radiation conditions were more favorable for maximum NEE, while low radiation conditions inhibited NEE. In most cases, when the sunny NEE was used as a baseline for comparison, the relative change in NEE (%NEE) was positive under cloudy sky conditions and negative under overcast sky conditions. The average maximal %NEE under cloudy sky conditions was 42.4% in spring, 34.1% in summer, 1.6% in autumn and -87.3% in winter. This study indicates that cloudy sky conditions promote photosynthetic rates and NEE in subtropical coniferous plantations.

Key words： eddy covariance; sky conditions; diffuse radiation; clearness index; NEE

Cite this article

HAN Jiayin , YE Shu , GUO Chuying , ZHANG Leiming , LI Shenggong , WANG Huimin . Cloudy Sky Conditions Promote Net Ecosystem CO2 Exchange in a Subtropical Coniferous Plantation across Seasons[J]. Journal of Resources and Ecology, 2019 , 10(2) : 137 -146 . DOI: 10.5814/j.issn.1674-764X.2019.02.004

1 Introduction

Solar radiation is the fundamental driver of photosynthesis by green plants. The quality and intensity of solar radiation have an important role in photosynthesis (Szeicz, 1974). When passing through the earth’s atmosphere, some solar radiation is absorbed, reflected or diffused. Solar radiation reaching the ground can be divided into direct radiation and diffuse radiation (Şen, 2008). Due to human activity and climate change, changes in atmospheric aerosols and cloud cover affect the solar radiation reaching the earth’s surface, for instance, “global dimming” after the 1950s and “global brightening” after the 1990s (Wild, 2009; Stjern and Hansen, 2010).
Dynamic changes in solar radiation and its components affect photosynthesis. The role of diffuse radiation in terrestrial ecosystem productivity has attracted attention in recent years. Many studies have found that increasing diffuse radiation has a positive effect on terrestrial carbon sinks (Gu et al., 1999; Alton et al., 2007; Knohl and Baldocchi, 2008; Mercado et al., 2009). For example, Gu et al. (2003) found that volcanic aerosols from the Mount Pinatubo eruption in 1991 increased diffuse radiation worldwide in the following two years, which enhanced noontime photosynthesis of a northern deciduous forest by 23% in 1992 and 8% in 1993 under cloudless conditions.
However, Alton (2008) recorded a general decrease in net primary production across 38 FLUXNET sites from different ecosystems because of a dramatic reduction in global radiation when clouds obscure the solar disk with higher diffuse fraction. Han et al. (2019) found no significant differences in daily net ecosystem productivity (NEP) between sunny and cloudy conditions, while the majority of daily NEP was significantly smaller under overcast conditions than under sunny and cloudy conditions. The effects of increasing diffuse radiation on net ecosystem CO2 exchange (NEE) remains unclear, and clarifying the mechanism by which diffuse radiation impacts terrestrial carbon sinks is necessary. China’s central and eastern regions have experienced frequent haze weather in recent years, reducing the frequency of sunny weather (Wu, 2012). These changes will have an impact on material cycles and energy flows in ecosystem (Wang et al., 2008).
Coniferous plantations are widely distributed in subtropical regions of southern China, and are an important land carbon sink globally (Yu et al., 2014). Many studies have focused on the effects of changes in solar radiation on carbon sequestration in this region. Based on the MAESTRA model, He et al. (2011) analyzed the effects of changes in total photosynthetically active radiation (PAR) and diffuse fraction on gross primary productivity (GPP) in Qianyanzhou (QYZ), indicating that an increasing diffuse fraction improves the absorption and utilization efficiency of the forest canopy on incident PAR. Based on a BEPS model, Li et al. (2014) found that the effects of changes in solar radiation on shaded leaves predominantly determined changes in canopy photosynthesis as shaded leaves contributed 67% to total GPP. Wang et al. (2015) found that NEE reached its maximum under a middle intensity of solar radiation. Nonetheless, there is a lack of research on the light responses of NEE across different seasons under various sky conditions, the influence of gloomy skies on NEE compared with sunny skies, and quantitative estimation of the effect of cloudy/overcast sky conditions on NEE.
In this study, a coniferous plantation in QYZ was selected. Using carbon flux data and conventional meteorological data for 2012, the effects of various sky conditions on NEE were studied. Our main questions were: 1) Is there seasonality in the light response of NEE under various sky conditions? 2) What is the relative change in NEE (%NEE) under cloudy/overcast sky conditions compared with sunny sky conditions?

2 Materials and methods

2.1 Site description

This study was conducted at the QYZ Ecological Research Station, Chinese Academy of Sciences, Ji’an City, Jiangxi, China (26°44′48″N, 115°04′13″E, and 102 m a.s.l.). The area is strongly affected by the East Asian monsoon climate. Annual mean precipitation and annual mean temperature are 1449.9 mm and 18.0°C, according to meteorological records (1985-2015) held by the China Ecosystem Research Network. The area is a typical hilly region. The main soil type is red soil, which is weathered from red sandstone and mud stone (Wang et al., 2011). Stand density in the site is ca. 833 stems ha-1. Zonal coniferous plantations were restored starting in 1985. The main tree species are slash pine (Pinus elliottii), Masson pine (Pinus massoniana), and Chinese fir (Cunninghamia lanceolata). Mean diameter at breast height and mean height of the trees are 20.9 cm and 17.5 m, respectively. Understory vegetation is dominated by Woodwardia japonica, Loropetalum chinense, and Dicranopteris dichotoma (Wang et al., 2012). In summer, the area is frequently susceptible to subtropical high-pressures, leading to seasonal droughts comprising how high temperature, low rainfall and low soil moisture (Wen et al., 2010).

2.2 Data sources

2.2.1 Eddy covariance (EC) measurement
Carbon flux data (i.e., NEE) are obtained from an EC system assembled on a flux tower at 39 m above aground level (a.g.l.) since 2002. The system consists of a three-dimensional sonic anemometer (Model CSAT-3, Campbell Scientific Inc., USA) and an open-path fast response infrared CO2/H2O analyzer (Model LI-7500, LI-COR Inc., USA). A datalogger (Model CR5000, Campbell Scientific Inc., USA) sampled data at 10 Hz, meanwhile half-hourly mean fluxes were calculated and stored using a standard procedure developed by ChinaFLUX (Yu et al., 2006).
2.2.2 Meteorological measurements
Global radiation was measured by a pyranometer (Model CM11, Kipp & Zonen Inc., Netherlands) at a height of 39 m a.g.l.; air temperature (Ta) and vapor pressure deficit (VPD) were measured by a temperature and relative humidity sensor (Model HMP45C, Vaisala Inc., Finland) at a height of 39 m a.g.l. All meteorological measurements were recorded at half-hourly intervals by three CR10X dataloggers (Model CR10XTD, Campbell Scientific Inc., USA) and a CR23X datalogger (Model CR23XTD, Campbell Scientific Inc., USA) with a 25-channel solid-state multiplexer (Model AM25T, Campbell Scientific Inc., USA) (Tang et al., 2014).
Diffuse radiation was measured by a pyranometer (Model SPN1, Delta-T Devices Ltd., England), which is at a 550 m horizontal distance from the flux tower, in the comprehensive meteorological observation field at a height of 1.5 m a.g.l. Diffuse radiation data were recorded at half-hourly intervals by a datalogger (Model CR3000, Campbell Scientific Inc., USA) (Han et al., 2015a).

2.3 Data processing and calculation

2.3.1 Flux calculation and corrections
The EC measurement system and EC data processing follow guidelines of the standard ChinaFLUX methodology (Yu et al., 2006). Specific preprocessing steps include the following: outlier eliminating, coordinate rotation, planar fit, Webb- Pearman-Leuning correction, and CO2 storage term correction (Webb et al., 1980; Wilczak et al., 2001; Baldocchi, 2003).
Data gaps were filled using nonlinear regression (Falge et al., 2001). For small gaps (≤ 2 h), missing data were linearly interpolated. Large gaps in daytime and nighttime data were treated separately when filling gaps in the CO2 datasets. Missing daytime flux data were estimated as a function of radiation using the Michaelis-Menten equation with a 10-day moving window. Missing nighttime flux data were estimated using the Lloyd and Taylor model (Lloyd and Taylor, 1994). All data processing and quality controls were performed in MATLAB 2014b (MathWorks Inc., USA).
2.3.2 Diffuse radiation calculation
Diffuse radiation data were missing from February 2012 due to power supply failure. This missing data was calculated using the Reindl model (Reindl et al., 1990) validated here (Han et al., 2015b) as follows:
kt = Ig/Ie (1)
Ie = Is[1 + 0.033cos(360td / 365)]sinβ (2)
sinβ = sinφ sinδ + cosφ cosδ cosω (3)
kd = 1.02 - 0.254kt + 0.0123sinβ (kt ≤ 0.3) (4)
kd = 1.4 - 1.749kt + 0.177sinβ (0.3 < kt < 0.78) (5)
kd = 0.486kt - 0.182sinβ (kt ≥ 0.78) (6)
Id = kd × Ig (7)
where, kt is the clearness index (unitless), Ig is the global radiation (W m-2), Ie is the extraterrestrial radiation (W m-2),Is is the solar constant (1367 W m-2), td is the day of year, β is the solar elevation angle (degree), φ is the local latitude (degree), δ is the declination of the sun (degree), ω the hour angle (degree), kd is the diffuse fraction (unitless), and Id is the diffuse radiation (W m-2).

2.4 Defining sunny, cloudy and overcast sky conditions

Because a full sunny day is rare, we used changes in kt coupled with sinβ and the diurnal variation of Ig to judge the sky conditions per half-day. Standard sunny sky conditions should meet the following three points: 1) kt smoothly grows with sinβ; 2) the diurnal variation curve of Ig is smooth; and 3) the maximal value of Ig is greater than 800 W m-2. Standard overcast sky conditions should meet the following two points: 1) the diurnal variation curve of Ig is not smooth; and 2) the maximal value of Ig is less than 400 W m-2. Except sunny and overcast sky conditions, the rest were deemed cloudy sky conditions. Seasonally, Ig and kt were plotted against sinβ and fitted by cubic polynomials in the sunny mornings and afternoons (Fig. 1).
Fig. 1 Scatterplots and regressions between global radiation (Ig), clearness index (kt) and the sine of solar elevation angles (sinβ) in 2012.
Note: (a) (e) spring, (b) (f) summer, (c) (g) autumn, and (d) (h) winter. Data were fitted by cubic polynomials in the morning (solid line) and afternoon (dashed line), respectively.

2.5 Light response of NEE

In order to analyze how NEE responds to Ig under various sky conditions, we used a form of exponential function to fit the response of NEE to Ig (Bassman and Zwier, 1991):
NEE = Rd - Pmax [1 - exp(- α × Ig / Pmax)] (8)
where, α is the apparent quantum yield (g C W-1 s-1), Pmax is the maximal photosynthetic rate (g C m-2 s-1), and Rd is the dark respiration (g C m-2 s-1).
Pmax can hardly be realized in practice. In order to compare potential photosynthetic rate in the same light intensity under various sky conditions we computed the potential photosynthetic rate under low (P150 at Ig = 150 W m-2) and high (P750 at Ig = 750 W m-2) light intensity.

2.6 Quantifying the influences of cloudy/overcast sky conditions on the NEE

In order to quantify the influence of cloudy/overcast sky conditions on NEE compared with sunny sky conditions we calculated %NEE (Gu et al., 1999) as follow:
%NEE = 100[NEE(β) - NEEs(β)] / NEEs(β) (9)
where, NEE(β) is the measured NEE under a given sky condition, and NEEs(β) is the NEE calculated from the regression
relationship between the measured sunny NEE and β.

3 Results

3.1 Light response of NEE under various sky conditions

The response of NEE to Ig was fitted to the exponential function (Fig. 2; Table 1). In most cases of each season, the light response curve (LRC) under overcast sky conditions was at the bottom, while the LRCs under cloudy and sunny conditions were at the middle and top, respectively (Fig. 2). This means that the potential photosynthetic rate under overcast sky conditions is the strongest. Only under high light intensity in autumn was the LRC under cloudy sky conditions at the upper part of the LRC under sunny sky conditions (Fig. 2c).
Table 1 Light response parameters of the net ecosystem CO2 exchange (NEE) response to global radiation
Season Weather α(g C W-1 s-1) Pmax(g C m-2 s-1) Rd(g C m-2 s-1) P150(g C m-2 s-1) P750(g C m-2 s-1) R2
Spring Sunny 1.1 × 10-3 1.14 0.07 0.08 0.52 0.88
Cloudy 2.6 × 10-3 0.97 0.09 0.23 0.75 0.96
Overcast 2.7 × 10-3 1.15 0.06 0.28 - 0.94
Summer Sunny 1.9 × 10-3 1.02 0.19 0.06 0.58 0.92
Cloudy 2.7 × 10-3 1.03 0.10 0.23 0.79 0.97
Overcast 4.8 × 10-3 0.76 0.13 0.34 - 0.96
Autumn Sunny 1.7 × 10-3 2.80 0.16 0.08 0.86 0.91
Cloudy 2.9 × 10-3 1.09 0.10 0.26 0.84 0.97
Overcast 3.5 × 10-3 1.42 0.12 0.32 - 0.63
Winter Sunny 1.0 × 10-3 1.34 0.06 0.08 0.51 0.90
Cloudy 1.8 × 10-3 0.66 0.00 0.22 0.57 0.98
Overcast 1.7 × 10-3 1.10 0.00 0.23 - 0.98

Note: α is the apparent quantum yield, Pmax is the maximal photosynthetic rate, Rd is the dark respiration, P150 is the potential photosynthetic rate at the low light intensity of 150 W m-2, P750 is the potential photosynthetic rate at the high light intensity of 750 W m-2, and R2 is the coefficient of determination. There is no strong light when it is overcast, so P750 under overcast sky (‘-’) was not listed.

Fig. 2 Light response curves of net ecosystem CO2 exchange (NEE) to global radiation (Ig) in 2012
Note: (a) spring, (b) summer, (c) autumn, and (d) winter. All points with standard error bars are averaged by the Ig data per 50 W m-2 under the same sky condition in the same season. All curves are best fitted by Eq. (8) and light response parameters are described in detail in Table 1.
Compared with sunny sky conditions, α under cloudy sky conditions increased by 136.4% in spring, 42.1% in summer, 70.6% in autumn, and 80% in winter, respectively; α under overcast sky conditions was increased by 145.5% in spring, 152.6% in summer, 105.9% in autumn, and 70% in winter, respectively (Table 1).
For P150 under cloudy sky conditions, it increased by 187.5% in spring, 283.3% in summer, 225% in autumn, and 175% in winter, compared with sunny sky conditions. P150 under overcast sky conditions increased by 250% in spring, 466.7% in summer, 300% in autumn, and 187.5% in winter (Table 1). For P750 under cloudy sky conditions, it changed by 44.2% in spring, 36.2% in summer, -2.3% in autumn, and 11.8% in winter compared with sunny sky conditions (Table 1).
Compared with sunny sky conditions, α, P150 and P750 under cloudy sky conditions increased by 82.3%, 217.7% and 22.5% on average, respectively; α and P150 under overcast sky conditions increased by 118.5% and 301% on average, respectively (Table 1). α, P150 and P750 increased linearly with kd (Fig. 3).
Fig. 3 Relationship between light response parameters ((a) α, (b) P150, and (c) P750) and diffuse fraction (kd) presented in Table 1.
Note: Only data under sunny and cloudy sky conditions are presented in (c). α is the apparent quantum yield, P150 is the potential photosynthetic rate under a low light intensity of 150 W m-2, P750 is the potential photosynthetic rate under a high light intensity of 750 W m-2, and R2 is the coefficient of determination.

3.2 Changes in NEE with kt

In order to further study the effects of diffuse radiation on NEE under various sky conditions, changes in NEE with kt were analyzed. Because kt is affected by β, we classified the data for every 10o interval of β. Here, only the results for 50o-60o and 80o-90o intervals of β are shown.
In general, the overcast data points were uppermost; cloudy and sunny data points were below the overcast data points (Fig. 4). In the analysis with major sunny data points, the quadratic regressions showed that maximal NEE (i.e., most negative value) occurred under cloudy sky conditions with the kt between 0.4 and 0.6 (Fig. 4a-c, f). In the analysis with minor sunny data points, results showed that NEE decreased as kt increased (Fig. 4d, e). The results for other intervals of β were similar.
Fig. 4 Scatterplots and quadratic regressions for net ecosystem CO2 exchange (NEE) and clearness index (kt) for (a-d) the 50o-60o and (e-f) 80o-90o intervals of solar elevation angles (β) in 2012.
Note: There are no 80o-90o intervals of β in autumn and winter.

3.3 Changes in environmental variables with kt

The results showed that Id had a unimodal trend with kt.When kt was between 0.4 and 0.6, Id reached its maximum (Fig. 5a-d). Generally, Ta and kt were positively correlated in summer and winter (Fig. 5f, h); Ta tended to increase first and then decrease in spring and autumn with kt (Fig. 5e, g). VPD was positively correlated with kt (Fig. 5i-l). The results for other intervals of β were similar.
Fig. 5 Changes in (a-d) diffuse radiation (Id), (e-h) air temperatures (Ta), and (i-l) vapor pressure deficit (VPD) with clearness index (kt) for 50o-60o and 80o-90o intervals of solar elevation angles (β) in 2012.
Note: There are no 80o-90o intervals of β in autumn and winter.

3.4 %NEE under cloudy and overcast sky conditions

Cubic regression equations were obtained using the relationship between NEE under sunny sky conditions and sinβ in each season (Fig. 6). According to Eq. (9), %NEE under cloudy and overcast sky conditions compared with sunny sky conditions were calculated respectively. When sunny NEE was used as a baseline for the comparison, most %NEE under cloudy sky conditions were greater than 0, while most %NEE under overcast sky conditions were less than 0 (Fig. 7). Table 2 showed that the maximal %NEE under cloudy sky conditions compared with sunny sky conditions for every 10o intervals of β in each season. The average maximal values of %NEE were 42.4% in spring, 34.1% in summer, 1.6% in autumn, and -87.3% in winter (Table 2).
Table 2 Maximal relative change in net ecosystem CO2 exchange (NEE) under cloudy sky conditions compared with sunny sky conditions (%NEE) for every 10° interval of solar elevation angle (β) in 2012
Season %NEE
(30°<β≤40°)		 %NEE
(40°<β≤50°)		 %NEE
(50°<β≤60°)		 %NEE
(60°<β≤70°)		 %NEE
(70°<β≤80°)		 %NEE
(80°<β≤90°)		 %NEE
(Mean)
Spring 39.8 42 18.3 26.4 39 86.4 42.4
Summer 38.9 27.1 31.2 30.6 41.4 35.4 34.1
Autumn 5.6 -12.8 -0.2 14 - - 1.6
Winter 39.9 -251.2 -50.7 - - - -87.3

Note: “-” represents no data in the intervals.

Fig. 6 Scatterplots and cubic regressions between the sunny net ecosystem CO2 exchange (NEE) and the sine of solar elevation angles (sinβ) in 2012
Note: The obtained cubic regression equations are used to calculate NEEs(β) in Eq. (9).
Fig. 7 Relationship between relative change in net ecosystem CO2 exchange (NEE) under (a-d) cloudy and (e-h) overcast sky conditions compared with sunny sky conditions (%NEE) and the clearness index (kt) for 50o-60o and 80o-90o intervals of solar elevation angles (β) in 2012.
Note: There are no 80 o-90o intervals of β in autumn and winter.

4 Discussion

4.1 Influence of cloudy and overcast sky conditions on photosynthesis

Generally, the light response parameters (α, P150, and P750) under cloudy sky conditions were improved compared with those under sunny sky conditions. Especially, α and P150 under overcast sky conditions were apparently higher than sunny sky conditions (Table 1). The relationship between the light response parameters and kd proves that ecosystem photosynthesis can be promoted when the diffuse radiation proportion is large (Fig. 3). This is similar to previous studies (Dengel and Grace, 2010; Kanniah et al., 2013; Han et al., 2019). These results suggest that gloomier sky conditions could improve the light use efficiency and potential photosynthetic capacity of ecosystems. Changes in light response parameters varied slightly in different seasons, but seasonal variations were not obvious. LRC under various sky conditions in different seasons did not show seasonal variation (Fig. 2), similar to Han et al. (2019).
Moderate radiation conditions (i.e., cloudy sky conditions) were more favorable for NEE to reach its maximal value, while low radiation conditions (i.e., overcast sky conditions) inhibited NEE (Fig. 4). This was consistent with variation in Id reaching its maximum value under moderate radiation condition (Fig. 5a-d). Studies by Zhang et al. (2010, 2011) had similar findings to the above. Because of the complexity of forest canopy and layers, solar radiation dominated by direct radiation under sunny sky conditions cannot penetrate the lower part of the forest (Şen, 2008). The strong direct radiation makes leaves in the canopy reach “light saturation”. Meanwhile, because Id is lower under sunny sky conditions, the photosynthesis of shade leaves below the forest is limited by light (Knohl and Baldocchi, 2008).
Id increases under cloudy sky conditions, which enables the shade leaves to receive more light for improving photosynthesis (Gu et al., 2002; Farquhar and Roderick, 2003; Alton et al., 2007). Reduced direct radiation also slows down “light saturation”. As a result, the carbon sequestration of the whole forest ecosystem is improved (Mercado et al., 2009). Nevertheless, higher kd but lower Id under overcast sky conditions cannot meet the radiation demand of photosynthesis (Alton, 2008) and ecosystem carbon sequestration is severely limited by light.
Changes in solar radiation are often accompanied by changes in other environmental factors. Coupled changes in various factors will affect ecosystem carbon sequestration. A decrease in Ta and VPD under cloudy sky conditions can reduce ecosystem respiration (Gu et al., 1999) and evapotranspiration (Law et al., 2002), improving photosynthesis (Freedman et al., 2001).

4.2 Carbon sequestration in the future

In most cases, compared with sunny sky conditions, cloudy sky conditions promoted NEE, but overcast sky conditions inhibited NEE. This result was similar to Gu et al. (1999). Under cloudy sky conditions, maximum %NEE showed seasonal variation which was higher in spring and summer, but lower in autumn and winter. The maximum %NEE was negative in winter, which may be caused by low Ta and reduced light durations. This pattern indicates that sunny and overcast sky conditions do not provide the ideal conditions for photosynthesis. By reducing direct radiation on the forest canopy and increasing diffuse radiation into the forest, cloudy conditions can enhance forest carbon sequestration. However, these results are from data in 2012 and the specific impact will need to be determined by a longer time series of data.
According to the frequency distribution of kt, there are more overcast sky conditions in winter (Fig. 8), which would decrease ecosystem carbon sequestration. The general pattern of current sky conditions is too “overcast” for ecosystem carbon sequestration. This indicates that the current pattern of sky conditions in QYZ still allow for more increases under cloudy conditions to enhance carbon uptake. Based on modeling, by the end of the 21st century, precipitation in the east Asian monsoon area will increase by 10-15% (Seo and Ok, 2013), which would mean more gloomy sky conditions and less carbon uptake. Besides, seasonal drought caused by sunny sky conditions in summer and autumn might weaken the promoting effect of cloudy sky conditions on NEE.
Fig. 8 Histograms of the clearness index (kt) in 2012

5 Conclusions

In this study, EC data and meteorological data for a subtropical coniferous plantation in 2012 were used to analyze the effects of various sky conditions on NEE. LRC under various sky conditions did not show seasonal variation. Light response parameters (α, P150, and P750) under cloudy sky conditions were improved compared with those under sunny sky conditions. Especially, α and P150 under overcast sky conditions were apparently higher than sunny sky conditions. Cloudy sky conditions were more favorable for maximum NEE with a kt between 0.4 and 0.6. In most cases, the average maximal %NEE was positive under cloudy sky conditions across seasons (except for winter) and negative under overcast sky conditions.

The authors have declared that no competing interests exist.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Alton P B, North P R, 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.Global Change Biology, 13(4): 776-787.

[2]
Alton P B.2008. Reduced carbon sequestration in terrestrial ecosystems under overcast skies compared to clear skies.Agricultural and Forest Meteorology, 148(10): 1641-1653.

[3]
Baldocchi D D.2003. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future.Global Change Biology, 9(4): 479-492.

[4]
Bassman J H, Zwier J C.1991. Gas exchange characteristics of Populus trichocarpa, Populus deltoides and Populus trichocarpa × P. deltoides clones. Tree Physiology, 8(2): 145-159.

[5]
Dengel S, Grace J.2010. Carbon dioxide exchange and canopy conductance of two coniferous forests under various sky conditions.Oecologia, 164(3): 797-808.

[6]
Falge E, Baldocchi D, Olson R, et al.2001. Gap filling strategies for defensible annual sums of net ecosystem exchange.Agricultural and Forest Meteorology, 107(1): 43-69.

[7]
Farquhar G D, Roderick M L.2003. Pinatubo, diffuse light, and the carbon cycle.Science, 299(5615): 1997-1998.

[8]
Freedman J M, Firzjarrald D R, Moore K E, et al.2001. Boundary layer clouds and vegetation-atmosphere feedbacks.Journal of Climate, 14(2): 180-197.

[9]
Gu L H, Baldocchi D D, Wofsy S C, et al.2003. Response of a deciduous forest to the Mount Pinatubo eruption: enhanced photosynthesis.Science, 299(5615): 2035-2038.

[10]
Gu L H, Baldocchi D, Verma S B, et al.2002. Advantages of diffuse radiation for terrestrial ecosystem productivity.Journal of Geophysical Research, D6(4050): 107.

[11]
Gu L H, Fuentes J D, Shugart H H, et al.1999. Responses of net ecosystem exchanges of carbon dioxide to changes in cloudiness: Results from two North American deciduous forests. Journal of Geophysical Research, 104(D24): 31421-31434.

[12]
Han J Y, Li S G, Zhang L M, et al.2015a. Comparative observation of diffuse radiation in Qianyanzhou during the spring of 2012.Chinese Journal of Applied Ecology, 26(3): 697-703. (in Chinese)

[13]
Han J Y, Li S G, Zhang L M, et al.2015b. Simulation and validation of diffuse radiation in Qianyanzhou area, Jiangxi, China.Chinese Journal of Applied Ecology, 26(10): 2991-2999. (in Chinese)

[14]
Han J Y, Li S G, Zhang L M, et al.2019. Effects of sky conditions on net ecosystem productivity of a subtropical coniferous plantation vary from half-hourly to daily timescales.Science of the Total Environment, 651: 3002-3014.

[15]
He X Z, Zhou T, Jia G S, et al.2011. Modeled effects of changes in the amount and diffuse fraction of PAR on forest GPP.Journal of Natural Resources, 26(4): 619-634. (in Chinese)

[16]
Kanniah K D, Beringer J, Hutley L.2013. Exploring the link between clouds, radiation, and canopy productivity of tropical savannas. Agricultural and Forest Meteorology, 182-183: 304-313.

[17]
Knohl A, Baldocchi D D.2008. Effects of diffuse radiation on canopy gas exchange processes in a forest ecosystem.Journal of Geophysical Research, 113(G2): G02023.

[18]
Law B E, Falge E, Gu L H, et al.2002. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation.Agricultural and Forest Meteorology, 113(1-4): 97-120.

[19]
Li D Q, Zhou Y L, Ju W M, et al.2014. Modelling the effects of changes in solar radiation on gross primary production in subtropical evergreen needle-leaf plantations.Chinese Journal of Plant Ecology, 38(3): 219-230. (in Chinese)

[20]
Lloyd J L, Taylor J A.1994. On the temperature dependence of soil respiration.Functional Ecology, 8(3): 315-323.

[21]
Mercado L M, Bellouin N, Sitch S, et al.2009. Impact of changes in diffuse radiation on the global land carbon sink.Nature, 458(7241): 1014-1017.

[22]
Reindl D T, Beckman W A, Duffie J A.1990. Diffuse fraction correlations.Solar Energy, 45(1): 1-7.

[23]
Şen Z.2008. Atmospheric environment and renewable energy. In: Şen Z (ed.). Solar Energy Fundamentals and Modeling Techniques: Atmosphere, Environment, Climate Change and Renewable Energy. Berlin: Springer, 21-45.

[24]
Seo K H, Ok J.2013. Assessing future changes in the East Asian summer monsoon using CMIP3 models: results from the best model ensemble.Journal of Climate, 26(5): 1807-1817.

[25]
Stjern C W, Hansen A W.2010. Global dimming and global brightening - an analysis of surface radiation and cloud cover data in northern Europe.International Journal of Climatology, 29(5): 643-653.

[26]
Szeicz C.1974. Solar radiation for plant growth.Journal of Applied Ecology, 11(2): 617-636.

[27]
Tang Y K, Wen X F, Sun X M, et al.2014. The limiting effect of deep soil water on evapotranspiration of a subtropical coniferous plantation subjected to seasonal drought.Advances in Atmospheric Sciences, 31(2): 385-395.

[28]
Wang K C, Dickinson R E, Liang S L.2008. Observational evidence on the effects of clouds and aerosols on net ecosystem exchange and evapotranspiration.Geophysical Research Letters, 35(10): L10401.

[29]
Wang M M, Zhang M, Wang H M, et al.2015. Effects of changes in solar radiation on net ecosystem exchange of carbon dioxide of planted subtropical coniferous forest in Qianyanzhou.Chinese Journal of Ecology, 34(2): 303-311. (in Chinese)

[30]
Wang Y D, Li Q K, Wang H M, et al.2011. Precipitation frequency controls interannual variation of soil respiration by affecting soil moisture in a subtropical forest plantation.Canadian Journal of Forest Research, 41(9): 1897-1906.

[31]
Wang Y D, Wang Z L, Wang H M, et al.2012. Rainfall pulse primarily drives litterfall respiration and its contribution to soil respiration in a young exotic pine plantation in subtropical China.Canadian Journal of Forest Research, 42(4): 657-666.

[32]
Webb E K, Pearman G I, Leuning R.1980. Correction of flux measurements for density effects due to heat and water vapour transfer.Quarterly Journal of the Royal Meteorological Society, 106(447): 85-100.

[33]
Wen X F, Wang H M, Wang J L, et al.2010. Ecosystem carbon exchanges of a subtropical evergreen coniferous plantation subjected to seasonal drought, 2003-2007.Biogeosciences, 7(1): 357-369.

[34]
Wilczak J M, Oncley S P, Stage S A.2001. Sonic anemometer tilt correction algorithms.Boundary-Layer Meteorology, 99(1): 127-150.

[35]
Wild M.2009. Global dimming and brightening: a review. Journal of Geophysical Research, 114: D00D16. doi:10.1029/2008JD011470.

[36]
Wu D.2012. Hazy weather research in China in the last decade: A review.Acta Scientiae Circumstantiae, 32(2): 257-269. (in Chinese)

[37]
Yu G R, Chen Z, Piao S L, et al.2014. High carbon dioxide uptake by subtropical forest ecosystems in the east Asian monsoon region.Proceedings of the National Academy of Sciences, 111(13): 4910-4915.

[38]
Yu G R, Fu Y L, Sun X M, et al.2006. Recent progress and future directions of ChinaFLUX.Science in China Series D: Earth Sciences, 49(Supp.II): 1-23.

[39]
Zhang M, Yu G R, Zhang L M, et al.2010. Impact of cloudiness on net ecosystem exchange of carbon dioxide in different types of forest ecosystems in China.Biogeosciences, 7(2): 711-722.

[40]
Zhang M, Yu G R, Zhuang J, et al.2011. Effects of cloudiness change on net ecosystem exchange, light use efficiency, and water use efficiency in typical ecosystems of China.Agricultural and Forest Meteorology, 151(7): 803-816.

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