Interactive comment on “Biophysical effects on the interannual variation in carbon dioxide exchange of an alpine meadow on the Tibetan Plateau”

This manuscript presents 4-year data set of CO2 exchange for a high elevation grass-land on the southeast margin of Tibetan Plateau. The statistic model HOS is used to partition the inter-annual variability in net ecosystem exchange between climatic variability and functional change. The annual patterns and inter-annual variability of NEE were showed in this study too. Many studies have revealed the relationship between the climate variables and the CO2 exchange. This paper means to discuss the biophysical effects on inter-annual variation in CO2 exchange. It is supposed to give us some new understandings. However, the authors just partition the climatic and biotic effects and give more analysis on the climatic effects. The key point should be focus on


Introduction
In the last decade, the carbon dioxide exchange in grassland ecosystems has attracted much attention (Baldocchi, 2008;Huang et al., 2008;Hunt et al., 2004;Jing et al., 2010;Suyker et al., 2003) because grasslands cover 32 % of the global land surface and make a substantial contribution to the carbon cycle on a global scale (Parton et al., 1995).The annual net ecosystem exchange (NEE) of grasslands has a large range, from −650 to 160 g C m −2 yr −1 , due to climate variability and land use changes (Gilmanov et al., 2007;Wang et al., 2016a).The climatic factors controlling CO 2 exchange also vary under different climate conditions (Du and Liu, 2013;Huang et al., 2016;Xu and Baldocchi, 2004).Most previous studies have focused on low-lying grasslands (Gilmanov et al., 2010).
Alpine meadows in China are the primary grassland type of the nation and are mainly distributed in the Qinghai-Tibetan plateau (DAHV and CISNR, 1996;Liu et al., 2008).The warming trend in high-altitude areas, such as the Tibetan Plateau and its south-east margin, has been observed to be more pronounced (Fan et al., 2011;Liu and Chen, 2000).Several studies of CO 2 exchange have been carried out on the Qinghai-Tibetan plateau, where the mean annual air temperature (T a ) is approximately 0 • C (Gu et al., 2003;Kato et  alpine meadow revealed that the annual CO 2 uptake was increased by the earlier onset of the growing season, which was caused by higher T a (Kato et al., 2006).The Lijiang alpine meadow is located in a much warmer area (the mean annual T a is 12.7 • C).A spring drought event and relatively low soil moisture were shown to significantly delay the start time of grass germination and reduce the annual CO 2 uptake (Wang et al., 2016b).How the annual CO 2 exchange responds to the mean annual T a is not clear for alpine meadow ecosystems.
Previous studies have attributed year-to-year changes in CO 2 exchange to climatic variability (Hui et al., 2003;Xu and Baldocchi, 2004).Fluxes may directly respond to climatic drivers or be indirectly affected by functional changes or changes in the flux-climate relationships (Polley et al., 2008).Statistical models have been used to partition the interannual variation (IAV) of the CO 2 exchange (Hui et al., 2003;Richardson et al., 2007;Teklemariam et al., 2010).For example, Shao et al. (2014) found that 77 % of the observed variation in NEE was explained by functional changes in the moist grassland in USA, while variations in climatic variables could better explain the IAV of NEE of a meadow in Denmark (Jensen et al., 2017) and mixed-grass prairies in the semi-arid area of USA (Polley et al., 2008).The relative importance of the direct and indirect effects of the climatic variables on the interannual variations in CO 2 exchange for alpine meadows in China has not been quantified.
The CO 2 exchange between the atmosphere and the Lijiang alpine meadow was measured using an eddy covariance technique from 2012 to 2015.The objectives of this study were to (1) examine the seasonal and interannual variation in NEE, gross primary production (GPP), ecosystem respiration (RE) and the parameters of ecosystem photosynthesis and RE; (2) investigate the main environmental controls of the total GPP, RE and NEE on seasonal and annual scales; and (3) partition the interannual variation in GPP, RE and NEE into climatic variability and vegetation growth.
2 Observation site and methods

Observation site
The observation site (27 • 10 N, 100 • 14 E, 3560 m a.s.l.) is located at Maoniuping in the Yulong snow mountains, to the north of Lijiang city on the south-east margin of the Tibetan Plateau, China.The study area has a plateau monsoon climate, which is influenced by the south-west and south-east monsoons.There are distinct wet and dry seasons, with a wet season from June to October.The 30-year mean annual total precipitation  at Lijiang city (2400 m a.s.l.) is 980.3 mm, and 85 % of the precipitation is concentrated in the wet season.The 30-year mean annual air temperature (MAT) is 12.6 • C (data from the Lijiang Meteorology Bureau).The dominant species in this alpine meadow are grass of the genus Kobresia Willd, with a maximum height of 20 cm, and the shrub Berberis Linn, with a maximum height of more than 60 cm.The surface is covered by green vegetation, litter and bare soil.The soil type is a loam, with a dark brown colour, which has a lower reflectance than the grass canopy (Guo et al., 2009).
Table 1.The average value of daily solar radiation (S in , MJ m −2 d −1 ), the mean annual air temperature (T a , • C), the mean annual vapour pressure deficit (VPD, kPa), the mean annual soil water content (SWC, m 3 m −3 ), the total amount of precipitation (PPT, mm) for the whole year and the wet season, and the maximum value of NDVI for each year from 2011 to 2015.The eddy covariance (EC) system was used to measure 3-D wind speed and the H 2 O and CO 2 concentrations at a height of 2.5 m, with a 10 Hz frequency.The system consisted of a three-dimensional sonic anemometer (CSAT3, Campbell Scientific, Logan, UT, USA) and an open-path CO 2 /H 2 O infrared gas analyzer (LI-7500A, LI-COR, Lincoln, NE, USA).The low response measurements (1/3 Hz frequency) made in this study were T a and relative humidity at a height of 2.5 m close to the EC system (HMP45C, Campbell Scientific).Net radiation (including shortwave and longwave radiation, CNR4, Kipp&Zonen, Delft, Netherlands) and photosynthetically active radiation (PAR; LI190SB, LI-COR) were measured at 1.5 m.Soil temperature (109-L, Campbell Scientific) and soil water content (SWC; CS616, Campbell Scientific) were measured at a depth of 5 cm below the ground.The precipitation (including solid precipitation in the winter) was measured using a weighing bucket precipitation gauge (T-200B, Geonor, Eiksmarka, Norway).All measurements were controlled by a data logger (CR3000, Campbell Scientific), and the data were stored on a 2-GB CF card.Four points around the flux tower were selected to investigate the variations in vegetation growth.The 250 × 250 m 2 gridded NDVI data at 16-day intervals (product name: MOD13Q1) for the four points were obtained from the Moderate Resolution Imaging Spectrometer (MODIS) on the EOS-1Terra satellite and were averaged to represent the meadow at this observation site.Observations affected by clouds were removed during this process, and the gaps were filled linearly.

Flux calculation and quality control
EddyPro software (version 5.1, LI-COR) was used to calculate the half-hourly CO 2 flux based on the 10 Hz raw data.After a spike detection (Vickers and Mahrt, 1997), the sector-  wise planar fit method was used to transform the coordinate system due to a terrain slope of approximately 10 • (Wilczak et al., 2001).The CO 2 flux was also subjected to a spectral loss correction (Moore, 1986) and density correction (WPL correction; Webb et al., 1980).
Stationary and integral turbulence characteristics tests were used for flux quality control (Foken and Wichura, 1996).When u * was less than 0.1 m s −1 , the CO 2 flux was dependent on u * and was discarded.Because there was a coniferous forest approximately 350 m to the north of the site, an analytical footprint model was used to determine whether the half-hourly CO 2 flux was influenced by the forest and needed to be removed (Kormann and Meixner, 2001).
After quality control, approximately 70 % of the CO 2 fluxes were subjected to further analysis.Linear interpolation was used to fill flux gaps of less than 2 h.To fill gaps of longer than 2 h, marginal distribution sampling, an improved look-up table method, was used (Falge et al., 2001;Lloyd and Taylor, 1994).

Data analysis
Using the homogeneity-of-slopes (HOS) model (Hui et al., 2003), the control of the CO 2 exchanges (NEE, GPP and RE) was statistically partitioned into four components: the interannual variation of environmental variables (SS i ), the seasonal variation of environmental variables (SS s ), variations of biological variables (SS f , NDVI in this study) and random error (SS e , resulting from measurement and analysis random error).To identify the significant control variables, a multi-  ple stepwise regression analysis of the CO 2 exchanges with environmental variables was conducted using SPSS 12.0 for Windows (SPSS Inc., Chicago, IL, USA).The environmental variables that were significantly correlated with fluxes were submitted for further HOS analysis, while the others were excluded from the analysis.To minimize errors, the daily NEE, GPP and RE were excluded from regressions if more than 50 % of the data points in the daytime (R n > 5 W m −2 ) were missing.More details of the HOS model are provided in Hui et al. (2003).
The relationship between daytime NEE (NEE daytime ) and PAR was described by the Michaelis-Menten model (Falge et al., 2001): where NEE sat is the NEE at the saturated light level, α is the apparent quantum yield (µmol CO 2 µmol −1 photons), and RE bulk is the bulk estimated RE.The Van 't Hoff equation was used to evaluate the relationship between the night-time NEE (NEE nighttime , µmol CO 2 m −2 s −1 ) and soil temperature at a depth of 5 cm (T s , • C; Aires et al., 2008): where a and b are the regression parameters.The temperature sensitivity coefficient (Q 10 ) of RE was determined using the following equation.
The partitioning of NEE into GPP and RE was based on the assumption that the sensitivity of RE to soil temperature was the same during the day and at night (Falge et al., 2001).The regression parameters derived from the night-time data were extrapolated to the daytime to calculate the daytime RE and the daily RE.The daily GPP was calculated as follows: 3 Results

Weather conditions and NDVI
The daily integrated solar radiation (S in ) varied from 1.15 to 32.40 MJ m −2 d −1 (Fig. 1a).The mean S in in spring (March to May) was 17.0 to 19.93 MJ m −2 d −1 and was clearly larger than in other seasons.In the wet season, the mean S in was 9.99 to 11.05 MJ m −2 d −1 .The MAT was 5.92 to 6.32 • C (Table 1).The daily mean T a ranged from 0.41 to 14.96 • C in the wet season and decreased to a minimum value of −9.06 • C in the winter.In contrast, the soil temperature never decreased below 0 • C, and the maximum value was 16.48 • C (Fig. 1b).The vapour pressure deficit (VPD) reached its maximum value of 1.07 kPa before the wet season (Fig. 1c).The VPD decreased to near 0 kPa, and the mean VPD for the wet season was 0.125 to 0.166 kPa.
The annual precipitation from 2012 to 2015 ranged from 1066.1 to 1257.4 mm.The precipitation during the wet season ranged from 906.1 to 1092.6 mm, accounting for 85 to 91 % of the annual total precipitation (Table 1).The mean annual SWC had a small interannual variability, from 0.227 to 0.233 m 3 m −3 .In the wet season, the SWC reached a maximum value of approximately 0.35 m 3 m −3 , and the minimum SWC was 0.15 m 3 m −3 (Fig. 1d).
The NDVI of this alpine meadow displayed a clear seasonal and interannual variation (Fig. 1e).The NDVI exceeded 0.4 at the end of April or in late May, depending on the amount and distribution of precipitation in the spring (March to May).The maximum NDVI for each year ranged from 0.60 (2012) to 0.72 (2013).In all 4 years, the NDVI decreased to below 0.4 at the end of October.

Seasonal and interannual variations in NEE sat , α
and Q 10 The daytime NEE and PAR were averaged, with PAR bins of 100 µmol m −2 s −1 to avoid random errors.For each month in the wet season, the daytime NEE decreased with PAR until a critical PAR was reached.Above the critical PAR, the daytime NEE increased and the CO 2 uptake was de- pressed (Fig. 2a).To derive NEE sat and α, the NEE and PAR data were used only when PAR was below the critical value.NEE sat showed a clear seasonal variation (Table 2).The mean NEE sat values for each month showed that NEE sat began to increase in June (−11.59µmol m −2 s −1 ) and reached a maximum in August (−20.14µmol m −2 s −1 ).
The highest NEE sat during the whole observation period occurred in August of 2014 (−23.75 µmol m −2 s −1 ).NEE sat then declined with grass senescence in September and October.The NEE sat in October (−9.36 µmol m −2 s −1 ) was less than half that in August.The interannual variations in NEE were also large.For example, NEE sat in September 2015 (−21.44 µmol m −2 s −1 ) was almost twice that in September 2013 (−11.43 µmol m −2 s −1 ; Table 2).On a monthly scale, 81 % of the variation in NEE sat could be explained by the mean NDVI (Fig. 2b).Over this meadow, NEE sat did not significantly correlate with SWC because the soil water conditions were always good in the wet season.At monthly intervals, there were large random errors in the regression between RE and T soil .For example, the R 2 for each month of the wet season in 2012 ranged from 0.04 to 0.12.Thus, in 2012, the data in the wet and dry season were combined to fit the regression (Fig. 3a).The Q 10 in the wet seasons was similar, at approximately 3.45 (Table 3), which was in the normal range of previous studies (1.2 to 3.7; Falge et al., 2001).These values were clearly higher than those for temperate grasslands (1.99 to 3.07; Wang et al., 2016a), Mediterranean grasslands (1.22 to 2.36; Aires et al., 2008) and the Haibei alpine meadow (1.50 to 2.27; Kato et al., 2004).Q 10 was lower in the dry season than in the wet season.

Seasonal and interannual variation in NEE, GPP and RE
The ecosystem started to absorb CO 2 (negative value of NEE) on DOY 165 in 2012, DOY 137 in 2013, DOY 116 in 2014 and DOY 104 in 2015, and then NEE decreased (Fig. 4).The minimum daily NEE for each year occurred The daily GPP increase started earlier than CO 2 uptake.The seasonal pattern of daily GPP was similar to that of NEE, although the amplitude of GPP variations was larger than that of NEE variations.The maximum daily GPP for each year was 6.02, 5.47, 6.23 and 5.95 g C m −2 d −1 for the 4 years from 2012 to 2015.Compared with the NEE and GPP, the seasonal variation in RE was smaller during the wet season.In particular, RE varied only slightly from June to August.
The annual GPP in 2014 and 2015 was clearly higher than in 2012 and 2013, as indicated by the larger NDVI (Fig. 5; Tables 1, 4).In contrast, the RE in 2014 was the highest of all 4 years because, although the Q 10 value was similar to the other years, it had the highest T a (Table 1).Therefore, the annual NEE in 2014 was similar to that in 2013, but lower than that in 2015, although the GPP was similar in 2014 and 2015.The spring drought resulted in a significantly lower NDVI in 2012 than in the other years; consequently, the annual GPP in 2012 was the lowest of all 4 years.The annual NEE for the 4 years followed the order of 2015 < 2014 < 2013 < 2012 (Table 4), which is consistent with the length of the CO 2 uptake period.4 Discussion

Partitioning the interannual variation in CO 2 exchange
The HOS model was used to partition the interannual variation (IAV) in CO 2 exchange into climatic variability and ecosystem functional change, which was reflected by the variability of the flux-climate relationship throughout the years (Hui et al., 2003).During the wet season, the daily NEE, GPP and RE were mainly related to T a (Fig. 6).The effect of PAR on NEE and GPP was very weak, with R values of −0.05 and 0.08, respectively.
A separate-slopes model was constructed for each year, and the multiple regression model was based on data from   the multiple regression model to 0.79 in the separate-slopes model.This means that the separate-slopes model accounted for 10.3 % more variation in the observed NEE than the multiple regression model, which was attributed to the functional change (SS f ).The other 89.7 % of the variation in the observed NEE was partitioned to interannual climatic variability (SS i , 7.7 %), seasonal climatic variation (SS s , 37.7 %) and random error (SS e , 44.3 %; Table 5).Therefore, most of the IAV in NEE, GPP and RE was attributable to the variation in climatic variables, in particular, climatic seasonal variation.This is in line with the findings reported for a Skjern meadow in Denmark and a temperate ombrotrophic bog in Canada (Jensen et al., 2017;Teklemariam et al., 2010).In contrast, Braswell et al. (1997) and Shao et al. (2014) found that functional change, rather than the direct effects of IAV in climate, accounted for more IAV in fluxes.Moreover, the contributions of different drivers to the IAV in GPP were similar to that of NEE, while the functional change in RE was twice that of NEE and GPP.The R 2 values for NEE, GPP and RE in the multiple regression model were 0.44, 0.53 and 0.59.It was considered reasonable that the largest random error was recorded for NEE.

Control of the interannual variation in the CO 2 exchange
To examine the interannual variation in CO 2 exchange, the cumulative NEE, GPP and RE in 2013, 2014 and 2015 were compared with the corresponding values in 2012 (Fig. 7).  Figure 10.Relationships between the annual GPP and precipitation (PPT) for this Lijiang site, the Haibei site (Kato et al., 2006;Fu et al., 2009), the Damxung site (Fu et al., 2009) and the semi-arid grassland sites (Fu et al., 2009;Du and Liu, 2013;Yang and Zhou, 2013).
creased throughout the entire year and was the largest of all the periods considered.The daily CO 2 uptake over this meadow ecosystem has previously been shown to increase with T a (Wang et al., 2016).Especially in the spring (March to May), the temperature affected the vegetation growth and GPP.From March to May, the cumulative T a was 592.3, 577.1, 633.1 and 647.6 • C in the 4 years from 2012 to 2015.Consequently, the cumulative GPP in the spring increased in the order of 2015 > 2014 > 2013.The exception was that the spring of 2012 had a higher T a , but a lower GPP than the spring of 2013.Compared with the GPP in 2013, the GPP in 2012 in-creased more significantly due to the higher T a from March to April.However, the drought in May 2012 delayed vegetation growth and reduced GPP.The differences in GPP cum for 2013−2012, 2014−2012 and 2015−2012 at the end of May were 20.0, 63.1 and 83.3 g C m −2 , representing 82.6, 42.9 and 59.7 % of the differences for the three periods.
From July to October, the NEE, GPP and RE were all strongly correlated with T a on a monthly scale (R 2 = 0.84, 0.86 and 0.73; Fig. 9a, b, c).The slope of the relationship between GPP and T a was much larger than for that between RE and T a , indicating that when T a increased, the alpine meadow ecosystem absorbed more CO 2 .The monthly GPP in July and August varied slightly between the 4 years, while the interannual variability of the GPP in September was the largest because the monthly means T a in September for 2012 (8.6 • C) and 2013 (8.8 • C) were significantly lower than those for 2014 (9.7 • C) and 2015 (10.0 • C).Consequently, the differences in GPP cum for 2014−2012 and 2015−2012 from September to October were 55.2 and 48.2 g C m −2 , representing 37.5 and 34.6 % of the differences for the two periods.
On the annual scale, the annual total NEE decreased with the MAT in 2012, 2013 and 2015 and then increased when the MAT was highest in 2014.The reason for this was that the annual total RE increased linearly with the MAT (R 2 = 0.97), while the relationship between the GPP and MAT was non-linear (Fig. 9d).The GPP became saturated as the MAT increased.In contrast, the annual NEE increased with the MAT in the Haibei alpine meadow, which is in line with previous studies showing that the annual NEE is comprehensively controlled by the temperature environment (Kato et al., 2006).

Comparison of annual CO 2 exchange with other sites
The annual GPP at the study site was much larger than that reported in semi-arid grasslands in Tibet and Canada (Flanagan et al., 2002;Yu et al., 2006), but much lower than that reported in moist grasslands in low-lying areas in Europe (Table 7).In China, the annual GPP for semi-arid grasslands and the Haibei alpine meadow increased slightly with annual precipitation (R 2 =0.94;Fig. 10).With similar annual precipitation, the annual GPP of the Damxung site was much lower than the Haibei site, possibly because of higher elevation.When the annual precipitation increased further, to over 1000 mm yr −1 , the annual GPP remained steady (Fig. 10).The annual GPP for the grassland ecosystems in China was always below 700 g C m −2 yr −1 .
In addition to temperature effects, the daily RE was also correlated with the daily GPP (RE = 0.44GPP + 0.63, R 2 = 0.82) during the wet season from 2012 to 2015.Due to the high elevation and low soil temperature in the summer, the percentage of RE to GPP for this meadow site was lower than for Mediterranean grass-lands (RE = 0.53GPP + 0.72, R 2 = 0.85, Aires et al., 2008; RE = 0.47GPP + 1.33, R 2 = 0.85, Xu and Baldocchi, 2004).The low level of RE resulted in a similar or even lower annual NEE (mean value: −161 g C m −2 yr −1 ) at Lijiang than in moist grasslands with a low elevation (Table 7).For example, the mean annual NEE for a meadow in Denmark (annual precipitation: 809 mm) was −156 g C m −2 yr −1 , while the mean annual NEE for a C3/C4 grassland in Japan (annual precipitation: 1156 mm) was −17 g C m −2 yr −1 .The ratio of RE to GPP ranged from 0.69 to 0.79 over the Lijiang alpine meadow, which was lower than the Haibei alpine meadow (Table 7).This is why the annual NEE at the Lijiang site was on average 25 % lower than at the Haibei site.In general, low RE / GPP ratios occurred in high-altitude and moist areas.The alpine meadow ecosystem (Lijiang and Haibei) had a lower RE / GPP ratio than most low-lying grasslands.Compared with semi-arid grasslands (RE / GPP: approximately 1.0), the RE / GPP ratios reported in moist grasslands are much lower, e.g. a sown grassland in the Netherlands (0.60) and a natural grassland in Italy (0.59; Gilmanov et al., 2007).

Conclusions
The 4-year EC data from 2012 to 2015 were used to investigate the interannual variation in the NEE, GPP and RE.The key parameters for ecosystem photosynthesis and respiration were determined for the different seasons of each year.The vegetation growth (NDVI) controlled NEE sat on a monthly scale, and the interannual variation in Q 10 for the wet and dry seasons was small.The seasonal variation in CO 2 exchange was affected by the seasonal pattern of T a and the soil moisture in the spring.In the spring, low T a and drought events delayed the start time of CO 2 uptake.In the late wet season, the higher T a in 2014 and 2015 resulted in later grass senescence and CO 2 release.The annual NEE decreased with the length of the CO 2 uptake period, but its relationship with the NDVI was not significant.For this alpine meadow, the HOS model suggests that most of the IAV in NEE, GPP and RE was attributed to the seasonal variation in climatic variables.On an annual scale, the annual RE increased linearly with the MAT, while the annual GPP became saturated when the MAT increased from 6.16 to 6.32 • C. Thus, the annual NEE decreased and then increased with the MAT.The low RE / GPP ratio at the study site was responsible for the lower annual NEE compared with some other grassland ecosystems with a larger GPP.

Figure 2 .
Figure 2. The relationship between daytime NEE and PAR (a) for August from 2012 to 2015.The NEE and PAR data were averaged with PAR bins of 100 µmol m −2 s −1 .(b) The relationship between NEE sat and NDVI on a monthly scale.

Figure 3 .
Figure 3. (a) Relationship between RE and T soil in 2012; (b) relationship between RE and Tsoil for the wet season from 2012 to 2015; RE and T soil were averaged with T soil bins of 1 • C.

Figure 4 .
Figure 4.The daily mean NEE, GPP and RE from 2012 to 2015.

Figure 6 .
Figure 6.Relationships between (a) NEE and T a , (b) GPP and T a and (c) RE and T a for the wet seasons from 2012 to 2015.

Figure 7 .
Figure 7. Seasonal variations in the differences of (a) NEE, (b) GPP and (c) RE from 2013 to 2012, from 2014 to 2012 and from 2015 to 2012.

Figure 8 .
Figure 8. Cumulative (a) T a and (b) GPP and (c) the daily mean SWC from March to May (DOY60 to 151) for 2012, 2013, 2014 and 2015.

Figure 9 .
Figure 9. Relationships between (a) NEE and T a ; (b) GPP and T a ; (c) RE and T s from July to October at a monthly scale and (d) relationship between the annual total CO 2 exchange fluxes and the mean annual T a , which were GPP = −1191T 2 a +14 930 T a −46 102, R 2 = 0.97 and RE = 276T a − 1235, R 2 = 0.97.

Table 3 .
The ecosystem respiration parameters using Eqs.(2) and (3) (a: µmol m −2 s −1 , b: Q 10 , R 2 ) for the wet and dry seasons from 2012 to 2015.The regression was based on the average values of RE and T soil with T soil bins of 1 • C.

Table 4 .
The annual total NEE, GPP and RE (g C m −2 yr −1 ) for each year from 2012 to 2015.

Table 5 .
The percentage of the contributions of the seasonal climatic variation (SS s ), interannual climatic variability (SS i ), the ecosystem functional change (SS f ) and random error (SS e ) to the interannual variations in NEE, GPP and RE.

Table 6 .
The GPP diff for2013-2012, 2014-2012 and 2015-2012during the periods from March to May, June, from June to July and from August to September.

Table 7 .
Comparison of mean annual temperature (MAT, • C), mean annual precipitation (MAP, mm yr −1 ), NEE (g C m −2 yr −1 ), GPP, RE and RE / GPP between this study and previous grassland studies.