In this paper we present an estimate of net ecosystem CO₂ exchange over China for the years 2001–2010 using the CarbonTracker Data Assimilation System for CO₂ (CTDAS). Additional Chinese and Asian CO₂ observations are used in CTDAS to improve our estimate. We found that the combined terrestrial ecosystems in China absorbed about −0.33 Pg C yr⁻¹ during 2001–2010. The uncertainty on Chinese terrestrial carbon exchange estimates as derived from a set of sensitivity experiments suggests a range of −0.29 to −0.64 Pg C yr⁻¹. This total Chinese terrestrial CO₂ sink is attributed to the three major biomes (forests, croplands, and grass/shrublands) with estimated CO₂ fluxes of −0.12 Pg C yr⁻¹ (range from −0.09 to −0.19 Pg C yr⁻¹), −0.12 Pg C yr⁻¹ (range from −0.09 to −0.26 Pg C yr⁻¹), and −0.09 Pg C yr⁻¹ (range from −0.09 to −0.17 Pg C yr⁻¹), respectively. The peak‐to‐peak amplitude of interannual variability of the Chinese terrestrial ecosystem carbon flux is 0.21 Pg C yr⁻¹ (~64% of mean annual average), with the smallest CO₂ sink (−0.19 Pg C yr⁻¹) in 2003 and the largest CO₂ sink (−0.40 Pg C yr⁻¹) in 2007. We stress that our estimate of terrestrial ecosystem CO₂ uptake based on inverse modeling strongly depends on a limited number of atmospheric CO₂ observations used. More observations in China specifically and in Asia in general are needed to improve the accuracy of terrestrial carbon budgeting for this region.

How well parameterization will improve gross primary production (GPP) estimation using the MODerate-resolution Imaging Spectroradiometer (MODIS) algorithm has been rarely investigated. We adjusted the parameters in the algorithm for 21 selected eddy-covariance flux towers which represented nine typical plant functional types (PFTs). We then compared these estimates of the MOD17A2 product, by the MODIS algorithm with default parameters in the Biome Property Look-Up Table, and by a two-leaf Farquhar model. The results indicate that optimizing the maximum light use efficiency (εmax) in the algorithm would improve GPP estimation, especially for deciduous vegetation, though it could not compensate the underestimation during summer caused by the one-leaf upscaling strategy. Adding the soil water factor to the algorithm would not significantly affect performance, but it could make the adjusted εmax more robust for sites with the same PFT and among different PFTs. Even with adjusted parameters, both one-leaf and two-leaf models would not capture seasonally photosynthetic dynamics, thereby we suggest that further improvement in GPP estimaiton is required by taking into consideration seasonal variations of the key parameters and variables.

Global land cover is an important parameter of the land surface and has been derived by various researchers based on remote sensing images. Each land cover product has its own disadvantages and limitations. Data fusion technology is becoming a notable method to fully integrate existing land cover information. In this paper, we developed a method to generate a synergetic global land cover map (synGLC) based on Bayes theorem. A state probability vector was defined to precisely and quantitatively describe the land cover classification of every pixel and reduce the errors caused by legends harmonization and spatial resampling. Simple axiomatic approaches were used to generate the prior land cover map, in which pixels with high consistency were regarded to be correct and then used as benchmark to obtain posterior land cover map. Validation results show that our hybrid land cover map (synGLC, the dataset is available on request) has the best overall performance compared with the existing global land cover products. Closed shrub-lands and permanent wetlands have the highest uncertainty in our fused land cover map. This novel method can be extensively applied to fusion of land cover maps with different legends, spatial resolutions or geographic ranges.

To gain insights into the impacts of emission reduction measures on the characteristics and sources of trace elements during the 2014 Asia-Pacific Economic Cooperation (APEC) summit, PM_2.5 samples were simultaneously collected from an urban site and a suburban site in Beijing from September 15th to November 12th, and fifteen metallic elements were analyzed, including five crustal elements (Mg, Al, K, Ca and Fe), nine trace metals (V, Cr, Mn, Co, Cu, Zn, Ag, Cd and Pb) and As. Most of the trace metals (V, Cr, Mn, As, Cd and Pb) decreased more than 40% due to the emission regulations during APEC, while the crustal elements decreased considerably (4–45%). Relative to the daytime, trace metals increased during the nighttime at both sites before the APEC summit, but no significant difference was observed during the APEC summit, suggesting suppressed emissions from anthropogenic activities. Five sources (dust, traffic exhaust, industrial sources, coal and oil combustion and biomass burning) were resolved using positive matrix factorization (PMF), which were collectively decreased by 30.7% at the urban site and 14.4% at the suburban site during the APEC summit. Coal and oil combustion regulations were the most effective for reducing the trace elements concentrations (urban site: 63.1%; suburban site: 52.0%), followed by measures to reduce traffic exhaust (52.8%) at the urban site and measures to reduce biomass burning (37.7%) at the suburban site. Our results signify that future control efforts of metallic elements in megacities like Beijing should prioritize coal and oil combustion, as well as traffic emissions.

Global retrieval of solar-induced chlorophyll fluorescence (SIF) using remote sensing by means of satellites has been developed rapidly in recent years. Exploring how SIF could improve the characterization of photosynthesis and its role in the land surface carbon cycle has gradually become a very important and active area. However, compared with other gross primary production (GPP) models, the robustness of the parameterization of the SIF model under different circumstances has rarely been investigated. In this study, we examined and compared the effects of temporal aggregation and meteorological conditions on the stability of model parameters for the SIF model ( $\epsilon /SI{F}_{\mathrm{yield}}$ ), the one-leaf light-use efficiency (SL-LUE) model ( ${\epsilon }_{\max }$ ), and the two-leaf LUE (TL-LUE) model ( ${\epsilon }_{\mathrm{msu}}$ and ${\epsilon }_{\mathrm{msh}}$ ). The three models were parameterized based on a maize–wheat rotation eddy-covariance flux tower data in Yucheng, Shandong Province, China by using the Metropolis–Hasting algorithm. The results showed that the values of the $\epsilon /SI{F}_{\mathrm{yield}}$ and ${\epsilon }_{\max }$ were similarly robust and considerably more stable than ${\epsilon }_{\mathrm{msu}}$ and ${\epsilon }_{\mathrm{msh}}$ for all temporal aggregation levels. Under different meteorological conditions, all the parameters showed a certain degree of fluctuation and were most affected at the mid-day scale, followed by the monthly scale and finally at the daily scale. Nonetheless, the averaged coefficient of variation ( $CV$ ) of $\epsilon /SI{F}_{\mathrm{yield}}$ was relatively small (15.0%) and was obviously lower than ${\epsilon }_{\max }$ ( $CV$ = 27.0%), ${\epsilon }_{\mathrm{msu}}$ ( $CV$ = 43.2%), and ${\epsilon }_{\mathrm{msh}}$ ( $CV$ = 53.1%). Furthermore, the SIF model’s performance for estimating GPP was better than that of the SL-LUE model and was comparable to that of the TL-LUE model. This study indicates that, compared with the LUE-based models, the SIF-based model without climate-dependence is a good predictor of GPP and its parameter is more likely to converge for different temporal aggregation levels and under varying environmental restrictions in croplands. We suggest that more flux tower data should be used for further validation of parameter convergence in other vegetation types.