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Surface ozone (O3) and fine particulate matter (PM2.5) are dominant air pollutants in China. Concentrations of these pollutants can show significant differences between urban and nonurban areas. However, such contrast has never been explored on the country level. This study investigates the spatiotemporal characteristics of urban-to-suburban and urban-to-background difference for O3 (Δ[O3]) and PM2.5 (Δ[PM2.5]) concentrations in China using monitoring data from 1171 urban, 110 suburban, and 15 background sites built by the China National Environmental Monitoring Center (CNEMC). On the annual mean basis, the urban-to-suburban Δ[O3] is −3.7 ppbv in Beijing-Tianjin-Hebei, 1.0 ppbv in the Yangtze River Delta, −3.5 ppbv in the Pearl River Delta, and −3.8 ppbv in the Sichuan Basin. On the contrary, the urban-to-suburban Δ[PM2.5] is 15.8, −0.3, 3.5 and 2.4 µg m−3 in those areas, respectively. The urban-to-suburban contrast is more significant in winter for both Δ[O3] and Δ[PM2.5]. In eastern China, urban-to-background differences are also moderate during summer, with −5.1 to 6.8 ppbv for Δ[O3] and −0.1 to 22.5 µg m−3 for Δ[PM2.5]. However, such contrasts are much larger in winter, with −22.2 to 5.5 ppbv for Δ[O3] and 3.1 to 82.3 µg m−3 for Δ[PM2.5]. Since the urban region accounts for only 2% of the whole country’s area, the urban-dominant air quality data from the CNEMC network may overestimate winter [PM2.5] but underestimate winter [O3] over the vast domain of China. The study suggests that the CNEMC monitoring data should be used with caution for evaluating chemical models and assessing ecosystem health, which require more data outside urban areas.

Accurate simulation of gross primary productivity (GPP) is essential for estimating the global carbon budget. However, GPP modeling is subject to various sources of uncertainties, among which the impacts of biases in climate forcing data have not been well quantified. Here, using a well-validated vegetation model, we compare site-level simulations using either ground-based meteorology or assimilated reanalyses to identify climate-driven uncertainties in the predicted GPP at 91 FLUXNET sites. Simulations yield the lowest root mean square errors (RMSE) in GPP relative to observations when all site-level meteorology and CO2 concentrations are used. Sensitivity tests conducted with Modern-Era Retrospective Analysis (MERRA) reanalyses increase GPP RMSE by 30%. Replacement of site-level CO2 with global annual average values provides limited contributions to these changes. In contrast, GPP uncertainties increase almost linearly with the biases in meteorology. Among all factors, photosynthetically active radiation (PAR), especially diffuse PAR, plays dominant roles in modulating GPP uncertainties. Simulations using all MERRA forcings but with site-level diffuse PAR help reduce over 50% of the climate-driven biases in GPP. Our study reveals that biases in meteorological forcings, especially the variabilities at diurnal to seasonal time scales, can induce significant uncertainties in the simulated GPP at FLUXET sites. We suggest cautions in simulating global GPP using climate reanalyses for dynamic global vegetation models and urgent improvements in climatic variability in reanalyses data, especially for diffuse radiation.

Isoprene is a key biogenic volatile organic compound of vital importance for global climate change and air quality. Previous studies projecting future changes of isoprene emissions showed a wide range of uncertainties due to the discrepancies in emission schemes, climate models, and future scenarios. Here, we use an ensemble of models from the Coupled Model Intercomparison Project version 6 (CMIP6) to explore the spatiotemporal variations of global isoprene emissions at present day and their changes by the end of 21st century. At present day, most models predict similar emission rates of 400 Tg C yr−1 but with large differences in the long-term trends. For models using the scheme of Model of Emissions of Gases and Aerosols from Nature, isoprene emissions show limited changes during historical period but significant enhancements after the year 2000. However, for models using another scheme with strong CO2 inhibition effects, isoprene emissions show decreasing trends during historical period and moderate increasing trends after the year 2000. By the end of 21st century, the ensemble projection shows increases of 21–57% in isoprene emissions with the largest enhancement for the strongest warming scenario. Attribution shows that temperature is the dominant driver for the increase of isoprene emissions, no matter whether the CO2 inhibition effects are considered or not. The enhanced isoprene emissions increase the risks of ozone pollution in a warmer climate.

The ecosystem plays an important role in mitigating global warming through carbon assimilation. In addition, climate change has both positive and negative impacts on ecosystem productivity. To combat climate change, the Paris Agreement has set the goal of limiting global warming well below 2 °C with an ultimate target of 1.5 °C above preindustrial levels. It remains unclear how ecosystem productivity will respond to these warming targets. Here, we project global changes in gross (GPP) and net (NPP) primary productivity under 1.5 °C/2 °C warming using a dynamic vegetation model coupled with a state-of-art climate model. We use site-level observations to validate the present-day simulations and the multimodel ensemble to validate the future projections. We identify the main drivers of ecosystem changes by distinguishing between the impacts of CO2 fertilization, fast climatic responses to CO2 radiative perturbation, and slow climatic feedbacks to ocean warming due to the large heat capacity. Compared with the present day, global GPP increases by 11.5% (20.9%) and NPP increases by 14.8% (27.1%) under the 1.5 °C (2 °C) warming scenario with similar magnitudes of change in the tropics and subtropics. Changes in the leaf area index mainly follow those of GPP/NPP but with more significant enhancement in boreal regions. CO2 fertilization causes the dominant contributions to GPP changes over 83% of land areas. The slow responses to ocean warming cause negative changes in GPP/NPP and partly offset regional CO2 fertilization over the tropics, where the climate is projected to be drier due to atmosphere-ocean feedback. The fast climatic responses to CO2 radiative perturbation further decrease GPP/NPP but with a moderate magnitude. The projection suggests that ecosystem productivity will increase in a warmer climate but with large spatial heterogeneity.

Aerosols affect ecosystem photosynthesis and evapotranspiration through the perturbations in Earth's energy balance and changes in climatic conditions. Aerosols enhance plant photosynthesis by increasing diffuse radiation, but meanwhile the reductions in direct radiation may dampen or even overweigh the diffuse fertilization effects, especially at the high loading of aerosols and cloud. Aerosol-induced cooling has positive (negative) impacts on photosynthesis at low (high) latitudes. The enhanced drought by aerosols may decrease both photosynthesis and evapotranspiration. On the global scale, the net effects of aerosols on ecosystems are limited due to the offset of radiative and climatic effects. Our review highlights the importance of understanding the interactions among aerosols, climate, and vegetation.

Diffuse radiation can promote ecosystem gross primary productivity (GPP) and strengthen land carbon sink. However, measurements of diffuse radiation at carbon flux sites are rare, limiting the explorations of diffuse fertilization efficiency (DFE), the percentage changes of GPP per unit diffuse photosynthetically active radiation (PAR). Here, we derive diffuse radiative fraction (Kd) at >200 FLUXNET sites based on an artificial neural network (ANN) and estimate DFE at individual sites around the world. Evaluations show that the ANN on average increases modeling-to-observation correlation coefficients and the Willmott index of Kd by 8% and reduces root-mean-square error by 14.7% compared to 11 empirical models. High Kd of 0.6–0.82 is located at mid-high latitudes and relatively low Kd of 0.46–0.58 is found at subtropical regions. The optimal Kd leading to maximum GPP is estimated to be 0.46 averaged for all FLUXNET sites. In response to 1 W m−2 enhancement of diffuse PAR, daytime GPP increases by 0.44–1.01% for tree species and 0.16–0.94% for non-tree species. Such GPP enhancement by diffuse radiation is on average 2.5 times of that by the same amount of direct radiation.