Vegetation controls the exchange of carbon, water, momentum and energy between the land and the atmosphere, and provides food, fibre, fuel and other valuable ecosystem services 1,2. Changes in vegetation structure and function are driven by climatic and environmental changes, and by human activities such as land-use change. Given that increased carbon storage in vegetation, such as through afforestation, could combat climate change 3,4 , quantifying vegetation change and its impact on carbon storage and climate has elicited considerable interest from scientists and policymakers. However, it is not possible to detect vegetation changes at the global scale using ground-based observations due to the heterogeneity of change and the lack of observations that can detect these changes both spatially and temporally. While monitoring the changes in some vegetation properties (for example, stem-size distribution and below-ground biomass) at the global scale remains impossible, satellite-based remote sensing has enabled continuous estimation of a few important metrics, including vegetation greenness, since the 1980s (Box 1). In 1986, a pioneering study by Tucker et al. 5 on remotely sensed normalized difference vegetation index (NDVI; a radiometric measure of vegetation greenness) (Box 1) revealed a close connection between vegetation canopy greenness and photosynthesis acti vity (as inferred from seasonal variations in atmospheric CO 2 concentration). This index was successfully used to constrain vegetation primary production globally 6. Using NDVI data from 1981 to 1991, Myneni et al. 7 reported an increasing trend in vegetation greenness in the Northern Hemisphere, which was subsequently observed across the globe 8-13. This 'vegetation greening' is defined as a statistically signi ficant increase in annual or seasonal vegetation greenness at a location resulting, for instance, from increases in average leaf size, leaf number per plant, plant density, species composition, duration of green-leaf presence due to changes in the growing season and increases in the number of crops grown per year. There has also been considerable interest in understanding the mechanisms or drivers of greening 11,14. Lucht et al. 14 and Xu et al. 10 revealed that warming has eased climatic constraints, facilitating increasing vegetation greenness over the high latitudes. Zhu et al. 11 further investigated key drivers of greenness trends and concluded that CO 2 fertilization is a major factor driving vegetation greening at the global scale. Subsequent studies based on fine-resolution and medium-resolution satel lite data 13 have shown the critical role of land-surface history, including afforestation and agricultural intensification, in enhancing vegetation greenness. The large spatial scale of vegetation greening and the robustness of its signal have led the Intergovernmental Panel on Climate Change (IPCC) special report on climate change Afforestation The conversion of treeless lands to forests through planting trees.

The global distribution of the optimum air temperature for ecosystem-level gross primary productivity (Topteco) is poorly understood, despite its importance for ecosystem carbon uptake under future warming. We provide empirical evidence for the existence of such an optimum, using measurements of in situ eddy covariance and satellite-derived proxies, and report its global distribution. Topteco is consistently lower than the physiological optimum temperature of leaf-level photosynthetic capacity, which typically exceeds 30 °C. The global average Topteco is estimated to be 23±6 ºC, with warmer regions having higher Topteco values than colder regions. In tropical forests, particularly, Topteco is close to growing-season air temperature and is projected to fall below it under all scenarios of future climate, suggesting a limited safe operating space for these ecosystems under future warming.

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Abstract. Field measurements of aboveground net primary productivity (ANPP) in temperate grasslands suggest that both positive and negative asymmetric responses to changes in precipitation (P) may occur. Under normal range of precipitation variability, wet years typically result in ANPP gains being larger than ANPP declines in dry years (positive asymmetry), whereas increases in ANPP are lower in magnitude in extreme wet years compared to reductions during extreme drought (negative asymmetry). Whether the current generation of ecosystem models with a coupled carbon–water system in grasslands are capable of simulating these asymmetric ANPP responses is an unresolved question. In this study, we evaluated the simulated responses of temperate grassland primary productivity to scenarios of altered precipitation with 14 ecosystem models at three sites: Shortgrass steppe (SGS), Konza Prairie (KNZ) and Stubai Valley meadow (STU), spanning a rainfall gradient from dry to moist. We found that (1) the spatial slopes derived from modeled primary productivity and precipitation across sites were steeper than the temporal slopes obtained from inter-annual variations, which was consistent with empirical data; (2) the asymmetry of the responses of modeled primary productivity under normal inter-annual precipitation variability differed among models, and the mean of the model ensemble suggested a negative asymmetry across the three sites, which was contrary to empirical evidence based on filed observations; (3) the mean sensitivity of modeled productivity to rainfall suggested greater negative response with reduced precipitation than positive response to an increased precipitation under extreme conditions at the three sites; and (4) gross primary productivity (GPP), net primary productivity (NPP), aboveground NPP (ANPP) and belowground NPP (BNPP) all showed concave-down nonlinear responses to altered precipitation in all the models, but with different curvatures and mean values. Our results indicated that most models overestimate the negative drought effects and/or underestimate the positive effects of increased precipitation on primary productivity under normal climate conditions, highlighting the need for improving eco-hydrological processes in those models in the future.