Global environmental change is rapidly altering the dynamics of terrestrial vegetation, with consequences for the functioning of the Earth system and provision of ecosystem services 1,2 . Yet how global vegetation is responding to the changing environment is not well established. Here we use three long-term satellite leaf area index (LAI) records and ten global ecosystem models to investigate four key drivers of LAI trends during 1982-2009. We show a persistent and widespread increase of growing season integrated LAI (greening) over 25% to 50% of the global vegetated area, whereas less than 4% of the globe shows decreasing LAI (browning). Factorial simulations with multiple global ecosystem models suggest that CO 2 fertilization e ects explain 70% of the observed greening trend, followed by nitrogen deposition (9%), climate change (8%) and land cover change (LCC) (4%). CO 2 fertilization e ects explain most of the greening trends in the tropics, whereas climate change resulted in greening of the high latitudes and the Tibetan Plateau. LCC contributed most to the regional greening observed in southeast China and the eastern United States. The regional e ects of unexplained factors suggest that the next generation of ecosystem models will need to explore the impacts of forest demography, di erences in regional management intensities for cropland and pastures, and other emerging productivity constraints such as phosphorus availability.Changes in vegetation greenness have been reported at regional and continental scales on the basis of forest inventory and satellite measurements 3-8 . Long-term changes in vegetation greenness are driven by multiple interacting biogeochemical drivers and land-use effects 9 . Biogeochemical drivers include the fertilization effects of elevated atmospheric CO 2 concentration (eCO 2 ), regional climate change (temperature, precipitation and radiation), and varying rates of nitrogen deposition. Land-use-related drivers involve changes in land cover and in land management intensity, including fertilization, irrigation, forestry and grazing 10 . None of these driving factors can be considered in isolation, given their strong interactions with one another. Previously, a few studies had investigated the drivers of global greenness trends 6,7,11 , with a limited number of models and satellite observations, which prevented an appropriate quantification of uncertainties 12 .Here, we investigate trends of leaf area index (LAI) and their drivers for the period 1982 to 2009 using three remotely sensed data sets (GIMMS3g, GLASS and GLOMAP) and outputs from ten ecosystem models run at global extent (see Supplementary Information). We use the growing season integrated leaf area index (hereafter, LAI; Methods) as the variable of our study. We first analyse global and regional LAI trends for the study period and differences between the three data sets. Using modelling results, we then quantify the contributions of CO 2 fertilization, climatic factors, nitrogen deposition and LCC to the observed trends...

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO 2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop-and regionspecific adaptation strategies to ensure food security for an increasing world population.climate change impact | global food security | major food crops | temperature increase | yield C rops are sensitive to climate change, including changes in temperature and precipitation, and to rising atmospheric CO 2 concentration (1, 2). Among the changes, temperature increase has the most likely negative impact on crop yields (3, 4), and regional temperature changes can be projected from climate models with more certainty than precipitation. Meteorological records show that mean annual temperatures over areas where wheat, rice, maize, and soybean are grown have increased by ∼1°C during the last century (Fig. 1A) and are expected to continue to increase over the next century (Fig. 1B) -more so if greenhouse gas emissions continue to increase. It is thus necessary to quantify the impact of temperature increase on global crop yields, including any spatial variations, to first assess the risk to world food security, and then to develop targeted adaptive strategies to feed a burgeoning world population (5).Several methods have been developed to assess the impact of temperature increase on crop yields (6). Process-based crop models characterize crop growth and development in daily time steps and can be used to simulate the temperature response of yield either in areas around the globe defined by grids or at selected field sites or points (1, 7). A third method, statistical modeling, uses observed regional yields and historical weather records to fit regression functions to predict crop responses (8,9). A fourth method is to artificially warm crops under nearnatural field conditions to directly measure the impact of increased Significance Agricultural production is vulnerable to climate change. Understanding climate change, especially the temperature impacts, is...

18The phenology of spring leaf unfolding influences regional and hemispheric-scale carbon 19 balances 2 , the long-term distribution of tree species 9 , and plant-animal interactions 10 . Changes in 20 the phenology of spring leaf unfolding can also exert biophysical feedbacks on climate by 21 modifying the surface albedo and energy budget 11,12 . Recent studies have reported significant 22 advances in spring phenology as a result of warming in most northern hemisphere regions 1,3,4 . 23Climate warming is projected to further increase 13 , but the future evolution of the phenology of 24 spring leaf unfolding remains uncertain -in view of the imperfect understanding of how the 25 underlying mechanisms respond to environmental stimuli 12,14 . In addition, the relative 26 contributions of each environmental stimulus, which together define the apparent temperature 27 sensitivity of the phenology of spring leaf unfolding (advances in days per degree Celsius 28 warming, S T ), may also change over time 6,8,15 . An improved characterization of the variation in 29 3 phenological responses to spring temperature is thus valuable, provided that it addresses temporal 1 and spatial scales relevant for regional projections. 2Numerous studies have reported advanced spring leaf unfolding which matches warming trends 3 over recent decades 1,3,4 . However, there is still debate regarding the linearity of leaf unfolding 4 response to the climate warming 6,7 . Recent experimental studies of warming using saplings have 5 shown that S T weakens as warming increases 7 . Experimental manipulation of temperature for 6 saplings or twigs, however, might elicit phenological responses that do not accurately reflect the 7 response of mature trees 16,17 . We therefore investigated the temporal changes in S T in adult trees 8 monitored in situ and exposed to real-world changes in temperature and other climate variables. 9These long-term data series were obtained across Central Europe from the Pan European regression for the entire period and for two 15-year periods, namely 1980-1994 and 1999-2013, 25 that had slight difference in preseason lengths (Extended Data Fig. 3a). The leaf unfolding dates (Fig. 1a). But the surprising result is that S T 3 significantly decreased by 40.0% from 4.0 ± 1.8 days °C -1 during 1980-1994 to 2.3 ± 1.6 days °C -4 1 during 1999-2013 (t=-37.3, df=5473, P<0.001) (Fig. 1b). All species show similar significant 5 decreases in S T (Fig. 1a), although the extent of reduction was species-specific. For example, 6Aesculus hippocastanum (see caption to Fig. 1 for English common names) had the largest 7 decrease in S T (-2.0 days °C -1 ), while S T decreased only slightly (but still significantly) in Fagus 8 sylvatica (-0.9 days °C -1 ) (Fig. 1a). Similar results were also obtained using a fixed preseason 9 length determined either in the time period 1980-1994 or in 1999-2013 10 and 3c). The declining S T could, however, also have been an artifact resulting from the 11 ‗encroachment' of leaf unfolding dates...

The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO 2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAI GS ) estimated by satellite datasets (average trend of 0.0070 yr À1, ranging from 0.0035 yr À1 to 0.0127 yr À1 ), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAI GS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAI GS (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAI GS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO 2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth.

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.

The timing of the end of the vegetation growing season (EOS) plays a key role in terrestrial ecosystem carbon and nutrient cycles. Autumn phenology is, however, still poorly understood, and previous studies generally focused on few species or were very limited in scale. In this study, we applied four methods to extract EOS dates from NDVI records between 1982 and 2011 for the Northern Hemisphere, and determined the temporal correlations between EOS and environmental factors (i.e., temperature, precipitation and insolation), as well as the correlation between spring and autumn phenology, using partial correlation analyses. Overall, we observed a trend toward later EOS in ~70% of the pixels in Northern Hemisphere, with a mean rate of 0.18 ± 0.38 days yr . Warming preseason temperature was positively associated with the rate of EOS in most of our study area, except for arid/semi-arid regions, where the precipitation sum played a dominant positive role. Interestingly, increased preseason insolation sum might also lead to a later date of EOS. In addition to the climatic effects on EOS, we found an influence of spring vegetation green-up dates on EOS, albeit biome dependent. Our study, therefore, suggests that both environmental factors and spring phenology should be included in the modeling of EOS to improve the predictions of autumn phenology as well as our understanding of the global carbon and nutrient balances.

In the Arctic, climate warming enhances vegetation activity by extending the length of the growing season and intensifying maximum rates of productivity. In turn, increased vegetation productivity reduces albedo, which causes a positive feedback on temperature. Over the Tibetan Plateau (TP), regional vegetation greening has also been observed in response to recent warming. Here, we show that in contrast to arctic regions, increased growing season vegetation activity over the TP may have attenuated surface warming. This negative feedback on growing season vegetation temperature is attributed to enhanced evapotranspiration (ET). The extra energy available at the surface, which results from lower albedo, is efficiently dissipated by evaporative cooling. The net effect is a decrease in daily maximum temperature and the diurnal temperature range, which is supported by statistical analyses of in situ observations and by decomposition of the surface energy budget. A daytime cooling effect from increased vegetation activity is also modeled from a set of regional weather research and forecasting (WRF) mesoscale model simulations, but with a magnitude smaller than observed, likely because the WRF model simulates a weaker ET enhancement. Our results suggest that actions to restore native grasslands in degraded areas, roughly one-third of the plateau, will both facilitate a sustainable ecological development in this region and have local climate cobenefits. More accurate simulations of the biophysical coupling between the land surface and the atmosphere are needed to help understand regional climate change over the TP, and possible larger scale feedbacks between climate in the TP and the Asian monsoon system.he Tibetan Plateau (TP) plays a key role in the Asian summer monsoon, a weather system affecting more than half of the world's population. The TP has experienced a pronounced warming over recent decades (1), with a warming rate of about twice the global average for the period 1960-2009 (2, 3), yet with heterogeneous patterns. Both observations and model studies show that recent climate change has had an impact on the structure and ecological functioning of TP grasslands (4-7). One robust observation is that temperature has increased more slowly during the day than during the night, thereby reducing the diurnal temperature range by about 0.23°C per decade over the period 1961-2003 (8). Understanding the mechanisms driving the spatiotemporal patterns of temperature change over the TP is critical for the development of adaptation strategies to protect its vulnerable grassland ecosystems and for better understanding the coupling between regional changes over the TP and the larger Asian monsoon system (9).Changes in vegetation albedo, emissivity, and evapotranspiration (ET) altogether exert feedbacks on climate (10-14). In the Arctic, it has been shown that a temperature-driven increase of vegetation productivity can produce a positive feedback to warming through reduced albedo, which increases the amount of solar radiation ...

Autumn phenology plays a critical role in regulating climate-biosphere interactions. However, the climatic drivers of autumn phenology remain unclear. In this study, we applied four methods to estimate the date of the end of the growing season (EOS) across China's temperate biomes based on a 30-year normalized difference vegetation index (NDVI) dataset from Global Inventory Modeling and Mapping Studies (GIMMS). We investigated the relationships of EOS with temperature, precipitation sum, and insolation sum over the preseason periods by computing temporal partial correlation coefficients. The results showed that the EOS date was delayed in temperate China by an average rate at 0.12 ± 0.01 days per year over the time period of 1982-2011. EOS of dry grassland in Inner Mongolia was advanced. Temporal trends of EOS determined across the four methods were similar in sign, but different in magnitude. Consistent with previous studies, we observed positive correlations between temperature and EOS. Interestingly, the sum of precipitation and insolation during the preseason was also associated with EOS, but their effects were biome dependent. For the forest biomes, except for evergreen needle-leaf forests, the EOS dates were positively associated with insolation sum over the preseason, whereas for dry grassland, the precipitation over the preseason was more dominant. Our results confirmed the importance of temperature on phenological processes in autumn, and further suggested that both precipitation and insolation should be considered to improve the performance of autumn phenology models.