The Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ) combines process‐based, large‐scale representations of terrestrial vegetation dynamics and land‐atmosphere carbon and water exchanges in a modular framework. Features include feedback through canopy conductance between photosynthesis and transpiration and interactive coupling between these ‘fast’ processes and other ecosystem processes including resource competition, tissue turnover, population dynamics, soil organic matter and litter dynamics and fire disturbance. Ten plants functional types (PFTs) are differentiated by physiological, morphological, phenological, bioclimatic and fire‐response attributes. Resource competition and differential responses to fire between PFTs influence their relative fractional cover from year to year. Photosynthesis, evapotranspiration and soil water dynamics are modelled on a daily time step, while vegetation structure and PFT population densities are updated annually. Simulations have been made over the industrial period both for specific sites where field measurements were available for model evaluation, and globally on a 0.5°° × 0.5°° grid. Modelled vegetation patterns are consistent with observations, including remotely sensed vegetation structure and phenology. Seasonal cycles of net ecosystem exchange and soil moisture compare well with local measurements. Global carbon exchange fields used as input to an atmospheric tracer transport model (TM2) provided a good fit to observed seasonal cycles of CO2 concentration at all latitudes. Simulated inter‐annual variability of the global terrestrial carbon balance is in phase with and comparable in amplitude to observed variability in the growth rate of atmospheric CO2. Global terrestrial carbon and water cycle parameters (pool sizes and fluxes) lie within their accepted ranges. The model is being used to study past, present and future terrestrial ecosystem dynamics, biochemical and biophysical interactions between ecosystems and the atmosphere, and as a component of coupled Earth system models.

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...

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.food security | AgMIP | ISI-MIP | climate impacts | agriculture T he magnitude, rate, and pattern of climate change impacts on agricultural productivity have been studied for approximately two decades. To evaluate these impacts, researchers use biophysical process-based models (e.g., refs. 1-5), agro-ecosystem models (e.g., ref. 6), and statistical analyses of historical data (e.g., refs. 7 and 8). Although these and other methods have been widely used to forecast potential impacts of climate change on future agricultural productivity, the protocols used in previous assessments have varied to such an extent that they constrain crossstudy syntheses and limit the ability to devise relevant adaptation options (9, 10). In this project we have brought together seven global gridded crop models (GGCMs) for a coordinated set of simulations of global crop yields under evolving climate conditions. This GGCM intercomparison was coordinated by the Agricultural Model Intercomparison and Improvement Project (AgMIP; 11) as part of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP; 12). In order to facilitate analyses across models and sectors, all global models are driven with consistent biascorrected climate forcings derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive (13). The objectives are to (i) establish the range of uncertainties of climate change impacts on crop productivity worldwide, (ii) determine key differences in current approaches used by crop modeling groups in global analyses, and (iii) propose improvements in GGCMs and in the methodologies for future intercomparisons to produce more reliable assessments.We examine the basic patterns of response to climate across crops, latitudes, time periods, regional temperatures, and atmospheric carbon dioxide concentrations [CO 2 ]. In anticipation of the wider scientific community using these model outputs and the expanded application of GGCMs, we introduce these models and present guidelines for their pract...

[1] We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5°× 0.5°spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 ± 7 J × 10 18 yr −1 ), H (164 ± 15 J × 10 18 yr −1), and GPP (119 ± 6 Pg C yr ) were similar to independent estimates. Our global TER estimate (96 ± 6 Pg C yr −1 ) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.