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Societal Impact Statement Native medicinal plants contribute essential health benefits to populations globally, constituting a major natural resource that human societies rely on. Being an integral part of terrestrial biodiversity, medicinal plants are detrimentally affected by ongoing climate and land‐use change, yet comprehensive studies on the risk that extinction will pose to medicinal biodiversity are lacking. Responding to ongoing scientific calls for conserving medicinal biodiversity, this study provides an integrated assessment of the impacts of environmental change on ironwort (Sideritis), a group of closely related endemic plants of great cultural significance as local medicinal resources in the Balkan Mountains. Summary Mountain habitats harbour unique biodiversity and provide vital resources for human well‐being, including natural medicinal resources, yet they are amongst the environments most impacted by global change. While there is ample evidence of recent rapid climate and land‐use change on mountain ecosystems, the impacts of these processes on the habitats of culturally important medicinal plants are still poorly understood. Here, we assess the potential loss of mountain habitats for medicinal plant resources over the past four decades using the culturally important ironwort, a group of endemic medicinal plants of the Balkan Mountains extensively used by local human populations and the pharmaceutical industry for treating cough and cold and gastrointestinal disorders. We used information collected from major European natural history museums to guide extensive field campaigns across 15 separate mountain ranges. We integrate field data with thousands of satellite images, station‐validated climate reanalysis data and habitat suitability modelling. We finally used machine learning to assess the relative roles of climate and vegetation rates of change in driving rates of habitat suitability change. We show that rising temperatures and ‘mountain greening’ erode the habitats of ironwort at alarming rates. About 50% of the total habitat area across all considered mountain ranges shows a significant decline in habitat suitability. These past trends will most likely continue in the future and could lead to widespread local extinction of the species and other medicinal plants that share similar ecological preferences, threatening their future contributions to societal well‐being.

Abstract Accurate reconstruction of seasonal atmospheric patterns of the past is essential for reliable prediction of how climate will evolve due to anthropogenic CO2 forcing. The Eocene ‘hot house’ climate, as the warmest epoch during the Cenozoic, is considered as a potential analogue for ‘high-CO2’ future climate scenarios. In this context, the reconstruction of variations in seasonality are as important as changes in mean annual conditions. Here we combine stable oxygen (δ18O) and dual clumped isotope (Δ47 + Δ48) measurements of a bivalve shell to determine sub-annual variations in sea surface temperatures and oceanic freshening in the Paris Basin during the Mid-Eocene Climate Optimum, 40 million years ago. Our reconstruction indicates to high mean annual temperatures with a small seasonal amplitude (33.3 °C ± 4.4 °C) and an enhanced fresh water input during the summer period. Our results implying a substantially warmer climate state with different hydrological conditions for Western Europe during the Eocene than previously suggested by proxy data or climate modelling.

Abstract Tree regeneration is a key process in forest dynamics, particularly in the context of forest resilience and climate change. Models are pivotal for assessing long‐term forest dynamics, and they have been in use for more than 50 years. However, there is a need to evaluate their capacity to accurately represent tree regeneration. We assess how well current models capture the overall abundance, species composition, and mortality of tree regeneration. Using 15 models built to capture long‐term forest dynamics at the stand, landscape, and global levels, we simulate tree regeneration at 200 sites representing large environmental gradients across Central Europe. The results are evaluated against extensive data from unmanaged forests. Most of the models overestimate recruitment levels, which is compensated only in some models by high simulated mortality rates in the early stages of individual‐tree dynamics. Simulated species diversity of recruitment generally matches observed ranges. Models simulating higher stand‐level species diversity do not feature higher species diversity in the recruitment layer. The effect of light availability on recruitment levels is captured better than the effects of temperature and soil moisture, but patterns are not consistent across models. Increasing complexity in the tree regeneration modules is not related to higher accuracy of simulated tree recruitment. Furthermore, individual model design is more important than scale (stand, landscape, and global) and approach (empirical and process‐based) for accurately capturing tree regeneration. Despite the mismatches between simulation results and data, it is remarkable that most models capture the essential features of the highly complex process of tree regeneration, while not having been parameterized with such data. We conclude that much can be gained by evaluating and refining the modeling of tree regeneration processes. This has the potential to render long‐term projections of forest dynamics under changing environmental conditions much more robust.

Abstract Tropical forests represent important supporting pillars for society, supplying global ecosystem services (ES), e.g., as carbon sinks for climate regulation and as crucial habitats for unique biodiversity. However, climate change impacts including implications for the economic value of these services have been rarely explored before. Here, we derive monetary estimates for the effect of climate change on climate regulation and habitat services for the forests of Central America. Our results projected ES declines in 24–62% of the study region with associated economic costs of $51–314 billion/year until 2100. These declines particularly affected montane and dry forests and had strong economic implications for Central America’s lower-middle income countries (losses of up to 335% gross domestic product). In addition, economic losses were mostly higher for habitat services than for climate regulation. This highlights the need to expand the focus from mere maximization of CO2 sequestration and avoid false incentives from carbon markets.

Measurement(s) plant trait Technology Type(s) various Factor Type(s) none Sample Characteristic - Organism Tracheophyta Sample Characteristic - Environment natural environment Sample Characteristic - Location global

Vegetation models for climate adaptation and mitigation strategies require spatially high-resolution climate input data in which the error with respect to observations has been previously corrected. To quantify the impact of bias correction, we examine the effects of quantile-mapping bias correction on the climate change signal (CCS) of climate, extremes, and biological variables from the convective regional climate model COSMO-CLM and two dynamic vegetation models (LPJ-GUESS and CARAIB). COSMO-CLM was driven and analyzed at 3 km horizontal resolution over Central Europe (CE) and the Iberian Peninsula (IP) for the transient period 1980–2070 under the RCP8.5 scenario. Bias-corrected and uncorrected climate simulations served as input to run the dynamic vegetation models over Wallonia. Main result of the impact of bias correction on the climate is a reduction of seasonal absolute precipitation by up to −55% with respect to uncorrected simulations. Yet, seasonal climate changes of precipitation and also temperature are marginally affected by bias correction. Main result of the impact of bias correction on changes in extremes is a robust spatial mean CCS of climate extreme indices over both domains. Yet, local biases can both over- and underestimate changes in these indices and be as large as the raw CCS. Changes in extremely wet days are locally enhanced by bias correction by more than 100%. Droughts in southern IP are exacerbated by bias correction, which increases changes in consecutive dry days by up to 14 days/year. Changes in growing season length in CE are affected by quantile mapping due to local biases ranging from 24 days/year in western CE to −24 days/year in eastern CE. The increase of tropical nights and summer days in both domains is largely affected by bias correction at the grid scale because of local biases ranging within ±14 days/year. Bias correction of this study strongly reduces the precipitation amount which has a strong impact on the results of the vegetation models with a reduced vegetation biomass and increases in net primary productivity. Nevertheless, there are large differences in the results of the two applied vegetation models.

The climate over Europe has been recorded to be hotter, drier, and more fire-prone over the last decade than ever before, leading to concerns about how climate change will alter fire weather in the future. A typical measure to estimate fire weather severity based on climate is the Canadian fire weather index (FWI). In this study, we used high-resolution, bias-corrected climate model output (∼9 km) from six CMIP6 climate models and four shared socio-economic pathway projections (SSPs) to calculate consistent and comparable daily FWI datasets for Europe from 1950 to 2080. Our study aims to identify regional and large-scale shifts in fire weather severity and its predictability over time to support adaptive planning. We show that irrespective of the future SSP, fire weather will become more severe, but the increase is much stronger under high greenhouse gas emissions. This leads to new areas being exposed to severe fire weather, such as central Europe and rapidly warming mountainous areas. Already fire-prone regions in southern Europe will experience more extreme conditions. We conclude that only the low-emission SSP1-2.6 pathway can prevent strong increases in fire weather beyond the 2050s. Fire surveillance and management will become more important, even in areas and in seasons where they have not been in the focus so far.

Summary: Medicinal plants and their bioactive molecules are integral components of nature and have supported the health of human societies for millennia. However, the prevailing view of medicinal biodiversity solely as an ecosystem-decoupled natural resource of commercial value prevents people from fully benefiting from the capacity of nature to provide medicines and from assessing the vulnerability of this capacity to the global environmental crisis. Emerging scientific and technological developments and traditional knowledge allow for appreciating medicinal plant resources from a planetary health perspective. In this Personal View, we highlight and integrate current knowledge that includes medicinal, biodiversity, and environmental change research in a transdisciplinary framework to evaluate natural medicinal resources and their vulnerability in the anthropocene. With Europe as an application case, we propose proxy spatial indicators for establishing the capacity, potential societal benefits, and economic values of native medicinal plant resources and the exposure of these resources to global environmental change. The proposed framework and indicators aim to be a basis for transdisciplinary research on medicinal biodiversity and could guide decisions in addressing crucial multiple Sustainable Development Goals, from accessible global health care to natural habitat protection and restoration.

As herbarium specimens are increasingly becoming digitised and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilise such information. In our study, we use deep learning to detect plant organs on digitised herbarium specimens with Faster R-CNN. For our experiment, we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model. The model worked particularly well on leaves and stems, while flowers were also present in large numbers in the sheets, but were not equally well recognised.

Abstract National and local governments need to step up efforts to effectively implement the post‐2020 global biodiversity framework of the Convention on Biological Diversity to halt and reverse worsening biodiversity trends. Drawing on recent advances in interdisciplinary biodiversity science, we propose a framework for improved implementation by national and subnational governments. First, the identification of actions and the promotion of ownership across stakeholders need to recognize the multiple values of biodiversity and account for remote responsibility. Second, cross‐sectorial implementation and mainstreaming should adopt scalable and multifunctional ecosystem restoration approaches and target positive futures for nature and people. Third, assessment of progress and adaptive management can be informed by novel biodiversity monitoring and modeling approaches handling the multidimensionality of biodiversity change.

Abstract Humanity is on a deeply unsustainable trajectory. We are exceeding planetary boundaries and unlikely to meet many international sustainable development goals and global environmental targets. Until recently, there was no broadly accepted framework of interventions that could ignite the transformations needed to achieve these desired targets and goals. As a component of the IPBES Global Assessment, we conducted an iterative expert deliberation process with an extensive review of scenarios and pathways to sustainability, including the broader literature on indirect drivers, social change and sustainability transformation. We asked, what are the most important elements of pathways to sustainability? Applying a social–ecological systems lens, we identified eight priority points for intervention (leverage points) and five overarching strategic actions and priority interventions (levers), which appear to be key to societal transformation. The eight leverage points are: (1) Visions of a good life, (2) Total consumption and waste, (3) Latent values of responsibility, (4) Inequalities, (5) Justice and inclusion in conservation, (6) Externalities from trade and other telecouplings, (7) Responsible technology, innovation and investment, and (8) Education and knowledge generation and sharing. The five intertwined levers can be applied across the eight leverage points and more broadly. These include: (A) Incentives and capacity building, (B) Coordination across sectors and jurisdictions, (C) Pre‐emptive action, (D) Adaptive decision‐making and (E) Environmental law and implementation. The levers and leverage points are all non‐substitutable, and each enables others, likely leading to synergistic benefits. Transformative change towards sustainable pathways requires more than a simple scaling‐up of sustainability initiatives—it entails addressing these levers and leverage points to change the fabric of legal, political, economic and other social systems. These levers and leverage points build upon those approved within the Global Assessment's Summary for Policymakers, with the aim of enabling leaders in government, business, civil society and academia to spark transformative changes towards a more just and sustainable world. A free Plain Language Summary can be found within the Supporting Information of this article.

Global, fast and accessible monitoring of biodiversity is one of the main pillars of the efforts undertaken in order to revert it loss. The Group on Earth Observations Biodiversity Observation Network (GEO-BON) provided an expert-based definition of the biological properties that should be monitored, the Essential Biodiversity Variables (EBVs). Initiatives to provide indicators for EBVs rely on global, freely available remote sensing (RS) products in combination with empirical models and field data, and are invaluable for decision making. In this study, we provide alternatives for the expansion and improvement of the EBV indicators, by suggesting current and future data from the European Space Agencýs COPERNICUS and explore the potential of RS-integrated Dynamic Global Vegetation Models (DGVMs) for the estimation of EBVs. Our review found that mainly due to the inclusion of the Sentinel constellation, Copernicus products have similar or superior potential for EBV indicator estimation in relation to their NASA counterparts. DGVMs simulate the ecosystem level EBVs (ecosystem function and structure), and when integrated with remote sensing data have great potential to not only offer improved estimation of current states but to provide projection of ecosystem impacts. We suggest that focus on producing EBV relevant outputs should be a priority within the research community, to support biodiversity preservation efforts.

Central America hosts many key biodiversity areas (KBAs), areas which represent unique and irreplaceable ecosystems of global importance for species conservation. However, large extents of these areas are not under legal protection and could be threatened by pressures from land use change (e.g. deforestation and agricultural expansion), high human population density (e.g. population growth and urban sprawl) and climate-driven biome shifts. Here, we simulated future biome stability under the influence of climate change across KBAs in the Mesoamerican biodiversity hot spot and combined the results with projections of land use and population density up to the end of the 21st century. We applied four forcing scenarios based on two global climate models (GFDL-ESM4 and IPSL-CM6A-LR) and two shared socio-economic pathways (SSP1-2.6 and SSP3-7.0), which represent a range from low to high emission pathways. Our model projected decreased biome stability in 39%–46% of protected areas in KBAs, whereas this number even increased to 59%–60% for unprotected areas in KBAs (depending on the climate scenario). While human interferences in protected parts of KBAs are expected to be limited, large parts of unprotected areas in KBAs were projected to be pressured by multiple factors at once and are reason for concern. In particular, high human population pressures (>10 people km ^−2 ) emerged as a main threat over 30%–44% of the unprotected area in KBAs. These were largely accompanied by pressures from land use and sporadically reinforced by pressures from climate-driven biome shifts. Among the hot spots facing multiple high pressures are some of the last tropical dry and montane forest ecosystems in Central America, which stresses the need for urgent conservation action.

It is unclear whether species’ responses to climate change tend to be adaptive or sufficient to keep up with climate change. Here, Radchuk et al. perform a meta-analysis showing that in birds phenology has advanced adaptively in some species, though not all the way to the new optima.

It is unclear whether CO2-stimulation of photosynthesis can propagate through slower ecosystem processes and lead to long-term increases in terrestrial carbon. Here the authors show that CO2-stimulation of photosynthesis leads to a 30% increase in forest regrowth over a decade of CO2 enrichment.

Forests provide important ecosystem services but are being affected by climate change, not only changes in temperature and precipitation but potentially also directly through the plant-physiological effects of increases in atmospheric CO2. We applied a tree-species-based dynamic model (LPJ-GUESS) at a high 5-km spatial resolution to project climate and CO2 impacts on tree species and thus forests in Great Britain. Climatic inputs consisted of a novel large climate scenario ensemble derived from a regional climate model (RCM) under an RCP 8.5 emission scenario. The climate change impacts were assessed using leaf area index (LAI) and net primary productivity (NPP) for the 2030s and the 2080s compared to baseline (1975–2004). The potential CO2 effects, which are highly uncertain, were examined using a constant CO2 level scenario for comparison. Also, a climate vulnerability index was developed to assess the potential drought impact on modeled tree species. In spite of substantial future reductions in rainfall, the mean projected LAI and NPP generally showed an increase over Britain, with a larger increment in Scotland, northwest England, and west Wales. The CO2 increase led to higher projected LAI and NPP, especially in northern Britain, but with little effect on overall geographical patterns. However, without accounting for plant-physiological effects of elevated CO2, NPP in Southern and Central Britain and easternmost parts of Wales showed a decrease relative to 2011, implying less ecosystem service provisioning, e.g., in terms of timber yields and carbon storage. The projected change of LAI and NPP varied from 5 to 100% of the mean change, due to the uncertainty arising from natural weather-induced variability, with Southeast England being most sensitive to this. It was also the most susceptible to climate change and drought, with reduced suitability for broad-leaved trees such as beech, small-leaved lime, and hornbeam. These could lead to important changes in woodland composition across Great Britain.

Vegetation responds to drought through a complex interplay of plant hydraulic mechanisms, posing challenges for model development and parameterization. We present a mathematical model that describes the dynamics of leaf water-potential over time while considering different strategies by which plant species regulate their water-potentials. The model has two parameters: the parameter λ describing the adjustment of the leaf water potential to changes in soil water potential, and the parameter Δψww describing the typical ‘well-watered’ leaf water potentials at non-stressed (near-zero) levels of soil water potential. Our model was tested and calibrated on 110 time-series datasets containing the leaf- and soil water potentials of 66 species under drought and non-drought conditions. Our model successfully reproduces the measured leaf water potentials over time based on three different regulation strategies under drought. We found that three parameter sets derived from the measurement data reproduced the dynamics of 53% of an drought dataset, and 52% of a control dataset [root mean square error (RMSE) < 0.5 MPa)]. We conclude that, instead of quantifying water-potential-regulation of different plant species by complex modeling approaches, a small set of parameters may be sufficient to describe the water potential regulation behavior for large-scale modeling. Thus, our approach paves the way for a parsimonious representation of the full spectrum of plant hydraulic responses to drought in dynamic vegetation models.

In addition to affecting individual species, climate change can modify species interactions. Coupling simulation models with networks between plants and animal pollinators and seed dispersers, Schleuninget al. show that animal persistence under climate change depends more strongly on plant persistence than vice versa.

Arctic ecosystems are particularly vulnerable to climate change because of Arctic amplification. Here, we assessed the climatic impacts of low-end, 1.5 °C, and 2.0 °C global temperature increases above pre-industrial levels, on the warming of terrestrial ecosystems in northern high latitudes (NHL, above 60 °N including pan-Arctic tundra and boreal forests) under the framework of the Inter-Sectoral Impact Model Intercomparison Project phase 2b protocol. We analyzed the simulated changes of net primary productivity, vegetation biomass, and soil carbon stocks of eight ecosystem models that were forced by the projections of four global climate models and two atmospheric greenhouse gas pathways (RCP2.6 and RCP6.0). Our results showed that considerable impacts on ecosystem carbon budgets, particularly primary productivity and vegetation biomass, are very likely to occur in the NHL areas. The models agreed on increases in primary productivity and biomass accumulation, despite considerable inter-model and inter-scenario differences in the magnitudes of the responses. The inter-model variability highlighted the inadequacies of the present models, which fail to consider important components such as permafrost and wildfire. The simulated impacts were attributable primarily to the rapid temperature increases in the NHL and the greater sensitivity of northern vegetation to warming, which contrasted with the less pronounced responses of soil carbon stocks. The simulated increases of vegetation biomass by 30–60 Pg C in this century have implications for climate policy such as the Paris Agreement. Comparison between the results at two warming levels showed the effectiveness of emission reductions in ameliorating the impacts and revealed unavoidable impacts for which adaptation options are urgently needed in the NHL ecosystems.

Vegetation biomass is a key and active component of the carbon cycle. Though China’s vegetation biomass in recent decades has been widely investigated, only two studies have quantitatively assessed its century-scale changes so far and reported totally opposite trends. This study provided the first multi-model estimates of China’s vegetation biomass change for the 20th century and its responses to historical changes in environmental and anthropogenic factors, based on simulations evaluated with the field observations from 3757 inventory plots in China and bias-corrected using machine learning (Gaussian process regression). A significant decline in vegetation biomass over the 20th century was shown by bias-corrected simulations from the six Dynamic Global Vegetation models (DGVMs) with trends ranging from −32.48 to −11.10 Tg C yr ^–1 and a mean trend of −17.74 Tg C yr ^–1 . Land use and land cover change (LULCC) was primarily responsible for the simulated downward trend (−50.71 to −24.28 Tg C yr ^–1 ), while increasing atmospheric CO _2 concentration lead to increased vegetation biomass (+9.27 to + 13.37 Tg C yr ^–1 ). Climate change had limited impacts on the long-term trend (−3.75 to + 5.06 Tg C yr ^–1 ). This study highlights the importance of LULCC for historical reconstruction and future projection of vegetation biomass over China. It also suggests that the incorrect change in China’s forest area for 1980–2000 in the LULCC dataset used as model input data of many existing and ongoing model intercomparison projects (MIPs) has likely led to inaccurate estimations of historical vegetation biomass changes in China.

The Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES), established in 2012 to counter the biodiversity crisis, requires the best scientific input available to function as a successful science-policy interface that addresses the knowledge needs of governments for safeguarding nature and its services. For the macroecological research community, IPBES presents a great opportunity to contribute knowledge, data and methods, and to help identify and address knowledge gaps and methodological impediments. Here, we outline our perspectives on how macroecology may contribute to IPBES. We focus on three essential topics for the IPBES process, where contributions by macroecologists will be invaluable: biodiversity data, biodiversity modelling, and modelling of ecosystem services. For each topic, we discuss the potential for contributions from the macroecological community, as well as limitations, challenges, and knowledge gaps. Overall, engagement of the macroecological community with IPBES should lead to mutual benefits. Macroecologists may profit as their contributions to IPBES may strengthen and inspire them as a community to design and conduct research that provides society-relevant results. Furthermore, macroecological contributions will help IPBES become a successful instrument of knowledge exchange and uncover the linkages between biodiversity and human well-being.

Historically, the expansion of soy plantations has been a major driver of land-use/cover change (LUCC) in Brazil. While a series of recent public actions and supply-chain commitments reportedly curbed the replacement of forests by soy, the expansion of the agricultural commodity still poses a considerable threat to the Amazonian and Cerrado biomes. Identification of areas under high risk of soy expansion is thus paramount to assist conservation efforts in the region. We mapped the areas suitable for undergoing transition to soy plantations in the Legal Amazon with a machine-learning approach adopted from the ecological modeling literature. Simulated soy expansion for the year 2014 exhibited favorable validation scores compared to other LUCC models. We then used our model to simulate how potential future infrastructure improvements would affect the 2014 probabilities of soy occurrence in the region. In addition to the 2.3 Mha of planted soy in the Legal Amazon in 2014, our model identified another 14.7 Mha with high probability of soy conversion in the region given the infrastructure conditions at that time. Out of those, pastures and forests represented 9.8 and 0.4 Mha, respectively. Under the new infrastructure scenarios simulated, the Legal Amazonian area under high risk of soy conversion increased by up to 2.1 Mha (14.6%). These changes led to up to 11.4 and 51.4% increases in the high-risk of conversion areas of pastures and forests, respectively. If conversion occurs in the identified high-risk areas, at least 4.8 Pg of CO2 could be released into the atmosphere, a value that represents 10 times the total CO2 emissions of Brazil in 2014. Our results highlight the importance of targeting conservation policies and enforcement actions, including the Soy Moratorium, to mitigate future forest cover loss associated with infrastructure improvements in the region.

Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%–40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.

This paper evaluates the ability of eight global vegetation models to reproduce recent trends and inter-annual variability of biomass in natural terrestrial ecosystems. For the purpose of this evaluation, the simulated trajectories of biomass are expressed in terms of the relative rate of change in biomass (RRB), defined as the deviation of the actual rate of biomass turnover from its equilibrium counterpart. Cumulative changes in RRB explain long-term changes in biomass pools. RRB simulated by the global vegetation models is compared with its observational equivalent, derived from vegetation optical depth reconstructions of above-ground biomass (AGB) over the period 1993–2010. According to the RRB analysis, the rate of global biomass growth described by the ensemble of simulations substantially exceeds the observation. The observed fluctuations of global RRB are significantly correlated with El Niño Southern Oscillation events (ENSO), but only some of the simulations reproduce this correlation. However, the ENSO sensitivity of RRB in the tropics is not significant in the observation, while it is in some of the simulations. This mismatch points to an important limitation of the observed AGB reconstruction to capture biomass variations in tropical forests. Important discrepancies in RRB were also identified at the regional scale, in the tropical forests of Amazonia and Central Africa, as well as in the boreal forests of north-western America, western and central Siberia. In each of these regions, the RRBs derived from the simulations were analyzed in connection with underlying differences in net primary productivity and biomass turnover rate ̶as a basis for exploring in how far differences in simulated changes in biomass are attributed to the response of the carbon uptake to CO _2 increments, as well as to the model representation of factors affecting the rates of mortality and turnover of foliage and roots. Overall, our findings stress the usefulness of using RRB to evaluate complex vegetation models and highlight the importance of conducting further evaluations of both the actual rate of biomass turnover and its equilibrium counterpart, with special focus on their background values and sources of variation. In turn, this task would require the availability of more accurate multi-year observational data of biomass and net primary productivity for natural ecosystems, as well as detailed and updated information on land-cover classification.

The impact of grazing activity on terrestrial carbon (C) sequestration has been noticed and studied worldwide. Recent efforts have been made to incorporate the disturbance into process-based land models. However, the performance of grazing models has not been well investigated at large scales. In this study, we performed a spatially explicit model uncertainty assessment in the world’s largest pasture ecosystem, the temperate Eurasian Steppe. Five grazing models were explicitly incorporated into a single terrestrial biogeochemical model to simulate regional C consumption from grazing activity ( C _graze ). First, we summarized the underlying mechanisms and explicitly compared the general functions used to describe the processes in different models. Then, the models (five models with 12 simulations) were run in parallel using the same forcing data and livestock distribution map in 2006. Results indicated that the modeled regional C _graze varied from 0.1–16.1 gC m ^−2 for the year. The corresponding ratios of C _graze to aboveground net primary productivity ANPP and net primary productivity (NPP) ranged from 0.08%–24.6% and 0.028%–11.2%, respectively. Parameter sensitivity was further analyzed. Model outputs are highly sensitive to the intake rate (i.e. feeding rate of livestock per day), half maximum intake rate, and initial livestock weight. Our results indicate that great uncertainty exists in simulating C _graze . We ascribed the major uncertainty to the different process description and poor parameterization. This study calls for more efforts to the comprehensive synthesis of usable dataset, the foundation of a standard observation system and the observe-based inter-comparison to evaluate models, which would facilitate more accurate assessment of C sequestration by pasture ecosystems and lead to better representation in earth system models.

The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). We evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions ( E _LUC ) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al ( 2015 ), and 2) the land CO _2 fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F _Jena and F _CAMS respectively. The model ensemble-mean NBP (that includes seven models with land-use change) is higher than but within the uncertainty of R + L, while the simulated positive NBP trend over the last 30 yr is lower than that from R + L and from the two inversion systems. ISIMIP2a biome models well capture the interannual variation of global net terrestrial ecosystem carbon fluxes. Tropical NBP represents 31 ± 17% of global total NBP during the past decades, and the year-to-year variation of tropical NBP contributes most of the interannual variation of global NBP. According to the models, increasing Net Primary Productivity (NPP) was the main cause for the generally increasing NBP. Significant global NBP anomalies from the long-term mean between the two phases of El Niño Southern Oscillation (ENSO) events are simulated by all models ( p < 0.05), which is consistent with the R + L estimate ( p = 0.06), also mainly attributed to NPP anomalies, rather than to changes in heterotrophic respiration (Rh). The global NPP and NBP anomalies during ENSO events are dominated by their anomalies in tropical regions impacted by tropical climate variability. Multiple regressions between R + L, F _Jena and F _CAMS interannual variations and tropical climate variations reveal a significant negative response of global net terrestrial ecosystem carbon fluxes to tropical mean annual temperature variation, and a non-significant response to tropical annual precipitation variation. According to the models, tropical precipitation is a more important driver, suggesting that some models do not capture the roles of precipitation and temperature changes adequately.

Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000–2010 total global GPP estimated from the model ensemble to be 117 ± 13 Pg C yr ^−1 (mean ± 1 standard deviation), which was higher than MODIS (112 Pg C yr ^−1 ), and close to the MTE (120 Pg C yr ^−1 ). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However, we observed large uncertainties across the eight ISIMIP2a models, which are probably due to the differences in the formulation of underlying photosynthetic processes. The results of this study are useful in understanding the contributions of different regions to global GPP and its spatiotemporal variability, how the model- and observational-based GPP estimates differ from each other in time and space, and the relative strength of the eight models. Our results also highlight the models’ ability to capture the seasonality of GPP that are essential for understanding the inter-annual and seasonal variability of GPP as a major component of the carbon cycle.

Simulating vegetation photosynthetic productivity (or gross primary production, GPP) is a critical feature of the biome models used for impact assessments of climate change. We conducted a benchmarking of global GPP simulated by eight biome models participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a) with four meteorological forcing datasets (30 simulations), using independent GPP estimates and recent satellite data of solar-induced chlorophyll fluorescence as a proxy of GPP. The simulated global terrestrial GPP ranged from 98 to 141 Pg C yr ^−1 (1981–2000 mean); considerable inter-model and inter-data differences were found. Major features of spatial distribution and seasonal change of GPP were captured by each model, showing good agreement with the benchmarking data. All simulations showed incremental trends of annual GPP, seasonal-cycle amplitude, radiation-use efficiency, and water-use efficiency, mainly caused by the CO _2 fertilization effect. The incremental slopes were higher than those obtained by remote sensing studies, but comparable with those by recent atmospheric observation. Apparent differences were found in the relationship between GPP and incoming solar radiation, for which forcing data differed considerably. The simulated GPP trends co-varied with a vegetation structural parameter, leaf area index, at model-dependent strengths, implying the importance of constraining canopy properties. In terms of extreme events, GPP anomalies associated with a historical El Niño event and large volcanic eruption were not consistently simulated in the model experiments due to deficiencies in both forcing data and parameterized environmental responsiveness. Although the benchmarking demonstrated the overall advancement of contemporary biome models, further refinements are required, for example, for solar radiation data and vegetation canopy schemes.