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In 2018 and 2019, central Europe was affected by two consecutive extreme dry and hot summers (DH18 and DH19). The DH18 event had severe impacts on ecosystems and likely affected vegetation activity in the subsequent year, for example through depletion of carbon reserves or damage from drought. Such legacies from drought and heat stress can further increase vegetation susceptibility to additional hazards. Temporally compound extremes such as DH18 and DH19 can, therefore, result in an amplification of impacts due to preconditioning effects of past disturbance legacies. Here, we evaluate how these two consecutive extreme summers impacted ecosystems in central Europe and how the vegetation responses to the first compound event (DH18) modulated the impacts of the second (DH19). To quantify changes in vegetation vulnerability to each compound event, we first train a set of statistical models for the period 2001–2017, which are then used to predict the impacts of DH18 and DH19 on enhanced vegetation index (EVI) anomalies from MODIS. These estimates correspond to expected EVI anomalies in DH18 and DH19 based on past sensitivity to climate. Large departures from the predicted values can indicate changes in vulnerability to dry and hot conditions and be used to identify modulating effects by vegetation activity and composition or other environmental factors on observed impacts. We find two regions in which the impacts of the two compound dry and hot (DH) events were significantly stronger than those expected based on previous climate–vegetation relationships. One region, largely dominated by grasslands and crops, showed much stronger impacts than expected in both DH events due to an amplification of their sensitivity to heat and drought, possibly linked to changing background CO2 and temperature conditions. A second region, dominated by forests and grasslands, showed browning from DH18 to DH19, even though dry and hot conditions were partly alleviated in 2019. This browning trajectory was mainly explained by the preconditioning role of DH18 on the impacts of DH19 due to interannual legacy effects and possibly by increased susceptibility to biotic disturbances, which are also promoted by warm conditions. Dry and hot summers are expected to become more frequent in the coming decades, posing a major threat to the stability of European forests. We show that state-of-the-art process-based models could not represent the decline in response to DH19 because they missed the interannual legacy effects from DH18 impacts. These gaps may result in an overestimation of the resilience and stability of temperate ecosystems in future model projections.

Satellite data reveal widespread changes in Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

Quantifying the net carbon flux from land use and land cover changes (fLULCC) is critical for understanding the global carbon cycle and, hence, to support climate change mitigation. However, large-scale fLULCC is not directly measurable and has to be inferred from models instead, such as semi-empirical bookkeeping models and process-based dynamic global vegetation models (DGVMs). By definition, fLULCC estimates are not directly comparable between these two different model types. As an important example, DGVM-based fLULCC in the annual global carbon budgets is estimated under transient environmental forcing and includes the so-called loss of additional sink capacity (LASC). The LASC results from the impact of environmental changes on land carbon storage potential of managed land compared to potential vegetation and accumulates over time, which is not captured in bookkeeping models. The fLULCC from transient DGVM simulations, thus, strongly depends on the timing of land use and land cover changes mainly because LASC accumulation is cut off at the end of the simulated period. To estimate the LASC, the fLULCC from pre-industrial DGVM simulations, which is independent of changing environmental conditions, can be used. Additionally, DGVMs using constant present-day environmental forcing enable an approximation of bookkeeping estimates. Here, we analyse these three DGVM-derived fLULCC estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO2-induced components and compare them to bookkeeping estimates. Averaged across the models, we find a global fLULCC (under transient conditions) of 2.0±0.6 PgC yr−1 for 2009–2018, of which ∼40 % are attributable to the LASC (0.8±0.3 PgC yr−1). From 1850 onward, the fLULCC accumulated to 189±56 PgC with 40±15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient fLULCC estimates to exceed estimates under present-day conditions, despite generally increased carbon stocks in the latter. Regional hotspots of high cumulative and annual LASC values are found in the USA, China, Brazil, equatorial Africa, and Southeast Asia, mainly due to deforestation for cropland. Distinct negative LASC estimates in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land), indicate that fLULCC estimates in these regions are lower in transient DGVM compared to bookkeeping approaches. Our study unravels the strong dependence of fLULCC estimates on the time a certain land use and land cover change event happened to occur and on the chosen time period for the forcing of environmental conditions in the underlying simulations. We argue for an approach that provides an accounting of the fLULCC that is more robust against these choices, for example by estimating a mean DGVM ensemble fLULCC and LASC for a defined reference period and homogeneous environmental changes (CO2 only).

Land-use models and integrated assessment models provide scenarios of land-use and land-cover (LULC) changes following pathways or storylines related to different socioeconomic and environmental developments. The large diversity of available scenario projections leads to a recognizable variability in impacts on land ecosystems and the levels of services provided. We evaluated 16 projections of future LULC until 2040 that reflected different assumptions regarding socioeconomic demands and modeling protocols. By using these LULC projections in a state-of-the-art dynamic global vegetation model, we simulated their effect on selected ecosystem service indicators related to ecosystem productivity and carbon sequestration potential, agricultural production and the water cycle. We found that although a common trend for agricultural expansion exists across the scenarios, where and how particular LULC changes are realized differs widely across models and scenarios. They are linked to model-specific considerations of some demands over others and their respective translation into LULC changes and also reflect the simplified or missing representation of processes related to land dynamics or other influencing factors (e.g., trade, climate change). As a result, some scenarios show questionable and possibly unrealistic features in their LULC allocations, including highly regionalized LULC changes with rates of conversion that are contrary to or exceed rates observed in the past. Across the diverging LULC projections, we identified positive global trends of net primary productivity (+10.2 % ± 1.4 %), vegetation carbon (+9.2 % ± 4.1 %), crop production (+31.2 % ± 12.2 %) and water runoff (+9.3 % ± 1.7 %), and a negative trend of soil and litter carbon stocks (−0.5 % ± 0.4 %). The variability in ecosystem service indicators across scenarios was especially high for vegetation carbon stocks and crop production. Regionally, variability was highest in tropical forest regions, especially at current forest boundaries, because of intense and strongly diverging LULC change projections in combination with high vegetation productivity dampening or amplifying the effects of climatic change. Our results emphasize that information on future changes in ecosystem functioning and the related ecosystem service indicators should be seen in light of the variability originating from diverging projections of LULC. This is necessary to allow for adequate policy support towards sustainable transformations.

The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle, with both recent historical baselines and future responses to environmental change poorly constrained by available observations. In the absence of large-scale observations, models used for global assessments tend to fall back on simplified assumptions of the turnover rates of biomass and soil carbon pools. In this study, the biomass carbon turnover times calculated by an ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to assess their current capability to accurately estimate biomass carbon turnover times in forests and how these times are anticipated to change in the future. Modelled baseline 1985–2014 global average forest biomass turnover times vary from 12.2 to 23.5 years between TBMs. TBM differences in phenological processes, which control allocation to, and turnover rate of, leaves and fine roots, are as important as tree mortality with regard to explaining the variation in total turnover among TBMs. The different governing mechanisms exhibited by each TBM result in a wide range of plausible turnover time projections for the end of the century. Based on these simulations, it is not possible to draw robust conclusions regarding likely future changes in turnover time, and thus biomass change, for different regions. Both spatial and temporal uncertainty in turnover time are strongly linked to model assumptions concerning plant functional type distributions and their controls. Thirteen model-based hypotheses of controls on turnover time are identified, along with recommendations for pragmatic steps to test them using existing and novel observations. Efforts to resolve uncertainty in turnover time, and thus its impacts on the future evolution of biomass carbon stocks across the world's forests, will need to address both mortality and establishment components of forest demography, as well as allocation of carbon to woody versus non-woody biomass growth.

International audience; The two most urgent and interlinked environmental challenges humanity faces are climate change and biodiversity loss. We are entering a pivotal decade for both the international biodiversity and climate change agendas with the sharpening of ambitious strategies and targets by the Convention on Biological Diversity and the United Nations Framework Convention on Climate Change. Within their respective Conventions, the biodiversity and climate interlinked challenges have largely been addressed separately. There is evidence that conservation actions that halt, slow or reverse biodiversity loss can simultaneously slow anthropogenic mediated climate change significantly. This review highlights conservation actions which have the largest potential for mitigation of climate change. We note that conservation actions have mainly synergistic benefits and few antagonistic trade-offs with climate change mitigation. Specifically, we identify direct co-benefits in 14 out of the 21 action targets of the draft post-2020 global biodiversity framework of the Convention on Biological Diversity, notwithstanding the many indirect links that can also support both biodiversity conservation and climate change mitigation. These relationships are context and scale-dependent; therefore, we showcase examples of local biodiversity conservation actions that can be incentivized, guided and prioritized by global objectives and targets. The close interlinkages between biodiversity, climate change mitigation, other nature's contributions to people and good quality of life are seldom as integrated as they should be in management and policy. This review aims to re-emphasize the vital relationships between biodiversity conservation actions and climate change mitigation in a timely manner, in support to major Conferences of Parties that are about to negotiate strategic frameworks and international goals for the decades to come.

Climate change (CLI), elevated CO2 concentration (CO2), and land use change (LUC) have strongly altered land evapotranspiration (ET) during the recent decades. The fingerprints of these drivers in ET change, however, have not previously been detected due to the lack of these three scenarios from global climate models (GCMs). Here we applied an optimal fingerprint method to detect and attribute ET change by integrated utilization of state-of-the-art global ecosystem models and GCMs. Results indicate that CLI provides the greatest contribution to increasing ET, and its fingerprint is detectable at different timescales. CO2 reduces ET in most areas covered by forests. LUC decreases ET over the tropics, while increases ET over temperate and high-latitude regions. To further subdivide the impacts of CLI, we extend the Budyko framework to quantify the contribution of precipitation (P) and potential evapotranspiration (PET) and find that the dominant role of CLI mainly depends on the contribution of P.

Year-to-year variability in CO2 fluxes can yield insight into climate-carbon cycle relationships, a fundamental yet uncertain aspect of the terrestrial carbon cycle. In this study, we use global observations from NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite for years 2015–2019 and a geostatistical inverse model to evaluate 5 years of interannual variability (IAV) in CO2 fluxes and its relationships with environmental drivers. OCO-2 launched in late 2014, and we specifically evaluate IAV during the time period when OCO-2 observations are available. We then compare inferences from OCO-2 with state-of-the-art process-based models (terrestrial biosphere model, TBMs). Results from OCO-2 suggest that the tropical grasslands biome (including grasslands, savanna, and agricultural lands within the tropics) makes contributions to global IAV during the 5 year study period that are comparable to tropical forests, a result that differs from a majority of TBMs. Furthermore, existing studies disagree on the environmental variables that drive IAV during this time period, and the analysis using OCO-2 suggests that both temperature and precipitation make comparable contributions. TBMs, by contrast, tend to estimate larger IAV during this time and usually estimate larger relative contributions from the extra-tropics. With that said, TBMs show little consensus on both the magnitude and the contributions of different regions to IAV. We further find that TBMs show a wide range of responses on the relationships of CO2 fluxes with annual anomalies in temperature and precipitation, and these relationships across most of the TBMs have a larger magnitude than inferred from OCO-2. Overall, the findings of this study highlight large uncertainties in process-based estimates of IAV during recent years and provide an avenue for evaluating these processes against inferences from OCO-2.

Forest disturbances that lead to the replacement of whole tree stands are a cornerstone of forest dynamics, with drivers that include fire, windthrow, biotic outbreaks and harvest. The frequency of disturbances may change over the next century with impacts on the age, composition and biomass of forests. However, the disturbance return time, that is, the mean interval between disturbance events, remains poorly characterized across the world’s forested biomes, which hinders the quantification of the role of disturbances in the global carbon cycle. Here we present the global distribution of stand-replacing disturbance return times inferred from satellite-based observations of forest loss. Prescribing this distribution within a vegetation model with a detailed representation of stand structure, we quantify the importance of stand-replacing disturbances for biomass carbon turnover globally over 2001–2014. The return time varied from less than 50 years in heavily managed temperate ecosystems to over 1,000 years in tropical evergreen forests. Stand-replacing disturbances accounted for 12.3% (95% confidence interval, 11.4–13.7%) of the annual biomass carbon turnover due to tree mortality globally, and in 44% of the forested area, biomass stocks are strongly sensitive to changes in the disturbance return time. Relatively small shifts in disturbance regimes in these areas would substantially influence the forest carbon sink that currently limits climate change by offsetting emissions. Forest stand-replacing disturbances significantly affect the biomass stocks in about a half of forested area globally, according to analyses of global forest loss from satellite data, together with a dynamic vegetation model.

Although the existence of a large carbon sink in terrestrial ecosystems is well-established, the drivers of this sink remain uncertain. It has been suggested that perturbations to forest demography caused by past land-use change, management, and natural disturbances may be causing a large component of current carbon uptake. Here we use a global compilation of forest age observations, combined with a terrestrial biosphere model with explicit modeling of forest regrowth, to partition the global forest carbon sink between old-growth and regrowth stands over the period 1981–2010. For 2001–2010 we find a carbon sink of 0.85 (0.66–0.96) Pg year −1 located in intact old-growth forest, primarily in the moist tropics and boreal Siberia, and 1.30 (1.03–1.96) Pg year −1 located in stands regrowing after past disturbance. Approaching half of the sink in regrowth stands would have occurred from demographic changes alone, in the absence of other environmental changes. These age-constrained results show consistency with those simulated using an ensemble of demographically-enabled terrestrial biosphere models following an independent reconstruction of historical land use and management. We estimate that forests will accumulate an additional 69 (44–131) Pg C in live biomass from changes in demography alone if natural disturbances, wood harvest, and reforestation continue at rates comparable to those during 1981–2010. Our results confirm that it is not possible to understand the current global terrestrial carbon sink without accounting for the sizeable sink due to forest demography. They also imply that a large portion of the current terrestrial carbon sink is strictly transient in nature.

Satellite data reveal widespread changes of Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change and consequent disturbances, such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at biome-level that can be scaled into a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies of vegetation changes, and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying Causal Counterfactual Theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). Our results do not support previously published accounts of dominant global-scale effects of CO2 fertilization. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last two decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slow-down in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

Global fire-vegetation models are widely used to assess impacts of environmental change on fire regimes and the carbon cycle and to infer relationships between climate, land use and fire. However, differences in model structure and parameterizations, in both the vegetation and fire components of these models, could influence overall model performance, and to date there has been limited evaluation of how well different models represent various aspects of fire regimes. The Fire Model Intercomparison Project (FireMIP) is coordinating the evaluation of state-of-the-art global fire models, in order to improve projections of fire characteristics and fire impacts on ecosystems and human societies in the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models to quantify their ability to reproduce a range of fire and vegetation benchmarks. The FireMIP models simulate a wide range in global annual total burnt area (39–536 Mha) and global annual fire carbon emission (0.91–4.75 Pg C yr−1) for modern conditions (2002–2012), but most of the range in burnt area is within observational uncertainty (345–468 Mha). Benchmarking scores indicate that seven out of nine FireMIP models are able to represent the spatial pattern in burnt area. The models also reproduce the seasonality in burnt area reasonably well but struggle to simulate fire season length and are largely unable to represent interannual variations in burnt area. However, models that represent cropland fires see improved simulation of fire seasonality in the Northern Hemisphere. The three FireMIP models which explicitly simulate individual fires are able to reproduce the spatial pattern in number of fires, but fire sizes are too small in key regions, and this results in an underestimation of burnt area. The correct representation of spatial and seasonal patterns in vegetation appears to correlate with a better representation of burnt area. The two older fire models included in the FireMIP ensemble (LPJ–GUESS–GlobFIRM, MC2) clearly perform less well globally than other models, but it is difficult to distinguish between the remaining ensemble members; some of these models are better at representing certain aspects of the fire regime; none clearly outperforms all other models across the full range of variables assessed.

In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.

We agree with Ceddia that investment can play a key role in achieving the transformative change that is necessary to reverse nature's ongoing decline. In our Review, we pointed to the large impact of extractive industries, the unequal distribution of wealth associated with trade flows, and the handful of transnational corporations that control the majority of supply chains in agriculture, fishing, logging, and mining. Changing investment in these sectors can profoundly affect the future of nature. More broadly, the impact of both public and private investment deserves more visibility ([ 1 ][1], [ 2 ][2]). It is also important to focus on the role that public policy and public opinion (including a more holistic view of economics and quality of life) can have in shaping investment ([ 3 ][3], [ 4 ][4]). A new economic system should build on and enhance the fabric of life rather than erode it. By rewarding actions that promote sustainability and penalizing actions that result in environmental deterioration, policies and attitudes can create powerful incentives for change in global financial and economic systems toward this vision. 1. [↵][5]1. E. S. Brondizio et al. 1. P. Balvanera et al ., in Global Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, E. S. Brondizio et al., Eds. (Secretariat of the Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services, Bonn, Germany, 2019). 2. [↵][6]1. E. S. Brondizio et al. 1. K. A. Brauman et al ., in Global Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, E. S. Brondizio et al., Eds. (Secretariat of the Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services, Bonn, Germany, 2019). 3. [↵][7]1. E. S. Brondizio et al. 1. K. M. A. Chan et al ., in Global Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, E. S. Brondizio et al., Eds. (Secretariat of the Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services, Bonn, Germany, 2019) 4. [↵][8]1. E. S. Brondizio et al. 1. J. Razzaque et al ., in Global Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, E. S. Brondizio et al., Eds. (Secretariat of the Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services, Bonn, Germany, 2019). [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #xref-ref-1-1 "View reference 1 in text" [6]: #xref-ref-2-1 "View reference 2 in text" [7]: #xref-ref-3-1 "View reference 3 in text" [8]: #xref-ref-4-1 "View reference 4 in text"

Most scenarios from integrated assessment models (IAMs) that project greenhouse gas emissions include the use of bioenergy as a means to reduce CO2 emissions or even to achieve negative emissions (together with CCS – carbon capture and storage). The potential amount of CO2 that can be removed from the atmosphere depends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiency with which CO2 produced by combustion is captured. While bioenergy crop yields can be simulated by models, estimates of the spatial distribution of bioenergy yields under current technology based on a large number of observations are currently lacking. In this study, a random-forest (RF) algorithm is used to upscale a bioenergy yield dataset of 3963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climatic and soil conditions as explanatory variables. The results are global yield maps of five important lignocellulosic bioenergy crops under current technology, climate and atmospheric CO2 conditions at a 0.5∘×0.5∘ spatial resolution. We also provide a combined “best bioenergy crop” yield map by selecting one of the five crop types with the highest yield in each of the grid cells, eucalypt and Miscanthus in most cases. The global median yield of the best crop is 16.3 t DM ha−1 yr−1 (DM – dry matter). High yields mainly occur in the Amazon region and southeastern Asia. We further compare our empirically derived maps with yield maps used in three IAMs and find that the median yields in our maps are > 50 % higher than those in the IAM maps. Our estimates of gridded bioenergy crop yields can be used to provide bioenergy yields for IAMs, to evaluate land surface models or to identify the most suitable lands for future bioenergy crop plantations. The 0.5∘×0.5∘ global maps for yields of different bioenergy crops and the best crop and for the best crop composition generated from this study can be download from https://doi.org/10.5281/zenodo.3274254 (Li, 2019).

Changes in vegetation structure are expected to influence the redistribution of heat and moisture; however, how variations in the leaf area index (LAI) affect this global energy partitioning is not yet quantified. Here, we estimate that a unit change in LAI leads to 3.66 ± 0.45 and −3.26 ± 0.41 W m−2 in latent (LE) and sensible (H) fluxes, respectively, over the 1982–2016 period. Analysis of an ensemble of data-driven products shows that these sensitivities increase by about 20% over the observational period, prominently in regions with a limited water supply, probably because of an increased transpiration/evaporation ratio. Global greening has caused a decrease in the Bowen ratio (B = H/LE) of −0.010 ± 0.002 per decade, which is attributable to the increased evaporative surface. Such a direct LAI effect on energy fluxes is largely modulated by plant functional types (PFTs) and background climate conditions. Land surface models (LSMs) misrepresent this vegetation control, possibly due to underestimation of the biophysical responses to changes in the water availability and poor representation of LAI dynamics. Changes in the leaf area index alter the distribution of heat and moisture. The change in energy partitioning related to leaf area, increasing latent and decreasing sensible fluxes over the observational period 1982–2016, is moderated by plant functional type and background climate.

Recent assessment reports by the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) have highlighted the risks to humanity arising from the unsustainable use of natural resources. Thus far, land, freshwater, and ocean exploitation have been the chief causes of biodiversity loss. Climate change is projected to be a rapidly increasing additional driver for biodiversity loss. Since climate change and biodiversity loss impact human societies everywhere, bold solutions are required that integrate environmental and societal objectives. As yet, most existing international biodiversity targets have overlooked climate change impacts. At the same time, climate change mitigation measures themselves may harm biodiversity directly. The Convention on Biological Diversity’s post-2020 framework offers the important opportunity to address the interactions between climate change and biodiversity and revise biodiversity targets accordingly by better aligning these with the United Nations Framework Convention on Climate Change Paris Agreement and the Sustainable Development Goals. We identify the considerable number of existing and proposed post-2020 biodiversity targets that risk being severely compromised due to climate change, even if other barriers to their achievement were removed. Our analysis suggests that the next set of biodiversity targets explicitly addresses climate change-related risks since many aspirational goals will not be feasible under even lower-end projections of future warming. Adopting more flexible and dynamic approaches to conservation, rather than static goals, would allow us to respond flexibly to changes in habitats, genetic resources, species composition, and ecosystem functioning and leverage biodiversity’s capacity to contribute to climate change mitigation and adaptation. We acknowledge the support by Rainer Krug, Hien Ngo, and Max Gueze in parts of the literature review. Pierre Lopez drew Fig. 1, using spatial data layers provided by Aidin Niamir (https://doi.org/10.5281/zenodo.3975694).

There is a clear need for transformative change in the land management and food production sectors to address the global land challenges of climate change mitigation, climate change adaptation, combatting land degradation and desertification, and delivering food security (referred to hereafter as “land challenges”). We assess the potential for 40 practices to address these land challenges and find that: Nine options deliver medium to large benefits for all four land challenges. A further two options have no global estimates for adaptation, but have medium to large benefits for all other land challenges. Five options have large mitigation potential (>3 Gt CO2eq/year) without adverse impacts on the other land challenges. Five options have moderate mitigation potential, with no adverse impacts on the other land challenges. Sixteen practices have large adaptation potential (>25 million people benefit), without adverse side effects on other land challenges. Most practices can be applied without competing for available land. However, seven options could result in competition for land. A large number of practices do not require dedicated land, including several land management options, all value chain options, and all risk management options. Four options could greatly increase competition for land if applied at a large scale, though the impact is scale and context specific, highlighting the need for safeguards to ensure that expansion of land for mitigation does not impact natural systems and food security. A number of practices, such as increased food productivity, dietary change and reduced food loss and waste, can reduce demand for land conversion, thereby potentially freeing‐up land and creating opportunities for enhanced implementation of other practices, making them important components of portfolios of practices to address the combined land challenges., There is a clear need for transformative change in the land management and food production sectors to address the global land challenges of climate change mitigation, climate change adaptation, combatting land degradation and desertification, and delivering food security (referred to hereafter as “land challenges”). We assess the potential for 40 practices to address these land challenges and find that most practices deliver across all land challenges. Some practices could result in competition for land, but those that reduce demand for land conversion potentially free‐up land, creating opportunities for enhanced implementation of other practices, making them important for addressing combined land challenge.

Iron can be a growth‐limiting nutrient for phytoplankton, modifying rates of net primary production, nitrogen fixation, and carbon export ‐ highlighting the importance of new iron inputs from the atmosphere. The bioavailable iron fraction depends on the emission source and the dissolution during transport. The impacts of anthropogenic combustion and land use change on emissions from industrial, domestic, shipping, desert, and wildfire sources suggest that Northern Hemisphere soluble iron deposition has likely been enhanced between 2% and 68% over the Industrial Era. If policy and climate follow the intermediate Representative Concentration Pathway 4.5 trajectory, then results suggest that Southern Ocean (>30°S) soluble iron deposition would be enhanced between 63% and 95% by 2100. Marine net primary productivity and carbon export within the open ocean are most sensitive to changes in soluble iron deposition in the Southern Hemisphere; this is predominantly driven by fire rather than dust iron sources. Changes in iron deposition cause large perturbations to the marine nitrogen cycle, up to 70% increase in denitrification and 15% increase in nitrogen fixation, but only modestly impacts the carbon cycle and atmospheric CO2 concentrations (1–3 ppm). Regionally, primary productivity increases due to increased iron deposition are often compensated by offsetting decreases downstream corresponding to equivalent changes in the rate of phytoplankton macronutrient uptake, particularly in the equatorial Pacific. These effects are weaker in the Southern Ocean, suggesting that changes in iron deposition in this region dominates the global carbon cycle and climate response.

Ecosystems responded asymmetrically to the 2018 European drought., In summer 2018, central and northern Europe were stricken by extreme drought and heat (DH2018). The DH2018 differed from previous events in being preceded by extreme spring warming and brightening, but moderate rainfall deficits, yet registering the fastest transition between wet winter conditions and extreme summer drought. Using 11 vegetation models, we show that spring conditions promoted increased vegetation growth, which, in turn, contributed to fast soil moisture depletion, amplifying the summer drought. We find regional asymmetries in summer ecosystem carbon fluxes: increased (reduced) sink in the northern (southern) areas affected by drought. These asymmetries can be explained by distinct legacy effects of spring growth and of water-use efficiency dynamics mediated by vegetation composition, rather than by distinct ecosystem responses to summer heat/drought. The asymmetries in carbon and water exchanges during spring and summer 2018 suggest that future land-management strategies could influence patterns of summer heat waves and droughts under long-term warming.

Forest production efficiency (FPE) metric describes how efficiently the assimilated carbon is partitioned into plants organs (biomass production, BP) or—more generally—for the production of organic matter (net primary production, NPP). We present a global analysis of the relationship of FPE to stand-age and climate, based on a large compilation of data on gross primary production and either BP or NPP. FPE is important for both forest production and atmospheric carbon dioxide uptake. We find that FPE increases with absolute latitude, precipitation and (all else equal) with temperature. Earlier findings—FPE declining with age—are also supported by this analysis. However, the temperature effect is opposite to what would be expected based on the short-term physiological response of respiration rates to temperature, implying a top-down regulation of carbon loss, perhaps reflecting the higher carbon costs of nutrient acquisition in colder climates. Current ecosystem models do not reproduce this phenomenon. They consistently predict lower FPE in warmer climates, and are therefore likely to overestimate carbon losses in a warming climate., Many models assume a universal carbon use efficiency across forest biomes, in contrast to assumptions of other process-based models. Here the authors analyse forest production efficiency across a wide range of climates to show a positive relationship with annual temperature and precipitation, indicating that ecosystem models are overestimating forest carbon losses under warming.

International audience; Robust estimates of CO2 budget, CO2 exchanged between the atmosphere and terrestrial biosphere, are necessary to better understand the role of the terrestrial biosphere in mitigating anthropogenic CO2 emissions. Over the past decade, this field of research has advanced through understanding of the differences and similarities of two fundamentally different approaches: “top-down” atmospheric inversions and “bottom-up” biosphere models. Since the first studies were undertaken, these approaches have shown an increasing level of agreement, but disagreements in some regions still persist, in part because they do not estimate the same quantity of atmosphere–biosphere CO2 exchange. Here, we conducted a thorough comparison of CO2 budgets at multiple scales and from multiple methods to assess the current state of the science in estimating CO2 budgets. Our set of atmospheric inversions and biosphere models, which were adjusted for a consistent flux definition, showed a high level of agreement for global and hemispheric CO2 budgets in the 2000s. Regionally, improved agreement in CO2 budgets was notable for North America and Southeast Asia. However, large gaps between the two methods remained in East Asia and South America. In other regions, Europe, boreal Asia, Africa, South Asia, and Oceania, it was difficult to determine whether those regions act as a net sink or source because of the large spread in estimates from atmospheric inversions. These results highlight two research directions to improve the robustness of CO2 budgets: (a) to increase representation of processes in biosphere models that could contribute to fill the budget gaps, such as forest regrowth and forest degradation; and (b) to reduce sink–source compensation between regions (dipoles) in atmospheric inversion so that their estimates become more comparable. Advancements on both research areas will increase the level of agreement between the top-down and bottom-up approaches and yield more robust knowledge of regional CO2 budgets.

International audience; Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model-data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB95) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB95, particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.

The springtime transition to regional-scale onset of photosynthesis and net ecosystem carbon uptake in boreal and tundra ecosystems are linked to the soil freeze-thaw state. We present evidence from diagnostic and inversion models constrained by satellite fluorescence and airborne CO2 from 2012 to 2014 indicating the timing and magnitude of spring carbon uptake in Alaska correlates with landscape thaw and ecoregion. Landscape thaw in boreal forests typically occurs in late April (DOY 111 ± 7) with a 29 ± 6 day lag until photosynthetic onset. North Slope tundra thaws 3 weeks later (DOY 133 ± 5) but experiences only a 20 ± 5 day lag until photosynthetic onset. These time lag differences reflect efficient cold season adaptation in tundra shrub and the longer dehardening period for boreal evergreens. Despite the short transition from thaw to photosynthetic onset in tundra, synchrony of tundra respiration with snow melt and landscape thaw delays the transition from net carbon loss (at photosynthetic onset) to net uptake by 13 ± 7 days, thus reducing the tundra net carbon uptake period. Two global CO2 inversions using a CASA-GFED model prior estimate earlier northern high latitude net carbon uptake compared to our regional inversion, which we attribute to (i) early photosynthetic-onset model prior bias, (ii) inverse method (scaling factor + optimization window), and (iii) sparsity of available Alaskan CO2 observations. Another global inversion with zero prior estimates the same timing for net carbon uptake as the regional model but smaller seasonal amplitude. The analysis of Alaskan eddy covariance observations confirms regional scale findings for tundra, but indicates that photosynthesis and net carbon uptake occur up to 1 month earlier in evergreens than captured by models or CO2 inversions, with better correlation to above-freezing air temperature than date of primary thaw. Further collection and analysis of boreal evergreen species over multiple years and at additional subarctic flux towers are critically needed.

International audience; This paper, developed under the framework of the RECCAP initiative, aims at providing improved estimates of the carbon and GHG (CO 2 , CH 4 and N 2 O) balance of continental Africa. The various components and processes of the African carbon and GHG budget are considered, existing data reviewed, and new data from different methodolo-gies (inventories, ecosystem flux measurements, models, and atmospheric inversions) presented. Uncertainties are quantified and current gaps and weaknesses in knowledge and monitoring systems described in order to guide future requirements. The majority of results agree that Africa is a small sink of carbon on an annual scale, with an average value of Published by Copernicus Publications on behalf of the European Geosciences Union. 382 R. Valentini et al.: A full greenhouse gases budget of Africa −0.61 ± 0.58 Pg C yr −1. Nevertheless, the emissions of CH 4 and N 2 O may turn Africa into a net source of radiative forcing in CO 2 equivalent terms. At sub-regional level, there is significant spatial variability in both sources and sinks, due to the diversity of biomes represented and differences in the degree of anthropic impacts. Southern Africa is the main source region; while central Africa, with its evergreen tropical forests, is the main sink. Emissions from land-use change in Africa are significant (around 0.32 ± 0.05 Pg C yr −1), even higher than the fossil fuel emissions: this is a unique feature among all the continents. There could be significant carbon losses from forest land even without deforestation, resulting from the impact of selective logging. Fires play a significant role in the African carbon cycle, with 1.03 ± 0.22 Pg C yr −1 of carbon emissions, and 90 % originating in savannas and dry woodlands. A large portion of the wild fire emissions are compensated by CO 2 uptake during the growing season, but an uncertain fraction of the emission from wood harvested for domestic use is not. Most of these fluxes have large in-terannual variability, on the order of ±0.5 Pg C yr −1 in standard deviation, accounting for around 25 % of the year-to-year variation in the global carbon budget. Despite the high uncertainty, the estimates provided in this paper show the important role that Africa plays in the global carbon cycle, both in terms of absolute contribution, and as a key source of interannual variability.

Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.

Land use and land cover changes (LULCC) modulate land surface energy, heat, moisture, and momentum fluxes. Using simulations performed with and without LULCC for five earth system models, averaged over the 2071–2100 period, we quantify the biophysical effects in response to a future realistic LULCC scenario (Representative Concentration Pathway RCP8.5) on 15 climate variables (i.e., atmospheric, radiative, wind, hydrologic variables, and heat fluxes). We find that climate models are able to simulate some robust and strong climate perturbations in response to LULCC. In tropical regions with substantial LULCC, significantly higher skin temperatures, less precipitation and soil moisture, less evaporation and clouds, more incoming radiation and stronger winds, more anticyclonic conditions and subsidence, are simulated in response to future LULCC. In midlatitude and high latitude, LULCC result in autumn cooling and higher tropospheric pressures, while East Asia is drier, warmer, with higher sensible heat flux and lower evaporation. The tropical wind strengthening and weakening of the hydrological cycle are comparable in magnitude to their future regional changes induced by greenhouse gases under RCP8.5, which make LULCC an indispensable forcing to take into account in future climatic assessments. Finally, our study reveals significant indirect atmospheric processes triggered by LULCC, implying substantial changes in incoming radiation, which dominate climatic responses over the direct effects, particularly in boreal regions.

International audience; Land-use and land-cover change (LULCC) represents one of the key drivers of global environmental change. However, the processes and drivers of anthropogenic land-use activity are still overly simplistically implemented in terrestrial biosphere models (TBMs). The published results of these models are used in major assessments of processes and impacts of global environmental change, such as the reports of the Intergovernmental Panel on Climate Change (IPCC). Fully coupled models of climate, land use and biogeochemical cycles to explore land use–climate interactions across spatial scales are currently not available. Instead, information on land use is provided as exogenous data from the land-use change modules of integrated assessment models (IAMs) to TBMs. In this article, we discuss, based on literature review and illustrative analysis of empirical and modeled LULCC data, three major challenges of this current LULCC representation and their implications for land use– climate interaction studies: (I) provision of consistent, harmonized, land-use time series spanning from historical reconstructions to future projections while accounting for uncertainties associated with different land-use mod-eling approaches, (II) accounting for sub-grid processes and bidirectional changes (gross changes) across spatial scales, and (III) the allocation strategy of independent land-use data at the grid cell level in TBMs. We discuss the factors that hamper the development of improved land-use representation, which sufficiently accounts for uncertainties in the land-use modeling process. We propose that LULCC data-provider and user communities should engage in the joint development and evaluation of enhanced LULCC time series, which account for the diversity of LULCC modeling and increasingly include empirically based information about sub-grid processes and land-use transition trajectories, to improve the representation of land use in TBMs. Moreover, we suggest concentrating on the development of integrated modeling frameworks that may provide further understanding of possible land–climate–society feedbacks. Published by Copernicus Publications on behalf of the European Geosciences Union. 370 R. Prestele et al.: Current challenges of implementing anthropogenic land-use and land-cover change

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

Scenario analyses have been used in multiple science-policy assessments to better understand complex plausible futures. Scenario archetype approaches are based on the fact that many future scenarios have similar underlying storylines, assumptions, and trends in drivers of change, which allows for grouping of scenarios into typologies, or archetypes, facilitating comparisons between a large range of studies. The use of scenario archetypes in environmental assessments foregrounds important policy questions and can be used to codesign interventions tackling future sustainability issues. Recently, scenario archetypes were used in four regional assessments and one ongoing global assessment within the Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services (IPBES). The aim of these assessments was to provide decision makers with policy-relevant knowledge about the state of biodiversity, ecosystems, and the contributions they provide to people. This paper reflects on the usefulness of the scenario archetype approach within science-policy processes, drawing on the experience from the IPBES assessments. Using a thematic analysis of (a) survey data collected from experts involved in the archetype analyses across IPBES assessments, (b) notes from IPBES workshops, and (c) regional assessment chapter texts, we synthesize the benefits, challenges, and frontiers of applying the scenario archetype approach in a science-policy process. Scenario archetypes were perceived to allow syntheses of large amounts of information for scientific, practice-, and policy-related purposes, streamline key messages from multiple scenario studies, and facilitate communication of them to end users. In terms of challenges, they were perceived as subjective in their interpretation, oversimplifying information, having a limited applicability across scales, and concealing contextual information and novel narratives. Finally, our results highlight what methodologies, applications, and frontiers in archetype-based research should be explored in the future. These advances can assist the design of future large-scale sustainability-related assessment processes, aiming to better support decisions and interventions for equitable and sustainable futures. © 2019 by the author(s). Nadia Sitas, Ryan Blanchard, and Patrick O'Farrell were supported by SwedBio at Stockholm Resilience Centre funded by the Swedish International Development Cooperation Agency (SIDA). Zuzana Harm??kov? and Reinette Biggs were supported by the GRAID programme funded by the Swedish International Development Agency (SIDA). Reinette Biggs was also supported by the South African Research Chairs Initiative (SARChI) (grant 98766) and the Swedish Research Council (grant 621-2014-5137). Eefje den Belder was funded by the Ministry of Agriculture, Nature and Food Quality of the Netherlands. Support to Paula A. Harrison was provided by UK Department of Environment, Food and Rural Affairs and the EU-funded IMPRESSIONS project (Grant Agreement 603416).

Fil: Díaz, Sandra. Consejo Nacional de investigaciones Científicas y Técnicas. Instituto Multidisciplinario de Biología Vegetal; Argentina. Fil: Díaz, Sandra. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Fil: Settele, Josef. Helmholtz Centre for Environmental Research–UFZ. Department of Community Ecology; Alemania. Fil: Settele, Josef. German Centre for Integrative Biodiversity Research–iDiv; Alemania. Fil: Brondízio, Eduardo S. Indiana University. Department of Anthropology; Estados Unidos. Fil: Ngo, Hien T. Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) Secretariat; Alemania. Fil: Agard, John. University of the West Indies. Department of Life Sciences; Trinidad y Tobago. Fil: Arneth, Almut. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research. Atmospheric Environmental Research; Alemania. Fil: Balvanera, Patricia. Universidad Nacional Autónoma de México. Instituto de Investigaciones en Ecosistemas y Sustentabilidad; México. Fil: Brauman, Kate A. University of Minnesota. Institute on the Environment; Estados Unidos. Fil: Butchart, Stuart H. BirdLife International; Reino Unido. Fil: Butchart, Stuart H. University of Cambridge. Department of Zoology; Reino Unido. Fil: Chan, Kai M. The University of British Columbia. Institute for Resources, Environment, and Sustainability; Canada. Fil: Garibaldi, Lucas Alejandro. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina. Fil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina. Fil: Ichii, Kazuhito. Chiba University. Center for Environmental Remote Sensing; Japón. Fil: Ichii, Kazuhito. National Institute for Environmental Studies. Center for Global Environmental Research; Japón. Fil: Liu, Jianguo. Michigan State University. Department of Fisheries and Wildlife. Center for Systems Integration and Sustainability; Estados Unidos. Fil: Subramanian, Suneetha M. United Nations University. Institute for the Advanced Study of Sustainability; Japón. Fil: Subramanian, Suneetha M. United Nations University. International Institute for Global Health; Malasia. Fil: Midgley, Guy F. Stellenbosch University. Department of Botany and Zoology. Global Change Biology Group; Sudáfrica. Fil: Miloslavich, Patricia. University of Tasmania. Institute for Marine and Antarctic Studies; Australia. Fil: Miloslavich, Patricia. Commonwealth Scientific and Industrial Research Organisation. Oceans and Atmosphere; Australia. Fil: Miloslavich, Patricia. Universidad Simón Bolívar. Departamento de Estudios Ambientales; Venezuela. Fil: Molnár, Zsolt. Magyar Tudományos Akadémia. Centre for Ecological Research Institute of Ecology and Botany; Hungría. Fil: Obura, David. Coastal Oceans Research and Development - Indian Ocean; Kenia. Fil: Obura, David. The University of Queensland. Global Climate Institute; Australia. Fil: Pfaff, Alexander. Duke University. Sanford School of Public Policy; Estados Unidos. Fil: Polasky, Stephen. University of Minnesota. Department of Applied Economics; Estados Unidos. Fil: Polasky, Stephen. University of Minnesota. Department of Ecology, Evolution, and Behavior; Estados Unidos. Fil: Purvis, Andy. Natural History Museum. Department of Life Sciences; Reino Unido. Fil: Purvis, Andy. Imperial College London. Grand Challenges in Ecosystems and the Environment; Reino Unido. Fil: Razzaque, Jona. University of the West of England. Faculty of Business and Law. Department of Law; Reino Unido. Fil: Reyers, Belinda. Stockholm University. Stockholm Resilience Centre; Suecia. Fil: Reyers, Belinda. Stellenbosch University. Department of Conservation Ecology; Sudáfrica. Fil: Roy Chowdhury, Rinku. Clark University. Graduate School of Geography; Estados Unidos. Fil: Shin, Yunne J. University of Montpellier; Francia. Fil: Shin, Yunne J. University of Cape Town. Marine Research Institute. Department of Biological Sciences; Sudáfrica. Fil: Visseren Hamakers, Ingrid. George Mason University. Department of Environmental Science and Policy; Estados Unidos. Fil: Visseren Hamakers, Ingrid. Radboud University. Institute for Management Research; Países Bajos. Fil: Willis, Katherine J. Royal Botanic Gardens; Reino Unido. Fil: Willis, Katherine J. University of Oxford. Department of Zoology. Long-Term Ecology Laboratory; Reino Unido. Fil: Zayas, Cynthia N. University of the Philippines. Center for International Studies; Filipinas. The human impact on life on Earth has increased sharply since the 1970s, driven by the demands of a growing population with rising average per capita income. Nature is currently supplying more materials than ever before, but this has come at the high cost of unprecedented global declines in the extent and integrity of ecosystems, distinctness of local ecological communities, abundance and number of wild species, and the number of local domesticated varieties. Such changes reduce vital benefits that people receive from nature and threaten the quality of life of future generations. Both the benefits of an expanding economy and the costs of reducing nature’s benefits are unequally distributed. The fabric of life on which we all depend—nature and its contributions to people—is unravelling rapidly. Despite the severity of the threats and lack of enough progress in tackling them to date, opportunities exist to change future trajectories through transformative action. Such action must begin immediately, however, and address the root economic, social, and technological causes of nature’s deterioration.

Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers on simulated burned area, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2 concentration, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1921. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trends in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the largest uncertainties in simulating global historical burned area are related to the representation of anthropogenic ignitions and suppression and effects of land use on vegetation and fire. In line with previous studies this highlights the need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire within Earth system model applications. Only two models show a strong response to atmospheric CO2 concentration. The effects of changes in atmospheric CO2 concentration on fire are complex and quantitative information of how fuel loads and how flammability changes due to this factor is missing. The response to lightning on global scale is low. The response of burned area to climate is spatially heterogeneous and has a strong inter-annual variation. Climate is therefore likely more important than the other factors for short-term variations and extremes in burned area. This study provides a basis to understand the uncertainties in global fire modelling. Both improvements in process understanding and observational constraints reduce uncertainties in modelling burned area trends.

Our understanding and quantification of global soil nitrous oxide (N2 O) emissions and the underlying processes remain largely uncertain. Here we assessed the effects of multiple anthropogenic and natural factors, including nitrogen fertilizer (N) application, atmospheric N deposition, manure N application, land cover change, climate change and rising atmospheric CO2 concentration, on global soil N2 O emissions for the period 1861-2016 using a standard simulation protocol with seven process-based terrestrial biosphere models. Results suggest global soil N2 O emissions have increased from 6.3 ± 1.1 Tg N2 O-N yr-1 in the pre-industrial period (the 1860s) to 10.0 ± 2.0 Tg N2 O-N yr-1 in the recent decade (2007-2016). Cropland soil emissions increased from 0.3 Tg N2 O-N yr-1 to 3.3 Tg N2 O-N yr-1 over the same period, accounting for 82% of the total increase. Regionally, China, South Asia and Southeast Asia underwent rapid increases in cropland N2 O emissions since the 1970s. However, US cropland N2 O emissions had been relatively flat in magnitude since the 1980s, and EU cropland N2 O emissions appear to have decreased by 14%. Soil N2 O emissions from predominantly natural ecosystems accounted for 67% of the global soil emissions in the recent decade but showed only a relatively small increase of 0.7 ± 0.5 Tg N2 O-N yr-1 (11%) since the 1860s. In the recent decade, N fertilizer application, N deposition, manure N application and climate change contributed 54%, 26%, 15% and 24%, respectively, to the total increase. Rising atmospheric CO2 concentration reduced soil N2 O emissions by 10% through the enhanced plant N uptake, while land cover change played a minor role. Our estimation here does not account for indirect emissions from soils and the directed emissions from excreta of grazing livestock. To address uncertainties in estimating regional and global soil N2 O emissions, this study recommends several critical strategies for improving the process-based simulations.

Climate change could pose a major challenge to efforts towards strongly increase food production over the coming decades. However, model simulations of future climate-impacts on crop yields differ substantially in the magnitude and even direction of the projected change. Combining observations of current maximum-attainable yield with climate analogues, we provide a complementary method of assessing the effect of climate change on crop yields. Strong reductions in attainable yields of major cereal crops are found across a large fraction of current cropland by 2050. These areas are vulnerable to climate change and have greatly reduced opportunity for agricultural intensification. However, the total land area, including regions not currently used for crops, climatically suitable for high attainable yields of maize, wheat and rice is similar by 2050 to the present-day. Large shifts in land-use patterns and crop choice will likely be necessary to sustain production growth rates and keep pace with demand., Simulations of the impact of future climate change on crop yield vary considerably. Here, the authors use a climate analogue approach to estimate the response of maximum attainable yield to climate change and predict that large shifts in land use and crop choice would be required to meet demand.

Disproportional warming in the northern high latitudes and large carbon stocks in boreal and (sub)arctic ecosystems have raised concerns as to whether substantial positive climate feedbacks from biogeochemical process responses should be expected. Such feedbacks occur when increasing temperatures lead, for example, to a net release of CO2 or CH4. However, temperature-enhanced emissions of biogenic volatile organic compounds (BVOCs) have been shown to contribute to the growth of secondary organic aerosol (SOA), which is known to have a negative radiative climate effect. Combining measurements in Eastern Siberia with model-based estimates of vegetation and permafrost dynamics, BVOC emissions, and aerosol growth, we assess here possible future changes in ecosystem CO2 balance and BVOC–SOA interactions and discuss these changes in terms of possible climate effects. Globally, the effects of changes in Siberian ecosystem CO2 balance and SOA formation are small, but when concentrating on Siberia and the Northern Hemisphere the negative forcing from changed aerosol direct and indirect effects become notable – even though the associated temperature response would not necessarily follow a similar spatial pattern. While our analysis does not include other important processes that are of relevance for the climate system, the CO2 and BVOC–SOA interplay serves as an example for the complexity of the interactions between emissions and vegetation dynamics that underlie individual terrestrial processes and highlights the importance of addressing ecosystem–climate feedbacks in consistent, process-based model frameworks.

Wildfires are by far the largest contributor to global biomass burning and constitute a large global source of atmospheric traces gases and aerosols. Such emissions have a considerable impact on air quality and constitute a major health hazard. Biomass burning also influences the radiative balance of the atmosphere and is thus not only of societal, but also of significant scientific interest. There is a common perception that climate change will lead to an increase in emissions as hot and dry weather events that promote wildfire will become more common. However, even though a few studies have found that the inclusion of CO2 fertilisation of photosynthesis and changes in human population patterns will tend to somewhat lower predictions of future wildfire emissions, no such study has included full ensemble ranges of both climate predictions and population projections, including the effect of different degrees of urbanisation.Here, we present a series of 124 simulations with the LPJ–GUESS–SIMFIRE global dynamic vegetation–wildfire model, including a semi-empirical formulation for the prediction of burned area based on fire weather, fuel continuity and human population density. The simulations use Climate Model Intercomparison Project 5 (CMIP5) climate predictions from eight Earth system models. These were combined with two Representative Concentration Pathways (RCPs) and five scenarios of future human population density based on the series of Shared Socioeconomic Pathways (SSPs) to assess the sensitivity of emissions to the effect of climate, CO2 and humans. In addition, two alternative parameterisations of the semi-empirical burned-area model were applied. Contrary to previous work, we find no clear future trend of global wildfire emissions for the moderate emissions and climate change scenario based on the RCP 4.5. Only historical population change introduces a decline by around 15 % since 1900. Future emissions could either increase for low population growth and fast urbanisation, or continue to decline for high population growth and slow urbanisation. Only for high future climate change (RCP8.5), wildfire emissions start to rise again after ca. 2020 but are unlikely to reach the levels of 1900 by the end of the 21st century. We find that climate warming will generally increase the risk of fire, but that this is only one of several equally important factors driving future levels of wildfire emissions, which include population change, CO2 fertilisation causing woody thickening, increased productivity and fuel load and faster litter turnover in a warmer climate.

Nitrogen (N) cycle dynamics and N deposition play an important role in determining the terrestrial biosphere's carbon (C) balance. We assess global and biome-specific N deposition effects on C sequestration rates with the dynamic global vegetation model LPJ-GUESS. Modeled CN interactions are evaluated by comparing predictions of the C and CN version of the model with direct observations of C fluxes from 68 forest FLUXNET sites. N limitation on C uptake reduced overestimation of gross primary productivity for boreal evergreen needleleaf forests from 56% to 18%, presenting the greatest improvement among forest types. Relative N deposition effects on C sequestration (dC/dN) in boreal, temperate, and tropical sites ranged from 17 to 26 kg C kg N-1 when modeled at site scale and were reduced to 12-22 kg C kg N-1 at global scale. We find that 19% of the recent (1990-2007) and 24% of the historical global C sink (1900-2006) was driven by N deposition effects. While boreal forests exhibit highest dC/dN, their N deposition-induced C sink was relatively low and is suspected to stay low in the future as no major changes in N deposition rates are expected in the boreal zone. N deposition induced a greater C sink in temperate and tropical forests, while predicted C fluxes and N-induced C sink response in tropical forests were associated with greatest uncertainties. Future work should be directed at improving the ability of LPJ-GUESS and other process-based ecosystem models to reproduce C cycle dynamics in the tropics, facilitated by more benchmarking data sets. Furthermore, efforts should aim to improve understanding and model representations of N availability (e.g., N fixation and organic N uptake), N limitation, P cycle dynamics, and effects of anthropogenic land use and land cover changes.

Croplands are vital ecosystems for human well-being and provide important ecosystem services such as crop yields, retention of nitrogen and carbon storage. On large (regional to global)-scale levels, assessment of how these different services will vary in space and time, especially in response to cropland management, are scarce. We explore cropland management alternatives and the effect these can have on future C and N pools and fluxes using the land-use-enabled dynamic vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator). Simulated crop production, cropland carbon storage, carbon sequestration and nitrogen leaching from croplands are evaluated and discussed. Compared to the version of LPJ-GUESS that does not include land-use dynamics, estimates of soil carbon stocks and nitrogen leaching from terrestrial to aquatic ecosystems were improved. Our model experiments allow us to investigate trade-offs between these ecosystem services that can be provided from agricultural fields. These trade-offs are evaluated for current land use and climate and further explored for future conditions within the two future climate change scenarios, RCP (Representative Concentration Pathway) 2.6 and 8.5. Our results show that the potential for carbon sequestration due to typical cropland management practices such as no-till management and cover crops proposed in previous studies is not realised, globally or over larger climatic regions. Our results highlight important considerations to be made when modelling C–N interactions in agricultural ecosystems under future environmental change and the effects these have on terrestrial biogeochemical cycles.

Land Surface Models (LSMs) are essential to reproduce biophysical processes modulated by vegetation and to predict the future evolution of the land‐climate system. To assess the performance of an ensemble of LSMs (JSBACH, JULES, ORCHIDEE, CLM, and LPJ‐GUESS) a consistent set of land surface energy fluxes and leaf area index (LAI) has been generated. Relationships of interannual variations of modeled surface fluxes and LAI changes have been analyzed at global scale across climatological gradients and compared with those obtained from satellite‐based products. Model‐specific strengths and deficiencies were diagnosed for tree and grass biomes. Results show that the responses of grasses are generally well represented in models with respect to the observed interplay between turbulent fluxes and LAI, increasing the confidence on how the LAI‐dependent partition of net radiation into latent and sensible heat are simulated. On the contrary, modeled forest responses are characterized by systematic bias in the relation between the year‐to‐year variability in LAI and net radiation in cold and temperate climates, ultimately affecting the amount of absorbed radiation due to LAI‐related effects on surface albedo. In addition, for tree biomes, the relationships between LAI and turbulent fluxes appear to contradict the experimental evidences. The dominance of the transpiration‐driven over the observed albedo‐driven effects might suggest that LSMs have the incorrect balance of these two processes. Such mismatches shed light on the limitations of our current understanding and process representation of the vegetation control on the surface energy balance and help to identify critical areas for model improvement., Key Points The covariability of land biophysics and changes in vegetation density predicted by Land Surface Models is compared with satellite dataBiophysical properties of vegetation are explored across climatological gradients of temperature and precipitationModel‐specific and systematic strengths and deficiencies are diagnosed separately for tree and grass biomes

Regional estimates of the effects of ozone pollution on forest growth depend on the availability of reliable injury functions that estimate a representative ecosystem response to ozone exposure. A number of such injury functions for forest tree species and forest functional types have recently been published and subsequently applied in terrestrial biosphere models to estimate regional or global effects of ozone on forest tree productivity and carbon storage in the living plant biomass. The resulting impacts estimated by these biosphere models show large uncertainty in the magnitude of ozone effects predicted. To understand the role that these injury functions play in determining the variability in estimated ozone impacts, we use the O-CN biosphere model to provide a standardised modelling framework. We test four published injury functions describing the leaf-level, photosynthetic response to ozone exposure (targeting the maximum carboxylation capacity of Rubisco (Vcmax) or net photosynthesis) in terms of their simulated whole-tree biomass responses against data from 23 ozone filtration/fumigation experiments conducted with young trees from European tree species at sites across Europe with a range of climatic conditions. Our results show that none of these previously published injury functions lead to simulated whole-tree biomass reductions in agreement with the observed dose–response relationships derived from these field experiments and instead lead to significant over- or underestimations of the ozone effect. By re-parameterising these photosynthetically based injury functions, we develop linear, plant-functional-type-specific dose–response relationships, which provide accurate simulations of the observed whole-tree biomass response across these 23 experiments.

An integrated understanding of the biogeochemical consequences of climate extremes and land use changes is needed to constrain land-surface feedbacks to atmospheric CO2 from associated climate change. Past assessments of the global carbon balance have shown particularly high uncertainty in Southeast Asia. Here, we use a combination of model ensembles to show that intensified land use change made Southeast Asia a strong source of CO2 from the 1980s to 1990s, whereas the region was close to carbon neutral in the 2000s due to an enhanced CO2 fertilization effect and absence of moderate-to-strong El Niño events. Our findings suggest that despite ongoing deforestation, CO2 emissions were substantially decreased during the 2000s, largely owing to milder climate that restores photosynthetic capacity and suppresses peat and deforestation fire emissions. The occurrence of strong El Niño events after 2009 suggests that the region has returned to conditions of increased vulnerability of carbon stocks., The carbon balance in Southeast Asia is highly uncertain. Here, the authors show that land use changes and occurrence of strong El Niño control decadal shifts in the carbon balance of this region.

Land use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and its location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land–climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies. The dataset consisting of both harmonized model simulation and remote sensing estimations is available at https://doi.org/10.5281/zenodo.1182145.

Imbalance-p paper contact with Marcos Fernández: m.fernandez@creaf.uab.cat The emission of isoprenoids (e.g. isoprene and monoterpenes) by plants plays an important defensive role against biotic and abiotic stresses. Little is known, however, about the functional traits linked to species-specific variability in the types and rates of isoprenoids emitted and about possible co-evolution of functional traits with isoprenoid emission type (isoprene emitter, monoterpene emitter or both). - We combined data for isoprene and monoterpene emission rates per unit dry mass with key functional traits (foliar nitrogen (N) and phosphorus (P) concentrations, and leaf mass per area) and climate for 113 plant species, covering the boreal, wet temperate, Mediterranean and tropical biomes. - Foliar N was positively correlated with isoprene emission, and foliar P was negatively correlated with both isoprene and monoterpene emission rate. Nonemitting plants generally had the highest nutrient concentrations, and those storing monoterpenes had the lowest concentrations. Our phylogenetic analyses found that the type of isoprenoid emission followed an adaptive, rather than a random model of evolution. - Evolution of isoprenoids may be linked to nutrient availability. Foliar N and P are good predictors of the type of isoprenoid emission and the rate at which monoterpenes, and to a lesser extent isoprene, are emitted.

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.

Achieving the long-term temperature goal of the Paris Agreement requires forest-based mitigation. Collective progress towards this goal will be assessed by the Paris Agreement’s Global stocktake. At present, there is a discrepancy of about 4 GtCO2 yr−1 in global anthropogenic net land-use emissions between global models (reflected in IPCC assessment reports) and aggregated national GHG inventories (under the UNFCCC). We show that a substantial part of this discrepancy (about 3.2 GtCO2 yr−1) can be explained by conceptual differences in anthropogenic forest sink estimation, related to the representation of environmental change impacts and the areas considered as managed. For a more credible tracking of collective progress under the Global stocktake, these conceptual differences between models and inventories need to be reconciled. We implement a new method of disaggregation of global land model results that allows greater comparability with GHG inventories. This provides a deeper understanding of model–inventory differences, allowing more transparent analysis of forest-based mitigation and facilitating a more accurate Global stocktake. The model–inventory discrepancy in net land-use carbon emissions mainly results from conceptual differences in estimating anthropogenic forest sinks. A revised disaggregation of global land model results allows greater comparability with inventories.

To support the assessments of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the IPBES Expert Group on Scenarios and Models is carrying out an intercomparison of biodiversity and ecosystem services models using harmonized scenarios (BES-SIM). The goals of BES-SIM are (1) to project the global impacts of land-use and climate change on biodiversity and ecosystem services (i.e., nature's contributions to people) over the coming decades, compared to the 20th century, using a set of common metrics at multiple scales, and (2) to identify model uncertainties and research gaps through the comparisons of projected biodiversity and ecosystem services across models. BES-SIM uses three scenarios combining specific Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) – SSP1xRCP2.6, SSP3xRCP6.0, SSP5xRCP8.6 – to explore a wide range of land-use change and climate change futures. This paper describes the rationale for scenario selection, the process of harmonizing input data for land use, based on the second phase of the Land Use Harmonization Project (LUH2), and climate, the biodiversity and ecosystem services models used, the core simulations carried out, the harmonization of the model output metrics, and the treatment of uncertainty. The results of this collaborative modeling project will support the ongoing global assessment of IPBES, strengthen ties between IPBES and the Intergovernmental Panel on Climate Change (IPCC) scenarios and modeling processes, advise the Convention on Biological Diversity (CBD) on its development of a post-2020 strategic plans and conservation goals, and inform the development of a new generation of nature-centred scenarios. ISSN:1991-9603 ISSN:1991-959X

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.

Nitrogen (N) is a key element in terrestrial ecosystems as it influences both plant growth and plant interactions with the atmosphere. Accounting for carbon–nitrogen interactions has been found to alter future projections of the terrestrial carbon (C) cycle substantially. Dynamic vegetation models (DVMs) aim to accurately represent both natural vegetation and managed land, not only from a carbon cycle perspective but increasingly so also for a wider range of processes including crop yields. We present here the extended version of the DVM LPJ-GUESS that accounts for N limitation in crops to account for the effects of N fertilisation on yields and biogeochemical cycling. The performance of this new implementation is evaluated against observations from N fertiliser trials and CO2 enrichment experiments. LPJ-GUESS captures the observed response to both N and CO2 fertilisation on wheat biomass production, tissue C to N ratios (C : N) and phenology. To test the model's applicability for larger regions, simulations are subsequently performed that cover the wheat-dominated regions of western Europe. When compared to regional yield statistics, the inclusion of C–N dynamics in the model substantially increase the model performance compared to an earlier version of the model that does not account for these interactions. For these simulations, we also demonstrate an implementation of N fertilisation timing for areas where this information is not available. This feature is crucial when accounting for processes in managed ecosystems in large-scale models. Our results highlight the importance of accounting for C–N interactions when modelling agricultural ecosystems, and it is an important step towards accounting for the combined impacts of changes in climate, [CO2] and land use on terrestrial biogeochemical cycles.

Recently a considerable amount of effort has been put into quantifying how interactions of the carbon and nitrogen cycle affect future terrestrial carbon sinks. Dynamic vegetation models, representing the nitrogen cycle with varying degree of complexity, have shown diverging constraints of nitrogen dynamics on future carbon sequestration. In this study, we use LPJ-GUESS, a dynamic vegetation model employing a detailed individual- and patch-based representation of vegetation dynamics, to evaluate how population dynamics and resource competition between plant functional types, combined with nitrogen dynamics, have influenced the terrestrial carbon storage in the past and to investigate how terrestrial carbon and nitrogen dynamics might change in the future (1850 to 2100; one representative "business-as-usual" climate scenario). Single-factor model experiments of CO2 fertilisation and climate change show generally similar directions of the responses of C–N interactions, compared to the C-only version of the model as documented in previous studies using other global models. Under an RCP 8.5 scenario, nitrogen limitation suppresses potential CO2 fertilisation, reducing the cumulative net ecosystem carbon uptake between 1850 and 2100 by 61%, and soil warming-induced increase in nitrogen mineralisation reduces terrestrial carbon loss by 31%. When environmental changes are considered conjointly, carbon sequestration is limited by nitrogen dynamics up to the present. However, during the 21st century, nitrogen dynamics induce a net increase in carbon sequestration, resulting in an overall larger carbon uptake of 17% over the full period. This contrasts with previous results with other global models that have shown an 8 to 37% decrease in carbon uptake relative to modern baseline conditions. Implications for the plausibility of earlier projections of future terrestrial C dynamics based on C-only models are discussed.

Animal products, i.e. meat, milk and eggs, provide an important component in global diets, but livestock dominate agricultural land use by area and are a major source of greenhouse gases. Cultural and personal associations with animal product consumption create barriers to moderating consumption, and hence reduced environmental impacts. Here we review alternatives to conventional animal products, including cultured meat, imitation meat and insects (i.e. entomophagy), and explore the potential change in global agricultural land requirements associated with each alternative. Stylised transformative consumption scenarios where half of current conventional animal products are substituted to provide at least equal protein and calories are considered. The analysis also considers and compares the agricultural land area given shifts between conventional animal product consumption. The results suggest that imitation meat and insects have the highest land use efficiency, but the land use requirements are only slightly greater for eggs and poultry meat. The efficiency of insects and their ability to convert agricultural by-products and food waste into food, suggests further research into insect production is warranted. Cultured meat does not appear to offer substantial benefits over poultry meat or eggs, with similar conversion efficiency, but higher direct energy requirements. Comparison with the land use savings from reduced consumer waste, including over-consumption, suggests greater benefits could be achieved from alternative dietary transformations considered. We conclude that although a diet with lower rates of animal product consumption is likely to create the greatest reduction in agricultural land, a mix of smaller changes in consumer behaviour, such as replacing beef with chicken, reducing food waste and potentially introducing insects more commonly into diets, would also achieve land savings and a more sustainable food system.