Beyond MRV: combining remote sensing and ecosystem modeling for geospatial monitoring and attribution of forest carbon fluxes over Maryland, USA

Members of the U.S. Climate Alliance, a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement, are considering policy options for inclusion of forest carbon in climate mitigation plans. These initiatives are generally limited by a lack of relevant data on forest carbon stocks and fluxes past-to-future. Previously, we developed a new forest carbon modeling system that combined high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above-ground forest carbon stocks, and projected future forest carbon sequestration potential for the state of Maryland. Here we extended this work to provide a consistent geospatial approach for monitoring changes in forest carbon stocks over time. Utilizing the same data and modeling system developed previously for planning, we integrated historical input data on weather and disturbance to reconstruct the history of vegetation dynamics and forest above-ground carbon stocks annually over the period 1984–2016 at 30 m resolution and provided an extension to 2023. Statewide, forested land had an average annual net above ground carbon sink of 1.37 TgC yr−1, comparable to prior estimates. However, unlike the prior estimates, there was considerable variation around this mean. The statewide net above ground flux ranged interannually from −0.65 to 2.77 Tg C yr−1. At the county scale, the average annual net above ground flux ranged spatially from 0.01 to 0.13 Tg C yr−1 and spatiotemporally from −0.43 to 0.24 Tg C yr−1. Attribution analyses indicate the primary importance of persistent and regrowing forests, vegetation structure, local disturbance, and rising CO2 to the mean flux, and the primary importance of weather to the large-scale interannual variability. These results have important implications for state climate mitigation planning, reporting and assessment. With this approach, it is now possible to monitor changes in forest carbon stocks spatiotemporally over policy relevant domains with a consistent framework that is also enabled for future planning.

Trends and Drivers of Regional Scale Terrestrial Sources and Sinks of Carbon Dioxide: An overview of the TRENDY Project.

The terrestrial biosphere plays a major role in the global carbon cycle, and there is a recognized need for regularly updated estimates of land-atmosphere exchange at regional and global scales. An international ensemble of Dynamic Global Vegetation Models (DGVMs), known as the “Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide” (TRENDY) project, quantifies land biophysical exchange processes and biogeochemistry cycles in support of the annual Global Carbon Budget assessments and the REgional Carbon Cycle Assessment and Processes, phase 2 project. DGVMs use a common protocol and set of driving data sets. A set of factorial simulations allows attribution of spatio-temporal changes in land surface processes to three primary global change drivers: changes in atmospheric CO2, climate change and variability, and Land Use and Land Cover Changes (LULCC). Here, we describe the TRENDY project, benchmark DGVM performance using remote-sensing and other observational data, and present results for the contemporary period. Simulation results show a large global carbon sink in natural vegetation over 2012–2021, attributed to the CO2 fertilization effect (3.8 ± 0.8 PgC/yr) and climate (−0.58 ± 0.54 PgC/yr). Forests and semi-arid ecosystems contribute approximately equally to the mean and trend in the natural land sink, and semi-arid ecosystems continue to dominate interannual variability. The natural sink is offset by net emissions from LULCC (−1.6 ± 0.5 PgC/yr), with a net land sink of 1.7 ± 0.6 PgC/yr. Despite the largest gross fluxes being in the tropics, the largest net land-atmosphere exchange is simulated in the extratropical regions.

Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050

Based on an extensive model intercomparison, we assessed trends in biodiversity and ecosystem services from historical reconstructions and future scenarios of land-use and climate change. During the 20th century, biodiversity declined globally by 2 to 11%, as estimated by a range of indicators. Provisioning ecosystem services increased several fold, and regulating services decreased moderately. Going forward, policies toward sustainability have the potential to slow biodiversity loss resulting from land-use change and the demand for provisioning services while reducing or reversing declines in regulating services. However, negative impacts on biodiversity due to climate change appear poised to increase, particularly in the higher-emissions scenarios. Our assessment identifies remaining modeling uncertainties but also robustly shows that renewed policy efforts are needed to meet the goals of the Convention on Biological Diversity.

Harnessing the Land Sector to Achieve U.S. Climate Goals: An all-of-society approach to meeting our climate goals and bolstering the carbon sink by 2035.

America is All In’s new in-depth analysis uses a multi-model approach to explore how the U.S. can expand and bolster its carbon sink. Through an all-of-society U.S. climate strategy—meaning integrated action at the federal, state, city, and business levels—current and additional action has the potential to deliver net emissions reductions from forests and farmland by over 70% by 2035 from 2021 levels– keeping the U.S on track toward its 2050 net-zero commitments and paving the way toward a sustainable, climate-resilient future.

Global Carbon Budget 2023

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based fCO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9±0.5 Gt C yr−1 (10.2±0.5 Gt C yr−1 when the cement carbonation sink is not included), and ELUC was 1.2±0.7 Gt C yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1±0.8 Gt C yr−1 (40.7±3.2 Gt CO2 yr−1). Also, for 2022, GATM was 4.6±0.2 Gt C yr−1 (2.18±0.1 ppm yr−1; ppm denotes parts per million), SOCEAN was 2.8±0.4 Gt C yr−1, and SLAND was 3.8±0.8 Gt C yr−1, with a BIM of −0.1 Gt C yr−1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1±0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959–2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade.

High-Fidelity Deep Approximation of Ecosystem Simulation over Long-Term at Large Scale

Ecosystem services, such as carbon sequestration, biodiversity, and climate regulation, play essential roles in combating climate change. Projection of ecosystem dynamics under various scenarios is critical in understanding potential impacts and informing policies and mitigation strategies. Ecosystem Demography (ED) model is a major mechanistic model for ecosystem dynamics projection, but its computational cost has been a major bottleneck in performing large-scale (e.g., global, national) projections at very high spatial resolution. We aim to approximate the ED model using deep neural networks at operational high accuracy to assist large-scale climate studies. The deep approximation is non-trivial due to challenges by long-term error accumulation (e.g., 40 years), highly diverse scenarios, and high cost in training data generation. We propose a Deep-ED approximation model to address the challenges with a multi-scale cumulative loss reduction structure, significance-based scenario partitioning, self-guided forwarding, and physics-aware active learning strategies. Experiment results in the northeastern US demonstrate the high accuracy of Deep-ED and its potential in large-scale ecosystem projection.

Conservation policies and management in the Ukrainian Emerald Network have maintained reforestation rate despite the war

The Russian-Ukrainian War, ongoing since 2014, impacts an area containing Emerald Network environmental-protection sites created through the implementation of the Bern Convention on the Conservation of European Wildlife and Natural Habitats. Here we explore the impact of this conflict on institutional links supporting environmental sustainability and conservation efforts. Using satellite data, we analyzed tree cover changes in the Luhansk region’s Emerald Network protected areas from 1996 to 2020. The results reveal that the implementation of Bern Convention conservation policies led to a shift from deforestation (−4% each) to reforestation (+8% and +10%) on both sides of the Emerald Network divided by the demarcation line in 2014. It also shows that despite the war, territories under Ukraine control after 2014 continued reforestation (+9%), while sites under Russian control experienced dramatic forest loss (−25%). These findings emphasize the significant consequences of warfare-induced separation of local institutions on conservation areas and underscore the positive impact of the Emerald Network establishment, both before and after the conflict’s onset.

The anthropogenic imprint on temperate and boreal forest demography and carbon turnover

The sweeping transformation of the biosphere by humans over the last millennia leaves only limited windows into its natural state. Much of the forests that dominated temperate and southern boreal regions have been lost and those that remain typically bear a strong imprint of forestry activities and past land-use change, which have changed forest age structure and composition. Here, we ask how would the dynamics, structure and function of temperate and boreal forests differ in the absence of forestry and the legacies of land-use change?

Land Cover and Land-Use Change in the Fifth National Climate Assesment

As Earth’s climate changes, many critical aspects of the land system are at risk. “Land system” refers to both land cover (the physical organization of the land surface) and land use (the intentional ways humans interact with the land). Land system here also refers to the physical and ecological functions of land and changes in organization and function over time. Escalating concerns about how climate and global environmental change impact land systems and ecosystem sustainability have driven extensive research efforts to understand these complex interactions.1,2,3,4,5 This chapter considers three broad categories describing the value of land systems to society and risks associated with climate change: 1 goods and services provided directly by land systems, 2 resilience of land systems in the face of disturbance, and 3 availability of options for future land use.

Maryland's Climate Pathway

On behalf of the Maryland Department of the Environment (MDE), I am honored to present Maryland’s Climate Pathway report. This report provides a comprehensive and ground-breaking analysis of Maryland’s opportunity to address climate change while building a new, green economy. MDE and our sister agencies are hard at work implementing policies to meet Maryland’s nation-leading climate goals and achieve environmental justice for all Marylanders. Please join us in this work with your feedback and support.

Changing cropland in changing climates: quantifying two decades of global cropland changes

Climate change is impacting global crop productivity, and agricultural land suitability is predicted to significantly shift in the future. Responses to changing conditions and increasing yield variability can range from altered management strategies to outright land use conversions that may have significant environmental and socioeconomic ramifications. However, the extent to which agricultural land use changes in response to variations in climate is unclear at larger scales. Improved understanding of these dynamics is important since land use changes will have consequences not only for food security but also for ecosystem health, biodiversity, carbon storage, and regional and global climate. In this study, we combine land use products derived from the Moderate Resolution Imaging Spectroradiometer with climate reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 to analyze correspondence between changes in cropland and changes in temperature and water availability from 2001 to 2018. While climate trends explained little of the variability in land cover changes, increasing temperature, extreme heat days, potential evaporation, and drought severity were associated with higher levels of cropland loss. These patterns were strongest in regions with more cropland change, and generally reflected underlying climate suitability—they were amplified in hotter and drier regions, and reversed direction in cooler and wetter regions. At national scales, climate response patterns varied significantly, reflecting the importance of socioeconomic, political, and geographic factors, as well as differences in adaptation strategies. This global-scale analysis does not attempt to explain local mechanisms of change but identifies climate-cropland patterns that exist in aggregate and may be hard to perceive at local scales. It is intended to supplement regional studies, providing further context for locally-observed phenomena and highlighting patterns that require further analysis.

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.

The drivers and impacts of Amazon forest degradation

Approximately 2.5 × 106 square kilometers of the Amazon forest are currently degraded by fire, edge effects, timber extraction, and/or extreme drought, representing 38% of all remaining forests in the region. Carbon emissions from this degradation total up to 0.2 petagrams of carbon per year (Pg C year−1), which is equivalent to, if not greater than, the emissions from Amazon deforestation (0.06 to 0.21 Pg C year−1). Amazon forest degradation can reduce dry-season evapotranspiration by up to 34% and cause as much biodiversity loss as deforestation in human-modified landscapes, generating uneven socioeconomic burdens, mainly to forest dwellers. Projections indicate that degradation will remain a dominant source of carbon emissions independent of deforestation rates. Policies to tackle degradation should be integrated with efforts to curb deforestation and complemented with innovative measures addressing the disturbances that degrade the Amazon forest.

Global Carbon Budget 2022

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9  ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Scientist-stakeholder relationships drive carbon data product transfer effectiveness within NASA program

Carbon cycle science is at the heart of research on global climate change and its long-term impacts, as it examines the exchange of carbon between the atmosphere, oceans, land, and the impact of fossil fuel emissions on this cycling. Given the urgency of the climate challenge, NASA's Carbon Monitoring System (CMS) requires all funded investigators to identify and work with stakeholder organizations at project inception to accelerate the transfer of the products developed by funded research into decision making systems. In this study, we contribute to the literature through the implementation of a quantitative analysis of 908 unique survey responses from funded investigators to explore the maturity of the scientist-stakeholder engagement. The paper employs multiple correspondence analysis to provide evidence to support policy options to increase stakeholder integration into research programs. Despite limitations of the dataset used, we demonstrated that multiple funding rounds, long-standing relationships between the stakeholder and scientist, and the scientific productivity of the Principal Investigator, including the ability to produce datasets and research papers on these datasets, all contribute to carbon products moving from research to operational use. The maturity of relationships between scientists and stakeholders was shown to result improved stakeholder engagement. The use of carbon products should be identified in every stage of the program, and that capacity building is needed to support both existing and newly identified stakeholders better understand and use CMS products. As federal, state, and local policy on climate adaptation and mitigation matures, the need for information on carbon will expand. Building of stakeholder-scientist relationships in CMS results in an effective generation and use of datasets to support this need and prototype ways that improved information needed for decision making can be created.

GEDI launches a new era of biomass inference from space

Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission's integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.

The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps

Underlying policy efforts to address global climate change is the scientific need to develop the methods to accurately measure and model carbon stocks and fluxes across the wide range of spatial and temporal scales in the Earth system. Initiated in 2010, the NASA Carbon Monitoring System is one of the most ambitious relevant science initiatives to date, exploiting the satellite remote sensing resources, computational capabilities, scientific knowledge, airborne science capabilities, and end-to-end system expertise that are major strengths of the NASA Earth Science program. Here we provide a synthesis of ‘Phase 2’ activities (2011–2019), encompassing 79 projects, 482 publications, and 136 data products. Our synthesis addresses four key questions: What has been attempted? What major results have been obtained? What major gaps and uncertainties remain? and What are the recommended next steps? Through this review, we take stock of what has been accomplished and identify future priorities toward meeting the nation’s needs for carbon monitoring reporting and verification.

Global Carbon Budget 2021

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1o. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4% relative to 2019, with fossil emissions at 9.5± 0.5GtCyr-1 (9.3± 0.5GtCyr-1 when the cement carbonation sink is included), and ELUC was 0.9± 0.7GtCyr-1, for a total anthropogenic CO2 emission of 10.2± 0.8GtCyr-1 (37.4± 2.9GtCO2). Also, for 2020, GATM was 5.0± 0.2GtCyr-1 (2.4± 0.1ppmyr-1), SOCEAN was 3.0± 0.4GtCyr-1, and SLAND was 2.9± 1GtCyr-1, with a BIM of -0.8GtCyr-1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45± 0.1ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of +4.8% (4.2% to 5.4%) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959-2020, but discrepancies of up to 1GtCyr-1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

Integrating remote sensing with ecology and evolution to advance biodiversity conservation

Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth’s biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth’s biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration.

Global evaluation of the Ecosystem Demography model (ED v3.0)

Terrestrial ecosystems play a critical role in the global carbon cycle but have highly uncertain future dynamics. Ecosystem modeling that includes the scaling up of underlying mechanistic ecological processes has the potential to improve the accuracy of future projections while retaining key process- level detail. Over the past two decades, multiple modeling advances have been made to meet this challenge, such as the Ecosystem Demography (ED) model and its derivatives, including ED2 and FATES. Here, we present the global evaluation of the Ecosystem Demography model (ED v3.0), which, like its predecessors, features the formal scaling of physiological processes for individual-based vegetation dynamics to ecosystem scales, together with integrated submodules of soil biogeochemistry and soil hydrology, while retaining explicit tracking of vegetation 3-D structure. This new model version builds on previous versions and provides the first global calibration and evaluation, global tracking of the effects of climate and land-use change on vegetation 3-D structure, spin-up process and input datasets, as well as numerous other advances. Model evaluation was performed with respect to a set of important benchmarking datasets, and model estimates were within observational constraints for multiple key variables, including (i) global patterns of dominant plant functional types (broadleaf vs. evergreen), (ii) the spatial distribution, seasonal cycle, and interannual trends for global gross primary production (GPP), (iii) the global interannual variability of net biome production (NBP) and (iv) global patterns of vertical structure, including leaf area and canopy height. With this global model version, it is now possible to simulate vegetation dynamics from local to global scales and from seconds to centuries with a consistent mechanistic modeling framework amendable to data from multiple traditional and new remote sensing sources, including lidar.

Anthropogenic emissions are the main contribution to the rise of atmospheric methane (1993-2017)

Atmospheric methane (CH4) concentrations have shown a puzzling resumption of growth since 2007 following a period of stabilization from 2000 to 2006. Multiple hypotheses have been proposed to explain the temporal variations in CH4 growth, which attributes the rise of atmospheric CH4 either to increases in emissions from fossil fuel activities, agriculture, and natural wetlands or to a decrease in the atmospheric chemical sink. Here, we use a comprehensive ensemble of CH4 source estimates and isotopic δ13C-CH4 source signature data to show that the resumption of CH4 growth is most likely due to increased anthropogenic emissions. Our emission scenarios that have the fewest biases with respect to isotopic composition suggest that the agriculture, landfill, and waste sectors were responsible for 53 ± 13% of the renewed growth over the period 2007–2017 compared to 2000–2006; industrial fossil fuel sources explained an additional 34 ± 24%, and wetland sources contributed the least at 13 ± 9%. The hypothesis that a large increase in emissions from natural wetlands drove the decrease in atmospheric δ13C-CH4 values cannot be reconciled with current process-based wetland CH4 models. This finding suggests the need for increased wetland measurements to better constrain the contemporary and future role of wetlands in the rise of atmospheric methane and climate feedbacks. Our findings highlight the predominant role of anthropogenic activities in driving the growth of atmospheric CH4 concentrations.

Land-use harmonization datasets for annual global carbon budgets

Land-use change has been the dominant source of anthropogenic carbon emissions for most of the historical period and is currently one of the largest and most uncertain components of the global carbon cycle. Advancing the scientific understanding on this topic requires that the best data be used as input to state-of-the-art models in well-organized scientific assessments. The Land-Use Harmonization 2 dataset (LUH2), previously developed and used as input for simulations of the 6th Coupled Model Intercomparison Project (CMIP6), has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2-GCB updates and extensions which incorporate annual wood harvest data updates from the Food and Agriculture Organization (FAO) of the United Nations for dataset years after 2015 and the History Database of the Global Environment (HYDE) gridded cropland and grazing area data updates (based on annual FAO cropland and grazing area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of 1 or more years in the FAO data releases. The resulting updated LUH2-GCB datasets have provided global, annual gridded land-use and land-use-change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, crop rotations, and pasture management and are used by both bookkeeping models and dynamic global vegetation models (DGVMs) for the GCB. For GCB 2019, a more significant update to LUH2 was produced, LUH2-GCB2019 (https://doi.org/10.3334/ORNLDAAC/1851, Chini et al., 2020b), to take advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil as far back as 1950. From 1951 to 2012 the LUH2-GCB2019 dataset begins to diverge from the version of LUH2 used for the World Climate Research Programme's CMIP6, with peak differences in Brazil in the year 2000 for grazing land (difference of 100 000 km2) and in the year 2009 for cropland (difference of 77 000 km2), along with significant sub-national reorganization of agricultural land-use patterns within Brazil. The LUH2-GCB2019 dataset provides the base for future LUH2-GCB updates, including the recent LUH2-GCB2020 dataset, and presents a starting point for operationalizing the creation of these datasets to reduce time lags due to the multiple input dataset and model latencies.

Geospatial assessment of the economic opportunity for reforestation in Maryland, USA

Afforestation and reforestation have the potential to provide effective climate mitigation through forest carbon sequestration. Strategic reforestation activities, which account for both carbon sequestration potential (CSP) and economic opportunity, can provide attractive options for policymakers who must manage competing social and environmental goals. In particular, forest carbon pricing can incentivize reforestation on private land, but this may require landholders to forego other profits. Here, we utilize an ambitious geospatial approach to quantify economic opportunities for reforestation in the state of Maryland (USA) based on high-resolution remoting sensing, ecosystem modeling, and economic analysis. Our results identify spatially-explicit areas of economic opportunity where the potential revenue from forest carbon outcompetes the expected profit of existing cropland at the hectare scale. Specifically, we find that under a baseline economic scenario of $20 per ton of carbon (5% rental rate) and decadal average crop profitability, a transition to forest on agricultural land would be more profitable than 23.2% of cropland in Maryland under a 20-year land-use commitment. Accounting for variations in carbon and crop pricing, 5.5% to 55.4% of cropland would be immediately outcompeted by expected forest carbon revenue, with the potential for an additional 0.5% to 10.6% of outcompeted cropland within 20 years. Under the baseline economic scenario, an annual allocation of $5.8 million towards a carbon rental program could protect 6.93 Tg C (2.2% of the state's total CSP) on reforested croplands. This moderate yearly cost is equal to 9.7% of Maryland's average annual auction proceeds from participation in the Regional Greenhouse Gas Initiative (between 2014-2018), and 19.3% of the average annual subsidy payments for corn, soy, and wheat allocated over the same period. This methodological approach may be useful for state governments, not-for-profit organizations, or regional climate initiatives interested in identifying strategic areas for reforestation.

Bookkeeping estimates of the net land-use change flux – a sensitivity study with the CMIP6 land-use dataset

The carbon flux due to land-use and land-cover change (net LULCC flux) historically contributed to a large fraction of anthropogenic carbon emissions while at the same time being associated with large uncertainties. This study aims to compare the contribution of several sensitivities underlying the net LULCC flux by assessing their relative importance in a bookkeeping model (Bookkeeping of Land Use Emissions, BLUE) based on a LULCC dataset including uncertainty estimates (the Land-Use Harmonization 2 (LUH2) dataset). The sensitivity experiments build upon the approach of Hurtt et al. (2011) and compare the impacts of LULCC uncertainty (a high, baseline and low land-use estimate), the starting time of the bookkeeping model simulation (850, 1700 and 1850), net area transitions versus gross area transitions (shifting cultivation) and neglecting wood harvest on estimates of the net LULCC flux. Additional factorial experiments isolate the impact of uncertainty from initial conditions and transitions on the net LULCC flux. Finally, historical simulations are extended with future land-use scenarios to assess the impact of past LULCC uncertainty in future projections. Over the period 1850–2014, baseline and low LULCC scenarios produce a comparable cumulative net LULCC flux, while the high LULCC estimate initially produces a larger net LULCC flux which decreases towards the end of the period and even becomes smaller than in the baseline estimate. LULCC uncertainty leads to slightly higher sensitivity in the cumulative net LULCC flux (up to 22 %; references are the baseline simulations) compared to the starting year of a model simulation (up to 15 %). The contribution from neglecting wood harvest activities (up to 28 % cumulative net LULCC flux) is larger than that from LULCC uncertainty, and the implementation of land-cover transitions (gross or net transitions) exhibits the smallest sensitivity (up to 13 %). At the end of the historical LULCC dataset in 2014, the LULCC uncertainty retains some impact on the net LULCC flux (±0.15 PgC yr−1 at an estimate of 1.7 PgC yr−1). Of the past uncertainties in LULCC, a small impact persists in 2099, mainly due to uncertainty of harvest remaining in 2014. However, compared to the uncertainty range of the LULCC flux estimated today, the estimates in 2099 appear to be indistinguishable. These results, albeit from a single model, are important for CMIP6 as they compare the relative importance of starting year, uncertainty of LULCC, applying gross transitions and wood harvest on the net LULCC flux. For the cumulative net LULCC flux over the industrial period, the uncertainty of LULCC is as relevant as applying wood harvest and gross transitions. However, LULCC uncertainty matters less (by about a factor of 3) than the other two factors for the net LULCC flux in 2014, and historical LULCC uncertainty is negligible for estimates of future scenarios.

The connection between forest degradation and urban energy demand in sub-Saharan Africa: a characterization based on high-resolution remote sensing data

Charcoal is a key energy source for urban households in sub-Saharan Africa and charcoal production is the main cause of forest degradation across the region. We used multitemporal high-resolution remote sensing optical imagery to quantify the extent and intensity of forest degradation associated with charcoal production and its impact on forest carbon stocks for the main supplying area of an African capital. This analysis documents the advance of forest degradation and quantifies its aboveground biomass removals over a seven-year period, registering that, between 2013 and 2016, the average annual area under charcoal production was 103 km2 and the annual aboveground biomass removals reached 1081 000 (SD = 2461) Mg. Kiln densities in the study area rose to 2 kilns-ha, with an average of 90.7 Mg ha−1 of extracted aboveground biomass. Charcoal production was responsible for the degradation of 55.5% of the mopane woodlands in the study area between 2013 and 2019. We estimated post-disturbance recovery times using an ecosystem model calibrated for the study area. The simulations showed that recovery times could require up to 150 years for current aboveground biomass extraction rates. The results of the remote sensing analysis and the simulations of the ecosystem model corroborate the unsustainability of the present patterns of charcoal production. The detailed characterization of the spatial and temporal patterns of charcoal production was combined with household survey information to quantify the impact of the urban energy demand of the Maputo urban area on forest carbon stocks. The analysis shows that Maputo charcoal demand was responsible for the annual degradation of up to 175.3 km2 and that the contribution of the study area to this demand fluctuated between 75% and 33% over the study period. The extent, advance pace and distance from urban centers documented in this study support the idea that forest degradation from charcoal production cannot merely be considered a peri-urban process. The intensity of the aboveground biomass (AGB) removals and its contribution to forest carbon stocks changes is significant at the national and regional levels.

Context and future directions for integrating forest carbon into sub-national climate mitigation planning in the RGGI region of the U.S.

International frameworks for climate mitigation that build from national actions have been developed under the United National Framework Convention on Climate Change and advanced most recently through the Paris Climate Agreement. In parallel, sub-national actors have set greenhouse gas (GHG) reduction goals and developed corresponding climate mitigation plans. Within the U.S., multi-state coalitions have formed to facilitate coordination of related science and policy. Here, utilizing the forum of the NASA Carbon Monitoring System's Multi-State Working Group, we collected and reviewed climate mitigation plans for 11 states in the Regional Greenhouse Gas Initiative region of the Eastern U.S. For each state we reviewed the (a) policy framework for climate mitigation, (b) GHG reduction goals, (c) inclusion of forest activities in the state's climate action plan, (d) existing science used to quantify forest carbon estimates, and (e) stated needs for forest carbon monitoring science. Across the region, we found important differences across all categories. While all states have GHG reduction goals and framework documents, nearly three-quarters of all states do not account for forest carbon when planning GHG reductions; those that do account for forest carbon use a variety of scientific methods with various levels of planning detail and guidance. We suggest that a common, efficient, standardized forest carbon monitoring system would provide important benefits to states and the geographic region as a whole. In addition, such a system would allow for more effective transparency and progress tracking to support state, national, and international efforts to increase ambition and implementation of climate goals.

High-resolution forest carbon modelling for climate mitigation planning over the RGGI region, USA

The inclusion of forest carbon in climate change mitigation planning requires the development of models able to project potential future carbon stocks—a step beyond traditional monitoring, reporting and verification frameworks. Here, we updated and expanded a high-resolution forest carbon modelling approach previously developed for the state of Maryland to 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain, which includes Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. In this study, we employ an updated version of the Ecosystem Demography (ED) model, an improved lidar initialization strategy, and an expanded calibration/validation approach. High resolution (90 m) wall-to-wall maps of present aboveground carbon, aboveground carbon sequestration potential, aboveground carbon sequestration potential gap (CSPG), and time to reach sequestration potential were produced over the RGGI domain where airborne lidar data were available, including 100% of eight states, 62% of Maine, 12% of New Jersey, and 0.65% of New York. For the eight states with complete data, an area of 228 552 km2, the contemporary forest aboveground carbon stock is estimated to be 1134 Tg C, and the forest aboveground CSPG is estimated to be larger at >1770 Tg C. Importantly, these estimates of the potential for added aboveground carbon sequestration in forests are spatially resolved, are further partitioned between continued growth of existing trees and new afforested/reforested areas, and include time estimates for realization. They are also assessed for sensitivity to potential changes in vegetation productivity and disturbance rate in response to climate change. The results from this study are intended as input into regional, state, and local planning efforts that consider future climate mitigation in forests along with other land-use considerations.

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the main set of future climate projections, based on concentration-driven simulations, within the Coupled Model Intercomparison Project phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century (2081–2100) encompassing the Tier 1 experiments based on the Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by close to 1.5 ∘C) reached at the upper end of the 5 %–95 % envelope of the highest scenario (SSP5-8.5). This is due to both the wider range of radiative forcing that the new scenarios cover and the higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensemble spreads, according to a set of initial condition ensemble simulations available under SSP3-7.0. These experiments suggest a tendency for internal variability to decrease along the course of the century in this scenario, a result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades around mid-century, as represented in SSP5-3.4OS, does not affect the end outcome of temperature and precipitation changes by 2100, which return to the same levels as those reached by the gradually increasing SSP4-3.4 (not erasing the possibility, however, that other aspects of the system may not be as easily reversible). Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level might be biased by the inclusion of models that have shown faster warming in the historical period than the observed. Those estimates show all scenarios reaching 1.5 ∘C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering between 20 and 27 years from present. The warming level of 2 ∘C of warming is reached as early as 2039 by the ensemble mean under SSP5-8.5 but as late as the mid-2060s under SSP1-2.6. The highest warming level considered (5 ∘C) is reached by the ensemble mean only under SSP5-8.5 and not until the mid-2090s.

High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA

Large-scale airborne lidar data collections can be used to generate high-resolution forest aboveground biomass maps at the state level and beyond as demonstrated in early phases of NASA's Carbon Monitoring System program. While products like aboveground biomass maps derived from these leaf-off lidar datasets each can meet state- or substate-level measurement requirements individually, combining them over multiple jurisdictions does not guarantee the consistency required in forest carbon planning, trading and reporting schemes. In this study, we refine a multi-state level forest carbon monitoring framework that addresses these spatial inconsistencies caused by variability in data quality and modeling techniques. This work is built upon our long term efforts to link airborne lidar, National Agricultural Imagery Program imagery and USDA Forest Service Forest Inventory and Analysis plot measurements for high-resolution forest aboveground biomass mapping. Compared with machine learning algorithms (r2 = 0.38, bias = −2.3, RMSE = 45.2 Mg ha−1), the use of a linear model is not only able to maintain a good prediction accuracy of aboveground biomass density (r2 = 0.32, bias = 4.0, RMSE = 49.4 Mg ha−1) but largely mitigates problems related to variability in data quality. Our latest effort has led to the generation of a consistent 30 m pixel forest aboveground carbon map covering 11 states in the Regional Greenhouse Gas Initiative region of the USA. Such an approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA's spaceborne lidar missions.

Harmonization of Global Land-Use Change and Management for the Period 850-2100 (LUH2) for CMIP6

Human land-use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth surface, with consequences for climate and other ecosystem services. In the future, land-use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World 45 Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community is developing the next generation of advanced Earth System Models (ESMs) to estimate the combined effects of human activities (e.g. land use and fossil fuel emissions) on the carbon-climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015-2100) from Integrated Assessment Model (IAM) teams, are required as input for these models. Here we present results from 50 the Land-use Harmonization 2 (LUH2) project, with the goal to smoothly connect updated historical reconstructions of land-use with new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land-use patterns, underlying land-use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds off a similar 55 effort from CMIP5, and is now provided at higher resolution (0.25 x 0.25 degree), over a longer time domain (850- 2100, with extensions to 2300), with more detail (including multiple crop and pasture types and associated management practices), using more input datasets (including Landsat remote sensing data), updated algorithms (wood harvest and shifting cultivation), and is assessed via a new diagnostic package. The new LUH2 products contain >50 times the information content of the datasets used in CMIP5, and are designed to enable new and improved estimates 60 of the combined effects of land-use on the global carbon-climate system.

Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation in land-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine) rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover.

Monitoring intra and inter annual dynamics of forest degradation from charcoal production in Southern Africa with Sentinel – 2 imagery

The contribution of forest degradation to changes in forest carbon stocks remains poorly quantified and constitutes a main source of uncertainty in the forest carbon budget. Charcoal production is a major source of forest degradation in sub-Saharan Africa. We used multitemporal Sentinel-2 imagery to monitor and quantify forest degradation extent in the main supplying area of a major urban center of southern Africa over a 4-year period. We implemented an indirect approach combining Sentinel-2 imagery to map kiln and field measurements to estimate AGB removals and carbon losses from charcoal production. This work generated 10 m resolution maps of forest degradation extent from charcoal production in the study area at quarterly intervals from 2016–2019. These maps reveal an intense and rapid forest degradation process and expose the spatial and temporal patterns of forest degradation from charcoal production with high detail. The total area under charcoal production over the study period reached 26,647 ha (SD = 320.8) and the forest degradation front advanced 10.5 km in a 4-year period, with an average of 19.4 ha of woodlands degraded daily. By the end of 2019, charcoal production disturbed most mopane stands in the study area and woodland fragmentation increased in 70.4 % of the mopane woodlands. We estimated that charcoal production was responsible for 2,568,761 Mg (SD = 42,130) of aboveground biomass extracted from the forest and 1,284,381 Mg (SD = 21,075) of carbon loss. The magnitude of these figures underlines the relevance of charcoal production as a main cause of forest cover change and remarks the existing uncertainties in the quantification of forest degradation processes. These results illustrate the potential of multitemporal medium resolution imagery to quantify forest degradation in sub-Saharan Africa and improve REDD + Monitoring, Reporting, and Verification systems in compliance with international reporting commitments.

Challenges in producing policy-relevant global scenarios of biodiversity and ecosystem services

Scenario-based modelling is a powerful tool to describe relationships between plausible trajectories of drivers, possible policy interventions, and impacts on biodiversity and ecosystem services. Model inter-comparisons are key in quantifying uncertainties and identifying avenues for model improvement but have been missing among the global biodiversity and ecosystem services modelling communities. The biodiversity and ecosystem services scenario-based inter-model comparison (BES-SIM) aims to fill this gap. We used global land-use and climate projections to simulate possible future impacts on terrestrial biodiversity and ecosystem services using a variety of models and a range of harmonized metrics. The goal of this paper is to reflect on the steps taken in BES-SIM, identify remaining methodological challenges, and suggest pathways for improvement. We identified five major groups of challenges; the need to: 1) better account for the role of nature in future human development storylines; 2) improve the representation of drivers in the scenarios by increasing the resolution (temporal, spatial and thematic) of land-use as key driver of biodiversity change and including additional relevant drivers; 3) explicitly integrate species- and trait-level biodiversity in ecosystem services models; 4) expand the coverage of the multiple dimensions of biodiversity and ecosystem services; and finally, 5) incorporate time-series or one-off historical data in the calibration and validation of biodiversity and ecosystem services models. Addressing these challenges would allow the development of more integrated global projections of biodiversity and ecosystem services, thereby improving their policy relevance in supporting the interlinked international conservation and sustainable development agendas.

Impact of fire and harvest on forest ecosystem services in a species‐rich area in the southern Appalachians

To mitigate and adapt to climate change, forest carbon sequestration and diversity of the ecosystem must be included in forest management planning, while satisfying the demand for wood products. The future provisions of ecosystem services under six realistic management scenarios were assessed to achieve that goal. These services were carbon sequestration, types and quantities of roundwood harvested, and different indicators of forest health—biomass of major species, species diversity, and variation of tree age. A spatially explicit forest succession model was combined with statistical analyses to conduct the assessment at the level of both the whole forest landscape and different ecological zones (ecozones) within. An important aspect of this study was to explore the effects of the biophysical heterogeneity of different ecological zones on the outcomes of different management scenarios. The study area was located in an area of the southern Appalachian Mountains in North Carolina with high tree diversity and active forest management activities. Along with a range of management practices, such richness in diversity allowed us to examine the complexity of the interaction between management activities and species competition. The results showed that fire suppression had a greater effect on increasing biomass carbon sequestration than any management scenario that involves harvest and replanting afterward, but at the expense of other indicators of forest health. The effect of fire on species composition was the largest in the xeric parts of the study area. Based on the study results, it was proposed that a low harvest intensity with a mix of fire and fire suppression across the landscape would best balance the need for roundwood products, biomass carbon sequestration, and desirable species composition. This study also demonstrated that the combination of a spatially explicit forest succession model and statistical analyses could be used to provide a robust and quantifiable projection of ecosystem service provisions and possible trade‐offs under different management scenarios.

Pervasive shifts in forest dynamics in a changing world

Forest dynamics arise from the interplay of environmental drivers and disturbances with the demographic processes of recruitment, growth, and mortality, subsequently driving biomass and species composition. However, forest disturbances and subsequent recovery are shifting with global changes in climate and land use, altering these dynamics. Changes in environmental drivers, land use, and disturbance regimes are forcing forests toward younger, shorter stands. Rising carbon dioxide, acclimation, adaptation, and migration can influence these impacts. Recent developments in Earth system models support increasingly realistic simulations of vegetation dynamics. In parallel, emerging remote sensing datasets promise qualitatively new and more abundant data on the underlying processes and consequences for vegetation structure. When combined, these advances hold promise for improving the scientific understanding of changes in vegetation demographics and disturbances.

Snowmelt velocity predicts vegetation green-wave velocity in mountainous ecological systems of North America

The timing of spring initiates an important period for resource availability for large trophic webs within ecosystems, including forage for grazing animals, flowers for pollinators, and the higher trophic levels that depend on these resources. Spring timing is highly variable across space, being influenced strongly by the departure of snow cover (i.e. snowmelt timing, in locations with a seasonal snowpack), climate, weather, elevation, and latitude. When spring timing occurs along a gradient (e.g. spring arriving later in higher elevations of mountainous terrain), the organisms that rely on spring resources often migrate to maintain an optimal position for spring resources – a phenomenon known as ‘surfing the green wave.’ While this behavior has been observed by tracking animals, there have been no studies to quantify the green wave as a movement across space and time. Furthermore, considering that snowmelt timing has moderate power to explain green-up timing for a given location, we ask the question: does snowmelt velocity predict green wave velocity? Here, we introduce the first continental maps of snowmelt and green wave velocity for North America from 2001 to 2016 as derived from the MODIS MCD12Q2 phenology dataset. We show that both snowmelt and green wave velocities are influenced strongly by topography, including slope and aspect. Furthermore, we quantify the relationships between snowmelt and green wave velocities according to three variables: direction, speed, and distance traveled. We conclude that mountainous ecoregions, such as the western North American cordillera, have the highest correspondence between snowmelt and green wave velocities, compared to flatter regions such as the Great Plains and tundra. This work will be of interest to wildlife ecologists, biologists, and land managers who seek to conserve migratory animals and the ecosystems that support them.

The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography

Obtaining accurate and widespread measurements of the vertical structure of the Earth’s forests has been a long-sought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6° N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 ​m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.

Monitoring forest degradation from charcoal production with historical Landsat imagery. A case study in southern Mozambique.

We used historical Landsat imagery to monitor forest degradation from charcoal production in the main supplying region of the Mozambican capital, Maputo, during a ten-year period (2008 – 2018). We applied a change detection method that exploits temporal NDVI dynamics associated with charcoal production. This forest degradation temporal sequence exposes the magnitude and the spatial and temporal dynamics of charcoal production, which is the main forest degradation driver in sub-Saharan Africa. The annual area under charcoal production has been steadily increasing since 2008 and reached 11,673 ha in 2018. The total forest degraded extent in the study area during the 10-year study period covered 79,630 ha, which represents 68 % of the available mopane woodlands in 2008. Only 5 % of the available mopane woodlands area remain undisturbed in the study area. Total gross carbon emissions associated charcoal production during this 10-year period were estimated in 1.13 Mt. These results mark forest degradation from charcoal production as the main driver of forest cover change in southern Mozambique. They also denote that, while charcoal production may be relatively localized in space, its implications for forest cover change a nd carbon emissions in a sub-Saharan African context are relevant at larger geographical scales. This study represents a proof of concept of the feasibility of medium resolution earth observation data to monitor forest degradation from charcoal production in the context of the growing urban energy demand. It also highlights the potential opportunities to improve REDD+ monitoring, reporting and verification efforts in sub-Saharan Africa as a first step toward designing effective management and policy interventions.

Global Carbon Budget 2019

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use change (ELUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), EFF was 9.5±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.9±0.02 GtC yr−1 (2.3±0.01 ppm yr−1), SOCEAN 2.5±0.6 GtC yr−1, and SLAND 3.2±0.6 GtC yr−1, with a budget imbalance BIM of 0.4 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr−1, reaching 10 GtC yr−1 for the first time in history, ELUC was 1.5±0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr−1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr−1 (2.4±0.1 ppm yr−1), SOCEAN was 2.6±0.6 GtC yr−1, and SLAND was 3.5±0.7 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein et al., 2019).

Potential Transient Response of Terrestrial Vegetation and Carbon in Northern North America from Climate Change

Terrestrial ecosystems and their vegetation are linked to climate. With the potential of accelerated climate change from anthropogenic forcing, there is a need to further evaluate the transient response of ecosystems, their vegetation, and their influence on the carbon balance, to this change. The equilibrium response of ecosystems to climate change has been estimated in previous studies in global domains. However, research on the transient response of terrestrial vegetation to climate change is often limited to domains at the sub-continent scale. Estimation of the transient response of vegetation requires the use of mechanistic models to predict the consequences of competition, dispersal, landscape heterogeneity, disturbance, and other factors, where it becomes computationally prohibitive at scales larger than sub-continental. Here, we used a pseudo-spatial ecosystem model with a vegetation migration sub-model that reduced computational intensity and predicted the transient response of vegetation and carbon to climate change in northern North America. The ecosystem model was first run with a current climatology at half-degree resolution for 1000 years to establish current vegetation and carbon distribution. From that distribution, climate was changed to a future climatology and the ecosystem model run for an additional 2000 simulation years. A model experimental design with different combinations of vegetation dispersal rates, dispersal modes, and disturbance rates produced 18 potential change scenarios. Results indicated that potential redistribution of terrestrial vegetation from climate change was strongly impacted by dispersal rates, moderately affected by disturbance rates, and marginally impacted by dispersal mode. For carbon, the sensitivities were opposite. A potential transient net carbon sink greater than that predicted by the equilibrium response was estimated on time scales of decades–centuries, but diminished over longer time scales. Continued research should further explore the interactions between competition, dispersal, and disturbance, particularly in regards to vegetation redistribution.

High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA

Accurate estimation of forest aboveground biomass at high-resolution continues to remain a challenge and long-term goal for carbon monitoring and accounting systems. Here, we present an exhaustive evaluation and validation of a robust, replicable and scalable framework that maps forest aboveground biomass over large areas at fine-resolution by linking airborne lidar and field data with machine learning algorithms. We developed this framework over multiple phases of bottom-up monitoring efforts within NASA's Carbon Monitoring Program. Lidar data were collected by different local and federal agencies and provided a wall-to-wall coverage of three states in the USA (Maryland, Pennsylvania and Delaware with a total area of 157 865 km2). We generated a set of standardized forestry metrics from lidar-derived imagery (i.e. canopy height model, CHM) to minimize inconsistency of data quality. We then estimated plot-scale biomass from field data that had the closet acquisition time to lidar data, and linked to lidar metrics using Random Forest models at four USDA Forest Service ecological regions. Additionally, we examined pixel-scale errors using independent field plot measurements across these ecoregions. Collectively, we estimate a total of ~680 Tg C in aboveground biomass over the Tri-State region (13 DE, 103 MD, 564 PA) circa 2011. A comparison with existing products at pixel-, county-, and state-scale highlighted the contribution of trees over 'non-forested' areas, including urban trees and small patches of trees, an important biomass component largely omitted by previous studies due to insufficient spatial resolution. Our results indicated that integrating field data and low point density (~1 pt m−2) airborne lidar can generate large-scale aboveground biomass products at an accuracy close to mainstream lidar forestry applications (R 2 = 0.46–0.54, RMSE = 51.4–54.7 Mg ha−1; and R 2 = 0.33–0.61, RMSE = 65.3–100.9 Mg ha−1; independent validation). Local, high-resolution lidar-derived biomass maps such as products rom this study, provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale mapping efforts and future development of a national carbon monitoring system.

Charcoal-related forest degradation dynamics in dry African woodlands: Evidence from Mozambique

Highlights

• Dry woodlands are progressively degraded by three distinct, sequential charcoal production waves.
• Even extremely selective charcoal production systems cause considerable modification to forest structure.
• The environmental consequences of an expanding producer population are more serious than charcoal poverty traps.

Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA

Forests are important ecosystems that are under increasing pressure from human use and environmental change, and have a significant ability to remove carbon dioxide from the atmosphere, and are therefore the focus of policy efforts aimed at reducing deforestation and degradation as well as increasing afforestation and reforestation for climate mitigation. Critical to these efforts is the accurate monitoring, reporting and verification of current forest cover and carbon stocks. For planning, the additional step of modeling is required to quantitatively estimate forest carbon sequestration potential in response to alternative land-use and management decisions. To be most useful and of decision- relevant quality, these model estimates must be at very high spatial resolution and with very high accuracy to capture important heterogeneity on the land surface and connect to monitoring efforts. Here, we present results from a new forest carbon monitoring and modeling system that combines high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above- ground forest carbon stocks, and project future forest carbon sequestration potential for the state of Maryland at 90 m resolution. Statewide, the contemporary above-ground carbon stock was estimated to be 110.8 Tg C (100.3–125.8 Tg C), with a corresponding mean above-ground biomass density of 103.7 Mg ha−1 hich was within 2% of independent empirically-based estimates. The forest above- ground carbon sequestration potential for the state was estimated to be much larger at 314.8 Tg C, and the forest above-ground carbon sequestration potential gap (i.e. potential-current) was estimated to be 204.1 Tg C, nearly double the current stock. These results imply a large statewide potential for future carbon sequestration from afforestation and reforestation activities. The high spatial resolution of the model estimates underpinning these totals demonstrate important heterogeneity across the state and can inform prioritization of actual afforestation/reforestation opportunities. With this approach, it is now possible to quantify both the forest carbon stock and future carbon sequestration potential over large policy relevant areas with sufficient accuracy and spatial resolution to significantly advance planning.

Taking climate model evaluation to the next level

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.

Global Carbon Budget 2018

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use and land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2008–2017), EFF was 9.4±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.7±0.02 GtC yr−1, SOCEAN 2.4±0.5 GtC yr−1, and SLAND 3.2±0.8 GtC yr−1, with a budget imbalance BIM of 0.5 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2017 alone, the growth in EFF was about 1.6 % and emissions increased to 9.9±0.5 GtC yr−1. Also for 2017, ELUC was 1.4±0.7 GtC yr−1, GATM was 4.6±0.2 GtC yr−1, SOCEAN was 2.5±0.5 GtC yr−1, and SLAND was 3.8±0.8 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 405.0±0.1 ppm averaged over 2017. For 2018, preliminary data for the first 6–9 months indicate a renewed growth in EFF of +2.7 % (range of 1.8 % to 3.7 %) based on national emission projections for China, the US, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. The analysis presented here shows that the mean and trend in the five components of the global carbon budget are consistently estimated over the period of 1959–2017, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations show (1) no consensus in the mean and trend in land-use change emissions, (2) a persistent low agreement among the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models, originating outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018, 2016, 2015a, b, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2018.

A protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios

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.

Quantifying long-term changes in carbon stocks and forest structure from Amazon forest degradation

Despite sustained declines in Amazon deforestation, forest degradation from logging and fire continues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arc of deforestation. Limited data on the magnitude of carbon losses and rates of carbon recovery following forest degradation have hindered carbon accounting efforts and contributed to incomplete national reporting to reduce emissions from deforestation and forest degradation (REDD+). We combined annual time series of Landsat imagery and igh-density airborne lidar data to characterize the variability, magnitude, and persistence of Amazon forest degradation impacts on aboveground carbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of the carbon stocks in intact forests, and differences persisted even after 15 years of regrowth. In comparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD. Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a history of one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD, respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires, selective logging before burning did not explain additional variability in modeled ACD loss and recovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure that can aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recovered faster than attributes of canopy structure that are critical for biodiversity in tropical forests, including the abundance of tall trees. We provide the first comprehensive look-up table of emissions factors for specific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon loss and recovery trajectories provide an important foundation for assessing the long-term contributions from forest degradation to regional carbon cycling and advance our understanding of the current state of frontier forests.

Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps

Spring snowmelt serves as the major hydrological contribution to many watersheds of the US West. Since the 1970s the conterminous western USA has seen an earlier arrival of spring snowmelt. The extremely low snowpack and early melt of 2015 in the Cascade Mountains may be a harbinger of winters to come, underscoring the interest in advancements in spring snowmelt monitoring. Target-of-opportunity and point measurements of snowmelt using meteorological stations or stream gauges are common sources of these data, however, there have been few attempts to identify snowmelt timing using remote sensing. In this study, we describe the creation of snowmelt timing maps (STMs) which identify the day of year that each pixel of a remotely sensed image transitions from “snow-covered” to “no snow” during the spring melt season, controlling for cloud coverage and ephemeral spring snow storms. Derived from the 500 m MODerate-resolution Imaging Spectroradiometer (MODIS) standard snow map, MOD10A2, this new dataset provides annual maps of snowmelt timing, with corresponding maps of cloud interference and interannual variability in snow coverage from 2001–2015. We first show that the STMs agree strongly with in-situ snow telemetry (SNOTEL) meteorological station measurements in terms of snowmelt timing. We then use the STMs to investigate the early snowmelt event of 2015 in the Cascade Mountains, USA, highlighting the protected areas of Mt. Rainier, Crater Lake, and Lassen Volcanic National Parks. In 2015 the Cascade Mountains experienced snowmelt 41 days earlier than the 2001–2015 average, with 25% of its land area melting>65 days earlier than average. The upper elevations of the Cascade Mountains experienced the greatest snowmelt anomaly. Our results are relevant to land managers and biologists as they plan adaptation strategies for mitigating the effects of climate change throughout temperate mountains.

Enhanced response of global wetland methane emissions to the 2015–2016 El Niño-Southern Oscillation event

Wetlands are thought to be the major contributor to interannual variability in the growth rate of atmospheric methane (CH4) with anomalies driven by the influence of the El Niño-Southern Oscillation (ENSO). Yet it remains unclear whether (i) the increase in total global CH4 emissions during El Niñ o versus La Niñ a events is from wetlands and (ii) how large the contribution of wetland CH4 emissions is to the interannual variability of atmospheric CH4. We used a terrestrial ecosystem model that includes permafrost and wetland dynamics to estimate CH4 emissions, forced by three separate meteorological reanalyses and one gridded observational climate dataset, to simulate the spatio-temporal dynamics of wetland CH4 emissions from 1980–2016. The simulations show that while wetland CH4 responds with negative annual anomalies during the El Niñ o events, the instantaneous growth rate of wetland CH4 emissions exhibits complex phase dynamics. We find that wetland CH4 instantaneous growth rates were declined at the onset of the 2015–2016 El Niñ o event but then increased to a record-high at later stages of the El Niñ o event (January through May 2016). We also find evidence for a step increase of CH4 emissions by 7.8±1.6 Tg CH4 yr−1 during 2007–2014 compared to the average of 2000–2006 from simulations using meteorological reanalyses, which is equivalent to a ∼3.5 ppb yr−1 rise in CH4 concentrations. The step increase is mainly caused by the expansion of wetland area in the tropics (30◦S–30◦N) due to an enhancement of tropical precipitation as indicated by the suite of the meteorological reanalyses. Our study highlights the role of wetlands, and the complex temporal phasing with ENSO, in driving the variability and trends of atmospheric CH4 concentrations. In addition, the need to account for uncertainty in meteorological forcings is highlighted in addressing the interannual variability and decadal-scale trends of wetland CH4 fluxes.

Attributing the Carbon Cycle Impacts of CMIP5 Historicaland Future Land Use and Land Cover Change in the Community Earth System Model (CESM1)

Historical global land use and land cover change (LULCC) emissions of 160 PgC represent a third of all human CO2 emissions from 1850 to 2010. Future land management decisions will have large impacts on the global carbon cycle, with scenarios ranging from continued deforestation and wood harvest to mitigation scenarios that reduce total emissions through afforestation and conversion to bioenergy. Here we present a systematic assessment of Community Earth System Model (CESM1) Coupled Model Intercomparison Project phase 5 (CMIP5) historic and projection simulations, with and without LULCC. For the historical period, CESM produced a Net LULCC flux of 123 PgC to the atmosphere, removing −130 PgC from ecosystems while increasing wood product pools by 7 PgC. Historical LULCC fluxes were equally divided between conversion and wood harvest fluxes. For the Representative Concentration Pathway (RCP) 4.5 afforestation scenario CESM produced a Net LULCC flux of 53 PgC to the atmosphere, removing −58 PgC out of the ecosystem and increasing wood product pools of 5 PgC. The afforestation offsets the Direct LULCC flux of 153 PgC with a negative Indirect LULCC flux of −94 PgC. For the RCP 8.5 high LULCC scenario, CESM produced a Net LULCC flux of 211 PgC, removing −227 PgC from ecosystems and increasing wood product pools of 15 PgC. The LULCC of the future RCP scenarios was dominated by wood harvest fluxes, which is a process that was not included in many of the CMIP5 models. The analysis framework also allowed the attribution of Indirect and Prior LULCC fluxes that offset the Direct LULCC fluxes in many cases.

Global Carbon Budget 2017

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on land-cover change data and bookkeeping models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emis- sions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2007–2016), EFF was 9.4 ± 0.5 GtC yr−1, ELUC 1.3 ± 0.7 GtC yr−1, GATM 4.7 ± 0.1 GtC yr−1, SOCEAN 2.4 ± 0.5 GtC yr−1, and SLAND 3.0 ± 0.8 GtC yr−1, with a budget imbalance BIM of 0.6 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1. Also for 2016, ELUC was 1.3 ± 0.7 GtC yr−1, GATM was 6.1 ± 0.2 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1, and SLAND was 2.7 ± 1.0 GtC yr−1, with a small BIM of −0.3 GtC. GATM continued to be higher in 2016 compared to the past decade (2007–2016), reflecting in part the high fossil emissions and the small SLAND consistent with El Niño conditions. The global atmospheric CO2 concentration reached 402.8 ± 0.1 ppm averaged over 2016. For 2017, preliminary data for the first 6–9 months indicate a renewed growth in EFF of +2.0 % (range of 0.8 to 3.0 %) based on national emissions projections for China, USA, and India, and projections of gross domestic product (GDP) corrected for recent changes in the carbon intensity of the economy for the rest of the world. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set (Le Quéré et al., 2016, 2015b, a, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017 (GCP, 2017).

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. Here we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO2 in 2004, and generates carbon uncertainty that is equivalent to 80% of the net effects of CO2 and climate and 124% of the effects of nitrogen deposition during 1850–2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. We conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.

Synergy between land use and climate change increases future fire risk in Amazon forests

Tropical forests have been a permanent feature of the Amazon basin for at least 55 million years, yet climate change and land use threaten the forest's future over the next century. Understory forest fires, which are common under the current climate in frontier forests, may accelerate Amazon forest losses from climate-driven dieback and deforestation. Far from land use frontiers, scarce fire ignitions and high moisture levels preclude significant burning, yet projected climate and land use changes may increase fire activity in these remote regions. Here, we used a fire model specifically parameterized for Amazon understory fires to examine the interactions between anthropogenic activities and climate under current and projected conditions. In a scenario of low mitigation efforts with substantial land use expansion and climate change – Representative Concentration Pathway (RCP) 8.5 – projected understory fires increase in frequency and duration, burning 4–28 times more forest in 2080–2100 than during 1990–2010. In contrast, active climate mitigation and land use contraction in RCP4.5 constrain the projected increase in fire activity to 0.9–5.4 times contemporary burned area. Importantly, if climate mitigation is not successful, land use contraction alone is very effective under low to moderate climate change, but does little to reduce fire activity under the most severe climate projections. These results underscore the potential for a fire-driven transformation of Amazon forests if recent regional policies for forest conservation are not paired with global efforts to mitigate climate change.

Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)

In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a special report in 2018 on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5 °C, including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity).

County-scale biomass map comparison: a case study for Sonoma, California

The amount of carbon stored in forests affects a wide range of regional- to global-scale climate change processes. However, current maps often show large differences in carbon accounting. In this study, we present a framework to evaluate and compare multiple recent biomass maps at the county scale (4119 km2). We first compare the differences in the forest and non-forest areas at the pixel and county levels from multiple maps. Map-based estimates of county-level mean and total biomass are compared to the United States Forest Service (USFS) sample-based estimates. Comparison of raster-based biomass products shows differences in mean and total biomass at both pixel- and county-levels. Despite all the maps using USFS's plot data for model training, only the three active sensor derived products compare well to USFS's estimates of total biomass (within 10%), while the three passive sensor derived map products underestimated total biomass by as much as 47%. Our evaluation demonstrates that the biomass map generated using combined Light Detection and Ranging (LiDAR) and auxiliary data achieve accurate estimates at plot-level (R2 = 0.67; RMSE = 97.9 Mg.ha-1). This comparison study confirmed that missing direct height information either from active sensors tends to underestimate total biomass and mean biomass density at county-level.

The PMIP4 contribution to CMIP6 – Part 3: The last millennium, scientific objective, and experimental design for the PMIP4 past1000 simulations

The pre-industrial millennium is among the periods selected by the Paleoclimate Model Intercomparison Project (PMIP) for experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and the fourth phase of the PMIP (PMIP4). The past1000 transient simulations serve to investigate the response to (mainly) natural forcing under background conditions not too different from today, and to discriminate between forced and internally generated variability on interannual to centennial timescales. This paper describes the motivation and the experimental set-ups for the PMIP4-CMIP6 past1000 simulations, and discusses the forcing agents orbital, solar, volcanic, and land use/land cover changes, and variations in greenhouse gas concentrations. The past1000 simulations covering the pre-industrial millennium from 850 Common Era (CE) to 1849 CE have to be complemented by historical simulations (1850 to 2014 CE) following the CMIP6 protocol. The external forcings for the past1000 experiments have been adapted to provide a seamless transition across these time periods. Protocols for the past1000 simulations have been divided into three tiers. A default forcing data set has been defined for the Tier 1 (the CMIP6 past1000) experiment. However, the PMIP community has maintained the flexibility to conduct coordinated sensitivity experiments to explore uncertainty in forcing reconstructions as well as parameter uncertainty in dedicated Tier 2 simulations. Additional experiments (Tier 3) are defined to foster collaborative model experiments focusing on the early instrumental period and to extend the temporal range and the scope of the simulations. This paper outlines current and future research foci and common analyses for collaborative work between the PMIP and the observational communities (reconstructions, instrumental data).

Vegetation demographics in Earth System Models: A review of progress and priorities

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.

Disturbance Distance: quantifying forests' vulnerability to disturbance under current and future conditions

Disturbances, both natural and anthropogenic, are critical determinants of forest structure, function, and distribution. The vulnerability of forests to potential changes in disturbance rates remains largely unknown. Here, we developed a framework for quantifying and mapping the vulnerability of forests to changes in disturbance rates. By comparing recent estimates of observed forest disturbance rates over a sample of contiguous US forests to modeled rates of disturbance resulting in forest loss, a novel index of vulnerability, Disturbance Distance, was produced. Sample results indicate that 20% of current US forestland could be lost if disturbance rates were to double, with southwestern forests showing highest vulnerability. Under a future climate scenario, the majority of US forests showed capabilities of withstanding higher rates of disturbance then under the current climate scenario, which may buffer some impacts of intensified forest disturbance.

Snowmelt timing, phenology, and growing season length in conifer forests of Crater Lake National Park, USA

Anthropogenic climate change is having significant impacts on montane and high-elevation areas globally. Warmer winter temperatures are driving reduced snowpack in the western USA with broad potential impacts on ecosystem dynamics of particular concern for protected areas. Vegetation phenology is a sensitive indicator of ecological response to climate change and is associated with snowmelt timing. Human monitoring of climate impacts can be resource prohibitive for land management agencies, whereas remotely sensed phenology observations are freely available at a range of spatiotemporal scales. Little work has been done in regions dominated by evergreen conifer cover, which represents many mountain regions at temperate latitudes. We used moderate resolution imaging spectroradiometer (MODIS) data to assess the influence of snowmelt timing and elevation on five phenology metrics (green up, maximum greenness, senescence, dormancy, and growing season length) within Crater Lake National Park, Oregon, USA from 2001 to 2012. Earlier annual mean snowmelt timing was significantly correlated with earlier onset of green up at the landscape scale. Snowmelt timing and elevation have significant explanatory power for phenology, though with high variability. Elevation has a moderate control on early season indicators such as snowmelt timing and green up and less on late-season variables such as senescence and growing season length. PCA results show that early season indicators and late season indicators vary independently. These results have important implications for ecosystem dynamics, management, and conservation, particularly of species such as whitebark pine (Pinus albicaulis) in alpine and subalpine areas.

Biospheric feedback effects in a synchronously coupled model of human and Earth systems

Fossil fuel combustion and land-use change are the two largest contributors to industrial-era increases in atmospheric CO₂ concentration. Projections of these are thus fundamental inputs for coupled Earth system models (ESMs) used to estimate the physical and biological consequences of future climate system forcing. While historical data sets are available to inform past and current climate analyses assessments of future climate change have relied on projections of energy and land use from energy–economic models, constrained by assumptions about future policy, land-use patterns and socio-economic development trajectories. Here we show that the climatic impacts on land ecosystems drive significant feedbacks in energy, agriculture, land use and carbon cycle projections for the twenty-first century. We find that exposure of human-appropriated land ecosystem productivity to biospheric change results in reductions of land area used for crops; increases in managed forest area and carbon stocks; decreases in global crop prices; and reduction in fossil fuel emissions for a low–mid-range forcing scenario7. The feedbacks between climate-induced biospheric change and human system forcings to the climate system—demonstrated here—are handled inconsistently, or excluded altogether, in the one-way asynchronous coupling of energy–economic models to ESMs used to date.

The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 ∘C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.

The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design.

Human land-use activities have resulted in large changes to the Earth's surface, with resulting implications for climate. In the future, land-use activities are likely to expand and intensify further to meet growing demands for food, fiber, and energy. The Land Use Model Intercomparison Project (LUMIP) aims to further advance understanding of the impacts of land-use and land-cover change (LULCC) on climate, specifically addressing the following questions. (1) What are the effects of LULCC on climate and biogeochemical cycling (past–future)? (2) What are the impacts of land management on surface fluxes of carbon, water, and energy, and are there regional land-management strategies with the promise to help mitigate climate change? In addressing these questions, LUMIP will also address a range of more detailed science questions to get at process-level attribution, uncertainty, data requirements, and other related issues in more depth and sophistication than possible in a multi-model context to date. There will be particular focus on the separation and quantification of the effects on climate from LULCC relative to all forcings, separation of biogeochemical from biogeophysical effects of land use, the unique impacts of land-cover change vs. land-management change, modulation of land-use impact on climate by land–atmosphere coupling strength, and the extent to which impacts of enhanced CO2 concentrations on plant photosynthesis are modulated by past and future land use. LUMIP involves three major sets of science activities: (1) development of an updated and expanded historical and future land-use data set, (2) an experimental protocol for specific LUMIP experiments for CMIP6, and (3) definition of metrics and diagnostic protocols that quantify model performance, and related sensitivities, with respect to LULCC. In this paper, we describe LUMIP activity (2), i.e., the LUMIP simulations that will formally be part of CMIP6. These experiments are explicitly designed to be complementary to simulations requested in the CMIP6 DECK and historical simulations and other CMIP6 MIPs including ScenarioMIP, C4MIP, LS3MIP, and DAMIP. LUMIP includes a two-phase experimental design. Phase one features idealized coupled and land-only model simulations designed to advance process-level understanding of LULCC impacts on climate, as well as to quantify model sensitivity to potential land-cover and land-use change. Phase two experiments focus on quantification of the historic impact of land use and the potential for future land management decisions to aid in mitigation of climate change. This paper documents these simulations in detail, explains their rationale, outlines plans for analysis, and describes a new subgrid land-use tile data request for selected variables (reporting model output data separately for primary and secondary land, crops, pasture, and urban land-use types). It is essential that modeling groups participating in LUMIP adhere to the experimental design as closely as possible and clearly report how the model experiments were executed.

Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification

The advent of mobile laser scanning has enabled time efficient and cost effective collection of forest structure information. To make use of this technology in calibrating or evaluating models of forest and landscape dynamics, there is a need to systematically and reproducibly automate the processing of LiDAR point clouds into quantities of forest structural components. Here we propose a method to classify vegetation structural components of an open-understorey eucalyptus forest, scanned with a ‘Zebedee’ mobile laser scanner. It detected 98% of the tree stems (N = 50) and 80% of the elevated understorey components (N = 15). Automatically derived DBH values agreed with manual field measurements with r² = 0.72, RMSE = 3.8 cm, (N = 27), and total basal area agreed within 1.5%. Though this methodological study was restricted to one ecosystem, the results are promising for use in applications such as fuel load, habitat structure, and biomass estimations.

The Impact of Fine-Scale Disturbances on the Predictability of Vegetation Dynamics and Carbon Flux

Predictions from forest ecosystem models are limited in part by large uncertainties in the current state of the land surface, as previous disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect. Likewise, future disturbances also present a challenge to prediction as their dynamics are episodic and complex and occur across a range of spatial and temporal scales. While large extreme events such as tropical cyclones, fires, or pest outbreaks can produce dramatic consequences, small fine-scale disturbance events are typically much more common and may be as or even more important. This study focuses on the impacts of these smaller disturbance events on the predictability of vegetation dynamics and carbon flux. Using data on vegetation structure collected for the same domain at two different times, i.e. “repeat lidar data”, we test high-resolution model predictions of vegetation dynamics and carbon flux across a range of spatial scales at an important tropical forest site at La Selva Biological Station, Costa Rica. We found that predicted height change from a height-structured ecosystem model compared well to lidar measured height change at the domain scale (~150 ha), but that the model-data mismatch increased exponentially as the spatial scale of evaluation decreased below 20 ha. We demonstrate that such scale-dependent errors can be attributed to errors predicting the pattern of fine-scale forest disturbances. The results of this study illustrate the strong impact fine-scale forest disturbances have on forest dynamics, ultimately limiting the spatial resolution of accurate model predictions.

Potential Vegetation and Carbon Redistribution in Northern North America from Climate Change

There are strong relationships between climate and ecosystems. With the prospect of anthropogenic forcing accelerating climate change, there is a need to understand how terrestrial vegetation responds to this change as it influences the carbon balance. Previous studies have primarily addressed this question using empirically based models relating the observed pattern of vegetation and climate, together with scenarios of potential future climate change, to predict how vegetation may redistribute. Unlike previous studies, here we use an advanced mechanistic, individually based, ecosystem model to predict the terrestrial vegetation response from future climate change. The use of such a model opens up opportunities to test with remote sensing data, and the possibility of simulating the transient response to climate change over large domains. The model was first run with a current climatology at half-degree resolution and compared to remote sensing data on dominant plant functional types for northern North America for validation. Future climate data were then used as inputs to predict the equilibrium response of vegetation in terms of dominant plant functional type and carbon redistribution. At the domain scale, total forest cover changed by ~2% and total carbon storage increased by ~8% in response to climate change. These domain level changes were the result of much larger gross changes within the domain. Evergreen forest cover decreased 48% and deciduous forest cover increased 77%. The dominant plant functional type changed on 58% of the sites, while total carbon in deciduous vegetation increased 107% and evergreen vegetation decreased 31%. The percent of terrestrial carbon from deciduous and evergreen plant functional types changed from 27%/73% under current climate conditions, to 54%/46% under future climate conditions. These large predicted changes in vegetation and carbon in response to future climate change are comparable to previous empirically based estimates, and motivate the need for future development with this mechanistic model to estimate the transient response to future climate changes.

Spatiotemporal patterns of terrestrial gross primary production

Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990–2009) from two observation-based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon-climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation-based data that show little IAV and trend, while the process-based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation-based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.

Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

Background Continental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-derived biomass map over Maryland, USA. We conduct detailed comparisons at pixel-, county-, and state-level. Results Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5–92.7 Mg ha ⁻¹ ). Discrepancies reduce with aggregation and the agreement among products improves at the county level. However, continental scale maps exhibit residual negative biases in mean (33.0–54.6 Mg ha ⁻¹ ) and total biomass (3.5–5.8 Tg) when compared to the high-resolution lidar biomass map. Three of the four continental scale maps reach near-perfect agreement at ~4 km and onward but do not converge with the high-resolution biomass map even at county scale. At the State level, these maps underestimate biomass by 30–80 Tg in forested and 40–50 Tg in non-forested areas. Conclusions Local discrepancies in continental scale biomass maps are caused by factors including data inputs, modeling approaches, forest/non-forest definitions and time lags. There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels. Local, high-resolution lidar-derived biomass maps provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale maps produced in carbon monitoring systems.

Sensitivity of Multi-Source SAR Backscatter to Changes in Forest Aboveground Biomass

Accurate estimates of forest aboveground biomass (AGB) after anthropogenic disturbance could reduce uncertainties in the carbon budget of terrestrial ecosystems and provide critical information to policy makers. Yet, the loss of carbon due to forest disturbance and the gain from post-disturbance recovery have not been sufficiently assessed. In this study, a sensitivity analysis was first conducted to investigate: (1) the influence of incidence angle and soil moisture on Synthetic Aperture Radar (SAR) backscatter; (2) the feasibility of cross-image normalization between multi-temporal and multi-sensor SAR data; and (3) the possibility of applying normalized backscatter data to detect forest biomass changes. An empirical model was used to reduce incidence angle effects, followed by cross-image normalization procedure to lessen soil moisture effect. Changes in forest biomass at medium spatial resolution (100 m) were mapped using both spaceborne and airborne SAR data. Results indicate that (1) the effect of incidence angle on SAR backscatter could be reduced to less than 1 dB by the correction model for airborne SAR data; (2) over 50% of the changes in SAR backscatter due to soil moisture could be eliminated by the cross-image normalization procedure; and (3) forest biomass changes greater than 100 Mg·ha⁻¹ or above 50% of 150 Mg·ha⁻¹ are detectable using cross-normalized SAR data.

Comparing MODIS Net Primary Production Estimates with Terrestrial National Forest Inventory Data in Austria

The mission of this study is to compare Net Primary Productivity (NPP) estimates using (i) forest inventory data and (ii) spatio-temporally continuous MODIS (MODerate resolution Imaging Spectroradiometer) remote sensing data for Austria. While forest inventories assess the change in forest growth based on repeated individual tree measurements (DBH, height etc.), the MODIS NPP estimates are based on ecophysiological processes such as photosynthesis, respiration and carbon allocation. We obtained repeated national forest inventory data from Austria, calculated a “ground-based” NPP estimate and compared the results with “space-based” MODIS NPP estimates using different daily climate data. The MODIS NPP estimates using local Austrian climate data exhibited better compliance with the forest inventory driven NPP estimates than the MODIS NPP predictions using global climate data sets. Stand density plays a key role in addressing the differences between MODIS driven NPP estimates versus terrestrial driven inventory NPP estimates. After addressing stand density, both results are comparable across different scales. As forest management changes stand density, these findings suggest that management issues are important in understanding the observed discrepancies between MODIS and terrestrial NPP.

Local cooling and warming effects of forests based on satellite observations

The biophysical effects of forests on climate have been extensively studied with climate models. However, models cannot accurately reproduce local climate effects due to their coarse spatial resolution and uncertainties, and field observations are valuable but often insufficient due to their limited coverage. Here we present new evidence acquired from global satellite data to analyse the biophysical effects of forests on local climate. Results show that tropical forests have a strong cooling effect throughout the year; temperate forests show moderate cooling in summer and moderate warming in winter with net cooling annually; and boreal forests have strong warming in winter and moderate cooling in summer with net warming annually. The spatiotemporal cooling or warming effects are mainly driven by the two competing biophysical effects, evapotranspiration and albedo, which in turn are strongly influenced by rainfall and snow. Implications of our satellite-based study could be useful for informing local forestry policies.

Future habitat loss and extinctions driven by land-use change in biodiversity hotspots under four scenarios of climate-change mitigation

Numerous species have been pushed into extinction as an increasing portion of Earth's land surface has been appropriated for human enterprise. In the future, global biodiversity will be affected by both climate change and land-use change, the latter of which is currently the primary driver of species extinctions. How societies address climate change will critically affect biodiversity because climate-change mitigation policies will reduce direct climate-change impacts; however, these policies will influence land-use decisions, which could have negative impacts on habitat for a substantial number of species. We assessed the potential impact future climate policy could have on the loss of habitable area in biodiversity hotspots due to associated land-use changes. We estimated past extinctions from historical land-use changes (1500–2005) based on the global gridded land-use data used for the Intergovernmental Panel on Climate Change Fifth Assessment Report and habitat extent and species data for each hotspot. We then estimated potential extinctions due to future land-use changes under alternative climate-change scenarios (2005–2100). Future land-use changes are projected to reduce natural vegetative cover by 26-58% in the hotspots. As a consequence, the number of additional species extinctions, relative to those already incurred between 1500 and 2005, due to land-use change by 2100 across all hotspots ranged from about 220 to 21000 (0.2% to 16%), depending on the climate-change mitigation scenario and biological factors such as the slope of the species–area relationship and the contribution of wood harvest to extinctions. These estimates of potential future extinctions were driven by land-use change only and likely would have been higher if the direct effects of climate change had been considered. Future extinctions could potentially be reduced by incorporating habitat preservation into scenario development to reduce projected future land-use changes in hotspots or by lessening the impact of future land-use activities on biodiversity within hotspots.

Global satellite monitoring of climate-induced vegetation disturbances

Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide.

Dynamics of MODIS evapotranspiration in South Africa

This paper describes the dynamics of evapotranspiration (ET) in South Africa using MOD16 ET satellite-derived data, and analyses the inter-dependency of variables used in the ET algorithm of Mu et al. (2011). Annual evapotranspiration is strongly dependent on rainfall and potential evapotranspiration (PET) in 4 climatically different regions of South Africa. Average ET in South Africa (2000–2012) was estimated to be 303 mm·a-1 or 481.4 x 109 m3·a1 (14% of PET and 67% of rainfall), mainly in the form of plant transpiration (T, 53%) and soil evaporation (Soil E, 39%). Evapotranspiration (ET) showed a slight tendency to decrease over the period 2000–2012 in all climatic regions, except in the south of the country (winter rainfall areas), although annual variations in ET resulted in the 13-year trends not being statistically significant. Evapotranspiration (ET) was spatially dependent on PET, T and vapour pressure deficit (VPD), in particular in winter rainfall and arid to semi-arid climatic regions. Assuming an average rainfall of 450 mm·a-1, and considering current best estimates of runoff (9% of rainfall), groundwater recharge (5%) and water withdrawal (2%), MOD16 ET estimates were about 15% short of the water balance closure in South Africa. The ET algorithm can be refined and tested for applications in restricted areas that are spatially heterogeneous and by accounting for soil water supply limiting conditions.

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

A data assimilation method to produce complete temporal sequences of synthetic medium-resolution images is presented. The method implements a Kalman filter recursive algorithm that integrates medium and moderate resolution imagery. To demonstrate the approach, time series of 30-m spatial resolution NDVI images at 16-day time steps were generated using Landsat NDVI images and MODIS NDVI products at four sites with different ecosystems and land cover-land use dynamics. The results show that the time series of synthetic NDVI images captured seasonal land surface dynamics and maintained the spatial structure of the landscape at higher spatial resolution. The time series of synthetic medium-resolution NDVI images were validated within a Monte Carlo simulation framework. Normalized residuals decreased as the number of available observations increased, ranging from 0.2 to below 0.1. Residuals were also significantly lower for time series of synthetic NDVI images generated at combined recursion (smoothing) than individually at forward and backward recursions (filtering). Conversely, the uncertainties of the synthetic images also decreased when the number of available observations increased and combined recursions were implemented.

Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt

Crop rotations (the practice of growing crops on the same land in sequential seasons) reside at the core of agronomic management as they can influence key ecosystem services such as crop yields, carbon and nutrient cycling, soil erosion, water quality, pest and disease control. Despite the availability of the Cropland Data Layer (CDL) which provides remotely sensed data on crop type in the US on an annual basis, crop rotation patterns remain poorly mapped due to the lack of tools that allow for consistent and efficient analysis of multi-year CDLs. This study presents the Representative Crop Rotations Using Edit Distance (RECRUIT) algorithm, implemented as a Python software package, to select representative crop rotations by combining and analyzing multi-year CDLs. Using CDLs from 2010 to 2012 for 5 states in the US Midwest, we demonstrate the performance and parameter sensitivity of RECRUIT in selecting representative crop rotations that preserve crop area and capture land-use changes. Selecting only 82 representative crop rotations accounted for over 90% of the spatio-temporal variability of the more than 13,000 rotations obtained from combining the multi-year CDLs. Furthermore, the accuracy of the crop rotation product compared favorably with total state-wide planted crop area available from agricultural census data. The RECRUIT derived crop rotation product was used to detect land-use conversion from grassland to crop cultivation in a wetland dominated part of the US Midwest. Monoculture corn and monoculture soybean cropping were found to comprise the dominant land-use on the newly cultivated lands.

Multi-scale sensitivity of Landsat and MODIS to forest disturbance associated with tropical cyclones

Multispectral data from satellites are widely used to study the effects of extreme weather events in forest ecosystems at a variety of spatial and temporal scales. Understanding the sensitivity of these data is important since these phenomena are projected to increase as climate changes. The Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat data were used to study the observed patterns of forest disturbance at different spatial scales in temperate forest (US Gulf Coast) produced by tropical cyclones Charley (2004), Katrina (2005), Rita (2005), and Gustav (2008), and in tropical rainforests (Australia) produced by cyclone Yasi (2011). The severity of forest disturbance was quantified by applying spectral mixture analysis to the MODIS and Landsat coverages. Field studies were used to verify and compare the results. At the local scale Landsat data was sensitive to forest disturbance both within and between forest types. Higher tree mortality was observed in tropical rainforests than in temperate forests. This observation may be explained by forest type characteristics such as stem density, forest adaptation, and depth of root systems. At the species level, Landsat showed a gradient of forest resilience to tropical cyclone winds that agreed with observational field studies. At the landscape scale, the observed topographic effects on disturbance patterns were well represented by the MODIS data. Positive covariance was observed between surface orientation and slope on the severity of disturbance. Greater levels of disturbance were observed on windward surfaces with steeper slopes. Finally, at the regional scale, MODIS reproduced the pattern of forest damage associated with cyclone winds. The highest level of forest disturbance was observed on the right side of the cyclone track in the northern hemisphere (US Gulf Coast forest ecosystems) and on the left side in the southern hemisphere (Australian rainforest). At the regional scale, forest disturbance was positively associated with the decrease of wind speeds in an inland direction. Tropical cyclone surface winds explained 20% of forest disturbance, although characteristics of cumulative processes could cause this to be underestimated.

Interacting Regional-Scale Regime Shifts for Biodiversity and Ecosystem Services

Current trajectories of global change may lead to regime shifts at regional scales, driving coupled human–environment systems to highly degraded states in terms of biodiversity, ecosystem services, and human well-being. For business-as-usual socioeconomic development pathways, regime shifts are projected to occur within the next several decades, to be difficult to reverse, and to have regional- to global-scale impacts on human society. We provide an overview of ecosystem, socioeconomic, and biophysical mechanisms mediating regime shifts and illustrate how these interact at regional scales by aggregation, synergy, and spreading processes. We give detailed examples of interactions for terrestrial ecosystems of central South America and for marine and coastal ecosystems of Southeast Asia. This analysis suggests that degradation of biodiversity and ecosystem services over the twenty-first century could be far greater than was previously predicted. We identify key policy and management opportunities at regional to global scales to avoid these shifts.

Data-driven diagnostics of terrestrial carbon dynamics over North America

The exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of the sink remain uncertain. We use a data-driven approach to upscale eddy covariance flux observations from towers to the continental scale by integrating flux observations, meteorology, stand age, aboveground biomass, and a proxy for canopy nitrogen concentrations from AmeriFlux and Fluxnet-Canada Research Network as well as a variety of satellite data streams from the MODIS sensors. We then use the resulting gridded flux estimates from March 2000 to December 2012 to assess the magnitude, distribution, and interannual variability of carbon fluxes for the U.S. and Canada. The mean annual gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP) of the U.S. over the period 2001–2012 were 6.84, 5.31, and 1.10 Pg C yr⁻¹ , respectively; the mean annual GPP, ER, and NEP of Canada over the same 12-year period were 3.91, 3.26, and 0.60 Pg C yr⁻¹ , respectively. The mean nationwide annual NEP of natural ecosystems over the period 2001–2012 was 0.53 Pg C yr⁻¹ for the U.S. and 0.49 Pg C yr⁻¹ for the conterminous U.S. Our estimate of the carbon sink for the conterminous U.S. was almost identical with the estimate of the First State of the Carbon Cycle Report (SOCCR). The carbon fluxes exhibited relatively large interannual variability over the study period. The main sources of the interannual variability in carbon fluxes included drought and disturbance. The annual GPP and NEP were strongly related to annual evapotranspiration (ET) for both the U.S. and Canada, showing that the carbon and water cycles were closely coupled. Our gridded flux estimates provided an independent, alternative perspective on ecosystem carbon exchange over North America.

From land use to land cover: restoring the afforestation signal in a coupled integrated assessment – earth system model and the implications for CMIP5 RCP simulations

Climate projections depend on scenarios of fossil fuel emissions and land use change, and the IPCC AR5 parallel process assumes consistent climate scenarios across Integrated Assessment and Earth System Models (IAMs and ESMs). The CMIP5 project used a novel "land use harmonization" based on the Global Land use Model (GLM) to provide ESMs with consistent 1500–2100 land use trajectories generated by historical data and four IAM projections. A direct coupling of the Global Change Assessment Model (GCAM), GLM, and the Community ESM (CESM) has allowed us to characterize and partially address a major gap in the CMIP5 land coupling design: the lack of a corresponding land cover harmonization. The CMIP5 CESM global afforestation is only 22% of GCAM's 2005 to 2100 RCP4.5 afforestation. Likewise, only 17% of GCAM's 2040 RCP4.5 afforestation, and zero pasture loss, were transmitted to CESM within the directly coupled model. This is a problem because afforestation was relied upon to achieve RCP4.5 climate stabilization. GLM modifications within the directly coupled model did not increase CESM afforestation. Modifying the land use translator in addition to GLM, however, enabled CESM to simulate 66% of GCAM's afforestation in 2040, and 94% of GCAM's pasture loss as grassland and shrubland losses. This additional afforestation increases vegetation carbon gain by 19 PgC and decreases atmospheric CO2 gain by 8 ppmv from 2005 to 2040, implying different RCP4.5 climate scenarios between CMIP5 GCAM and CESM. Although the IAMs and ESMs were not expected to have exactly the same climate forcing, due in part to different terrestrial carbon cycles and atmospheric radiation algorithms, the ESMs were expected to project climates representative of the RCP scenarios. Similar land cover inconsistencies exist in other CMIP5 model results, primarily because land cover information is not shared between models. High RCP4.5 afforestation might also contribute to inconsistencies as some ESMs might impose bioclimatic limits to potential forest area and have different rates of forest growth than projected by RCP4.5. Further work to harmonize land cover among models will be required to address this problem.

Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth’s human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects.

Remote Sensing Assessment of Forest Disturbance across Complex Mountainous Terrain: The Pattern and Severity of Impacts of Tropical Cyclone Yasi on Australian Rainforests

Topography affects the patterns of forest disturbance produced by tropical cyclones. It determines the degree of exposure of a surface and can alter wind characteristics. Whether multispectral remote sensing data can sense the effect of topography on disturbance is a question that deserves attention given the multi-scale spatial coverage of these data and the projected increase in intensity of the strongest cyclones. Here, multispectral satellite data, topographic maps and cyclone surface wind data were used to study the patterns of disturbance in an Australian rainforest with complex mountainous terrain produced by tropical cyclone Yasi (2011). The cyclone surface wind data (H*wind) was produced by the Hurricane Research Division of the National Oceanic and Atmospheric Administration (HRD/NOAA), and this was the first time that this data was produced for a cyclone outside of United States territory. A disturbance map was obtained by applying spectral mixture analyses on satellite data and presented a significant correlation with field-measured tree mortality. Our results showed that, consistent with cyclones in the southern hemisphere, multispectral data revealed that forest disturbance was higher on the left side of the cyclone track. The highest level of forest disturbance occurred in forests along the path of the cyclone track (±30°). Levels of forest disturbance decreased with decreasing slope and with an aspect facing off the track of the cyclone or away from the dominant surface winds. An increase in disturbance with surface elevation was also observed. However, areas affected by the same wind intensity presented increased levels of disturbance with increasing elevation suggesting that complex terrain interactions act to speed up wind at higher elevations. Yasi produced an important offset to Australia’s forest carbon sink in 2010. We concluded that multispectral data was sensitive to the main effects of complex topography on disturbance patterns. High resolution cyclone wind surface data are needed in order to quantify the effects of topographic accelerations on cyclone related forest disturbances.

Global carbon budget 2013

Accurate assessment of anthropogenic carbon dioxide (CO₂) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO₂ emissions from fossil-fuel combustion and cement production (E_FF) are based on energy statistics, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO₂ concentration is measured directly and its rate of growth (G_ATM) is computed from the annual changes in concentration. The mean ocean CO₂ sink (S_OCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated for the first time in this budget with data products based on surveys of ocean CO₂ measurements. The global residual terrestrial CO₂ sink (S_LAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO₂ and land cover change (some including nitrogen–carbon interactions). All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2003–2012), E_FF was 8.6 ± 0.4 GtC yr⁻¹, E_LUC 0.9 ± 0.5 GtC yr⁻¹, G_ATM 4.3 ± 0.1 GtC yr⁻¹, S_OCEAN 2.5 ± 0.5 GtC yr⁻¹, and S_LAND 2.8 ± 0.8 GtC yr⁻¹. For year 2012 alone, E_FF grew to 9.7 ± 0.5 GtC yr⁻¹, 2.2% above 2011, reflecting a continued growing trend in these emissions, G_ATM was 5.1 ± 0.2 GtC yr⁻¹, S_OCEAN was 2.9 ± 0.5 GtC yr⁻¹, and assuming an E_LUC of 1.0 ± 0.5 GtC yr⁻¹ (based on the 2001–2010 average), S_LAND was 2.7 ± 0.9 GtC yr⁻¹. G_ATM was high in 2012 compared to the 2003–2012 average, almost entirely reflecting the high EFF. The global atmospheric CO₂ concentration reached 392.52 ± 0.10 ppm averaged over 2012. We estimate that E_FF will increase by 2.1% (1.1–3.1%) to 9.9 ± 0.5 GtC in 2013, 61% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the economy. With this projection, cumulative emissions of CO₂ will reach about 535 ± 55 GtC for 1870–2013, about 70% from E_FF (390 ± 20 GtC) and 30% from ELUC (145 ± 50 GtC).

High-Resolution Global Maps of 21st-Century Forest Cover Change

Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil’s well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.

The impacts of tropical cyclones on the net carbon balance of eastern US forests (1851–2000)

In temperate forests of the eastern US, tropical cyclones are a principal agent of catastrophic wind damage, with dramatic impacts on the structure and functioning of forests. Substantial progress has been made to quantify forest damage and resulting gross carbon emissions from tropical cyclones. However, the net effect of storms on the carbon balance of forests depends not only on the biomass lost in single events, but also on the uptake during recovery from a mosaic of past events. This study estimates the net impacts of tropical cyclones on the carbon balance of US forests over the period 1851–2000. To track both disturbance and recovery and to isolate the effects of storms, a modeling framework is used combining gridded historical estimates of mortality and damage with a mechanistic model using an ensemble approach. The net effect of tropical cyclones on the carbon balance is shown to depend strongly on the spatial and temporal scales of analysis. On average, tropical cyclones contribute a net carbon source over latter half of the 19th century. However, throughout much of the 20th century a regional carbon sink is estimated resulting from periods of forest recovery exceeding damage. The large-scale net annual flux resulting from tropical cyclones varies by up to 50 Tg C yr−1, an amount equivalent to 17%–36% of the US forest carbon sink.

Historical warming reduced due to enhanced land carbon uptake

Previous studies have demonstrated the importance of enhanced vegetation growth under future elevated atmospheric CO2 for 21st century climate warming. Surprisingly no study has completed an analogous assessment for the historical period, during which emissions of greenhouse gases increased rapidly and land-use changes (LUC) dramatically altered terrestrial carbon sources and sinks. Using the Geophysical Fluid Dynamics Laboratory comprehensive Earth System Model ESM2G and a reconstruction of the LUC, we estimate that enhanced vegetation growth has lowered the historical atmospheric CO2 concentration by 85 ppm, avoiding an additional 0.31 ± 0.06 °C warming. We demonstrate that without enhanced vegetation growth the total residual terrestrial carbon flux (i.e., the net land flux minus LUC flux) would be a source of 65–82 Gt of carbon (GtC) to atmosphere instead of the historical residual carbon sink of 186–192 GtC, a carbon saving of 251–274 GtC.

Effect of anthropogenic land-use and land- cover changes on climate and land carbon storage in CMIP5 projections for the 21st century

The effects of land-use changes on climate are assessed using specified-concentration simulations complementary to the representative concentration pathway 2.6 (RCP2.6) and RCP8.5 scenarios performed for phase 5 of the Coupled Model Intercomparison Project (CMIP5). This analysis focuses on differences in climate and land–atmosphere fluxes between the ensemble averages of simulations with and without land-use changes by the end of the twenty-first century. Even though common land-use scenarios are used, the areas of crops and pastures are specific for each Earth system model (ESM). This is due to different interpretations of land-use classes. The analysis reveals that fossil fuel forcing dominates land-use forcing. In addition, the effects of land-use changes are globally not significant, whereas they are significant for regions with land-use changes exceeding 10%. For these regions, three out of six participating models—the Second Generation Canadian Earth System Model (CanESM2); Hadley Centre Global Environmental Model, version 2 (Earth System) (HadGEM2-ES); and Model for Interdisciplinary Research on Climate, Earth System Model (MIROC-ESM)—reveal statistically significant changes in mean annual surface air temperature. In addition, changes in land surface albedo, available energy, and latent heat fluxes are small but significant for most ESMs in regions affected by land-use changes. These climatic effects are relatively small, as land-use changes in the RCP2.6 and RCP8.5 scenarios are small in magnitude and mainly limited to tropical and subtropical regions. The relative importance of the climatic effects of land-use changes is higher for the RCP2.6 scenario, which considers an expansion of biofuel croplands as a climate mitigation option. The underlying similarity among all models is the loss in global land carbon storage due to land-use changes.

Greenhouse Gas Policy Influences Climate via Direct Effects of Land-Use Change

Proposed climate mitigation measures do not account for direct biophysical climate impacts of land-use change (LUC), nor do the stabilization targets modeled for phase 5 of the Coupled Model Intercomparison Project (CMIP5) representative concentration pathways (RCPs). To examine the significance of such effects on global and regional patterns of climate change, a baseline and an alternative scenario of future anthropogenic activity are simulated within the Integrated Earth System Model, which couples the Global Change Assessment Model, Global Land-Use Model, and Community Earth System Model. The alternative scenario has high biofuel utilization and approximately 50% less global forest cover than the baseline, standard RCP4.5 scenario. Both scenarios stabilize radiative forcing from atmospheric constituents at 4.5 W m⁻² by 2100. Thus, differences between their climate predictions quantify the biophysical effects of LUC. Offline radiative transfer and land model simulations are also utilized to identify forcing and feedback mechanisms driving the coupled response. Boreal deforestation is found to strongly influence climate because of increased albedo coupled with a regional-scale water vapor feedback. Globally, the alternative scenario yields a twenty-first-century warming trend that is 0.5°C cooler than baseline, driven by a 1 W m⁻² mean decrease in radiative forcing that is distributed unevenly around the globe. Some regions are cooler in the alternative scenario than in 2005. These results demonstrate that neither climate change nor actual radiative forcing is uniquely related to atmospheric forcing targets such as those found in the RCPs but rather depend on particulars of the socioeconomic pathways followed to meet each target.

Understorey fire frequency and the fate of burned forests in southern Amazonia

Recent drought events underscore the vulnerability of Amazon forests to understorey fires. The long-term impact of fires on biodiversity and forest carbon stocks depends on the frequency of fire damages and deforestation rates of burned forests. Here, we characterized the spatial and temporal dynamics of understorey fires (1999–2010) and deforestation (2001–2010) in southern Amazonia using new satellite-based estimates of annual fire activity (greater than 50 ha) and deforestation (greater than 10 ha). Understorey forest fires burned more than 85 500 km2 between 1999 and 2010 (2.8% of all forests). Forests that burned more than once accounted for 16 per cent of all understorey fires. Repeated fire activity was concentrated in Mato Grosso and eastern Pará, whereas single fires were widespread across the arc of deforestation. Routine fire activity in Mato Grosso coincided with annual periods of low night-time relative humidity, suggesting a strong climate control on both single and repeated fires. Understorey fires occurred in regions with active deforestation, yet the interannual variability of fire and deforestation were uncorrelated, and only 2.6 per cent of forests that burned between 1999 and 2008 were deforested for agricultural use by 2010. Evidence from the past decade suggests that future projections of frontier landscapes in Amazonia should separately consider economic drivers to project future deforestation and climate to project fire risk.

Sensitivity of Climate Mitigation Strategies to Natural Disturbances

The present and future concentration of atmospheric carbon dioxide depends on both anthropogenic and natural sources and sinks of carbon. Most proposed climate mitigation strategies rely on a progressive transition to carbon-efficient technologies to reduce industrial emissions, substantially supported by policies to maintain or enhance the terrestrial carbon stock in forests and other ecosystems. This strategy may be challenged if terrestrial sequestration capacity is affected by future climate feedbacks, but how and to what extent is little understood. Here, we show that climate mitigation strategies are highly sensitive to future natural disturbance rates (e.g. fires, hurricanes, droughts), because of the potential effect of disturbances on the terrestrial carbon balance. Generally, altered disturbance rates affect the pace of societal and technological transitions required to achieve the mitigation target, with substantial consequences on the energy sector and the global economy. An understanding of the future dynamics and consequences of natural disturbances on terrestrial carbon balance is thus essential for developing robust climate mitigation strategies and policies.

Essential Biodiversity Variables

Reducing the rate of biodiversity loss and averting dangerous biodiversity change are international goals, reasserted by the Aichi Targets for 2020 by Parties to the United Nations (UN) Convention on Biological Diversity (CBD) after failure to meet the 2010 target (1, 2). However, there is no global, harmonized observation system for delivering regular, timely data on biodiversity change (3). With the first plenary meeting of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) soon under way, partners from the Group on Earth Observations Biodiversity Observation Network (GEO BON) (4) are developing—and seeking consensus around—Essential Biodiversity Variables (EBVs) that could form the basis of monitoring programs worldwide.

Sustainable bioenergy production from marginal lands in the US Midwest

Legislation on biofuels production in the USA¹; and Europe^2,3; is directing food crops towards the production of grain-based ethanol^2,3;, which can have detrimental consequences for soil car- bon sequestration⁴;, nitrous oxide emissions⁵;, nitrate pollution⁶;, biodiversity⁷; and human health⁸;. An alternative is to grow lignocellulosic (cellulosic) crops on ‘marginal’ lands⁹;. Cellulosic feedstocks can have positive environmental outcomes^10,11; and could make up a substantial proportion of future energy port- folios^12,13;. However, the availability of marginal lands for cellulosic feedstock production, and the resulting greenhouse gas (GHG) emissions, remains uncertain. Here we evaluate the potential for marginal lands in ten Midwestern US states to produce sizeable amounts of biomass and concurrently mitigate GHG emissions. In a comparative assessment of six alternative cropping systems over 20 years, we found that successional herbaceous vegetation, once well established, has a direct GHG emissions mitigation capacity that rivals that of purpose-grown crops (-851 ±46 grams of CO2 equivalent emissions per square metre per year (gCO₂e m^-2 yr^-1)). If fertilized, these communities have the capacity to produce about 63 ± 5 gigajoules of ethanol energy per hectare per year. By con- trast, an adjacent, no-till corn–soybean–wheat rotation produces on average 41 ± 1 gigajoules of biofuel energy per hectare per year and has a net direct mitigation capacity of -397 ± 32 gCO₂e m^-2 yr^-1; a continuous corn rotation would probably produce about 62 ±7 gigajoules of biofuel energy per hectare per year, with 13% less mitigation. We also perform quantitative modelling of successional vegetation on marginal lands in the region at a resolution of 0.4 hectares, constrained by the requirement that each modelled loca- tion be within 80 kilometres of a potential biorefinery. Our results suggest that such vegetation could produce about 21 gigalitres of ethanol per year from around 11 million hectares, or approximately 25 per cent of the 2022 target for cellulosic biofuel mandated by the US Energy Independence and Security Act of 2007, with no initial carbon debt nor the indirect land-use costs associated with food- based biofuels. Other regional-scale aspects of biofuel sustainability2, such as water quality^11,14; and biodiversity¹⁵;, await future study.

A “Matchstick Model” of microwave backscatter from a forest

Given that the majority of biomass is contained in the stems of trees within forests (as much as 90%), a new radar modelling approach is proposed here wherein the stems are the major biomass contributor in the context of Synthetic Aperture Radar backscatter sensitivity to forest biomass. The new model regards stems are as “matchsticks” consisting of constant radius, constant density, and constant dielectric properties. Furthermore, by considering only the larger constituents of the forest a clearer understanding of the correlation of biomass with backscatter from P and VHF Band SAR can be obtained. Using backscatter data, and specifically the trends, produced from the RT2 radiative transfer model this paper considers the effects of SAR scattering when interacting with forest stands consisting of identical vertical stems, or matchsticks. SAR frequencies of 50 MHz (VHF) and 429 MHz (P Band) are used to generate a comparative radar interaction data. These not only allow a comparison of the scattering of SAR signals of different wavelengths but also of how interactions with stems can reveal novel clues as to the source of the backscatter saturation phenomenon of forests. Removing the random volume scattering aspect of a forest, the canopy, would be expected to eliminate the saturation behaviour which occurs at increasing levels of biomass density, an effect believed to be due to extinction effects, but is shown here to not be the case. Results reveal that saturation behaviour can occur even for the low number density case where increasing the size of stems through the radii associated with Rayleigh, Mie and Optical scattering will result in backscatter saturation as the biomass density is increased. Consistent with this finding, a forest of solely vertical stems will exhibit saturation behaviour at lower biomass density values when lower planting densities of similar stems are used. In this study the backscatter model considers the ground contribution to be negligible but does include the double bounce as a result of interactions between the stems and ground. Also considered are Mie backscatter oscillations which are shown to average out when using both non-vertical stems and random radius values about a mean, both independently and collectively. These “Mie” effects can also be removed by altering the incident SAR angle. These findings allow a reduction of the backscattering scenario of forests to a two-region problem involving solely Rayleigh and Optical scattering. This serves to further provide an explanation as to the origin of saturation, whereby the balance between the Optical scattering increase and the matchstick number density decrease cancel each other out. The important conclusion is that backscatter does not measure biomass, but measures structural trends that are correlated with biomass in different ways, driven by stand level competition, resource use, etc. As a result different forest growth regimes should expect different backscatter–biomass trends.

A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H₁₀₀, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H₁₀₀ and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 10²–10⁶ plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H₁₀₀.

Characterizing 3D vegetation structure from space: Mission requirements

Human and natural forces are rapidly modifying the global distribution and structure of terrestrial ecosystems on which all of life depends, altering the global carbon cycle, affecting our climate now and for the foreseeable future, causing steep reductions in species diversity, and endangering Earth's sustainability.
To understand changes and trends in terrestrial ecosystems and their functioning as carbon sources and sinks, and to characterize the impact of their changes on climate, habitat and biodiversity, new space assets are urgently needed to produce high spatial resolution global maps of the three-dimensional (3D) structure of vegetation, its biomass above ground, the carbon stored within and the implications for atmospheric green house gas concentrations and climate. These needs were articulated in a 2007 National Research Council (NRC) report (NRC, 2007) recommending a new satellite mission, DESDynI, carrying an L-band Polarized Synthetic Aperture Radar (Pol-SAR) and a multi-beam lidar (Light RAnging And Detection) operating at 1064 nm. The objectives of this paper are to articulate the importance of these new, multi-year, 3D vegetation structure and biomass measurements, to briefly review the feasibility of radar and lidar remote sensing technology to meet these requirements, to define the data products and measurement requirements, and to consider implications of mission durations. The paper addresses these objectives by synthesizing research results and other input from a broad community of terrestrial ecology, carbon cycle, and remote sensing scientists and working groups. We conclude that:

(1) Current global biomass and 3-D vegetation structure information is unsuitable for both science and management and policy. The only existing global datasets of biomass are approximations based on combining land cover type and representative carbon values, instead of measurements of actual biomass. Current measurement attempts based on radar and multispectral data have low explanatory power outside low biomass areas. There is no current capability for repeatable disturbance and regrowth estimates.
(2) The science and policy needs for information on vegetation 3D structure can be successfully addressed by a mission capable of producing (i) a first global inventory of forest biomass with a spatial resolution 1 km or finer and unprecedented accuracy (ii) annual global disturbance maps at a spatial resolution of 1 ha with subsequent biomass accumulation rates at resolutions of 1 km or finer, and (iii) transects of vertical and horizontal forest structure with 30 m along-transect measurements globally at 25 m spatial resolution, essential for habitat characterization.

We also show from the literature that lidar profile samples together with wall-to-wall L-band quad-pol-SAR imagery and ecosystem dynamics models can work together to satisfy these vegetation 3D structure and biomass measurement requirements. Finally we argue that the technology readiness levels of combined pol-SAR and lidar instruments are adequate for space flight. Remaining to be worked out, are the particulars of a lidar/pol-SAR mission design that is feasible and at a minimum satisfies the information and measurement requirement articulated herein.

Harmonization of Land-Use Scenarios for the Period 1500-2100: 600 Years of Global Gridded Annual Land-Use Transitions, Wood Harvest, and Resulting Secondary Lands

In preparation for the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the international community is developing new advanced Earth System Models (ESMs) to assess the combined effects of human activities (e.g. land use and fossil fuel emissions) on the carbon-climate system. In addition, four Representative Concentration Pathway (RCP) scenarios of the future (2005–2100) are being provided by four Integrated Assessment Model (IAM) teams to be used as input to the ESMs for future carbon-climate projections (Moss et al. 2010). The diversity of approaches and requirements among IAMs and ESMs for tracking land-use change, along with the dependence of model projections on land-use history, presents a challenge for effectively passing data between these communities and for smoothly transitioning from the historical estimates to future projections. Here, a harmonized set of land-use scenarios are presented that smoothly connects historical reconstructions of land use with future projections, in the format required by ESMs. The land-use harmonization strategy estimates fractional land-use patterns and underlying land-use transitions annually for the time period 1500–2100 at 0.5°×0.5° resolution. Inputs include new gridded historical maps of crop and pasture data from HYDE 3.1 for 1500–2005, updated estimates of historical national wood harvest and of shifting cultivation, and future information on crop, pasture, and wood harvest from the IAM implementations of the RCPs for the period 2005–2100. The computational method integrates these multiple data sources, while minimizing differences at the transition between the historical reconstruction ending conditions and IAM initial conditions, and working to preserve the future changes depicted by the IAMs at the grid cell level. This study for the first time harmonizes land-use history data together with future scenario information from multiple IAMs into a single consistent, spatially gridded, set of land-use change scenarios for studies of human impacts on the past, present, and future Earth system.

The Representative Concentration Pathways: An Overview

Abstract This paper summarizes the development process and main characteristics of the Representative Concentration Pathways (RCPs), a set of four new pathways developed for the climate modeling community as a basis for long-term and near-term modeling experiments. The four RCPs together span the range of year 2100 radiative forcing values found in the open literature, i.e. from 2.6 to 8.5 W/m2. The RCPs are the product of an innovative collaboration between integrated assessment modelers, climate modelers, terrestrial ecosystem modelers and emission inventory experts. The resulting product forms a comprehensive data set with high spatial and sectoral resolutions for the period extending to 2100. Land use and emissions of air pollutants and greenhouse gases are reported mostly at a 0.5×0.5 degree spatial resolution, with air pollutants also provided per sector (for well-mixed gases, a coarser resolution is used). The underlying integrated assessment model outputs for land use, atmospheric emissions and concentration data were harmonized across models and scenarios to ensure consistency with historical observations while preserving individual scenario trends. For most variables, the RCPs cover a wide range of the existing literature. The RCPs are supplemented with extensions (Extended Concentration Pathways, ECPs), which allow

The HadGEM2-ES implementation of CMIP5 centennial simulations

The scientific understanding of the Earth's climate system, including the central question of how the climate system is likely to respond to human-induced perturbations, is comprehensively captured in GCMs and Earth System Models (ESM). Diagnosing the simulated climate response, and comparing responses across different models, is crucially dependent on transparent assumptions of how the GCM/ESM has been driven – especially because the implementation can involve subjective decisions and may differ between modelling groups performing the same experiment. This paper outlines the climate forcings and setup of the Met Office Hadley Centre ESM, HadGEM2-ES for the CMIP5 set of centennial experiments. We document the prescribed greenhouse gas concentrations, aerosol precursors, stratospheric and tropospheric ozone assumptions, as well as implementation of land-use change and natural forcings for the HadGEM2-ES historical and future experiments following the Representative Concentration Pathways. In addition, we provide details of how HadGEM2-ES ensemble members were initialised from the control run and how the palaeoclimate and AMIP experiments, as well as the "emission-driven" RCP experiments were performed.

Building a Model of Collaboration Between Historically Black and Historically White Universities

Despite increases over the last two decades in the number of degrees awarded to students from underrepresented groups in science, technology, engineering, and mathematics (STEM) disciplines, enhancing diversity in these disciplines remains a challenge. This article describes a strategic approach to this challenge—the development of a collaborative partnership between two universities: the historically Black Elizabeth City State University and the historically White University of New Hampshire. The partnership, a type of learning organiza- tion built on three mutually agreed upon principles, strives to enhance opportunities for underrepresented students to pursue careers in the STEM disciplines. This article further describes six promising practices that framed the partnership, which resulted in the submission of nine proposals to federal agencies and the funding of four grants that led to the implementation, research, learning, and evaluation that followed.

Mapping Canopy Damage from Understory Fires in Amazon Forests

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997–2004) and MODIS (2000–2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image- derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars b50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997–2002. MODIS data were suitable for mapping medium (50–500 ha) and large (N500 ha) burn scars that accounted for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km²) were an order of magnitude higher than during the 1997–1998 El Niño event (124 km² and 39 km², respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.

Using ICESat's Geoscience Laser Altimeter System (GLAS) to assess large-scale forest disturbance caused by hurricane Katrina

In 2005, hurricane Katrina resulted in a large disturbance to U.S. forests. Recent estimates of damage from hurricane Katrina have relied primarily on optical remote sensing and field data. This paper is the first large- scale study to use satellite-based lidar data to quantify changes in forest structure from that event. GLAS data for the years prior to and following hurricane Katrina were compared to wind speed, forest cover, and damage data to assess the adequacy of sensor sampling, and to estimate changes in Mean Canopy Height (MCH) over all areas that experienced tropical force winds and greater. Statistically significant decreases in MCH post-Katrina were found to increase with wind intensity: Tropical Storm ΔMCH = − 0.5 m, Category 1 ΔMCH = − 2 m, and Category 2 ΔMCH = − 4 m. A strong relationship was also found between changes in non-photosynthetic vegetation (ΔNPV), a metric previously shown to be related to storm damage, and post- storm MCH. The season of data acquisition was shown to influence calculations of MCH and MCH loss, but did not preclude the detection of major large-scale patterns of damage. Results from this study show promise for using space-borne lidar for large-scale assessments of forest disturbance, and highlight the need for future data on vegetation structure from space.

Scenarios for Global Biodiversity in the 21st Century

Quantitative scenarios are coming of age as a tool for evaluating the impact of future socioeconomic development pathways on biodiversity and ecosystem services. We analyze global terrestrial, freshwater, and marine biodiversity scenarios using a range of measures including extinctions, changes in species abundance, habitat loss, and distribution shifts, as well as comparing model projections to observations. Scenarios consistently indicate that biodiversity will continue to decline over the 21st century. However, the range of projected changes is much broader than most studies suggest, partly because there are major opportunities to intervene through better policies, but also because of large uncertainties in projections.

Climate Mitigation and the Future of Tropical Landscapes

Land-use change to meet 21st-century demands for food, fuel, and fiber will depend on many interactive factors, including global policies limiting anthropogenic climate change and realized improve- ments in agricultural productivity. Climate-change mitigation policies will alter the decision-making environment for land management, and changes in agricultural productivity will influence cultivated land expansion. We explore to what extent future increases in agricultural productivity might offset conversion of tropical forest lands to crop lands under a climate mitigation policy and a contrasting no-policy scenario in a global integrated assessment model. The Global Change Assessment Model is applied here to simulate a mitigation policy that stabilizes radiative forcing at 4.5 W m!2 (approximately 526 ppm CO2) in the year 2100 by introducing a price for all greenhouse gas emis- sions, including those from land use. These scenarios are simulated with several cases of future agricultural productivity growth rates and the results downscaled to produce gridded maps of potential land-use change. We find that tropical forests are preserved near their present-day extent, and bioenergy crops emerge as an effective mitigation option, only in cases in which a climate mitigation policy that includes an economic price for land-use emissions is in place, and in which agricultural productivity growth continues throughout the century. We find that idealized land-use emissions price assumptions are most effective at limiting deforestation, even when cropland area must increase to meet future food demand. These findings empha- size the importance of accounting for feedbacks from land-use change emissions in global climate change mitigation strategies.

Linking models and data on vegetation structure

For more than a century, scientists have recognized the importance of vegetation structure in understanding forest dynamics. Now future satellite missions such as Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI) hold the potential to provide unprecedented global data on vegetation structure needed to reduce uncertainties in terrestrial carbon dynamics. Here, we briefly review the uses of data on vegetation structure in ecosystem models, develop and analyze theoretical models to quantify model-data requirements, and describe recent progress using a mechanistic modeling approach utilizing a formal scaling method and data on vegetation structure to improve model predictions. Generally, both limited sampling and coarse resolution averaging lead to model initialization error, which in turn is propagated in subsequent model prediction uncertainty and error. In cases with representative sampling, sufficient resolution, and linear dynamics, errors in initialization tend to compensate at larger spatial scales. However, with inadequate sampling, overly coarse resolution data or models, and nonlinear dynamics, errors in initialization lead to prediction error. A robust model-data framework will require both models and data on vegetation structure sufficient to resolve important environmental gradients and tree-level heterogeneity in forest structure globally.

Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica

In this paper we present the results of an experiment to measure forest structure and biomass dynamics over the tropical forests of La Selva Biological Station in Costa Rica using a medium resolution lidar. Our main objective was to observe changes in forest canopy height, related height metrics, and biomass, and from these map sources and sinks of carbon across the landscape. The Laser Vegetation Imaging Sensor (LVIS) measured canopy structure over La Selva in 1998 and again in 2005. Changes in waveform metrics were related to field-derived changes in estimated aboveground biomass from a series of old growth and secondary forest plots. Pairwise comparisons of nearly coincident lidar footprints between years showed canopy top height changes that coincided with expected changes based on land cover types. Old growth forests had a net loss in height of -0.33 m, while secondary forests had net gain of 2.08 m. Multiple linear regressionwas used to relate lidar metrics with biomass changes for combined old growth and secondary forest plots, giving an r² of 0.65 and an RSE of 10.5 Mg/ha, but both parametric and bootstrapped confidence intervals were wide, suggesting weaker model performance. The plot level relationships were then used to map biomass changes across La Selva using LVIS at a 1 ha scale. The spatial patterns of biomass changes matched expected patterns given the distribution of land cover types at La Selva, with secondary forests showing a gain of 25 Mg/ha and old growth forests showing little change (2 Mg/ha). Prediction intervals were calculated to assess uncertainty for each 1 ha cell to ascertain whether the data and methods used could confidently estimate the sign (source or sink) of the biomass changes. The resulting map showed most of the old growth areas as neutral (no net biomass change), with widely scattered and isolated sources and sinks. Secondary forests in contrast were mostly sinks or neutral, but were never sources. By quantifying both the magnitude of biomass changes and the sensitivity of lidar to detect them, this work will help inform the formulation of future space missions focused on biomass dynamics, such as NASA’s Deformation Ecosystem Structure and Dynamics of Ice mission.

Regional forest growth rates measured by combining ICESat GLAS and Landsat data

Forest canopy heights derived from ICESat Geoscience Laser Altimeter System (GLAS) lidar data were combined with Landsat-based disturbance history maps to assess forest regeneration rates in three regions of the eastern United States (Maine, Virginia, and Mississippi). GLAS observations were screened for topographic relief and waveform quality, and canopy heights were obtained by visual inspection of each waveform. Regressing the GLAS heights against the age of last disturbance yielded vertical growth rates of 0.6 m/yr (Maine), 1.0 m/yr (Virginia), and 1.2 m/yr (Mississippi). Growth rates, when combined with height-biomass allometric relations, can be converted to estimates of aboveground wood productivity. The study demonstrates that large-footprint lidar data can be used to measure vertical growth rates when averaged spatially, thus providing unique information on forest regeneration for carbon cycle studies.

Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

Abrupt forest disturbances generating gaps >0.001 km² impact roughly 0.4–0.7 million km² a⁻¹. Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., ∼1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth's forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information.

Impacts of tropical cyclones on U.S. forest tree mortality and carbon flux from 1851 to 2000

Tropical cyclones cause extensive tree mortality and damage to forested ecosystems. A number of patterns in tropical cyclone frequency and intensity have been identified. There exist, however, few studies on the dynamic impacts of historical tropical cyclones at a continental scale. Here, we synthesized field measurements, satellite image analyses, and empirical models to evaluate forest and carbon cycle impacts for historical tropical cyclones from 1851 to 2000 over the continental U.S. Results demonstrated an average of 97 million trees affected each year over the entire United States, with a 53-Tg annual biomass loss, and an average carbon release of 25 Tg y⁻¹. Over the period 1980–1990, released CO₂ potentially offset the carbon sink in forest trees by 9–18% over the entire United States. U.S. forests also experienced twice the impact before 1900 than after 1900 because of more active tropical cyclones and a larger extent of forested areas. Forest impacts were primarily located in Gulf Coast areas, particularly southern Texas and Louisiana and south Florida, while significant impacts also occurred in eastern North Carolina. Results serve as an important baseline for evaluating how potential future changes in hurricane frequency and intensity will impact forest tree mortality and carbon balance.

Carbon cycling under 300 years of land use change: Importance of the secondary vegetation sink

We have developed a dynamic land model (LM3V) able to simulate ecosystem dynamics and exchanges of water, energy, and CO₂ between land and atmosphere. LM3V is specifically designed to address the consequences of land use and land management changes including cropland and pasture dynamics, shifting cultivation, logging, fire, and resulting patterns of secondary regrowth. Here we analyze the behavior of LM3V, forced with the output from the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric model AM2, observed precipitation data, and four historic scenarios of land use change for 1700–2000. Our analysis suggests a net terrestrial carbon source due to land use activities from 1.1 to 1.3 GtC/a during the 1990s, where the range is due to the difference in the historic cropland distribution. This magnitude is substantially smaller than previous estimates from other models, largely due to our estimates of a secondary vegetation sink of 0.35 to 0.6 GtC/a in the 1990s and decelerating agricultural land clearing since the 1960s. For the 1990s, our estimates for the pastures' carbon flux vary from a source of 0.37 to a sink of 0.15 GtC/a, and for the croplands our model shows a carbon source of 0.6 to 0.9 GtC/a. Our process-based model suggests a smaller net deforestation source than earlier bookkeeping models because it accounts for decelerated net conversion of primary forest to agriculture and for stronger secondary vegetation regrowth in tropical regions. The overall uncertainty is likely to be higher than the range reported here because of uncertainty in the biomass recovery under changing ambient conditions, including atmospheric CO2 concentration, nutrients availability, and climate.

Using lidar data and a height-structured ecosystem model to estimate forest carbon stocks and fluxes over mountainous terrain

Accurately predicting forest dynamics and associated carbon fluxes requires both knowledge of the current state of the ecosystem and an understanding of the underlying processes and environmental conditions that influence the ecosystem processes. Here, we apply a combination of light detection and ranging (lidar) remote sensing (LVIS), an individual-based height-structured ecosystem model (ED), and detailed topographic and climate data to address these requirements to predict carbon dynamics at the Hubbard Brook Experimental Forest (HBEF) in the White Mountains of New Hampshire. Lidar data provided substantial constraints on model estimates of carbon stocks and annual net ecosystem production (ANEP). Lidar-initialized model estimates of carbon stocks (10.77 kg C m⁻²) were within 5% of the field estimates over the domain and accounted for the 44% decrease in carbon stocks observed between minimum and maximum elevation at HBEF. Lidar-initialized model estimates of ANEP (0.023 kg C m⁻² year⁻¹) also compared favorably with recent field estimates. Model estimates of ANEP strongly depended on fine-scale (1 ha) lidar data on vegetation structure, environmental gradients, and the dynamics of disturbance events. Substituting fine-scale (1 ha) data on vegetation structure and climate with domain-wide inputs increased model estimates of ANEP by 84%. Substituting fine-scale climate data with domain-wide inputs but initializing with fine-scale data on vegetation structure increased estimates of ANEP by 40%. Model simulations initialized with spatial heterogeneity in environmental conditions but that lacked corresponding spatial heterogeneity in vegetation structure were the most problematic because this configuration had serious inconsistencies in areas where the domain mean canopy height exceeded the local potential for vegetation (e.g., at high elevations). Lastly, failing to account for increased natural disturbance rates with elevation increased model estimates of ANEP by 43%. This research demonstrates that the combination of lidar data and a height-structured ecosystem model can be a powerful tool for estimating forest carbon stocks and fluxes, even in complex mountainous environments. To be most useful for constraining model predictions, lidar data need to be at the scale of the underlying environmental heterogeneity that determines plant vital rates.

Clustered disturbances lead to bias in large-scale estimates based on forest sample plots

Assessments from field plots steer much of our current understanding of global change impacts on forest ecosystem structure and function. Recent widespread observations of net carbon accumulation in field plots have suggested that terrestrial ecosystems may be a carbon sink possibly resulting from climate change and/or CO₂ fertilization. We hypothesize that field plots may inadequately sample inherently rare mortality events, leading to bias when plot level measurements are scaled up to larger domains. In this study, we constructed a simple computer simulation model of forest dynamics to investigate the effects of disturbance patterns on landscape-scale carbon balance estimates. The model was constructed to be a balanced biosphere at the landscape-scale with a uniform spatial pattern of forest growth rates. Disturbance gap-size distributions across the landscape were modelled with a power-law distribution. Small and frequent disturbances result in a well-mixed heterogeneous forest where even small sample plots represented domain-wide behaviour. However, with disturbances dominated by large and rare events, sample plots as large as 50 ha displayed significant bias towards growth. We suggest that the accuracy of domain level estimates of carbon balance from sample plots are highly sensitive to the distribution of disturbance events across the landscape, and to the number, size and distribution of field plots that comprise the estimate. Assumptions that small clusters of field plots may be representative of domain-wide conditions should only be made very cautiously, and warrant further investigation for verification

The effects of deforestation on the hydrological cycle in Amazonia: a review on scale and resolution

This paper reviews the effects of deforestation on the hydrological cycle in Amazonia according to recent modeling and observational studies performed within different spatial scales and resolutions. The predictions that follow from future scenarios of a complete deforestation in the region point to a restrained water cycle, while the simulated effects of small, disturbed areas show a contrasting tendency. Differences between coarsely spatially averaged observations and finely sampled data sets have also been encountered. These contrasts are only partially explained by the different spatial resolutions among models and observations, since they seem to be further associated with the weakening of precipitation recycling under scenarios of extensive deforestation and with the potential intensification of convection over areas of land-surface heterogeneity. Therefore, intrinsic and interrelated scale and heterogeneity dependencies on the impact of deforestation in Amazonia on the hydrological cycle are revealed and the acknowledgement of the relevance of these dependencies sets a few challenges for the future.

Hurricane Katrina's Carbon Footprint on U.S. Gulf Coast Forests

Hurricane Katrina's impact on U.S. Gulf Coast forests was quantified by linking ecological field studies, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) image analyses, and empirically based models. Within areas affected by relatively constant wind speed, tree mortality and damage exhibited strong species-controlled gradients. Spatially explicit forest disturbance maps coupled with extrapolation models predicted mortality and severe structural damage to ~320 million large trees totaling 105 teragrams of carbon, representing 50 to 140% of the net annual U.S. forest tree carbon sink. Changes in disturbance regimes from increased storm activity expected under a warming climate will reduce forest biomass stocks, increase ecosystem respiration, and may represent an important positive feedback mechanism to elevated atmospheric carbon dioxide.

The Tension between Fire Risk and Carbon Storage: Evaluating U.S. Carbon and Fire Management Strategies through Ecosystem Models

Fire risk and carbon storage are related environmental issues because fire reduction results in carbon storage through the buildup of woody vegetation, and stored carbon is a fuel for fires. The sustainability of the U.S. carbon sink and the extent of fire activity in the next 100 yr depend in part on the type and effectiveness of fire reduction employed. Previous studies have bracketed the range of dynamics from continued fire reduction to the complete failure of fire reduction activities. To improve these estimates, it is necessary to explicitly account for fire reduction in terrestrial models. A new fire reduction submodel that estimates the spatiotemporal pattern of reduction across the United States was developed using gridded data on biomass, climate, land-use, population, and economic factors. To the authors’ knowledge, it is the first large-scale, gridded fire model that explicitly accounts for fire reduction. The model was calibrated to 1° × 1° burned area statistics [Global Burnt Area 2000 Project (GBA-2000)] and compared favorably to three important diagnostics. The model was then implemented in a spatially explicit ecosystem model and used to analyze 1620 scenarios of future fire risk and fire reduction strategies. Under scenarios of climate change and urbanization, burned area and carbon emissions both increased in scenarios where fire reduction efforts were not adjusted to match new patterns of fire risk. Fuel reducing management strategies reduced burned area and fire risk, but also limited carbon storage. These results suggest that to promote carbon storage and minimize fire risk in the future, fire reduction efforts will need to be increased and spatially adjusted and will need to employ a mixture of fuel-reducing and non-fuel-reducing strategies.

The contributions of land-use change, CO₂ fertilization, and climate variability to the Eastern US carbon sink

Atmospheric measurements and land-based inventories imply that terrestrial ecosystems in the northern hemisphere are taking up significant amounts of anthropogenic carbon dioxide (CO₂) emissions; however, there is considerable disagreement about the causes of this uptake, and its expected future trajectory. In this paper, we use the ecosystem demography (ED) model to quantify the contributions of disturbance history, CO₂ fertilization and climate variability to the past, current, and future terrestrial carbon fluxes in the Eastern United States. The simulations indicate that forest regrowth following agricultural abandonment accounts for uptake of 0.11 Pg C yr^-1 in the 1980s and 0.15 Pg C yr^-1 in the 1990s, and regrowth following forest harvesting accounts for an additional 0.1 Pg C yr^-1 of uptake during both these decades. The addition of CO₂ fertilization into the model simulations increases carbon uptake rates to 0.38 Pg C yr^-1 in the 1980s and 0.47 Pg C yr^-1 in the 1990s. Comparisons of predicted aboveground carbon uptake to regional-scale forest inventory measurements indicate that the model’s predictions in the absence of CO₂ fertilization are 14% lower than observed, while in the presence of CO₂ fertilization, predicted uptake rates are 28% larger than observed. Comparable results are obtained from comparisons of predicted total Net Ecosystem Productivity to the carbon fluxes observed at the Harvard Forest flux tower site and in model simulations free-air CO₂ enrichment (FACE) experiments. These results imply that disturbance history is the principal mechanism responsible for current carbon uptake in the Eastern United States, and that conventional biogeochemical formulations of plant growth overestimate the response of plants to rising CO₂ levels. Model projections out to 2100 imply that the carbon uptake arising from forest regrowth will increasingly be dominated by forest regrowth following harvesting. Consequently, actual carbon storage declines to near zero by the end of the 21st century as the forest regrowth that has occurred since agricultural abandonment comes into equilibrium with the landscape’s new disturbance regime. Incorporating interannual climate variability into the model simulations gives rise to large interannual variation in regional carbon fluxes, indicating that long-term measurements are necessary to detect the signature of processes that give rise to long-term uptake and storage.

Broadening students horizons: the development, delivery, and assessment of a new course in Earth System Science.

Earth System Science is an exceptionally interdisciplinary field requiring knowledge and skills from multiple scientific disciplines. Many important questions lie at the intersection of traditional disciplines and require a systems level approach. The emerging educational challenge is to train the next generation of scientists to address these topics. Here, we describe the development, delivery, and assessment of a new course in Earth System Science designed for advanced undergraduates and beginning graduate students. The course was designed to meet specific learning objectives, delivered in an inquiry-based learning environment, and assessed to determine the extent to which the learning objectives had been attained. The course consisted of readings from both texts and primary literature, lectures by UNH professors and NASA scientists, computer modeling labs, and interdisciplinary student-team research projects. Results emphasize the importance of pre-planning and resources, establishing clear and concise student learning objectives, creating of an inquiry-based learning centered environment, role-modeling how Earth System Science research is done, and meeting student demand and institutional challenges. This class can serve as a model course for upper level undergraduates and beginning graduate students to expand their disciplinary scope, skills, and readiness to address Earth System Science questions.

A water balance model to study the hydrological response to different scenarios of deforestation in Amazonia

Amazonia encloses some of the largest watersheds in the world, experiencing substantial amounts of rainfall annually and producing more runoff to the ocean than any other region. Amazonia experiences one of the highest rates of deforestation in the world and the hydrological effects of such a disturbance have already been investigated by several studies. Contrasting results exist, especially when different scales and degrees of heterogeneity are considered. This paper assesses the dependency of the hydrological impact of deforestation on these factors through application of a gridded water balance model. The model simulates different scenarios of deforestation based on straightforward water balance calculations. In all experiments performed, the scenarios conform to observations of decreased evapotranspiration within disturbed sites. Initially, by implying an uncoupling between small deforested áreas and circulation, the model suggests an increase in runoff locally. However, when the land-atmosphere coupling caused by intermediate levels of deforestation is reproduced through deviations on circulation, the model confirms that the water cycle may or may not become regionally accelerated, depending on the degree of heterogeneity associated. Finally, by simulating a scenario of complete deforestation, the model confirms expectations of a less intense water cycle in Amazonia. Due to the broad range of numerical models and observation networks currently available, the importance of the proper representation of both scale and heterogeneity of deforestation to the correct assessment of its hydrological effects is emphasized.

Despite our model results, there is need for more mechanistic studies on coupled land-surface and atmosphere interactions under varying conditions.

The underpinnings of land-use history: three centuries of global gridded land-use transitions, wood harvest activity, and resulting secondary lands.

To accurately assess the impacts of human land use on the Earth system, information is needed on the current and historical patterns of land-use activities. Previous global studies have focused on developing reconstructions of the spatial patterns of agriculture. Here, we provide the first global gridded estimates of the underlying land conversions (land-use transitions), wood harvesting, and resulting secondary lands annually, for the period 1700–2000. Using data-based historical cases, our results suggest that 42–68% of the land surface was impacted by land-use activities (crop, pasture, wood harvest) during this period, some multiple times. Secondary land area increased 10–44 × 106 km²; about half of this was forested. Wood harvest and shifting cultivation generated 70–90% of the secondary land by 2000; permanent abandonment and relocation of agricultural land accounted for the rest. This study provides important new estimates of globally gridded land-use activities for studies attempting to assess the consequences of anthropogenic changes to the Earth's surface over time.

Field work and statistical analyses for enhanced interpretation of satellite fire data

Because their broad spatial and temporal coverage, satellites provide the main source of fire data for Amazonia. A key to the application of these tools for environmental studies is the appropriate interpretation of the data they provide. To enhance the interpretation of satellite fire data for this region, we collected ground-based data on fires in 2001 and 2002 using a simple and passive method, and statistically related these data to corresponding estimates from AVHRR and MODIS fire products using error matrices. Multiple methods of analyses from simple to complex produced qualitatively similar results. Total accuracies for both fire products were very high (> 99%) and dominated by accurate (> 99%) non-fire detection. Kappa statistics and fire-detection accuracies were substantially lower, with omission errors higher than commission errors. Results calculated using several different sets of spatial-matching parameters of analysis showed that Kappa was 1–10.6% for AVHRR, and 0–1.4% for MODIS. User's accuracy for fires was 0–40% for AVHRR and 3–100% for MODIS. Producer's accuracy for fires was 0–8% for AVHRR and 0–1% for MODIS. Statistical evaluations of potential explanatory factors showed that fire size and sampling time were dominant factors for low accuracies. Results from this study indicate that current satellite fire products are providing a limited sample of the fire activity in the region, and that ground-based analyses can substantially contribute to the interpretation of these products.

Beyond Potential Vegetation: Combining LIDAR Data and a Height-Structured Model for Carbon Studies

Carbon estimates from terrestrial ecosystem models are limited by large uncertainties in the current state of the land surface. Natural and anthropogenic disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect or assess with conventional methods. In this study, we combined two recent advances in remote sensing and ecosystem modeling to improve model carbon stock and flux estimates at a tropical forest study site at La Selva, Costa Rica (10°25′ N, 84°00′ W). Airborne lidar remote sensing was used to measure spatial heterogeneity in the vertical structure of vegetation. The ecosystem demography model (ED) was used to estimate the consequences of this heterogeneity for regional estimates of carbon stocks and fluxes. Lidar data provided substantial constraints on model estimates of both carbon stocks and net carbon fluxes. Lidar-initialized ED estimates of aboveground biomass were within 1.2% of regression-based approaches, and corresponding model estimates of net carbon fluxes differed substantially from bracketing alternatives. The results of this study provide a promising illustration of the power of combining lidar data on vegetation height with a height-structured ecosystem model. Extending these analyses to larger scales will require the development of regional and global lidar data sets, and the continued development and application of height structured ecosystem models.

Human-induced changes in US biogenic volatile organic compound emissions: evidence from long-term forest inventory data

Volatile organic compounds (VOCs) emitted by woody vegetation influence global climate forcing and the formation of tropospheric ozone. We use data from over 250,000 re-surveyed forest plots in the eastern US to estimate emission rates for the two most important biogenic VOCs (isoprene and monoterpenes) in the 1980s and 1990s, and then compare these estimates to give a decadal change in emission rate. Over much of the region, particularly the southeast, we estimate that there were large changes in biogenic VOC emissions: half of the grid cells (1° × 1°) had decadal changes in emission rate outside the range -2.3% to +16.8% for isoprene, and outside the range 0.2–17.1% for monoterpenes. For an average grid cell the estimated decadal change in heatwave biogenic VOC emissions (usually an increase) was three times greater than the decadal change in heatwave anthropogenic VOC emissions (usually a decrease, caused by legislation). Leaf-area increases in forests, caused by anthropogenic disturbance, were the most important process increasing biogenic VOC emissions. However, in the southeast, which had the largest estimated changes, there were substantial effects of ecological succession (which decreased monoterpene emissions and had location-specific effects on isoprene emissions), harvesting (which decreased monoterpene emissions and increased isoprene emissions) and plantation management (which increased isoprene emissions, and decreased monoterpene emissions in some states but increased monoterpene emissions in others). In any given region, changes in a very few tree species caused most of the changes in emissions: the rapid changes in the southeast were caused almost entirely by increases in sweetgum (Liquidambar styraciflua) and a few pine species. Therefore, in these regions, a more detailed ecological understanding of just a few species could greatly improve our understanding of the relationship between natural ecological processes, forest management, and biogenic VOC emissions.

Projecting the future of the U.S. carbon sink

Atmospheric and ground-based methods agree on the presence of a carbon sink in the coterminous United States (the United States minus Alaska and Hawaii), and the primary causes for the sink recently have been identified. Projecting the future behavior of the sink is necessary for projecting future net emissions. Here we use two models, the Ecosystem Demography model and a second simpler empirically based model (Miami Land Use History), to estimate the spatio-temporal patterns of ecosystem carbon stocks and fluxes resulting from land-use changes and fire suppression from 1700 to 2100. Our results are compared with other historical reconstructions of ecosystem carbon fluxes and to a detailed carbon budget for the 1980s. Our projections indicate that the ecosystem recovery processes that are primarily responsible for the contemporary U.S. carbon sink will slow over the next century, resulting in a significant reduction of the sink. The projected rate of decrease depends strongly on scenarios of future land use and the long-term effectiveness of fire suppression.

Consistent Land- and Atmosphere-Based U.S. Carbon Sink Estimates

For the period 1980-89, we estimate a carbon sink in the coterminous United States between 0.30 and 0.58 petagrams of carbon per year (petagrams of carbon = 1015 grams of carbon). The net carbon flux from the atmosphere to the land was higher, 0.37 to 0.71 petagrams of carbon per year, because a net flux of 0.07 to 0.13 petagrams of carbon per year was exported by rivers and commerce and returned to the atmosphere elsewhere. These land-based estimates are larger than those from previous studies (0.08 to 0.35 petagrams of carbon per year) because of the inclusion of additional processes and revised estimates of some component fluxes. Although component estimates are uncertain, about one-half of the total is outside the forest sector. We also estimated the sink using atmospheric models and the atmospheric concentration of carbon dioxide (the tracer-transport inversion method). The range of results from the atmosphere-based inversions contains the land-based estimates. Atmosphere- and land-based estimates are thus consistent, within the large ranges of uncertainty for both methods. Atmosphere-based results for 1980-89 are similar to those for 1985-89 and 1990-94, indicating a relatively stable U.S. sink throughout the period.

Contributions of land-use history to carbon accumulation in US forests

Carbon accumulation in forests has been attributed to historical changes in land use and the enhancement of tree growth by CO₂ fertilization, N deposition, and climate change. The relative contribution of land use and growth enhancement is estimated by using inventory data from five states spanning a latitudinal gradient in the eastern United States. Land use is the dominant factor governing the rate of carbon accumulation in these states, with growth enhancement contributing far less than previously reported. The estimated fraction of aboveground net ecosystem production due to growth enhancement is 2.0 ± 4.4%, with the remainder due to land use.