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DOE OSTI2024

The key role of forest disturbance in reconciling estimates of the northern carbon sink

Northern forests are an important carbon sink, but our understanding of the driving factors is limited due to discrepancies between dynamic global vegetation models (DGVMs) and atmospheric inversions. We show that DGVMs simulate a 50% lower sink (1.1 ± 0.5 PgC yr –1 over 2001–2021) across North America, Europe, Russia, and China compared to atmospheric inversions (2.2 ± 0.6 PgC yr –1 ). We explain why DGVMs underestimate the carbon sink by considering how they represent disturbance processes, specifically the overestimation of fire emissions, and the lack of robust forest demography resulting in lower forest regrowth rates than observed. We reconcile net sink estimates by using alternative disturbance-related fluxes. We estimate carbon uptake through forest regrowth by combining satellite-derived forest age and biomass maps. We calculate a regrowth flux of 1.1 ± 0.1 PgC yr –1 , and combine this with satellite-derived estimates of fire emissions (0.4 ± 0.1 PgC yr –1 ), land-use change emissions from bookkeeping models (0.9 ± 0.2 PgC yr –1 ), and the DGVM-estimated sink from CO 2 fertilisation, nitrogen deposition, and climate change (2.2 ± 0.9 PgC yr –1 ). The resulting ‘bottom-up’ net flux of 2.1 ± 0.9 PgC yr –1 agrees with atmospheric inversions. The reconciliation holds at regional scales, increasing confidence in our results.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Global Carbon Budget 2024

Abstract. 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 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) 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 global net uptake of CO2 by the ocean (SOCEAN, called the ocean sink) is estimated with global ocean biogeochemistry models and observation-based fCO2 products (fCO2 is the fugacity of CO2). The global net uptake of CO2 by the land (SLAND, called the land sink) 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 sum of all sources and sinks results in the carbon budget imbalance (BIM), a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2023, EFOS increased by 1.3 % relative to 2022, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (10.3 ± 0.5 GtC yr−1 when the cement carbonation sink is not included), and ELUC was 1.0 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1 ± 0.9 GtC yr−1 (40.6 ± 3.2 GtCO2 yr−1). Also, for 2023, GATM was 5.9 ± 0.2 GtC yr−1 (2.79 ± 0.1 ppm yr−1; ppm denotes parts per million), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 2.3 ± 1.0 GtC yr−1, with a near-zero BIM (−0.02 GtC yr−1). The global atmospheric CO2 concentration averaged over 2023 reached 419.31 ± 0.1 ppm. Preliminary data for 2024 suggest an increase in EFOS relative to 2023 of +0.8 % (−0.2 % to 1.7 %) globally and an atmospheric CO2 concentration increase by 2.87 ppm, reaching 422.45 ppm, 52 % 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 1959–2023, with a near-zero overall budget imbalance, although discrepancies of up to around 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 the following: (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) 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 mean ocean sink. This living-data update documents changes in methods and datasets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2024 (Friedlingstein et al., 2024).

Friedlingstein, Pierre (ORCID:0000000333094739)↗

DOE OSTI2026

Global Carbon Budget 2025

Accurate assessment of anthropogenic carbon dioxide (CO 2 ) 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 synthesise datasets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO 2 emissions (E FOS ) are based on energy and cement production data. Emissions from land-use change (E LUC ) are estimated by bookkeeping models based on land-use data. The global atmospheric CO 2 growth rate (G ATM ) is computed from changes in concentration measured at surface stations. The global net uptake of CO 2 by the ocean (S OCEAN ) is estimated with global ocean biogeochemistry models and observation-based fCO 2 -products. The global net uptake of CO 2 by the land (S LAND ) is estimated with dynamic global vegetation models. Additional lines of evidence are provided by atmospheric inversions, atmospheric oxygen measurements, ocean interior observation-based estimates, and Earth System Models. This year, we introduced corrections on the E LUC , S OCEAN and S LAND estimates. The sum of all sources and sinks results in the carbon budget imbalance (B IM ), a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ± 1σ. For the year 2024, E FOS increased by 1.1 % relative to 2023, with fossil emissions at 10.3 ± 0.5 GtC yr −1 (including the cement carbonation sink, 0.2 GtC yr −1 ), E LUC was 1.3 ± 0.7 GtC yr −1 , for total anthropogenic CO 2 emissions of 11.6 ± 0.9 GtC yr −1 (42.4 ± 3.2 GtCO2 yr −1 ). Also, for 2024, G ATM was 7.9 ± 0.2 GtC yr−1 (3.73 ± 0.1 ppm yr −1 ), 2.2 GtC above the 2023 growth rate. SOCEAN was 3.4 ± 0.4 GtC yr −1 and S LAND was 1.9 ± 1.1 GtC yr −1 , leaving a large negative B IM (−1.7 GtC yr −1 ), suggesting that the total sink or G ATM is strongly overestimated in 2024. The global atmospheric CO 2 concentration averaged over 2024 reached 422.8 ± 0.1 ppm. Preliminary data for 2025 suggest an increase in E FOS relative to 2024 of +1.0 % (0.2 % to 1.7 %) globally, and atmospheric CO 2 concentration increasing by 2.1 ppm reaching 425.6 ppm, 53 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2024, with a near-zero overall budget imbalance, although discrepancies of up to around 1 GtC yr −1 persist for the representation of annual to decadal variability in CO 2 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 CO 2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the mean ocean sink. This living data update documents changes in methods and datasets applied to this most-recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2025 (Friedlingstein et al., 2025c).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2009

UAVSAR - A New Airborne L-Band Radar for Repeat Pass Interferometry

NASA/JPL has developed a new airborne Synthetic Aperture Radar (SAR) which has become available for use by the scientific community in January, 2009. Pod mounted, the UAVSAR was designed to be portable among a variety of aircraft, including unmanned aerial systems (UAS). The instrument operates in the L-Band, has a resolution under 2m from a GPS altitude of 12Km and a swath width of approximately 20Km. UAVSAR currently flies on a modified Gulfstream-III aircraft, operated by NASA s Dryden Flight Research Center at Edwards, California. The G-III platform enables repeat-pass interferometric measurements, by using a modified autopilot and precise kinematic differential GPS to repeatedly fly the aircraft within a specified 10m tube. The antenna is electronically steered along track to assure that the antenna beam can be directed independently, regardless of speed and wind direction. The instrument can be controlled remotely, AS AN OPTION, using the Research Environment for Vehicle Embedded Analysis on Linux (REVEAL). This allows simulation of the telepresence environment necessary for flight on UAS. Potential earth science research and applications include surface deformation, volcano studies, ice sheet dynamics, and vegetation structure.

Mace, Thomas H.↗

NASA NTRS2017

The Impact of Alternative Trait-Scaling Hypotheses for the Maximum Photosynthetic Carboxylation Rate (V (sub cmax)) on Global Gross Primary Production

The maximum photosynthetic carboxylation rate (V (sub cmax)) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global V(sub cmax) distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2 petagrams of Carbon (PgC) per year, 65 percent of the range of a recent model intercomparison of global GPP. The variation in GPP propagated through to a 27percent coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r equals 0.85-0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand V(sub cmax) variation in the field, particularly in northern latitudes.

chlorophyll fluorescence↗

NASA NTRS2017

Historical Carbon Dioxide Emissions Caused by Land-Use Changes are Possibly Larger than Assumed

The terrestrial biosphere absorbs about 20% of fossil-fuel CO2 emissions. The overall magnitude of this sink is constrained by the difference between emissions, the rate of increase in atmospheric CO2 concentrations, and the ocean sink. However, the land sink is actually composed of two largely counteracting fluxes that are poorly quantified: fluxes from land-use change andCO2 uptake by terrestrial ecosystems. Dynamic global vegetation model simulations suggest that CO2 emissions from land-use change have been substantially underestimated because processes such as tree harvesting and land clearing from shifting cultivation have not been considered. As the overall terrestrial sink is constrained, a larger net flux as a result of land-use change implies that terrestrial uptake of CO2 is also larger, and that terrestrial ecosystems might have greater potential to sequester carbon in the future. Consequently, reforestation projects and efforts to avoid further deforestation could represent important mitigation pathways, with co-benefits for biodiversity. It is unclear whether a larger land carbon sink can be reconciled with our current understanding of terrestrial carbon cycling. Our possible underestimation of the historical residual terrestrial carbon sink adds further uncertainty to our capacity to predict the future of terrestrial carbon uptake and losses.

Arneth, A.↗

NASA NTRS2016

Global Carbon Budget 2016

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 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 and consistency within and among components, alongside methodology and data limitations. 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 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 (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as +/- 1(sigma), reflecting the current capacity to characterize the annual estimates of each component of the global carbon budget. For the last decade available (2006-2015), EFF was 9.3+/-0.5 GtC/yr, ELUC 1.0+/-0.5 GtC/yr,GATM 4.5+/-0.1 GtC/yr, SOCEAN 2.6+/-0.5 GtC/yr, and SLAND 3.1+/-0.9 GtC/yr. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9+/-0.5 GtC/yr, showing a slowdown in growth of these emissions compared to the average growth of 1.8/yr that took place during 2006-2015.Also, for 2015, ELUC was 1.3+/-0.5 GtC/yr, GATM was 6.3+/-0.2 GtC/yr, SOCEAN was 3.0+/-0.5 GtC/yr, and SLAND was 1.9+/-0.9 GtC/yr. GATM was higher in 2015 compared to the past decade (2006-2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4+/-0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2% (range of -1.0 to +1.8% ) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Nino conditions of 2015-2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565+/-55 GtC (2075+/-205 GtCO2) for 1870-2016, about 75% from EFF and 25% from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set.

Quéré, Corinne Le↗

NASA NTRS2018

Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis

The tropical rainforests are the largest reserves of terrestrial carbon and, therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and on-going drought in the Congo region for more than two decades, there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. However, there is no single ecosystem model that quantifies the relationship between vegetation health in these rainforests and climatic factors. Small scale studies have used statistical correlation measure and simple linear regression to model climate-vegetation interactions, but suffer from the lack of comprehensive data representation as well as simplistic assumptions about dependency of the target on the covariates. In this paper we use genetic programming (GP) based symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space to build individual models for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Our method GP-tree shows that the rainforests exhibit tremendous resiliency in the face of extreme climatic events by adapting to changing conditions.

genetic programming↗

NASA NTRS2019

Historical (1700–2012) Global Multi-Model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)

Fire emissions are critical for carbon and nutrient cycles, climate, and air quality. Dynamic Global Vegetation Models (DGVMs) with interactive fire modeling provide important estimates for long-term and large-scale changes of fire emissions. Here we present the first multi-model estimates of global gridded historical fire emissions for 1700-2012, including carbon and 33 species of trace gases and aerosols. The dataset is based on simulations of nine DGVMs with different state-of-the-art global fire models that participated in the Fire Modeling Intercomparison Project (FireMIP), using the same and standardized protocols and forcing data, and the most up-to-date fire emission factor table from field and laboratory studies over various land cover types. We evaluate the simulations of present-day fire emissions by comparing them with satellite-based products. Evaluation results show that most DGVMs simulate present-day global fire emission totals within the range of satellite-based products, and can capture the high emissions over the tropical savannas, low emissions over the arid and sparsely vegetated regions, and the main features of seasonality. However, most of the models fail to simulate the interannual variability, partly due to a lack of modeling peat fires and tropical deforestation fires. Historically, all models show only a weak trend in global fire emissions before ~1850s, consistent with multi-source merged historical reconstructions. The long-term trends among DGVMs are quite different for the 20th century, with some models showing an increase and others a decrease in fire emissions, mainly as a result of the discrepancy in their simulated responses to human population density change and land-use and land-cover change (LULCC). Our study provides a basic dataset for developing regional and global multi-source merged historical reconstructions and merging methods, and analyzing historical changes of fire emissions and their uncertainties as well as their role in the Earth system. It also highlights the importance of accurately modeling the responses of fire emissions to LULCC and population density change in reducing uncertainties in historical reconstructions of fire emissions and providing more reliable future projections.

Li, Fang↗

NASA NTRS2019

Multimodel Analysis of Future Land Use and Climate Change Impacts on Ecosystem Functioning

Land use and climate changes both affect terrestrial ecosystems. Here, we used three combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1xRCP26, SSP3xRCP60, and SSP5xRCP85) as input to three dynamic global vegetation models to assess the impacts and associated uncertainty on several ecosystem functions: terrestrial carbon storage and fluxes, evapotranspiration, surface albedo, and runoff. We also performed sensitivity simulations in which we kept either land use or climate (including atmospheric CO2) constant from year 2015 on to calculate the isolated land use versus climate effects. By the 2080–2099 period, carbon storage increases by up to 87 ± 47 Gt (SSP1xRCP26) compared to present day, with large spatial variance across scenarios and models. Most of the carbon uptake is attributed to drivers beyond future land use and climate change, particularly the lagged effects of historic environmental changes. Future climate change typically increases carbon stocks in vegetation but not soils, while future land use change causes carbon losses, even for net agricultural abandonment (SSP1xRCP26). Evapotranspiration changes are highly variable across scenarios, and models do not agree on the magnitude or even sign of change of the individual effects. A calculated decrease in January and July surface albedo (up to −0.021 ± 0.007 and −0.004 ± 0.004 for SSP5xRCP85) and increase in runoff (+67 ± 6 mm/year) is largely driven by climate change. Overall, our results show that future land use and climate change will both have substantial impacts on ecosystem functioning. However, future changes can often not be fully explained by these two drivers and legacy effects have to be considered.

Krause, A.↗

NASA NTRS2020

Critical land change information enhances the understanding of carbon balance in the United States

Large‐scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine‐scale processes including land use and land cover change into a large‐scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1‐km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.

carbon sequestration↗

NASA NTRS2019

Multimodel Analysis of Future Land Use and Climate Change Impacts on Ecosystem Functioning

land use change↗

NASA NTRS2021

Land-Use Harmonization Datasets for Global Carbon Budget 2019 and Beyond

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 the best models in well-organized scientific assessments. The Land-Use Harmonization dataset (LUH2), previously developed and used as input for CMIP6 simulations, has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessment. These annual LUH2-GCB updates and extensions have incorporated annual FAO wood harvest data updates for dataset years after 2015 and HYDE gridded agriculture area data updates (based on annual FAO agricultural area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of one 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 toagricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, and crop rotationsand pasture managementand are used by both book-keeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB. For GCB 2019,a more significant update to LUH2 was produced (LUH2-GCB2019) to correct cropland and grazing area errors in the underlying input datasets for the globally important region of Brazil, as far back as 1950. From 1951-2012 the LUH2-GCB2019 dataset begins to diverge from the LUH2 v2hdataset, 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. These LUH2-GCB2019 corrections for Brazil provide the base for future LUH2-GCB updates including the recent LUH2-GCB2020 dataset, and present a starting point for operationalizing the creation of these datasets toreduce time-lags due to the multiple input dataset and model latencies.

L Chini↗

NASA NTRS2021

Modelled land use and land cover change emissions – a spatio-temporal comparison of different approaches

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

land use and land cover change↗

NASA NTRS2021

Are Land-Use Change Emissions in Southeast Asia Decreasing or Increasing?

Southeast Asia is a region known for active land-use changes (LUC) over the past 60 years; yet, how trends in net CO2 uptake and release resulting from LUC activities (net LUC flux) have changed through past decades remains uncertain. The level of uncertainty in net LUC flux from process-based models is so high that it cannot be concluded that newer estimates are necessarily more reliable than older ones. Here, we examined net LUC flux estimates of Southeast Asia for the 1980s−2010s from older and newer sets of Dynamic Global Vegetation Model simulations (TRENDY v2 and v7, respectively), and forcing data used for running those simulations, along with two book-keeping estimates (H&N and BLUE). These estimates yielded two contrasting historical LUC transitions, such that TRENDY v2 and H&N showed a transition from increased emissions from the 1980s to 1990s to declining emissions in the 2000s, while TRENDY v7 and BLUE showed the opposite transition. We found that these contrasting transitions originated in the update of LUC forcing data, which reduced the loss of forest area during the 1990s. Further evaluation of remote sensing studies, atmospheric inversions, and the history of forestry and environmental policies in Southeast Asia supported the occurrence of peak emissions in the 1990s and declining thereafter. However, whether LUC emissions continue to decline in Southeast Asia remains uncertain as key processes in recent years, such as conversion of peat forest to oil-palm plantation, are yet to be represented in the forcing data, suggesting a need for further revision.

land-use change↗

NASA NTRS2023

A Multi-Satellite Framework to Rapidly Evaluate Extreme Biosphere Cascades: The Western US 2021 Drought and Heatwave

The increasing frequency and intensity of climate extremes and complex ecosystem responses motivate the need for integrated observational studies at low-latency to determine biosphere responses and carbon-climate feedbacks. Here, we develop a satellite-based rapid attribution workflow and demonstrate its use at a 1–2-month latency to attribute drivers of the carbon cycle feedbacks during the 2020-2021 Western US drought and heatwave. In the first half of 2021, concurrent negative photosynthesis anomalies and large positive column CO 2 anomalies were detected with satellites. Using a simple atmospheric mass balance approach, we estimate a surface carbon efflux anomaly of 132 TgC in June 2021, a magnitude corroborated 28 independently with a dynamic global vegetation model. Integrated satellite observations of hydrologic processes, representing the soil-plant-atmosphere continuum (SPAC), show that these surface carbon flux anomalies are largely due to substantial reductions in photosynthesis because of a spatially widespread moisture-deficit propagation through the SPAC between 2020 and 2021. A causal model indicates deep soil moisture stores partially drove photosynthesis, maintaining its values in 2020 and driving its declines throughout 2021. The causal model also suggests legacy effects may have amplified photosynthesis deficits in 2021 beyond the direct effects of environmental forcing. The integrated, observation framework presented here provides a valuable first assessment of a biosphere extreme response and an independent testbed for improving drought propagation and mechanisms in models. The rapid identification of extreme carbon anomalies and hotspots can also aid mitigation and adaptation decisions.

Causal model↗

NASA NTRS2022

Global Carbon Budget 2022

Accurate assessment of anthropogenic carbon dioxide (CO 2 ) 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 CO 2 emissions (E FOS ) are based on energy statistics and cement production data, while emissions from land-use change (E LUC ), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly, and its growth rate (G ATM ) is computed from the annual changes in concentration. The ocean CO 2 sink (S OCEAN ) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO 2 sink (S LAND ) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (B IM ), 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, E FOS 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 E LUC was 1.1 ± 0.7 GtC yr −1 , for a total anthropogenic CO 2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr −1 (40.0 ± 2.9 GtCO 2 ). Also, for 2021, G ATM was 5.2 ± 0.2 GtC yr −1 (2.5 ± 0.1 ppm yr −1 ), S OCEAN was 2.9 ± 0.4 GtC yr −1 , and S LAND was 3.5 ± 0.9 GtC yr −1 , with a B IM of −0.6 GtC yr −1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO 2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in E FOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO 2 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 CO 2 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 CO 2 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).

Pierre Friedlingstein↗

NASA NTRSDate not supplied

FluxSat: Long-term Earth Science Data Record (ESDR) for Terrestrial Gross Primary Production (GPP) based on satellite data calibrated with eddy covariance data

Gross primary production (GPP), the amount of carbon dioxide (CO 2 ) assimilated by plants through photosynthesis, is one of the most variable and uncertain components of the global carbon cycle. Global GPP has been estimated with a number of process-based models, data-driven, and hybrid approaches. Dynamic global vegetation models (DGVMs), driven by observed environmental changes, are used for global carbon budget assessments and long-term (climate) prediction. Benchmarking these and other models globally with data-driven GPP estimates is critical for understanding the land sink and ensuring accurate forecasts of the carbon cycle. In addition, global data-driven GPP estimates are crucial for studies of interannual variability, including trends that are linked to mechanisms with large uncertainties, such as the indirect CO 2 fertilization effect related to greening. In response to a community need for a GPP data set that well captures spatio-temporal variability, we developed FluxSat, a data-driven approach that optimizes the use of satellite reflectance data from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, calibrated using ground-based eddy covariance (EC) data. We are enhancing (spatially, higher resolution) and extending FluxSat (in time, with additional sensors) to create a high quality long term GPP Earth System Data Record (ESDR) for use in model benchmarking, carbon cycle modeling, and studies of trends and interannual variability. Our team’s objectives are to: 1. Update and document the current MODIS FluxSat GPP (daily, 0.05o and 0.5o resolutions) products with latest available MODIS and EC data sets; 2. Extend FluxSat GPP record forward in time with the Visible Infrared Imaging Radiometer Suite (VIIRS) on operational weather satellites going forward; 3. Extend FluxSat GPP record backward in time using the Advanced Very High Resolution Radiometer (AVHRR) on weather satellites dating back to 1981; 4. Provide higher spatial resolution MODIS and VIIRS GPP (0.0083o). 5. Thoroughly evaluate all FluxSat products with independent data; and 6. Create a homogenized long-term GPP record spanning 40+ years. We will discuss plans for this long-term data set that is supported through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program.

gross Primary Production↗