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

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 synthesise 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 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), ELUC was 1.1 ± 0.7 GtC yr-1, for a total anthropogenic CO2 emission of 11.1 ± 0.8 GtC yr-1 (40.8 ± 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. total estimated sources too low or sinks 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 +1.1 % (0 % to 1.7 %) globally, and atmospheric CO2 concentration reaching 417.3 ppm, more than 50 % above pre-industrial level. 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 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 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 (Friedlingstein et al., 2022a; Friedlingstein et al., 2020; Friedlingstein et al., 2019; Le Quéré et al., 2018b, 2018a, 2016, 2015b, 2015a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Land cover and land management change (LCLMC) has been highlighted for its critical role in mitigation scenarios, both in terms of global mitigation and local adaptation. Yet, the climate effect of individual LCLMC options, their dependence on the background climate and the local vs. non-local responses are still poorly understood across different Earth system models (ESMs). Here we simulate the climatic effects of LCLMC using three state-of-the-art ESMs, including the Community Earth System Model (CESM), the Max Planck Institute for Meteorology Earth System Model (MPI-ESM) and the European Consortium Earth System Model (EC-EARTH). We assess the LCLMC effects using the following four idealized experiments: (i) a fully afforested world, (ii) a world fully covered by cropland, (ii) a fully afforested world with extensive wood harvesting and (iv) a full-cropland world with extensive irrigation. In these idealized sensitivity experiments, performed under present-day climate conditions, the effects of the different LCLMC strategies represent an upper bound for the potential of global mitigation and local adaptation. To disentangle the local and non-local effects from the LCLMC, a checkerboard-like LCLMC perturbation, i.e. alternating grid boxes with and without LCLMC, is applied. The local effects of deforestation on surface temperature are largely consistent across the ESMs and the observations, with a cooling in boreal latitudes and a warming in the tropics. However, the energy balance components driving the change in surface temperature show less consistency across the ESMs and the observations. Additionally, some biases exist in specific ESMs, such as a strong albedo response in CESM mid-latitudes and a soil-thawing-driven warming in boreal latitudes in EC-EARTH. The non-local effects on surface temperature are broadly consistent across ESMs for afforestation, though larger model uncertainty exists for cropland expansion. Irrigation clearly induces a cooling effect; however, the ESMs disagree whether these are mainly local or non-local effects. Wood harvesting is found to have no discernible biogeophysical effects on climate. Overall, our results underline the potential of ensemble simulations to inform decision making regarding future climate consequences of land-based mitigation and adaptation strategies., Earth System Dynamics, 14 (3), ISSN:2190-4987, ISSN:2190-4979

With the focus of climate policy shifting from pledges to implementation, there is an increasing need to track progress on climate change mitigation at country level, especially for the land-use sector. Despite new tools and models offering unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO2 fluxes between the national greenhouse gas inventories (NGHGIs) used to assess compliance with the Paris Agreement, and the Global Carbon Budget and IPCC assessment reports, both based on global bookkeeping models (BMs). Recent evidence showed that these differences are mainly due to inconsistent definitions of anthropogenic forest CO2 fluxes. In particular, the part of the land sink that is caused by the indirect effects of human-induced environmental change (e.g., fertilization effect on vegetation growth due to increase atmospheric CO2 concentration, climate change) on managed lands is treated as non-anthropogenic by BMs, while in most cases is considered anthropogenic in NGHGIs. In addition, countries use a broader definition of managed land than BMs. Building on previous studies, we implement an approach that adds the CO2 sink due to environmental change from countries’ managed forest area (estimated by Dynamic Global Vegetation Models, DGVMs) to the original land-use flux from BMs. This sum is expected to be conceptually more comparable to NGHGIs. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of land categories (i.e., forest land, deforestation, organic soils, other land uses) and countries. Our results confirm a large difference in land use CO2 fluxes between the ensemble mean of the BMs, estimating a source of 4.3 GtCO2 yr-1 globally for the period 2001–2020, and NGHGIs, which estimate a sink of -1.7 GtCO2 yr-1. Most of this 6.0 GtCO2 yr-1 gap is found on forest land (3.8 GtCO2 yr-1), with differences also for deforestation (1.1 GtCO2 yr-1), other land uses (1.0 GtCO2 yr-1), and to a lesser extent for organic soils (0.1 GtCO2 yr-1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-5.1 GtCO2 yr-1) to BMs estimates, the gap between BMs and NGHGIs becomes significantly smaller both globally (residual gap: 0.9 GtCO2 yr-1) and in most regions and countries. The remaining differences mostly reflect smaller net emissions from deforestation and agricultural land in the NGHGIs of developing countries than in the BMs. By reconciling most of the differences between NGHGIs and global models (BMs and DGVMs), offering a blueprint for operationalizing future comparisons, and identifying areas to be further investigated, this study represents an important step forward for increasing transparency and confidence in land-use CO2 flux estimates at the country level. This is crucial to support land-based mitigation investments and assess the countries’ collective progress under the Paris Agreement’s Global Stocktake.

Rainfall-intense summer monsoon seasons on the Indian subcontinent that are exceeding long-term averages cause widespread floods and landslides. Here we show that the latest generation of coupled climate models robustly project an intensification of very rainfall-intense seasons (June–September). Under the shared socioeconomic pathway SSP5-8.5, very wet monsoon seasons as observed in only 5 years in the period 1965–2015 are projected to occur 8 times more often in 2050–2100 in the multi-model average. Under SSP2-4.5, these seasons become only a factor of 6 times more frequent, showing that even modest efforts to mitigate climate change can have a strong impact on the frequency of very strong rainfall seasons. Besides, we find that the increasing risk of extreme seasonal rainfall is accompanied by a shift from days with light rainfall to days with moderate or heavy rainfall. Additionally, the number of wet days is projected to increase. © 2022. The Authors.

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

Land surface models have an increasing scope. Initially designed to capture the feedbacks between the land and the atmosphere as part of weather and climate prediction, they are now used as a critical tool in the urgent need to inform policy about land-use and water-use management in a world that is changing physically and economically. This paper outlines the way that models have evolved through this change of purpose and what might the future hold. It highlights the importance of distinguishing between advances in the science within the modelling components, with the advances of how to represent their interaction. This latter aspect of modelling is often overlooked but will increasingly manifest as an issue as the complexity of the system, the time and space scales of the system being modelled increase. These increases are due to technology, data availability and the urgency and range of the problems being studied.

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 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 first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gases 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 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1 and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021, suggest a rebound in EFOS relative to 2020 of +4.9 % (4.1 % to 5.7 %) 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 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 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 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 (Friedlingstein et al., 2020; Friedlingstein et al., 2019; Le Quéré et al., 2018b, 2018a, 2016, 2015b, 2015a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

Land management and anthropogenic land cover change (LMLCC) plays a key role in the global carbon budget. For example, approximately half of the terrestrial biomass has been removed by LMLCC to date. Conversely, large potentials for carbon dioxide removal are invoked when vegetation-based negative emission technologies such as afforestation are discussed. Previous studies on LMLCC effects on the carbon cycle focused on the direct effect of tree removal or regrowth on carbon fluxes. However, a suite of studies has shown that LMLCC has an important influence on climate via biogeophysical effects through changes in energy and water fluxes. This influence can reach far beyond the location of LMLCC, called the "nonlocal effect" of LMLCC on climate. This raises the question if LMLCC can also have non-negligible effects on the carbon cycle remote from the LMLCC location itself. Our study establishes the concept how to investigate strength and patterns of the unintended nonlocal side-effects of LMLCC on carbon stocks and fluxes.Therefore, we conducted three different fully-coupled atmosphere-ocean-land experiments of idealized global cropland expansion with and without cropland irrigation as well as global re-/afforestation starting from today's state over a 150-year period under present day solar and trace gas forcing. All experiments were simulated by three different earth system models (MPI-ESM, EC-EARTH and CESM) to additionally quantify inter-model uncertainty and potentially uncover specific model biases. Here only CESM and MPI-ESM results are presented. To separate the local and nonlocal effects we use a checkerboard approach of grid boxes with and without LMLCC as proposed by Winckler et al., 2017. That is, we separate the carbon stock changes due to LMLCC at the location of LMLCC (local effect) from those induced by climate change caused by remote LMLCC (nonlocal effect). The total effect is the sum of both, the local and nonlocal effect.The results of MPI-ESM (CESM) show that the global nonlocal effect on vegetation carbon (cVeg) accounts for 6% (3%) and 4% (0.6%) of total cVeg changes for crop expansion and afforestation simulation, respectively. Additionally, applying irrigation to crop expansion strongly increases the nonlocal climate induced cVeg change by 52% (610%) of total cVeg change for MPI-ESM (CESM). The nonlocal effect of regions with largest carbon changes exhibit partly much larger nonlocal/total ratio. For instance, the nonlocal cVeg change in the Congo basin after cropland expansion accounts for more than 30% of total cVeg change. Furthermore, in some regions, the nonlocal effect of cVeg can be opposite to the local effect, and may thus reduce the total effect of the LMLCC practice compared to what would be expected from the local effect alone.Overall, the results from MPI-ESM and CESM indicate that the nonlocal carbon effect is important in key regions and can even become globally important for the irrigation practices. In addition to local effects, these unintended nonlocal effects need to be considered when the impacts of a LMLCC practice on the entire carbon cycle (e.g., also with regard to a potential carbon dioxide removal method) will be assessed.

The frequency and severity of droughts and heatwaves are projected to increase under global warming. However, their impacts on the terrestrial biosphere and the anthropogenic CO2 sink remain poorly understood. Here we analyse the effects of six hypothetical climate scenarios with stationary climate but differing drought-heat signatures on vegetation distribution and land carbon dynamics, as modelled by seven state-of-the-art dynamic global vegetation models. The six forcing scenarios are sampled from a long climate model simulation and consist of a control scenario representing a natural climate, a scenario with no hot and dry extremes, one with no compound hot and dry extremes but univariate extremes are possible, one with only hot extremes, one with only dry extremes, and one with both hot and dry extremes. Models show substantial differences in their vegetation coverage response to the different scenarios. While in some models, climate with no droughts and heatwaves favours tree growth, this is not the case for other models, where grasses benefit. Similarly, climates with frequent droughts promote grasses in some models and reduce forest growth in others. Models tend to agree that a climate with frequent concurrent droughts and heatwaves leads to reduced tree cover and increased grass cover. The changes in coverage are mirrored by changes in gross and net carbon fluxes. The stark differences among model responses illustrate the different modelling processes dealing with heat and drought stress and how they are differently affected by the extremes. Overall, this comparison helps quantify model uncertainties and process differences that are important for how vegetation behaves under extreme climate events.Our study illustrates how factorial model experiments can be employed to disentangle the impacts from single and compound extremes. The findings from this model comparison may also help to identify sources of uncertainty in carbon cycle projections.

Land cover and land management changes (LCLMC) have often been highlighted as crucial regarding climate change mitigation (e.g., enhanced carbon uptake on land through afforestation), but their potential for adaptation has also been suggested (e.g., local cooling through irrigation). Regarding the latter, the effects of LCLMC on the climate remain uncertain. LCLMC can have strong implications on surface moisture fluxes and have even been linked to changes in large scale atmospheric circulation. Here, we study the effects of three LCLMC (i) global afforestation, (ii) global cropland expansion and (iii) large-scale irrigation extension on climate by employing three fully coupled Earth System Models (CESM, MPI-ESM, and EC-EARTH). Sensitivity simulations were performed under present-day conditions and extreme LCLMC, of which the effects on moisture fluxes and atmospheric circulation are investigated. We do this by first analyzing the surface moisture fluxes using monthly precipitation and evaporation data to perform a moisture convergence analysis, before performing a moisture tracking analysis with the Water Accounting Model (WAM-2 layers) , this model solves the atmospheric moisture balance and requires sub-daily data from the sensitivity experiments as an input.Here we focus on the results from CESM, cropland expansion has shown to cause an average shift southward of the Intertropical convergence zone as well as a weakening in westerlies strength and consequent decrease in moisture transport. This causes an increase in continental moisture sources over most of the Northern Hemisphere. Afforestation, in contrast, shows an average shift northward of the Intertropical convergence zone and enhanced westerlies and moisture transport. Lastly, irrigation expansion enhances the moisture convergence over areas where irrigation is applied, causing an increase in both precipitation and evapotranspiration.

Natural and anthropogenic disturbances, in particular forest management, affect forest age structures all around the globe. Forest age structures in turn influence key land surface processes, such as photosynthesis and thus the carbon cycle. Yet, many dynamic global vegetation models (DGVMs), including those used as land surface models (LSMs) in Earth system models (ESMs), do not account for subgrid forest age structures, despite being used to investigate land-use effects on the global carbon budget or simulating biogeochemical responses to climate change. In this paper we present a new scheme to introduce forest age classes in hierarchical tile-based DGVMs combining benefits of recently applied approaches the first being a computationally efficient age-dependent simulation of all relevant processes, such as photosynthesis and respiration, using a restricted number of age classes and the second being the tracking of the exact forest age, which is a prerequisite for any implementation of age-based forest management. This combination is achieved by using the tile hierarchy to track the area fraction for each age on an aggregated plant functional type level, whilst simulating the relevant processes for a set of age classes. We describe how we implemented this scheme in JSBACH4, the LSM of the ICOsahedral Non-hydrostatic Earth system model (ICON-ESM). Subsequently, we compare simulation output to global observation-based products for gross primary production, leaf area index, and above-ground biomass to assess the ability of simulations with and without age classes to reproduce the annual cycle and large-scale spatial patterns of these variables. The comparisons show decreasing differences and increasing computation costs with an increasing number of distinguished age classes. The results demonstrate the benefit of the introduction of age classes, with the optimal number of age classes being a compromise between computation costs and error reduction.

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., Global Biogeochemical Cycles, 36 (1), ISSN:0886-6236, ISSN:1944-9224

Purpose of Review As demand for food and fiber, but also for negative emissions, brings most of the Earth’s land surface under management, we aim to consolidate the scientific progress of recent years on the climatic effects of global land use change, including land management, and related land cover changes (LULCC). Recent Findings We review the methodological advances in both modeling and observations to capture biogeochemical and biogeophysical LULCC effects and summarize the knowledge on underlying mechanisms and on the strength of their effects. Recent studies have raised or resolved several important questions related to LULCC: How can we derive CO2 fluxes related to LULCC from satellites? Why are uncertainties in LULCC-related GHG fluxes so large? How can we explain that estimates of afforestation/reforestation potentials diverge by an order of magnitude? Can we reconcile the seemingly contradicting results of models and observations concerning the cooling effect of high-latitude deforestation? Summary Major progress has been achieved in understanding the complementarity of modeling, observations, and inventories for estimating the impacts of various LULCC practices on carbon, energy, and water fluxes. Emerging fields are the operationalization of the recently achieved integration of approaches, such as a full greenhouse gas balance of LULCC, mapping of emissions from global models to country-reported emissions data, or model evaluation against local biogeophysical observations. Fundamental challenges remain, however, e.g., in separating anthropogenic from natural land use dynamics and accurately quantifying the first. Recent progress has laid the foundation for future research to integrate the local to global scales at which the various effects act, to create co-benefits between global mitigation, including land-based carbon dioxide removal, and changes in local climate for effective adaptation strategies.

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21

The Response Function Identification method introduced in the first part of this study is applied here to investigate the land carbon cycle in the Max Planck Institute for Meteorology Earth System Model. We identify from standard C4MIP 1 % experiments the linear response functions that generalize the land carbon sensitivities β and γ. The identification of these generalized sensitivities is shown to be robust by demonstrating their predictive power when applied to experiments not used for their identification. The linear regime for which the generalized framework is valid is estimated, and approaches to improve the quality of the results are proposed. For the generalized γ-sensitivity, the response is found to be linear for temperature perturbations until at least 6 K. When this sensitivity is identified from a 2 x CO2 experiment instead of the 1 % experiment, its predictive power improves, indicating an enhancement in the quality of the identification. For the generalized β-sensitivity, the linear regime is found to extend up to CO2 perturbations of 100 ppm. We find that nonlinearities in the β-response arise mainly from the nonlinear relationship between Net Primary Production and CO2. By taking instead of CO2 the resulting Net Primary Production as forcing, the response is approximately linear until CO2 perturbations of about 850 ppm. Taking Net Primary Production as forcing also substantially improves the spectral resolution of the generalized β-sensitivity. For the best recovery of this sensitivity, we find a spectrum of internal time scales with two peaks, at 4 and 100 years. Robustness of this result is demonstrated by two independent tests. We find that the two-peak spectrum can be explained by the different characteristic time scales of functionally different elements of the land carbon cycle. The peak at 4 years results from the collective response of carbon pools whose dynamics is governed by fast processes, namely pools representing living vegetation tissues (leaves, fine roots, sugars, and starches) and associated litter. The peak at 100 years results from the collective response of pools whose dynamics is determined by slow processes, namely the pools that represent the wood in stem and coarse roots, the associated litter, and the soil carbon (humus). Analysis of the response functions that characterize these two groups of pools shows that the pools with fast dynamics dominate the land carbon response only for times below 2 years. For times above 25 years the response is completely determined by the pools with slow dynamics. From 100 years onwards only the humus pool contributes to the land carbon response.

Existent methods to identify linear response functions from data require tailored perturbation experiments, e.g. impulse or step experiments. And if the system is noisy, these experiments need to be repeated several times to obtain a good statistics. In contrast, for the method developed here, data from only a single perturbation experiment at arbitrary perturbation is sufficient if in addition data from an unperturbed (control) experiment is available. To identify the linear response function for this ill-posed problem we invoke regularization theory. The main novelty of our method lies in the determination of the level of background noise needed for a proper estimation of the regularization parameter: This is achieved by comparing the frequency spectrum of the perturbation experiment with that of the additional control experiment. The resulting noise level estimate can be further improved for linear response functions known to be monotonic. The robustness of our method and its advantages are investigated by means of a toy model. We discuss in detail the dependence of the identified response function on the quality of the data (signal-to-noise ratio) and on possible nonlinear contributions to the response. The method development presented here prepares in particular for the identification of carbon-cycle response functions in Part II of this study. But the core of our method, namely our new approach to obtain the noise level for a proper estimation of the regularization parameter, may find applications in solving also other types of linear ill-posed problems.

Despite their sparse vegetation, dryland regions exert a huge influence over global biogeochemical cycles because they cover more than 40% of the world surface (Schimel 2010 Science 327 418–9). It is thought that drylands dominate the inter-annual variability (IAV) and long-term trend in the global carbon (C) cycle (Poulter et al 2014 Nature 509 600–3, Ahlstrom et al 2015 Science 348 895–9, Zhang et al 2018 Glob. Change Biol. 24 3954–68). Projections of the global land C sink therefore rely on accurate representation of dryland C cycle processes; however, the dynamic global vegetation models (DGVMs) used in future projections have rarely been evaluated against dryland C flux data. Here, we carried out an evaluation of 14 DGVMs (TRENDY v7) against net ecosystem exchange (NEE) data from 12 dryland flux sites in the southwestern US encompassing a range of ecosystem types (forests, shrub- and grasslands). We find that all the models underestimate both mean annual C uptake/release as well as the magnitude of NEE IAV, suggesting that improvements in representing dryland regions may improve global C cycle projections. Across all models, the sensitivity and timing of ecosystem C uptake to plant available moisture was at fault. Spring biases in gross primary production (GPP) dominate the underestimate of mean annual NEE, whereas models’ lack of GPP response to water availability in both spring and summer monsoon are responsible for inability to capture NEE IAV. Errors in GPP moisture sensitivity at high elevation forested sites were more prominent during the spring, while errors at the low elevation shrub and grass-dominated sites were more important during the monsoon. We propose a range of hypotheses for why model GPP does not respond sufficiently to changing water availability that can serve as a guide for future dryland DGVM developments. Our analysis suggests that improvements in modeling C cycle processes across more than a quarter of the Earth’s land surface could be achieved by addressing the moisture sensitivity of dryland C uptake.

To track progress towards keeping warming well below 2 °C, as agreed upon in the Paris Agreement, comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions (GHG) is required. Here we provide a dataset on anthropogenic GHG emissions 1970–2019 with a broad country and sector coverage. We build the dataset from recent releases of the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases, and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with data on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three bookkeeping models. We provide an assessment of the uncertainties in each greenhouse gas at the 90 % confidence interval (5th–95th percentile) by combining statistical analysis and comparisons of global emissions inventories with an expert judgement informed by the relevant scientific literature. We identify important data gaps: CH4 and N2O emissions could be respectively 10–20 % higher than reported in EDGAR once all emissions are accounted. F-gas emissions estimates for individual species in EDGARv5 do not align well with atmospheric measurements and the F-gas total exceeds measured concentrations by about 30 %. However, EDGAR and official national emission reports under the UNFCCC do not comprehensively cover all relevant F-gas species. Excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are larger than the sum of the reported species. GHG emissions in 2019 amounted to 59 ± 6.6 GtCO2eq: CO2 emissions from FFI were 38 ± 3.0 Gt, CO2 from LULUCF 6.6 ± 4.6 Gt, CH4 11 ± 3.3 GtCO2eq, N2O 2.4 ±1.5 GtCO2eq and F-gases 1.6 ± 0.49 GtCO2eq. Our analysis of global, anthropogenic GHG emission trends over the past five decades (1970–2019) highlights a pattern of varied, but sustained emissions growth. There is high confidence that global anthropogenic greenhouse gas emissions have increased every decade. Emission growth has been persistent across different (groups of) gases. While CO2 has accounted for almost 75 % of the emission growth since 1970 in terms of CO2eq as reported here, the combined F-gases have grown at a faster rate than other GHGs, albeit starting from low levels in 1970. Today, F-gases make a non-negligible contribution to global warming – even though CFCs and HCFCs, regulated under the Montreal Protocol and not included in our estimates, have contributed more. There is further high confidence that global anthropogenic GHG emission levels were higher in 2010-2019 than in any previous decade and GHG emission levels have grown across the most recent decade. While average annual greenhouse gas emissions growth slowed between 2010–2019 compared to 2000–2009, the absolute increase in average decadal GHG emissions from the 2000s to the 2010s has been the largest since the 1970s – and within all human history as suggested by available long-term data. We note considerably higher rates of change in GHG emissions between 2018 and 2019 than for the entire decade 2010–2019, which is numerically comparable with the period of high GHG emissions growth during the 2000s, but we place low confidence in this finding as the majority of the growth is driven by highly uncertain increases in CO2-LULUCF emissions as well as the use of preliminary data and extrapolation methodologies for these most recent years. While there is a growing number of countries today on a sustained emission reduction trajectory, our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. We conclude by highlighting that tracking progress in climate policy requires substantial investments in independent GHG emission accounting and monitoring as well as the available national and international statistical infrastructures. The data associated with this article (Minx et al. 2021) can be found at https://doi.org/10.5281/zenodo.5053056.

Fluxes from deforestation, changes in land cover, land use and management practices (FLUC for simplicity) contributed to approximately 14 % of anthropogenic CO2 emissions in 2009–2018. Estimating FLUC accurately in space and in time remains, however, challenging, due to multiple sources of uncertainty in the calculation of these fluxes. This uncertainty, in turn, is propagated to global and regional carbon budget estimates, hindering the compilation of a consistent carbon budget and preventing us from constraining other terms, such as the natural land sink. Uncertainties in FLUC estimates arise from many different sources, including differences in model structure (e.g. process based vs. bookkeeping) and model parameterisation. Quantifying the uncertainties from each source requires controlled simulations to separate their effects. Here, we analyse differences between the two bookkeeping models used regularly in the global carbon budget estimates since 2017: the model by Hansis et al. (2015) (BLUE) and that by Houghton and Nassikas (2017) (HN2017). The two models have a very similar structure and philosophy, but differ significantly both with respect to FLUC intensity and spatiotemporal variability. This is due to differences in the land-use forcing but also in the model parameterisation. We find that the larger emissions in BLUE compared to HN2017 are largely due to differences in C densities between natural and managed vegetation or primary and secondary vegetation, and higher allocation of cleared and harvested material to fast turnover pools in BLUE than in HN2017. Besides parameterisation and the use of different forcing, other model assumptions cause differences: in particular that BLUE represents gross transitions which leads to overall higher carbon losses that are also more quickly realised than HN2017.

Brazil is currently the largest contributor of land use and land cover change (LULCC) carbon dioxide net emissions worldwide, representing 17%–29% of the global total. There is, however, a lack of agreement among different methodologies on the magnitude and trends in LULCC emissions and their geographic distribution. Here we perform an evaluation of LULCC datasets for Brazil, including those used in the annual global carbon budget (GCB), and national Brazilian assessments over the period 2000–2018. Results show that the latest global HYDE 3.3 LULCC dataset, based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps, can represent the observed spatial variation in LULCC over the last decades, representing an improvement on the HYDE 3.2 data previously used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than estimates based on MapBiomas. We use HYDE 3.3 and MapBiomas as input to a global bookkeeping model (bookkeeping of land use emission, BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine Brazil’s LULCC emissions over the period 2000–2019. Results show mean annual LULCC emissions of 0.1–0.4 PgC yr−1, compared with 0.1–0.24 PgC yr−1 reported by the Greenhouse Gas Emissions Estimation System of land use changes and forest sector (SEEG/LULUCF) and by FAO in its latest assessment of deforestation emissions in Brazil. Both JULES-ES and BLUE now simulate a slowdown in emissions after 2004 (−0.006 and −0.004 PgC yr−2 with HYDE 3.3, −0.014 and −0.016 PgC yr−2 with MapBiomas, respectively), in agreement with the Brazilian INPE-EM, global Houghton and Nassikas book-keeping models, FAO and as reported in the 4th national greenhouse gas inventories. The inclusion of Earth observation data has improved spatial representation of LULCC in HYDE and thus model capability to simulate Brazil’s LULCC emissions. This will likely contribute to reduce uncertainty in global LULCC emissions, and thus better constrains GCB assessments.

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

Forests are the main source of biomass production from solar energy and take up around 2.4±0.4 PgC per year globally. Future changes in climate may affect forest growth and productivity. Currently, state-of-the-art Earth system models use prescribed wood harvest rates in future climate projections. These rates are defined by integrated assessment models (IAMs), only accounting for regional wood demand and largely ignoring the supply side from forests. Therefore, we assess how global growth and harvest potentials of forests change when they are allowed to respond to changes in environmental conditions. For this, we simulate wood harvest rates oriented towards the actual rate of forest growth. Applying this growth-based harvest rule (GB) in JSBACH, the land component of the Max Planck Institute's Earth system model, forced by several future climate scenarios, we realized a growth potential 2 to 4 times (3–9 PgC yr−1) the harvest rates prescribed by IAMs (1–3 PgC yr−1). Limiting GB to managed forest areas (MF), we simulated a harvest potential of 3–7 PgC yr−1, 2 to 3 times higher than IAMs. This highlights the need to account for the dependence of forest growth on climate. To account for the long-term effects of wood harvest as integrated in IAMs, we added a life cycle analysis, showing that the higher supply with MF as an adaptive forest harvesting rule may improve the net mitigation effects of forest harvest during the 21st century by sequestering carbon in anthropogenic wood products.

The increase in atmospheric CO2 driven by anthropogenic emissions is the main radiative forcing causing climate change. But this increase is not only a result from emissions, but also from changes in the global carbon cycle. These changes arise from feedbacks between climate and the carbon cycle that drive CO2 into or out of the atmosphere in addition to the emissions, thereby either accelerating or buffering climate change. Therefore, understanding the contribution of these feedbacks to the global response of the carbon cycle is crucial in advancing climate research. Currently, this contribution is quantified by the α-β-γ framework (Friedlingstein et al., 2003). But this quantification is only valid for a particular perturbation scenario and time period. In contrast, a recently proposed generalization (Rubino et al., 2016) of this framework for weak perturbations quantifies this contribution for all scenarios and at different time scales. Thereby, this generalization provides a systematic framework to investigate the response of the global carbon cycle in terms of the climate-carbon cycle feedbacks. In the present work we employ this framework to study these feedbacks and the airborne fraction in different CMIP5 models. We demonstrate (1) that this generalization of the α-β-γ framework consistently describes the linear dynamics of the carbon cycle in the MPI-ESM; and (2) how by this framework the climate-carbon cycle feedbacks and airborne fraction are quantified at different time scales in CMIP5 models. Our analysis shows that, independently of the perturbation scenario, (1) the net climate-carbon cycle feedback is negative at all time scales; (2) the airborne fraction generally decreases for increasing time scales; and (3) the land biogeochemical feedback dominates the model spread in the airborne fraction at all time scales. This last result therefore emphasizes the need to improve our understanding of this particular feedback.References:P. Friedlingstein, J.-L. Dufresne, P. Cox, and P. Rayner. How positive is the feedback between climate change and the carbon cycle? Tellus B, 55(2):692–700, 2003.M. Rubino, D. Etheridge, C. Trudinger, C. Allison, P. Rayner, I. Enting, R. Mulvaney, L. Steele, R. Langenfelds, W. Sturges, et al. Low atmospheric CO2 levels during the Little Ice Age due to cooling-induced terrestrial uptake. Nature Geoscience, 9(9):691–694, 2016.

The global carbon budget including CO2 fluxes among different reservoirs and atmospheric carbon growth rate vary substantially in interannual to decadal time-scales. Reconstructing and predicting the variable global carbon cycle is of essential value of tracing the fate of carbon and the corresponding climate and ecosystem changes. For the first time, we extend our prediction system based on the Max Planck Institute Earth system model (MPI-ESM) from concentration-driven to emission-driven taking into account the interactive carbon cycle and hence enabling prognostic atmospheric carbon increment. By assimilating atmospheric and oceanic observational data products into MPI-ESM decadal prediction system, we can reproduce the observed variations of the historical global carbon cycle globally. The reconstruction from the fully coupled model enables quantification of global carbon budget within a close Earth system and therefore avoids the budget imbalance term of budgeting the carbon with standalone models. Our reconstructions of carbon budget provide a novel approach for supporting global carbon budget and understanding the dominating processes. Retrospective predictions based on the emission-driven hindcasts, which are initiated from the reconstructions, show predictive skill in the atmospheric carbon growth rate, air-sea CO2 fluxes, and air-land CO2 fluxes. The air-sea CO2 fluxes have higher predictive skill up to 5 years, and the air-land CO2 fluxes and atmospheric carbon growth rate show predictive skill of 2 years. Our results also suggest predictions based on Earth system models enable reproducing and further predicting the evolution of atmospheric CO2 concentration changes. The earth system predictions will provide valuable inputs for understanding the global carbon cycle and supporting climate relevant policy development.

Land use and land cover change (LULCC) has a significant role for the global carbon cycle. Despite big improvements in LULCC emission modelling, related uncertainties remain relatively high. Major uncertainties in quantifying LULCC emissions stem from uncertainties in underlying LULCC datasets. The novel, high-resolution (~1 km×1 km) LULCC dataset HIstoric Land Dynamics Assessment+ (HILDA+) reflects gross transitions derived from multiple remote sensing products and offers an alternative to existing land use change datasets, serving as forcing for process-based and bookkeeping models. By incorporating HILDA+ in the “bookkeeping of land use emissions” (BLUE) model, which is one of the three bookkeeping models used in the Global Carbon Budget 2020, we gain a different temporal and spatial perspective on LULCC estimates and related sources of uncertainty. We compare our results to emission estimates based on LUH2, which is broadly used as LULCC forcing for process-based and bookkeeping models. First results of our analysis show overall lower LULCC emissions for the estimates based on HILDA+. For the time period 1990-2019, mean yearly emission estimates based on HILDA+ are 0.595 GtC yr−1 compared to 1.368 GtC yr−1 based on LUH2. Reasons are lower emissions from cropland expansion and less carbon uptake from vegetation regrowth after abandonment of managed land (i.e. a smaller carbon sink). Furthermore, fewer discontinuities in the BLUE runs with the HILDA+ forcing compared to the LUH2-based estimates suggests a better representation of land use dynamics and their effects. Overall, the simulations based on HILDA+ capture spatial heterogeneity to a greater degree and provide a more detailed picture of local sources and sinks, which is crucial for (1) a better representation of component fluxes (e.g. deforestation, degradation) and (2) effective mitigation and adaptation policies.

Extreme summer temperatures in western and central Europe have become more frequent and heatwaves more prolonged over the past decades. The summer of 2018 was one of the driest and hottest in the observational record and led to losses in vegetation productivity in central Europe by up to 50%. Legacy effects from such extreme summers can affect ecosystem functioning over several years, as vegetation slowly recovers. In 2019 an extremely dry and hot summer was registered again in the region, imposing stress conditions at a time when ecosystems were still recovering from summer 2018.Using Enhanced Vegetation Index (EVI) fields from MODIS, we evaluate how ecosystems in central Europe responded to the occurrence of two consecutive extreme summers. We find that only ca. 21% of the area negatively impacted by drought in summer 2018 fully recovered in 2019.We find that the strongest EVI anomalies in 2018/19 diverge from the long-term relationships between EVI and climate, indicating an increase in ecosystem vulnerability to heat and drought events. Furthermore, 18% of the area showed a worsening of plant status during summer 2019 in spite of drought alleviation, which could be explained by interannual legacy effects from 2018, such as impaired growth and increased biotic disturbances.Land-surface models do not simulate interannual legacy effects from summer 2018 and thereby underestimate the impact of drought in 2019 on ecosystems. The poor representation of drought-induced damage and mortality and lack of biotic disturbances in these models may result in an overestimation of the resilience and stability of temperate ecosystems in the future.

Land cover and land management (LCLM) changes have been highlighted for their critical role in low-end warming scenarios, both in terms of global mitigation and local adaptation. Yet the overall potential of LCLM options and their combination is still poorly understood. Here we model the climatic effects of four LCLM options using three state-of-the-art Earth system models, including the Community Earth System Model (CESM), the Max Planck Institute Earth System Model (MPI-ESM) and the European Consortium Earth System Model (EC-EARTH). The considered LCLM options represent idealized conditions:(i) a fully afforested world, (ii) a fully deforested world, (ii) a fully afforested world with extensive wood harvesting, and (iv) a fully deforested world with extensive irrigation. In these idealized sensitivity experiments, ran under present-day climate conditions, the effects of the different LCLM strategies represent an upper bound of the potential for global mitigation and local adaptation. To disentangle the local and non-local effects from the LCLM changes, a checkerboard perturbation, as proposed by Winckler et al. (2017) is applied.Our first results show that deforestation leads to a pronounced warming in 2m air temperature in CESM over most regions, being most pronounced in the tropics (up to 4°C). In contrast, in the boreal regions of North America and Asia, deforestation causes a ~1°C cooling in 2m air temperature. In CESM, the local effect seems to dominate the temperature response from deforestation, while the resulting non-local effect overall has a smaller magnitude. This contrasts to the effect from afforestation, of which the non-local component dominates the 2m air temperature signal. Afforestation indeed shows a strong local cooling in the tropics and a slight local warming in the temperate and boreal regions, yet, the local cooling is regionally offset by a global, non-local warming of up to 2 °C. In a next step, we will extend this analysis to the ensemble of Earth system models and increase our process-based understanding of these results and their implications on hot extremes as well as the effects on other temperature metrics (surface temperature and temperature of the lowest level of atmospheric column). Finally, we will perform a subgrid-scale comparison of the effects of LCLM on temperature.References:Winckler, J., Reick, C.H., Pongratz, J., 2017. Robust identification of local biogeophysical effects of land-cover change in a global climate model, American Meteorological society, 30(2), DOI: 10.1175/JCLI-D-16-0067.1

Brazil is responsible for about one third of the global land use and land cover change (LULCC) carbon dioxide emissions. However, there is a disagreement among different methodologies on the magnitude and trends in emissions and their geographic distribution. One of the main uncertainties is associated with different LULCC datatasets used as input in the different approaches. In this work we perform an evaluation of LULCC datasets for Brazil, including the global dataset (HYDE 3.2) used in the annual Global Carbon Budget (GCB), and national Brazilian dataset (MapBiomas) over the period 2000-2018. We also analyze the latest global HYDE 3.3 dataset based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps. Results show that the new HYDE 3.3 can represent well the observed spatial variation in cropland and pastures areas over the last decades compared to national data (MapBiomas) and shows an improvement compared to HYDE 3.2 used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than national estimates from MapBiomas. Finally, we used HYDE 3.3 as input to two different approaches included in GCB, a global bookkeeping model (BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine the impact of the new version of HYDE dataset on Brazil’s land-use emissions trends over the period 2000-2017. Both JULES-ES and BLUE now simulate a negative land-use emissions trend for the last two decades. This negative trend is in agreement with Brazilian INPE-EM, global H&N bookkeeping models, FAO and as reported in National GHG inventories (NGHGI), although magnitudes differ among approaches. Overall, the inclusion of the multi-annual ESA CCI Land Cover dataset to allocate spatially the FAO statistical data has improved spatial representation of agricultural area change in Brazil in the last two decades, contributing to improve global model capability to simulate Brazil’s LULCC emissions in agreement with national trends estimates and spatial distribution.

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

Resolving regional carbon budgets is critical for informing land-based mitigation policy. For nine regions covering nearly the whole globe, we collected inventory estimates of carbon-stock changes complemented by satellite estimates of biomass changes where inventory data are missing. The net land–atmospheric carbon exchange (NEE) was calculated by taking the sum of the carbon-stock change and lateral carbon fluxes from crop and wood trade, and riverine-carbon export to the ocean. Summing up NEE from all regions, we obtained a global ‘bottom-up’ NEE for net land anthropogenic CO2 uptake of –2.2 ± 0.6 PgC yr−1 consistent with the independent top-down NEE from the global atmospheric carbon budget during 2000–09. This estimate is so far the most comprehensive global bottom-up carbon budget accounting, which set up an important milestone for global carbon-cycle studies. By decomposing NEE into component fluxes, we found that global soil heterotrophic respiration amounts to a source of CO2 of 39 PgC yr−1 with an interquartile of 33–46 PgC yr−1—a much smaller portion of net primary productivity than previously reported., info:eu-repo/semantics/published

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 (BLUE) based on a LULCC dataset including uncertainty estimates (the 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 %, reference 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 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 three) than the other two factors for the net LULCC flux in 2014 and historical LULCC uncertainty is negligible for estimates of future scenarios.

Fluxes from deforestation, changes in land-cover, land-use and management practices (FLUC for simplicity) contributed to circa 14 % of anthropogenic CO2 emissions in 2009–2018. Estimating FLUC accurately in space and in time remains, however, challenging, due to multiple sources of uncertainty in the calculation of these fluxes. This uncertainty, in turn, is propagated to global and regional carbon budget estimates, hindering the compilation of a consistent carbon budget and preventing us from constraining other terms, such as the natural land sink. Uncertainties in FLUC estimates arise from many different sources, including differences in model structure (e.g., process- based vs. bookkeeping) and model parameterization. Quantifying the uncertainties from each source requires controlled simulations to separate their effects. Here we analyze differences between the two bookkeeping models used regularly in the global carbon budget estimates since 2017: the model by Hansis et al. (Hansis et al., 2015) (BLUE) and that by Houghton and Nassikas (Houghton and Nassikas, 2017) (HN2017). The two models have a very similar structure and philosophy, but differ significantly both with respect to FLUC intensity and spatio-temporal variability. This is due to differences in the land-use forcing, but also in the model parameterization. We find that the larger emissions in BLUE compared to HN2017 are largely due to differences in C densities between natural and managed vegetation or primary and secondary vegetation, and higher allocation of cleared and harvested material to fast turnover pools in BLUE than in HN2017. Beside parameterization and the use of different forcing, other model assumptions cause differences, in particular that BLUE represents gross transitions which leads to overall higher carbon losses that are also more quickly realized than HN2017.

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

Non-technical summaryWe summarize some of the past year's most important findings within climate change-related research. New research has improved our understanding of Earth's sensitivity to carbon dioxide, finds that permafrost thaw could release more carbon emissions than expected and that the uptake of carbon in tropical ecosystems is weakening. Adverse impacts on human society include increasing water shortages and impacts on mental health. Options for solutions emerge from rethinking economic models, rights-based litigation, strengthened governance systems and a new social contract. The disruption caused by COVID-19 could be seized as an opportunity for positive change, directing economic stimulus towards sustainable investments.Technical summaryA synthesis is made of ten fields within climate science where there have been significant advances since mid-2019, through an expert elicitation process with broad disciplinary scope. Findings include: (1) a better understanding of equilibrium climate sensitivity; (2) abrupt thaw as an accelerator of carbon release from permafrost; (3) changes to global and regional land carbon sinks; (4) impacts of climate change on water crises, including equity perspectives; (5) adverse effects on mental health from climate change; (6) immediate effects on climate of the COVID-19 pandemic and requirements for recovery packages to deliver on the Paris Agreement; (7) suggested long-term changes to governance and a social contract to address climate change, learning from the current pandemic, (8) updated positive cost–benefit ratio and new perspectives on the potential for green growth in the short- and long-term perspective; (9) urban electrification as a strategy to move towards low-carbon energy systems and (10) rights-based litigation as an increasingly important method to address climate change, with recent clarifications on the legal standing and representation of future generations.Social media summaryStronger permafrost thaw, COVID-19 effects and growing mental health impacts among highlights of latest climate science.

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO 2 ), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO 2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well-characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results and inverse modeling estimates over the period 1990–2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the member states following the IPCC Guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO 2 fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO 2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO 2 emissions (including cement production) account for 2624 Tg CO 2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588 ( ± 463 Tg CO 2 ). The inversion reports 2700 Tg CO 2 ( ± 480 Tg CO 2 ), which is well in line with the national inventories. Over 2011–2015, the CO 2 land sources and sinks from NGHGI estimates report −90 Tg C yr −1 ± 30 Tg C yr −1 while all other BU approaches report a mean sink of −98 Tg C yr −1 ( ± 362 Tg of C from dynamic global vegetation models only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr −1 ± 400 Tg C yr −1 ). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of “CO 2 flux” obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4626578 (Petrescu et al., 2020a).

International audience; Atmospheric carbon dioxide concentration ([CO 2 ]) is increasing, which increases leaf-scale photosynthesis and intrinsic water-use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO 2] increase and thus climate change. However, ecosystem CO2 responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO 2]-driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO 2] (iCO 2) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre-industrial times. Established theory, supported by experiments, indicates that iCO 2 is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO 2 responses are high in comparison to experiments and predictions from theory. Plantmortality and soil carbon iCO 2 responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO2 , albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.

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.

Quantifying the anthropogenic fluxes of CO2 is important to understand the evolution of carbon sink capacities, on which the required strength of our mitigation efforts directly depends. For the historical period, the global carbon budget (GCB) can be compiled from observations and model simulations as is done annually in the Global Carbon Project's (GCP) carbon budgets. However, the historical budget only considers a single realization of the Earth system and cannot account for internal climate variability. Understanding the distribution of internal climate variability is critical for predicting the future carbon budget terms and uncertainties. We present here a decomposition of the GCB for the historical period and the RCP4.5 scenario using single‐model large ensemble simulations from the Max Planck Institute Grand Ensemble (MPI‐GE) to capture internal variability. We calculate uncertainty ranges for the natural sinks and anthropogenic emissions that arise from internal climate variability, and by using this distribution, we investigate the likelihood of historical fluxes with respect to plausible climate states. Our results show these likelihoods have substantial fluctuations due to internal variability, which are partially related to El Niño‐Southern Oscillation (ENSO). We find that the largest internal variability in the MPI‐GE stems from the natural land sink and its increasing carbon stocks over time. The allowable fossil fuel emissions consistent with 3 C warming may be between 9 and 18 Pg C yr−1. The MPI‐GE is generally consistent with GCP's global budgets with the notable exception of land‐use change emissions in recent decades, highlighting that human action is inconsistent with climate mitigation goals., Key Points: We use a single‐model large ensemble to estimate uncertainties from internal climate variability in the global carbon budget. The land sink accounts for most internal climate uncertainty which may permit 9–18 Pg C yr−1 in allowable emissions by 2050 (for 3°C warming)., European Union's Horizon 2020

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results, and inverse modelling estimates, over the period 1990–2018. BU and TD products are compared with European national GHG inventories (NGHGI) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGI, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGI with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO2 fossil) and the use of different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities (CO2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO2 emissions (including cement production) account for 2624 Tg CO2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGI and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), well in line with the national inventories. Over 2011–2015, the CO2 land sources/sinks from NGHGI estimates report −90 Tg C yr−1 ± 30 Tg C while all other BU approaches report a mean sink of −98 Tg yr−1 (± 362 Tg C from DGVMs only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr−1 ± 400 T g C yr−1). This concludes that a) current independent approaches are consistent with NGHGI b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of CO2 flux obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288883 (Petrescu et al., 2020).

Background: The climate mitigation target of limiting the temperature increase below 2 °C above the pre-industrial levels requires the efforts from all countries. Tracking the trajectory of the land carbon sink efficiency is thus crucial to evaluate the nationally determined contributions (NDCs). Here, we define the instantaneous land sink efficiency as the ratio of natural land carbon sinks to emissions from fossil fuel and land-use and land-cover change with a value of 1 indicating carbon neutrality to track its temporal dynamics in the past decades.Results: Land sink efficiency has been decreasing during 1957-1990 because of the increased emissions from fossil fuel. After the effect of the Pinatubo eruption diminished (after 1994), the land sink efficiency firstly increased before 2009 and then began to decrease again after 2009. This reversal around 2009 is mostly attributed to changes in land sinks in Latin America in response to climate variations.Conclusions: The decreasing trend of land sink efficiency in the recent years reveals greater challenges in climate change mitigation, and impacts of climate on land carbon sinks need to be accurately quantified to assess the implementation of climate mitigation policies.

Background The climate mitigation target of limiting the temperature increase below 2 °C above the pre-industrial levels requires the efforts from all countries. Tracking the trajectory of the land carbon sink efficiency is thus crucial to evaluate the nationally determined contributions (NDCs). Here, we define the instantaneous land sink efficiency as the ratio of natural land carbon sinks to emissions from fossil fuel and land-use and land-cover change with a value of 1 indicating carbon neutrality to track its temporal dynamics in the past decades. Results Land sink efficiency has been decreasing during 1957–1990 because of the increased emissions from fossil fuel. After the effect of the Mt. Pinatubo eruption diminished (after 1994), the land sink efficiency firstly increased before 2009 and then began to decrease again after 2009. This reversal around 2009 is mostly attributed to changes in land sinks in tropical regions in response to climate variations. Conclusions The decreasing trend of land sink efficiency in recent years reveals greater challenges in climate change mitigation, and that climate impacts on land carbon sinks must be accurately quantified to assess the effectiveness of regional scale climate mitigation policies.