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This work documents the ICON‐Earth System Model (ICON‐ESM V1.0), the first coupled model based on the ICON (ICOsahedral Non‐hydrostatic) framework with its unstructured, icosahedral grid concept. The ICON‐A atmosphere uses a nonhydrostatic dynamical core and the ocean model ICON‐O builds on the same ICON infrastructure, but applies the Boussinesq and hydrostatic approximation and includes a sea‐ice model. The ICON‐Land module provides a new framework for the modeling of land processes and the terrestrial carbon cycle. The oceanic carbon cycle and biogeochemistry are represented by the Hamburg Ocean Carbon Cycle module. We describe the tuning and spin‐up of a base‐line version at a resolution typical for models participating in the Coupled Model Intercomparison Project (CMIP). The performance of ICON‐ESM is assessed by means of a set of standard CMIP6 simulations. Achievements are well‐balanced top‐of‐atmosphere radiation, stable key climate quantities in the control simulation, and a good representation of the historical surface temperature evolution. The model has overall biases, which are comparable to those of other CMIP models, but ICON‐ESM performs less well than its predecessor, the Max Planck Institute Earth System Model. Problematic biases are diagnosed in ICON‐ESM in the vertical cloud distribution and the mean zonal wind field. In the ocean, sub‐surface temperature and salinity biases are of concern as is a too strong seasonal cycle of the sea‐ice cover in both hemispheres. ICON‐ESM V1.0 serves as a basis for further developments that will take advantage of ICON‐specific properties such as spatially varying resolution, and configurations at very high resolution., Plain Language Summary: ICON‐ESM is a completely new coupled climate and earth system model that applies novel design principles and numerical techniques. The atmosphere model applies a non‐hydrostatic dynamical core, both atmosphere and ocean models apply unstructured meshes, and the model is adapted for high‐performance computing systems. This article describes how the component models for atmosphere, land, and ocean are coupled together and how we achieve a stable climate by setting certain tuning parameters and performing sensitivity experiments. We evaluate the performance of our new model by running a set of experiments under pre‐industrial and historical climate conditions as well as a set of idealized greenhouse‐gas‐increase experiments. These experiments were designed by the Coupled Model Intercomparison Project (CMIP) and allow us to compare the results to those from other CMIP models and the predecessor of our model, the Max Planck Institute for Meteorology Earth System Model. While we diagnose overall satisfactory performance, we find that ICON‐ESM features somewhat larger biases in several quantities compared to its predecessor at comparable grid resolution. We emphasize that the present configuration serves as a basis from where future development steps will open up new perspectives in earth system modeling., Key Points: This work documents ICON‐ESM 1.0, the first version of a coupled model based on the ICON framework. Performance of ICON‐ESM is assessed by means of CMIP6 Diagnosis, Evaluation, and Characterization of Klima experiments at standard CMIP‐type resolution. ICON‐ESM reproduces the observed temperature evolution. Biases in clouds, winds, sea‐ice, and ocean properties are larger than in MPI‐ESM., European Union H2020 ESM2025, European Union H2020 COMFORT, European Union H2020ESiWACE2, Deutsche Forschungsgemeinschaft TRR181, Deutsche Forschungsgemeinschaft EXC 2037, European Union H2020, Deutscher Wetterdienst, Bundesministerium fuer Bildung und Forschung, http://esgf-data.dkrz.de/search/cmip6-dkrz/, https://mpimet.mpg.de/en/science/modeling-with-icon/code-availability, http://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=RUBY-0_ICON-_ESM_V1.0_Model

The Global Carbon Budget 2018 (GCB2018) estimated by the atmospheric CO₂ growth rate, fossil fuel emissions, and modeled (bottom-up) land and ocean fluxes cannot be fully closed, leading to a“budget imbalance,” highlighting uncertainties in GCB components. However, no systematic analysis has been performed on which regions or processes contribute to this term. To obtain deeper insight on the sources of uncertainty in global and regional carbon budgets, we analyzed differences in Net Biome Productivity (NBP) for all possible combinations of bottom-up and top-down data sets in GCB 2018: (i) 16 dynamic global vegetation models (DGVMs), and (ii) 5 atmospheric inversions that match the atmospheric CO₂ growth rate. We find that the global mismatch between the two ensembles matches well the GCB 2018 budget imbalance, with Brazil, Southeast Asia, and Oceania as the largest contributors. Differences between DGVMs dominate global mismatches, while at regional scale differences between inversions contribute the most to uncertainty. At both global and regional scales, disagreement on NBP interannual variability between the two approaches explains a large fraction of differences. We attribute this mismatch to distinct responses to El Niño–Southern Oscillation variability between DGVMs and inversions and to uncertainties in land use change emissions, especially in South America and Southeast Asia. We identify key needs to reduce uncertainty in carbon budgets: reducing uncertainty in atmospheric inversions (e.g., through more observations in the tropics) and in land use change fluxes, including more land use processes and evaluating land use transitions (e.g., using high-resolution remote-sensing), and, finally, improving tropical hydroecological processes and fire representation within DGVMs.

Accurate assessment of anthropogenic carbon dioxide (CO$_{2}$) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – 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 and synthesize data sets and methodology 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$_{OCEAN}$) and terrestrial CO$_{2}$ sink (S$_{LAND}$) are estimated with global process models constrained by observations. 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 last decade available (2010–2019), E$_{FOS}$ was 9.6 ± 0.5 GtC yr$^{-1}$ excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and E$_{LUC}$ was 1.6 ± 0.7 GtC yr$^{-1}$. For the same decade, G$_{ATM}$ was 5.1 ± 0.02 GtC yr$^{-1}$ (2.4 ± 0.01 ppm yr$_{-1}$), S$_{OCEAN}$ 2.5 ± 0.6 GtC yr$^{-1}$, and S$_{LAND}$ 3.4 ± 0.9 GtC yr$^{-1}$, with a budget imbalance B$_{IM}$ of −0.1 GtC yr$^{-1}$ indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in E$_{FOS}$ was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr$^{-1}$ excluding the cement carbonation sink (9.7 ± 0.5 GtC yr$^{-1}$ when cement carbonation sink is included), and E$_{LUC}$ was 1.8 ± 0.7 GtC yr$^{-1}$, for total anthropogenic CO$_{2}$ emissions of 11.5 ± 0.9 GtC yr$^{-1}$ (42.2 ± 3.3 GtCO$_{2}$). Also for 2019, G$_{ATM}$ was 5.4 ± 0.2 GtC yr$^{-1}$ (2.5 ± 0.1 ppm yr$^{-1}$), S$_{OCEAN}$ was 2.6 ± 0.6 GtC yr$^{-1}$, and S$_{LAND}$ was 3.1 ± 1.2 GtC yr$^{-1}$, with a B$_{IM}$ of 0.3 GtC. The global atmospheric CO$_{2}$ concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in E$_{FOS}$ relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr$^{-1}$ persist for the representation of semi-decadal variability in CO$_{2}$ fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO$_{2}$ flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. 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., 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).

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

The Global Carbon Budget 2018 (GCB2018) estimated by the atmospheric CO 2 growth rate, fossil fuel emissions, and modeled (bottom-up) land and ocean fluxes cannot be fully closed, leading to a “budget imbalance,” highlighting uncertainties in GCB components. However, no systematic analysis has been performed on which regions or processes contribute to this term. To obtain deeper insight on the sources of uncertainty in global and regional carbon budgets, we analyzed differences in Net Biome Productivity (NBP) for all possible combinations of bottom-up and top-down data sets in GCB2018: (i) 16 dynamic global vegetation models (DGVMs), and (ii) 5 atmospheric inversions that match the atmospheric CO 2 growth rate. We find that the global mismatch between the two ensembles matches well the GCB2018 budget imbalance, with Brazil, Southeast Asia, and Oceania as the largest contributors. Differences between DGVMs dominate global mismatches, while at regional scale differences between inversions contribute the most to uncertainty. At both global and regional scales, disagreement on NBP interannual variability between the two approaches explains a large fraction of differences. We attribute this mismatch to distinct responses to El Niño–Southern Oscillation variability between DGVMs and inversions and to uncertainties in land use change emissions, especially in South America and Southeast Asia. We identify key needs to reduce uncertainty in carbon budgets: reducing uncertainty in atmospheric inversions (e.g., through more observations in the tropics) and in land use change fluxes, including more land use processes and evaluating land use transitions (e.g., using high-resolution remote-sensing), and, finally, improving tropical hydroecological processes and fire representation within DGVMs.

Quantifying the net carbon flux from land use and land cover changes (f$_{LULCC}$) is critical for understanding the global carbon cycle and, hence, to support climate change mitigation. However, large-scale f$_{LULCC}$ 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, f$_{LULCC}$ estimates are not directly comparable between these two different model types. As an important example, DGVM-based f$_{LULCC}$ 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 f$_{LULCC}$ 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 f$_{LULCC}$ 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 f$_{LULCC}$ estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO$_{2}$-induced components and compare them to bookkeeping estimates. Averaged across the models, we find a global f$_{LULCC}$ (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 f$_{LULCC}$ accumulated to 189��56 PgC with 40��15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient f$_{LULCC}$ 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 f$_{LULCC}$ estimates in these regions are lower in transient DGVM compared to bookkeeping approaches. Our study unravels the strong dependence of f$_{LULCC}$ 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 f$_{LULCC}$ that is more robust against these choices, for example by estimating a mean DGVM ensemble f$_{LULCC}$ and LASC for a defined reference period and homogeneous environmental changes (CO$_{2}$ only).

The Global Carbon Budget 2018 (GCB2018) estimated by the atmospheric CO₂ growth rate, fossil fuel emissions, and modeled (bottom-up) land and ocean fluxes cannot be fully closed, leading to a“budget imbalance,” highlighting uncertainties in GCB components. However, no systematic analysis has been performed on which regions or processes contribute to this term. To obtain deeper insight on the sources of uncertainty in global and regional carbon budgets, we analyzed differences in Net Biome Productivity (NBP) for all possible combinations of bottom-up and top-down data sets in GCB 2018: (i) 16 dynamic global vegetation models (DGVMs), and (ii) 5 atmospheric inversions that match the atmospheric CO₂ growth rate. We find that the global mismatch between the two ensembles matches well the GCB 2018 budget imbalance, with Brazil, Southeast Asia, and Oceania as the largest contributors. Differences between DGVMs dominate global mismatches, while at regional scale differences between inversions contribute the most to uncertainty. At both global and regional scales, disagreement on NBP interannual variability between the two approaches explains a large fraction of differences. We attribute this mismatch to distinct responses to El Niño–Southern Oscillation variability between DGVMs and inversions and to uncertainties in land use change emissions, especially in South America and Southeast Asia. We identify key needs to reduce uncertainty in carbon budgets: reducing uncertainty in atmospheric inversions (e.g., through more observations in the tropics) and in land use change fluxes, including more land use processes and evaluating land use transitions (e.g., using high-resolution remote-sensing), and, finally, improving tropical hydroecological processes and fire representation within DGVMs.

Variability in climate exerts a strong influence on vegetation productivity (gross primary productivity; GPP), and therefore has a large impact on the land carbon sink. However, no direct observations of global GPP exist, and estimates rely on models that are constrained by observations at various spatial and temporal scales. Here, we assess the consistency in GPP from global products which extend for more than three decades; two observation‐based approaches, the upscaling of FLUXNET site observations (FLUXCOM) and a remote sensing derived light use efficiency model (RS‐LUE), and from a suite of terrestrial biosphere models (TRENDYv6). At local scales, we find high correlations in annual GPP among the products, with exceptions in tropical and high northern latitudes. On longer time scales, the products agree on the direction of trends over 58% of the land, with large increases across northern latitudes driven by warming trends. Further, tropical regions exhibit the largest interannual variability in GPP, with both rainforests and savannas contributing substantially. Variability in savanna GPP is likely predominantly driven by water availability, although temperature could play a role via soil moisture‐atmosphere feedbacks. There is, however, no consensus on the magnitude and driver of variability of tropical forests, which suggest uncertainties in process representations and underlying observations remain. These results emphasize the need for more direct long‐term observations of GPP along with an extension of in situ networks in underrepresented regions (e.g., tropical forests). Such capabilities would support efforts to better validate relevant processes in models, to more accurately estimate GPP.

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

Accurate assessment of anthropogenic carbon dioxide (CO$_{2}$) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – 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 and synthesize data sets and methodology 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$_{OCEAN}$) and terrestrial CO$_{2}$ sink (S$_{LAND}$) are estimated with global process models constrained by observations. 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 last decade available (2010–2019), E$_{FOS}$ was 9.6 ± 0.5 GtC yr$^{-1}$ excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and E$_{LUC}$ was 1.6 ± 0.7 GtC yr$^{-1}$. For the same decade, G$_{ATM}$ was 5.1 ± 0.02 GtC yr$^{-1}$ (2.4 ± 0.01 ppm yr$_{-1}$), S$_{OCEAN}$ 2.5 ± 0.6 GtC yr$^{-1}$, and S$_{LAND}$ 3.4 ± 0.9 GtC yr$^{-1}$, with a budget imbalance B$_{IM}$ of −0.1 GtC yr$^{-1}$ indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in E$_{FOS}$ was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr$^{-1}$ excluding the cement carbonation sink (9.7 ± 0.5 GtC yr$^{-1}$ when cement carbonation sink is included), and E$_{LUC}$ was 1.8 ± 0.7 GtC yr$^{-1}$, for total anthropogenic CO$_{2}$ emissions of 11.5 ± 0.9 GtC yr$^{-1}$ (42.2 ± 3.3 GtCO$_{2}$). Also for 2019, G$_{ATM}$ was 5.4 ± 0.2 GtC yr$^{-1}$ (2.5 ± 0.1 ppm yr$^{-1}$), S$_{OCEAN}$ was 2.6 ± 0.6 GtC yr$^{-1}$, and S$_{LAND}$ was 3.1 ± 1.2 GtC yr$^{-1}$, with a B$_{IM}$ of 0.3 GtC. The global atmospheric CO$_{2}$ concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in E$_{FOS}$ relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr$^{-1}$ persist for the representation of semi-decadal variability in CO$_{2}$ fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO$_{2}$ flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. 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., 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).

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.

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