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Abstract Emissions from fossil fuel exploitation are a leading contributor to global anthropogenic methane emissions, but are highly uncertain. The lack of reliable estimates hinders monitoring of the progress on pledges towards methane reductions. Here we analyze methane emissions from exploitation of coal, oil and gas for major producing nations across a suite of bottom-up inventories and global inversions. Larger disagreement in emissions exists for the oil/gas sector across the inventories compared to coal, arising mostly from disparate data sources for emission factors. Moreover, emissions reported to the United Nations Framework Convention on Climate Change are lower than other bottom-up and inversion estimates, with many countries lacking reporting in the past decades. Finally, comparison with previous global inversions, revealed a strong influence of the prior inventory on the inferred sub-sectoral emissions magnitude. This study highlights the need to improve consensus on the methodological inputs among the bottom-up inventories in order to obtain more consistent inverse modelling results at the sub-sectoral level.

The northern high latitude permafrost region has been an important contributor to the carbon sink since the 1980s. A new study finds that as tree cover increases, respiratory CO2 loss during late-growing season offsets photosynthetic CO2 uptake and leads to a slower rate of increasing annual net CO2 uptake.

Abstract 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.

Measurement(s) carbon dioxide emission • oxygen combustion Technology Type(s) digital curation • computational modeling technique Factor Type(s) annual and monthly fossil carbon dioxide emissions estimates • annual and monthly oxygen combustion estimates Sample Characteristic - Environment climate system Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13333643

The carbon balance in Southeast Asia is highly uncertain. Here, the authors show that land use changes and occurrence of strong El Niño control decadal shifts in the carbon balance of this region.

Measurements of Pollution in the Troposphere (MOPITT) satellite and ground-based carbon monoxide (CO) measurements both suggest a widespread downward trend in CO concentrations over East Asia during the period 2005–2016. This negative trend is inconsistent with global bottom-up inventories of CO emissions, which show a small increase or stable emissions in this region. We try to reconcile the observed CO trend with emission inventories using an atmospheric inversion of the MOPITT CO data that estimates emissions from primary sources, secondary production, and chemical sinks of CO. The atmospheric inversion indicates a ~ −2% yr ^−1 decrease in emissions from primary sources in East Asia from 2005–2016. The decreasing emissions are mainly caused by source reductions in China. The regional MEIC inventory for China is the only bottom up estimate consistent with the inversion-diagnosed decrease of CO emissions. According to the MEIC data, decreasing CO emissions from four main sectors (iron and steel industries, residential sources, gasoline-powered vehicles, and construction materials industries) in China explain 76% of the inversion-based trend of East Asian CO emissions. This result suggests that global inventories underestimate the recent decrease of CO emission factors in China which occurred despite increasing consumption of carbon-based fuels, and is driven by rapid technological changes with improved combustion efficiency and emission control measures.

Abstract The atmospheric CO2 growth rate is a fundamental measure of climate forcing. NOAA's growth rate estimates, derived from in situ observations at the marine boundary layer (MBL), serve as the benchmark in policy and science. However, NOAA's MBL‐based method encounters challenges in accurately estimating the whole‐atmosphere CO2 growth rate at sub‐annual scales. Here we introduce the Growth Rate from Satellite Observations (GRESO) method as a complementary approach to estimate the whole‐atmosphere CO2 growth rate utilizing satellite data. Satellite CO2 observations offer extensive atmospheric coverage that extends the capability of the current NOAA benchmark. We assess the sampling errors of the GRESO and NOAA methods using 10 atmospheric transport model simulations. The simulations generate synthetic OCO‐2 satellite and NOAA MBL data for calculating CO2 growth rates, which are compared against the global sum of carbon fluxes used as model inputs. We find good performance for the NOAA method (R = 0.93, RMSE = 0.12 ppm year−1 or 0.25 PgC year−1). GRESO demonstrates lower sampling errors (R = 1.00; RMSE = 0.04 ppm year−1 or 0.09 PgC year−1). Additionally, GRESO shows better performance at monthly scales than the NOAA method (R = 0.76 vs. 0.47, respectively). Due to CO2's atmospheric longevity, the NOAA method accurately captures growth rates over 5‐year intervals. GRESO's robustness across partial coverage configurations (ocean or land data) shows that satellites can be promising tools for low‐latency CO2 growth rate information, provided the systematic biases are minimized using in situ observations. Along with accurate and calibrated NOAA in situ data, satellite‐derived growth rates can provide information about the global carbon cycle at sub‐annual scales.

Abstract The Surface Ocean CO2 Atlas (SOCAT) of CO2 fugacity (fCO2) observations is a key resource supporting annual assessments of CO2 uptake by the ocean and its side effects on the marine ecosystem. SOCAT data are usually released with a lag of up to 1.5 years which hampers timely quantification of recent variations of carbon fluxes between the Earth System components, not only with the ocean. This study uses a statistical ensemble approach to analyze fCO2 with a latency of one month only based on the previous SOCAT release and a series of predictors. Results indicate a modest degradation in a retrospective prediction test for 2021–2022. The generated fCO2 and fluxes for January–August 2023 show a progressive reduction in the Equatorial Pacific source following the La Niña retreat. A breaking‐record decrease in the northeastern Atlantic CO2 sink has been diagnosed on account of the marine heatwave event in June 2023.

Abstract We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies’ effectiveness and make adjustments quickly.

Southwestern North America (SWNA) continuously experienced megadroughts and large wildfires in 2020 and 2021. Here, we quantified their impact on the terrestrial carbon budget using net biome production (NBP) estimates from an ensemble of atmospheric inversions assimilating in-situ CO _2 and Carbon Observatory – 2 (OCO-2) satellite XCO _2 retrievals (OCO-2 v10 MIP Extension), two satellite-based gross primary production (GPP) datasets, and two fire CO _2 emission datasets. We found that the 2020 – 2021 drought and associated wildfires in SWNA led to a large CO _2 loss, an ensemble mean of 95.07 TgC estimated by the satellite inversions using both nadir and glint XCO _2 retrievals (LNLG) within the OCO-2 v10 MIP, greater than 80% of SWNA’s annual total carbon sink. Moreover, the carbon loss in 2020 was mainly contributed by fire emissions while in 2021 mainly contributed by drought impacts on terrestrial carbon uptake. In addition, the satellite inversions indicated the huge carbon loss was mainly contributed by fire emissions from forests and grasslands along with carbon uptake reductions due to drought impacts on grasslands and shrublands. This study provides a process understanding of how some droughts and following wildfires affect the terrestrial carbon budget on a regional scale.

Abstract The Global Stocktake (GST), implemented by the Paris Agreement, requires rapid developments in the capabilities to quantify annual greenhouse gas (GHG) emissions and removals consistently from the global to the national scale and improvements to national GHG inventories. In particular, new capabilities are needed for accurate attribution of sources and sinks and their trends to natural and anthropogenic processes. On the one hand, this is still a major challenge as national GHG inventories follow globally harmonized methodologies based on the guidelines established by the Intergovernmental Panel on Climate Change, but these can be implemented differently for individual countries. Moreover, in many countries the capability to systematically produce detailed and annually updated GHG inventories is still lacking. On the other hand, spatially-explicit datasets quantifying sources and sinks of carbon dioxide, methane and nitrous oxide emissions from Earth Observations (EO) are still limited by many sources of uncertainty. While national GHG inventories follow diverse methodologies depending on the availability of activity data in the different countries, the proposed comparison with EO-based estimates can help improve our understanding of the comparability of the estimates published by the different countries. Indeed, EO networks and satellite platforms have seen a massive expansion in the past decade, now covering a wide range of essential climate variables and offering high potential to improve the quantification of global and regional GHG budgets and advance process understanding. Yet, there is no EO data that quantifies greenhouse gas fluxes directly, rather there are observations of variables or proxies that can be transformed into fluxes using models. Here, we report results and lessons from the ESA-CCI RECCAP2 project, whose goal was to engage with National Inventory Agencies to improve understanding about the methods used by each community to estimate sources and sinks of GHGs and to evaluate the potential for satellite and in-situ EO to improve national GHG estimates. Based on this dialogue and recent studies, we discuss the potential of EO approaches to provide estimates of GHG budgets that can be compared with those of national GHG inventories. We outline a roadmap for implementation of an EO carbon-monitoring program that can contribute to the Paris Agreement.

Abstract The magnitude and spatial pattern of anomalous net biome exchange (NBE) induced by the 2015/16 El Niño over Amazonian rainforests remain uncertain. We here investigated them using multi‐model posterior NBE products in the Orbiting Carbon Observatory‐2 (OCO‐2) version 10 modeling intercomparison project. Results suggest that relative to the annual NBE average in 2017/18, larger anomalous carbon release occurred over the eastern and northern Amazonian rainforests in 2015/16, with a total flux of approximately 0.4 PgC yr−1 after assimilating satellite‐observed column CO2 concentrations (XCO2) over land. We further find that this anomalous spatial pattern was predominantly determined by soil dryness, while the total positive NBE anomaly was dominated by higher temperature with its contribution of approximately 68～70%. We believe that atmospheric inversions assimilating more satellite‐observed XCO2 in future can provide us more comprehensive understanding how Amazonian rainforests cope with the abiotic stresses induced by El Niño events.

The dominant driver of variations in global land carbon sink remains unclear. Here the authors show that the seasonal compensation of temperature effects on land carbon sink in the Northern Hemisphere could induce a global water dominance.

Measurement(s) atmospheric carbon dioxide, methane and carbon monoxide Technology Type(s) numerical simulation Factor Type(s) None Sample Characteristic - Organism long-lived greenhouse gases Sample Characteristic - Environment atmosphere Sample Characteristic - Location global atmosphere

Abstract The ability of global transport models to go up in resolution becomes discriminating for greenhouse gas atmospheric inversions. This paper describes the porting on Graphics Processing Units of the global transport model currently used in the European operational Copernicus Atmosphere Monitoring Service (CAMS) for CO2 and N2O inversions. It represents an important milestone to achieve sub‐degree resolution. The code includes not only the direct model but also its tangent‐linear and its adjoint versions which are needed in variational inversions. Tests were carried out for CO2 at a resolution of 2.50° in longitude, 1.27° in latitude and 79 layers in the vertical, corresponding to 1,626,768 3D cells, 4.5 times more than the current standard resolution of the model used in the CAMS reanalyzes. A month's worth of computation of the tangent‐linear and of the adjoint versions now takes 2.5 min, including 50 s for reading meteorological data.

The COVID-19 pandemic has stopped many human activities, which has had significant impact on emissions of greenhouse gases. Here, the authors present daily estimates of country-level CO2 emissions for different economic sectors and show that there has been a 8.8% decrease in global CO2 emissions in the first half of 2020.

Precise and high-resolution carbon dioxide (CO2) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO2 Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO2 emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO2) retrievals. Our study on the global CO2 emissions responds to the growing and urgent need for high-quality, fine-grained, near-real-time CO2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO2 emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt. Public summary: • We present the first near-real-time Global Gridded Daily CO2 Emissions Dataset (GRACED) • GRACED can be updated in near real time with a spatial resolution of 0.1° and a temporal resolution of 1 day • GRACED shows gridded emissions of seven sectors: power, industry, residential consumption, ground transport, domestic aviation, international aviation, and international shipping • Regular updates of GRACED will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt on various spatial scales

The Paris Agreement of the United Nations Framework Convention on Climate Change is a binding international treaty signed by 196 nations to limit their greenhouse gas emissions through ever-reducing Nationally Determined Contributions and a system of 5-yearly Global Stocktakes in an Enhanced Transparency Framework. To support this process, the European Commission initiated the design and development of a new Copernicus service element that will use Earth observations mainly to monitor anthropogenic carbon dioxide (CO2) emissions. The CO2 Human Emissions (CHE) project has been successfully coordinating efforts of its 22 consortium partners, to advance the development of a European CO2 monitoring and verification support (CO2MVS) capacity for anthropogenic CO2 emissions. Several project achievements are presented and discussed here as examples. The CHE project has developed an enhanced capability to produce global, regional and local CO2 simulations, with a focus on the representation of anthropogenic sources. The project has achieved advances towards a CO2 global inversion capability at high resolution to connect atmospheric concentrations to surface emissions. CHE has also demonstrated the use of Earth observations (satellite and ground-based) as well as proxy data for human activity to constrain uncertainties and to enhance the timeliness of CO2 monitoring. High-resolution global simulations (at 9 km) covering the whole of 2015 (labelled CHE nature runs) fed regional and local simulations over Europe (at 5 km and 1 km resolution) and supported the generation of synthetic satellite observations simulating the contribution of a future dedicated Copernicus CO2 Monitoring Mission (CO2M).

Tropical land ecosystems contain vast carbon reservoirs, but their influence on atmospheric CO2 is poorly understood. Here the authors use new carbon-observing satellites to reveal a large emission source over northern tropical Africa, where there are large soil carbon stores and substantial land use changes.

Abstract Background Childhood-onset spondyloarthropathies usually start with enthesitis and peripheral arthritis. However, axial disease may develop afterward. Patients are most often classified, following revised (Edmonton 2011) ILAR criteria, as enthesitis-related arthritis, psoriatic arthritis, or unclassified juvenile idiopathic arthritis, particularly in cases of psoriasis in the patient or a first-degree relative. In adults, peripheral spondyloarthritis is classified by ASAS criteria. Methods We retrospectively studied patients with childhood-onset spondyloarthropathies followed for more than one year in our referral centre. We did not exclude patients with a personal or familial history of psoriasis. Results We included 114 patients followed between January 2008 and December 2015 for a median of 2.5 years (IQR = 2.3). Sixty-nine per-cent of patients fulfilled the revised ILAR classification criteria for enthesitis-related arthritis, and 92% the ASAS criteria for peripheral spondyolarthritis (p

Siberia experienced an unprecedented strong and persistent heatwave in winter to spring of 2020. Using bottom–up and top–down approaches, we evaluated seasonal and annual CO _2 fluxes of 2020 in the northern hemisphere (north of 30 °N), focusing on Siberia where the pronounced heatwave occurred. We found that, over Siberia, CO _2 respiration loss in response to the pronounced positive winter temperature anomaly was greater than in previous years. However, continued warming in the spring enhanced photosynthetic CO _2 uptake, resulting in the largest seasonal transition in net ecosystem CO _2 exchange; that is, the largest magnitude of the switch from the net CO _2 loss in winter to net CO _2 uptake in spring until June. However, this exceptional transition was followed by the largest reduction in CO _2 uptake in late summer due to multiple environmental constraints, including a soil moisture deficit. Despite a substantial increase of CO _2 uptake by 22 ± 9 gC m ^−2 in the spring in response to the heatwave, the mean annual CO _2 uptake over Siberia was slightly lower (3 ± 13 gC m ^−2 yr ^−1 ) than the average of the previous five years. These results highlight the highly dynamic response of seasonal carbon fluxes to extreme temperature anomalies at high latitudes, indicating a seasonal compensation between abnormal uptake and release of CO _2 in response to extreme warmth that may limit carbon sink capacity in high northern latitudes.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20254-5

The relationship between terrestrial carbon sinks and atmospheric modes of variability remains uncertain. Here, the authors show that the coupling of the North Atlantic Oscillation and East-Atlantic patterns explains variations in the European CO2sink from 1982 to 2012.

National annual inventories of CO2 emitted during fossil fuel consumption (FFCO2) bear 5–10% uncertainties for developed countries, and are likely higher at intra annual scales or for developing countries. Given the current international efforts of mitigating actions, there is a need for independent verifications of these inventories. Atmospheric inversion assimilating atmospheric gradients of CO2 and radiocarbon measurements could provide an independent way of monitoring FFCO2 emissions. A strategy would be to deploy such measurements over continental scale networks and to conduct continental to global scale atmospheric inversions targeting the national and one-month scale budgets of the emissions. Uncertainties in the high-resolution distribution of the emissions could limit the skill for such a large-scale inversion framework. This study assesses the impact of such uncertainties on the potential for monitoring the emissions at large scale. In practice, it is more specifically dedicated to the derivation, typical quantification and analysis of critical sources of errors that affect the inversion of FFCO2 emissions when solving for them at a relatively coarse resolution with a coarse grid transport model. These errors include those due to the mismatch between the resolution of the transport model and the spatial variability of the actual fluxes and concentrations (i.e. the representation errors) and those due to the uncertainties in the spatial and temporal distribution of emissions at the transport model resolution when solving for the emissions at large scale (i.e. the aggregation errors). We show that the aggregation errors characterize the impact of the corresponding uncertainties on the potential for monitoring the emissions at large scale, even if solving for them at the transport model resolution. We propose a practical method to quantify these sources of errors, and compare them with the precision of FFCO2 measurements (i.e. the measurement errors) and the errors in the modelling of atmospheric transport (i.e. the transport errors). The results show that both the representation and measurement errors can be much larger than the aggregation errors. The magnitude of representation and aggregation errors is sensitive to sampling heights and temporal sampling integration time. The combination of these errors can reach up to about 50% of the typical signals, i.e. the atmospheric large-scale mean afternoon FFCO2 gradients between sites being assimilated by the inversion system. These errors have large temporal auto-correlation scales, but short spatial correlation scales. This indicates the need for accounting for these temporal auto-correlations in the atmospheric inversions and the need for dense networks to limit the impact of these errors on the inversion of FFCO2 emissions at large scale. More generally, comparisons of the representation and aggregation errors to the errors in simulated FFCO2 gradients due to uncertainties in current inventories suggest that the potential of inversions using global coarse-resolution models (with typical horizontal resolution of a couple of degrees) to retrieve FFCO2 emissions at sub-continental scale could be limited, and that meso-scale models with smaller representation errors would effectively increase the potential of inversions to constrain FFCO2 emission estimates.

The authors wish to make the following corrections to this paper [...]

Uncertainty quantification is critical in the inversion of CO2 surface fluxes from atmospheric concentration measurements. Here, we estimate the main hyperparameters of the error covariance matrices for a priori fluxes and CO2 concentrations, that is, the variances and the correlation lengths, using real, continuous hourly CO2 concentration data in the context of the Ring 2 experiment of the North American Carbon Program Mid Continent Intensive. Several criteria, namely maximum likelihood (ML), general cross-validation (GCV) and χ 2 test are compared for the first time under a realistic setting in a mesoscale CO2 inversion. It is shown that the optimal hyperparameters under the ML criterion assure perfect χ 2 consistency of the inverted fluxes. Inversions using the ML error variances estimates rather than the prescribed default values are less weighted by the observations, because the default values underestimate the model-data mismatch error, which is assumed to be dominated by the atmospheric transport error. As for the spatial correlation length in prior flux errors, the Ring 2 network is sparse for GCV, and this method fails to reach an optimum. In contrast, the ML estimate (e.g. an optimum of 20 km for the first week of June 2007) does not support long spatial correlations that are usually assumed in the default values.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

The MEthane Remote sensing Lidar missioN (MERLIN) aims at demonstrating the spaceborne active measurement of atmospheric methane, a potent greenhouse gas, based on an Integrated Path Differential Absorption (IPDA) nadir-viewing LIght Detecting and Ranging (Lidar) instrument. MERLIN is a joint French and German space mission, with a launch currently scheduled for the timeframe 2021/22. The German Space Agency (DLR) is responsible for the payload, while the platform (MYRIADE Evolutions product line) is developed by the French Space Agency (CNES). The main scientific objective of MERLIN is the delivery of weighted atmospheric columns of methane dry-air mole fractions for all latitudes throughout the year with systematic errors small enough (

The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). We evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions ( E _LUC ) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al ( 2015 ), and 2) the land CO _2 fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F _Jena and F _CAMS respectively. The model ensemble-mean NBP (that includes seven models with land-use change) is higher than but within the uncertainty of R + L, while the simulated positive NBP trend over the last 30 yr is lower than that from R + L and from the two inversion systems. ISIMIP2a biome models well capture the interannual variation of global net terrestrial ecosystem carbon fluxes. Tropical NBP represents 31 ± 17% of global total NBP during the past decades, and the year-to-year variation of tropical NBP contributes most of the interannual variation of global NBP. According to the models, increasing Net Primary Productivity (NPP) was the main cause for the generally increasing NBP. Significant global NBP anomalies from the long-term mean between the two phases of El Niño Southern Oscillation (ENSO) events are simulated by all models ( p < 0.05), which is consistent with the R + L estimate ( p = 0.06), also mainly attributed to NPP anomalies, rather than to changes in heterotrophic respiration (Rh). The global NPP and NBP anomalies during ENSO events are dominated by their anomalies in tropical regions impacted by tropical climate variability. Multiple regressions between R + L, F _Jena and F _CAMS interannual variations and tropical climate variations reveal a significant negative response of global net terrestrial ecosystem carbon fluxes to tropical mean annual temperature variation, and a non-significant response to tropical annual precipitation variation. According to the models, tropical precipitation is a more important driver, suggesting that some models do not capture the roles of precipitation and temperature changes adequately.