In support of the global stocktake of the Paris Agreement
on climate change, this study presents a comprehensive framework to process
the results of an ensemble of atmospheric inversions in order to ...make their
net ecosystem exchange (NEE) carbon dioxide (CO2) flux suitable for
evaluating national greenhouse gas inventories (NGHGIs) submitted by
countries to the United Nations Framework Convention on Climate Change
(UNFCCC). From inversions we also deduced anthropogenic methane (CH4)
emissions regrouped into fossil and agriculture and waste emissions, as well as
anthropogenic nitrous oxide (N2O) emissions. To compare inversion
results with national reports, we compiled a new global harmonized database
of emissions and removals from periodical UNFCCC inventories by Annex I
countries, and from sporadic and less detailed emissions reports by
non-Annex I countries, given by national communications and biennial update
reports. No gap filling was applied. The method to reconcile inversions with
inventories is applied to selected large countries covering ∼90 % of the global land carbon uptake for CO2 and top emitters of
CH4 and N2O. Our method uses results from an ensemble of global
inversions produced by the Global Carbon Project for the three greenhouse
gases, with ancillary data. We examine the role of CO2 fluxes caused by
lateral transfer processes from rivers and from trade in crop and wood
products and the role of carbon uptake in unmanaged lands, both not
accounted for by NGHGIs. Here we show that, despite a large spread across
the inversions, the median of available inversion models points to a larger
terrestrial carbon sink than inventories over temperate countries or groups
of countries of the Northern Hemisphere like Russia, Canada and the European
Union. For CH4, we find good consistency between the inversions
assimilating only data from the global in situ network and those using
satellite CH4 retrievals and a tendency for inversions to diagnose
higher CH4 emission estimates than reported by NGHGIs. In particular,
oil- and gas-extracting countries in central Asia and the Persian Gulf region
tend to systematically report lower emissions compared to those estimated by
inversions. For N2O, inversions tend to produce higher anthropogenic
emissions than inventories for tropical countries, even when attempting to
consider only managed land emissions. In the inventories of many non-Annex I
countries, this can be tentatively attributed to a lack of reporting
indirect N2O emissions from atmospheric deposition and from leaching to
rivers, to the existence of natural sources intertwined with managed
lands, or to an underestimation of N2O emission factors for direct
agricultural soil emissions. Inversions provide insights into seasonal and
interannual greenhouse gas fluxes anomalies, e.g., during extreme events
such as drought or abnormal fire episodes, whereas inventory methods are
established to estimate trends and multi-annual changes. As a much denser
sampling of atmospheric CO2 and CH4 concentrations by different
satellites coordinated into a global constellation is expected in the coming
years, the methodology proposed here to compare inversion results with
inventory reports (e.g., NGHGIs) could be applied regularly for monitoring
the effectiveness of mitigation policy and progress by countries to meet the
objective of their pledges. The dataset constructed by this study is
publicly available at https://doi.org/10.5281/zenodo.5089799 (Deng et al., 2021).
Emission of greenhouse gases (GHGs) and removals from land, including both anthropogenic and natural fluxes, require reliable quantification, including estimates of uncertainties, to support credible ...mitigation action under the Paris Agreement. This study provides a state-of-the-art scientific overview of bottom-up anthropogenic emissions data from agriculture, forestry and other land use (AFOLU) in the European Union (EU281). The data integrate recent AFOLU emission inventories with ecosystem data and land carbon models and summarize GHG emissions and removals over the period 1990–2016. This compilation of bottom-up estimates of the AFOLU GHG emissions of European national greenhouse gas inventories (NGHGIs), with those of land carbon models and observation-based estimates of large-scale GHG fluxes, aims at improving the overall estimates of the GHG balance in Europe with respect to land GHG emissions and removals. Whenever available, we present uncertainties, its propagation and role in the comparison of different estimates. While NGHGI data for the EU28 provide consistent quantification of uncertainty following the established IPCC Guidelines, uncertainty in the estimates produced with other methods needs to account for both within model uncertainty and the spread from different model results. The largest inconsistencies between EU28 estimates are mainly due to different sources of data related to human activity, referred to here as activity data (AD) and methodologies (tiers) used for calculating emissions and removals from AFOLU sectors. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.3662371 (Petrescu et al., 2020).
Significant climate risks are associated with a positive carbon–temperature feedback in northern latitude carbon-rich ecosystems, making an accurate analysis of human impacts on the net greenhouse ...gas balance of wetlands a priority. Here, we provide a coherent assessment of the climate footprint of a network of wetland sites based on simultaneous and quasi-continuous ecosystem observations of CO ₂ and CH ₄ fluxes. Experimental areas are located both in natural and in managed wetlands and cover a wide range of climatic regions, ecosystem types, and management practices. Based on direct observations we predict that sustained CH ₄ emissions in natural ecosystems are in the long term (i.e., several centuries) typically offset by CO ₂ uptake, although with large spatiotemporal variability. Using a space-for-time analogy across ecological and climatic gradients, we represent the chronosequence from natural to managed conditions to quantify the “cost” of CH ₄ emissions for the benefit of net carbon sequestration. With a sustained pulse–response radiative forcing model, we found a significant increase in atmospheric forcing due to land management, in particular for wetland converted to cropland. Our results quantify the role of human activities on the climate footprint of northern wetlands and call for development of active mitigation strategies for managed wetlands and new guidelines of the Intergovernmental Panel on Climate Change (IPCC) accounting for both sustained CH ₄ emissions and cumulative CO ₂ exchange.
Significance Wetlands are unique ecosystems because they are in general sinks for carbon dioxide and sources of methane. Their climate footprint therefore depends on the relative sign and magnitude of the land–atmosphere exchange of these two major greenhouse gases. This work presents a synthesis of simultaneous measurements of carbon dioxide and methane fluxes to assess the radiative forcing of natural wetlands converted to agricultural or forested land. The net climate impact of wetlands is strongly dependent on whether they are natural or managed. Here we show that the conversion of natural wetlands produces a significant increase of the atmospheric radiative forcing. The findings suggest that management plans for these complex ecosystems should carefully account for the potential biogeochemical effects on climate.
Regional land carbon budgets provide insights into the spatial distribution of the land uptake of atmospheric carbon dioxide and can be used to evaluate carbon cycle models and to define baselines ...for land-based additional mitigation efforts. The scientific community has been involved in providing observation-based estimates of regional carbon budgets either by downscaling atmospheric CO.sub.2 observations into surface fluxes with atmospheric inversions, by using inventories of carbon stock changes in terrestrial ecosystems, by upscaling local field observations such as flux towers with gridded climate and remote sensing fields, or by integrating data-driven or process-oriented terrestrial carbon cycle models. The first coordinated attempt to collect regional carbon budgets for nine regions covering the entire globe in the RECCAP-1 project has delivered estimates for the decade 2000-2009, but these budgets were not comparable between regions due to different definitions and component fluxes being reported or omitted. The recent recognition of lateral fluxes of carbon by human activities and rivers that connect CO.sub.2 uptake in one area with its release in another also requires better definitions and protocols to reach harmonized regional budgets that can be summed up to a globe scale and compared with the atmospheric CO.sub.2 growth rate and inversion results. In this study, using the international initiative RECCAP-2 coordinated by the Global Carbon Project, which aims to be an update to regional carbon budgets over the last 2 decades based on observations for 10 regions covering the globe with a better harmonization than the precursor project, we provide recommendations for using atmospheric inversion results to match bottom-up carbon accounting and models, and we define the different component fluxes of the net land atmosphere carbon exchange that should be reported by each research group in charge of each region. Special attention is given to lateral fluxes, inland water fluxes, and land use fluxes.
This study aims at estimating errors to be accounted for in atmospheric inversions of methane (CH
4
) emissions at the European scale. Four types of errors are estimated in the concentration space ...over the model domain and at selected measurement sites. Furthermore, errors in emission inventories are estimated at country and source sector scales. A technically ready method is used, which is implemented by running a set of simulations of hourly CH
4
mixing ratios for 2015 using two area-limited transport models at three horizontal resolutions with multiple data sets of emissions and boundary and initial conditions as inputs. The obtained error estimates provide insights into how these errors could be treated in an inverse modelling system for inverting CH
4
emissions over Europe. The main results show that sources of transport errors may better be controlled alongside the emissions, which differs from usual inversion practices. The average total concentration error is estimated at 29 ppb. The assessed error of total CH
4
emissions is 22% and emission errors are heterogeneous at sector (23-49%) and country scales (16-124%), with largest errors occurring in the waste sector due to uncertainties in activity data and emission factors and in Finland due to uncertainties in natural wetland emissions.
Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the ...Paris Agreement. This study provides a consolidated synthesis of CH4 andN2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990–2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 TgCH4yr-1 (EDGAR v5.0) and 19.0 TgCH4yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 TgCH4yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 TgCH4yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 TgCH4yr-1) and surface network (24.4 TgCH4yr-1). The magnitude of natural peatland emissions from the JSBACH–HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 TgCH4yr-1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 TgN2Oyr-1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 TgN2Oyr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 TgN2Oyr-1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at 10.5281/zenodo.4590875 (Petrescu et al., 2020b).
When a peatland is drained and cultivated, it behaves as a notable source of CO
2
. However, we lack temporally and spatially explicit estimates of carbon losses from cultivated peatlands. Using a ...process-based land surface model that explicitly includes representation of peatland processes, we estimate that northern peatlands converted to croplands emitted 72 Pg C over 850–2010, with 45% of this source having occurred before 1750. This source surpassed the carbon accumulation by high-latitude undisturbed peatlands (36 to 47 Pg C). Carbon losses from the cultivation of northern peatlands are omitted in previous land-use emission assessments. Adding this ignored historical land-use emission implies an 18% larger terrestrial carbon storage since 1750 to close the historical global carbon budget. We also show that carbon emission per unit area decrease with time since drainage, suggesting that time since drainage should be accounted for in inventories to refine land-use emissions from cultivated peatlands.
Crop cultivation of northern peatlands emitted large amount of CO
2
over the period 850–2010.
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 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 (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 NGHGIs and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), which is well in line with the national inventories. Over 2011-2015, the CO2 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 "CO2 flux"obtained from different approaches. The referenced datasets related to figures are visualized.
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 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 (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 NGHGIs and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), which is well in line with the national inventories. Over 2011–2015, the CO2 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 “CO2 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).
Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to ...monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as 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. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr-1 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr-1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr-1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH4 yr-1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH4 yr-1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr-1. For N2O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr-1, close to the NGHGI data (0.8±55 % Tg N2O yr-1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr-1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at 10.5281/zenodo.7553800 (Petrescu et al., 2023).
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