In:
Earth System Science Data, Copernicus GmbH, Vol. 15, No. 3 ( 2023-03-21), p. 1197-1268
Abstract:
Abstract. 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 https://doi.org/10.5281/zenodo.7553800 (Petrescu et al.,
2023).
Type of Medium:
Online Resource
ISSN:
1866-3516
DOI:
10.5194/essd-15-1197-2023
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2023
detail.hit.zdb_id:
2475469-9
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