• Energy Research

AbstractEast Asia (China, Japan, Koreas, and Mongolia) has been the world's economic engine over at least the past two decades, exhibiting a rapid increase in fossil fuel emissions of greenhouse gases (GHGs) and has expressed the recent ambition to achieve climate neutrality by mid‐century. However, the GHG balance of its terrestrial ecosystems remains poorly constrained. Here, we present a synthesis of the three most important long‐lived greenhouse gases (CO2, CH4, and N2O) budgets over East Asia during the decades of 2000s and 2010s, following a dual constraint approach. We estimate that terrestrial ecosystems in East Asia is close to neutrality of GHGs, with a magnitude of between −46.3 ± 505.9 Tg CO2eq yr−1(the top‐down approach) and −36.1 ± 207.1 Tg CO2eq yr−1(the bottom‐up approach) during 2000–2019. This net GHG sink includes a large land CO2sink (−1229.3 ± 430.9 Tg CO2 yr−1based on the top‐down approach and −1353.8 ± 158.5 Tg CO2 yr−1based on the bottom‐up approach) being offset by biogenic CH4and N2O emissions, predominantly coming from the agricultural sectors. Emerging data sources and modeling capacities have helped achieve agreement between the top‐down and bottom‐up approaches, but sizable uncertainties remain in several flux terms. For example, the reported CO2flux from land use and land cover change varies from a net source of more than 300 Tg CO2 yr−1to a net sink of ∼−700 Tg CO2 yr−1. Although terrestrial ecosystems over East Asia is close to GHG neutral currently, curbing agricultural GHG emissions and additional afforestation and forest managements have the potential to transform the terrestrial ecosystems into a net GHG sink, which would help in realizing East Asian countries' ambitions to achieve climate neutrality.

Abstract To understand the spatio-temporal variations of the atmospheric greenhouse gases in the Arctic region, we have conducted systematic observations and numerical model simulations. Long-term observations for CO2, CH4 and related constituents at Ny-Alesund helped in separating the oceanic and terrestrial uptake rates of CO2 and revealed the importance of biogenic CH4 emissions to atmospheric CH4 variations. Shipboard observations of atmospheric CH4 in the Arctic Ocean indicated several peaks associated with long-range transports of the continental source signals. An underestimation of climatological O2 flux is demonstrated by the shipboard O2/N2 observations over the North Pacific and Arctic Sea. Clear seasonal cycles and long-term trends of the greenhouse gases were observed in the upper troposphere and lower stratosphere over the Eurasian continent at 8.5–12.5 km in altitude. Forward and inverse modeling studies and direct observations of CH4 emission from West Siberian wetlands were conducted to evaluate the CH4 fluxes estimated by previous bottom-up studies. To be consistent with the atmospheric CH4 observations, the prescribed CH4 emission needs downward and upward revisions at Hudson Bay Lowland and West Siberia, respectively. The terrestrial ecosystem model study showed increasing trends in the CH4 emission in inland North America, Alaska and northern part of West Siberian Lowland.

Abstract. 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 and N2O 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 modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. 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 NGHGI 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 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates 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 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−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 NGHGI and inversions and account for 5.2 Tg CH4 yr−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 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−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 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU 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 EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).

AbstractQuantification of CO2 fluxes at the Earth’s surface is required to evaluate the causes and drivers of observed increases in atmospheric CO2 concentrations. Atmospheric inversion models disaggregate observed variations in atmospheric CO2 concentration to variability in CO2 emissions and sinks. They require prior constraints fossil CO2 emissions. Here we describe GCP-GridFED (version 2019.1), a gridded fossil emissions dataset that is consistent with the national CO2 emissions reported by the Global Carbon Project (GCP). GCP-GridFEDv2019.1 provides monthly fossil CO2 emissions estimates for the period 1959–2018 at a spatial resolution of 0.1°. Estimates are provided separately for oil, coal and natural gas, for mixed international bunker fuels, and for the calcination of limestone during cement production. GCP-GridFED also includes gridded estimates of O2 uptake based on oxidative ratios for oil, coal and natural gas. It will be updated annually and made available for atmospheric inversions contributing to GCP global carbon budget assessments, thus aligning the prior constraints on top-down fossil CO2 emissions with the bottom-up estimates compiled by the GCP.

AbstractIn the framework of the RECCAP2 initiative, we present the greenhouse gas (GHG) and carbon (C) budget of Europe. For the decade of the 2010s, we present a bottom‐up (BU) estimate of GHG net‐emissions of 3.9 Pg CO2‐eq. yr−1 (using a global warming potential on a 100 years horizon), which are largely dominated by fossil fuel emissions. In this decade, terrestrial ecosystems acted as a net GHG sink of 0.9 Pg CO2‐eq. yr−1, dominated by a CO2 sink that was partially counterbalanced by net emissions of CH4 and N2O. For CH4 and N2O, we find good agreement between BU and top‐down (TD) estimates from atmospheric inversions. However, our BU land CO2 sink is significantly higher than the TD estimates. We further show that decadal averages of GHG net‐emissions have declined by 1.2 Pg CO2‐eq. yr−1 since the 1990s, mainly due to a reduction in fossil fuel emissions. In addition, based on both data driven BU and TD estimates, we also find that the land CO2 sink has weakened over the past two decades. A large part of the European CO2 and C sinks is located in Northern Europe. At the same time, we find a decreasing trend in sink strength in Scandinavia, which can be attributed to an increase in forest management intensity. These are partly offset by increasing CO2 sinks in parts of Eastern Europe and Northern Spain, attributed in part to land use change. Extensive regions of high CH4 and N2O emissions are mainly attributed to agricultural activities and are found in Belgium, the Netherlands and the southern UK. We further analyzed interannual variability in the GHG budgets. The drought year of 2003 shows the highest net‐emissions of CO2 and of all GHGs combined.

Abstract. The source and sinks of carbon dioxide (CO2) and methane (CH4) due to anthropogenic and natural biospheric activities were estimated for the South Asia region (Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka). Flux estimates were based on top-down methods that use inversions of atmospheric data, and bottom-up methods that use field observations, satellite data, and terrestrial ecosystem models. Based on atmospheric CO2 inversions, the net biospheric CO2 flux in South Asia (equivalent to the Net Biome Productivity, NBP) was a sink, estimated at −104 ± 150 Tg C yr−1 during 2007–2008. Based on the bottom-up approach, the net biospheric CO2 flux is estimated to be −191 ± 193 Tg C yr−1 during the period of 2000–2009. This last net flux results from the following flux components: (1) the Net Ecosystem Productivity, NEP (net primary production minus heterotrophic respiration) of −220 ± 186 Tg C yr−1 (2) the annual net carbon flux from land-use change of −14 ± 50 Tg C yr−1, which resulted from a sink of −16 Tg C yr−1 due to the establishment of tree plantations and wood harvest, and a source of 2 Tg C yr−1 due to the expansion of croplands; (3) the riverine export flux from terrestrial ecosystems to the coastal oceans of +42.9 Tg C yr−1; and (4) the net CO2 emission due to biomass burning of +44.1 ± 13.7 Tg C yr−1. Including the emissions from the combustion of fossil fuels of 444 Tg C yr−1 for the decades of 2000s, we estimate a net CO2 land-to-atmosphere flux of 297 Tg C yr−1. In addition to CO2, a fraction of the sequestered carbon in terrestrial ecosystems is released to the atmosphere as CH4. Based on bottom-up and top-down estimates, and chemistry-transport modeling, we estimate that 37 ± 3.7 Tg C-CH4 yr−1 were released to atmosphere from South Asia during the 2000s. Taking all CO2 and CH4 fluxes together, our best estimate of the net land-to-atmosphere CO2-equivalent flux is a net source of 334 Tg C yr−1 for the South Asia region during the 2000s. If CH4 emissions are weighted by radiative forcing of molecular CH4, the total CO2-equivalent flux increases to 1148 Tg C yr−1 suggesting there is great potential of reducing CH4 emissions for stabilizing greenhouse gases concentrations.

Nitrous oxide emissions are presented from three independent atmospheric inversion frameworks. The frameworks are: 1) INVICAT: an inversion using the atmospheric transport model, TOMCAT and a 4D-Var optimisation method; 2) JAMSTEC: an inversion using the MIROC4-ACTM atmospheric transport model and a Bayesian analytical optimisation method; and 3) PYVAR: an inversion using the LMDZ5 atmospheric transport model and a 4D-var optimisation method. The emissions were optimised monthly and have been re-gridded from the model native resolution to 1.0 by 1.0 degrees. The files for TOMCAT and LMDZ5 (i.e. the inversion frameworks INVICAT and PYVAR, respectively) contain two flux variables: 1) the prior fluxes as estimated a priori, and 2) the posterior fluxes as estimated by the inversion. The file for the JAMSTEC inversion, contains five flux variables: 1) flux_apri_land: the prior fluxes over land, 2) flux_apri_ocean: the prior fluxes over ocean, 3) flux_apri_fossil: the prior estimate of emissions from combustion, 4) flux_apos_land: posterior fluxes over land estimated by the inversion, and 5) flux_apos_ocean: the posterior fluxes over ocean estimated by the inversion. Note that flux_apri_fossil was not optimised in the inversion but for the total posterior N2O emission, needs to be added to the flux_apos_ocean and flux_apos_land variables. {"references": ["Thompson, R. L. et al. Nitrous oxide emissions 1999 to 2009 from a global atmospheric inversion, Atmos. Chem. Phys. 14, 1801-1817 (2014)", "Wilson, C. et al. Development of a variational flux inversion system (INVICAT v1.0) using the TOMCAT chemical transport model. Geosci Model Dev 7(5), 2485\u20132500 (2014)", "Patra, P. K. et al. Improved Chemical Tracer Simulation by MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM). SOLA 14, 91\u201396 (2018)."]}