Nitrous oxide (N2O), like carbon dioxide, is a long-lived greenhouse gas that accumulates in the atmosphere. Over the past 150 years, increasing atmospheric N2O concentrations have contributed to stratospheric ozone depletion1 and climate change2, with the current rate of increase estimated at 2 per cent per decade. Existing national inventories do not provide a full picture of N2O emissions, owing to their omission of natural sources and limitations in methodology for attributing anthropogenic sources. Here we present a global N2O inventory that incorporates both natural and anthropogenic sources and accounts for the interaction between nitrogen additions and the biochemical processes that control N2O emissions. We use bottom-up (inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and top-down (atmospheric inversion) approaches to provide a comprehensive quantification of global N2O sources and sinks resulting from 21 natural and human sectors between 1980 and 2016. Global N2O emissions were 17.0 (minimum–maximum estimates: 12.2–23.5) teragrams of nitrogen per year (bottom-up) and 16.9 (15.9–17.7) teragrams of nitrogen per year (top-down) between 2007 and 2016. Global human-induced emissions, which are dominated by nitrogen additions to croplands, increased by 30% over the past four decades to 7.3 (4.2–11.4) teragrams of nitrogen per year. This increase was mainly responsible for the growth in the atmospheric burden. Our findings point to growing N2O emissions in emerging economies—particularly Brazil, China and India. Analysis of process-based model estimates reveals an emerging N2O–climate feedback resulting from interactions between nitrogen additions and climate change. The recent growth in N2O emissions exceeds some of the highest projected emission scenarios3,4, underscoring the urgency to mitigate N2O emissions.
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The relevant codes used in this study are archived in the box site of the International Center for Climate and Global Change Research at Auburn University (https://auburn.box.com/).
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This paper is the result of a collaborative international effort under the umbrella of the Global Carbon Project (a project of Future Earth and a research partner of the World Climate Research Programme) and International Nitrogen Initiative. This research was made possible partly by Andrew Carnegie Fellowship award no. G-F-19-56910; NSF grant nos 1903722,1243232 and 1922687; NASA grant nos NNX14AO73G, NNX10AU06G, NNX11AD47G and NNX14AF93G; NOAA grant nos NA16NOS4780207 and NA16NOS4780204; National Key R&D Program of China (grant no. 2017YFA0604702); National Natural Science Foundation of China (grant no. 41961124006); and OUC-AU Joint Center Program. E.T.B., P.R., G.P.P., R.L.T. and P.S. acknowledge funding support from VERIFY project (EC H2020 grant no. 776810); P.S. also acknowledges funding from the EC H2020 grant no. 641816 (CRESCENDO); A.I. acknowledges funding support from JSPS KAKENHI grant (no. 17H01867); G.B., F.J. and S.L. acknowledge support from the Swiss National Science Foundation (no. 200020_172476) and EC H2020 grant no. 821003 (Project 4C) and no. 820989 (Project COMFORT); A.L. acknowledges support from DFG project SFB754/3; S.Z. acknowledges support from EC H2020 grant no. 647204; K.C.W. and D.B.M. acknowledge support from NASA (IDS grant no. NNX17AK18G) and NOAA (grant no. NA13OAR4310086); P.A.R. acknowledges NASA Award NNX17AI74G; M.M. acknowledges support from the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS) Environmental Change Programme (2016-2021); B.D.E. acknowledges the support from ARC Linkage Grants LP150100519 and LP190100271; M.J.P. acknowledges the US Department of Energy grant no. DE-SC0012536, Lawrence Livermore National Laboratory B628407 and NASA MAP program grant no. NNX13AL12G; S.B. was supported by the EC H2020 with the CRESCENDO project (grant no. 641816) and by the COMFORT project (grant no. 820989), and also acknowledges the support of the team in charge of the CNRM-CM climate model; F.Z. acknowledges the support from the National Natural Science Foundation of China (41671464). Supercomputing time was provided by the Météo-France/DSI supercomputing center. P.K.P. is partly supported by Environment Research and Technology Development Fund (#2-1802) of the Ministry of the Environment, Japan; R.L. acknowledges support from the French state aid managed by the ANR under the ‘Investissements d’avenir’ programme with the reference ANR-16-CONV-0003. NOAA ground-based observations of atmospheric N2O are supported by NOAA’s Climate Program Office under the Atmospheric Chemistry Carbon Cycle and Climate (AC4) theme. The AGAGE stations measuring N2O are supported by NASA (USA) grants NNX16AC98G to MIT and NNX16AC97G and NNX16AC96G to SIO, and by BEIS (UK) for Mace Head, NOAA (USA) for Barbados, and CSIRO and BoM (Australia) for Cape Grim. F.N.T. acknowledges funding from FAO regular programme. The views expressed in this publication are those of the author(s) and do not necessarily reflect the views or policies of FAO. P.C. acknowledges support from ERC Synergy Grant Imbalance-P and the ANR Cland Convergence Institute. We also thank S. Frolking for constructive comments and suggestions that have helped to improve this paper. The statements made and views expressed are solely the responsibility of the authors.
The authors declare no competing interests.
Peer review information Nature thanks Steve Frolking, Arvin Mosier and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
Global mean growth rates (solid lines, during 1995–2017) and atmospheric N2O concentration (dashed lines, during 1980–2017) are from the AGAGE6 (green), NOAA5 (orange) and CSIRO (blue) networks. Global mean growth rates were calculated with annual time steps and are shown as 12-month moving averages. Growth rates are not calculated before 1995 owing to insufficient data and higher uncertainties on the measurements.
BU and TD represent bottom-up and top-down methods, respectively. The colour codes are the same as that used in Table 1 and Figs. 1–3. We use both approaches, including 22 bottom-up and five top-down estimates of N2O fluxes from land and oceans. For sources estimated by the bottom-up approach, we include six process-based terrestrial biosphere modelling studies16; five process-based ocean biogeochemical models99; one nutrient budget model30,60,61; five inland water modelling studies35,36,50,51,68; one statistical model SRNM based on spatial extrapolation of field measurements17; and four greenhouse-gas inventories: EDGAR v4.3.2100, FAOSTAT101, GAINS41, and GFED4s102. In addition, previous studies regarding estimates of surface sink58,73, lightning53,54, atmospheric production56,57,103, aquaculture31,62 and model-based tropospheric sink81 and observed stratospheric sink1 are included in the current synthesis. aRef. 31 and ref. 62 provide global aquaculture N2O emissions in 2013 and in 2009, respectively; and the nutrient budget model30,60,61 provides nitrogen flows in global freshwater and marine aquaculture over the period 1980–2016. bModel-based estimates of N2O emissions from inland and coastal waters include rivers and reservoirs35,36, lakes51, estuaries35, coastal zones (that is, seagrasses, mangroves, saltmarsh and intertidal saltmarsh)68 and coastal upwelling50.
Extended Data Fig. 3 Comparison of annual total N2O emissions at global and regional scales estimated by bottom-up and top-down approaches.
The blue lines represent the mean N2O emission from bottom-up methods and the shaded areas show minimum and maximum estimates; the gold lines represent the mean N2O emission from top-down methods and the shaded areas show minimum and maximum estimates.
a, Direct emission from agricultural soils associated with mineral fertilizer, manure and crop residue inputs, and cultivation of organic soils based on EDGAR v4.3.2, GAINS, FAOSTAT, NMIP/DLEM and SRNM/DLEM estimates. NMIP/DLEM or SRNM/DLEM indicates the combination of N2O emission estimated by NMIP or SRNM from croplands with N2O emission from intensively managed grassland (pasture) by estimated by DLEM. b, Direct emission from the global total area under permanent meadows and pasture, due to manure nitrogen deposition (left on pasture) based on EDGAR v4.3.2, FAOSTAT and GAINS estimates. c, Emission from manure management based on FAOSTAT, GAINS and EDGAR v4.3.2. d, Aquaculture N2O emission based on a nutrient budget model30, ref. 31 and ref. 62; the solid line represents the ‘best estimate’ that is the product of emission factor (1.8%) and nitrogen waste from aquaculture provided by the nutrient budget model; the dashed lines represent the minimum and maximum values.
a, Emission from fossil fuel combustion based on EDGAR v4.3.2 and GAINS estimates. b, Emission from industry based on EDGAR v4.3.2 and GAINS estimates. c, Emission from waste and waste water based on EDGAR v4.3.2 and GAINS estimates. d, Emission from biomass burning based on FAOSTAT, DLEM, and GFED4s estimates.
Extended Data Fig. 6 Global N2O emissions from natural soils, inland and coastal waters and due to change in climate, atmospheric CO2 and nitrogen deposition.
a, Changes in global soil N2O fluxes due to changing CO2 and climate. b, Global natural soil N2O emissions without consideration of land use change (for example, deforestation) and without consideration of indirect anthropogenic effects via global change (that is, climate, increased CO2 and atmospheric nitrogen deposition). The estimates are based on NMIP estimates during 1980–2016 including six process-based land biosphere models. Here, we also subtracted the difference between including and not including emissions from secondary forests (that grow back after pasture or cropland abandonment) as part of natural soil emissions based on NMIP estimates. The solid lines represent the ensemble and dashed lines show the minimum and maximum values. c, Global anthropogenic N2O emission from inland waters, estuaries, coastal zones based on models (model-based), FAOSTAT, GAINS and EDGAR v4.3.2 estimates. d, Emission due to atmospheric nitrogen deposition on land based on NMIP, FAOSTAT/EDGAR v4.3.2 and GAINS/EDGAR v4.3.2. FAOSTAT/EDGAR v4.3.2 or GAINS/EDGAR v4.3.2 indicates the combination of agricultural source estimates from FAOSTAT or GAINS with non-agricultural source estimates from EDGAR v4.3.2. A process-based model DLEM36 and a mechanistic stochastic model35,51 were used to estimate N2O emission from inland waters and estuaries, whereas site-level emission rates of N2O were upscaled to estimate global N2O fluxes from the global seagrass area68.
The blue line represents the mean forest N2O reduction caused by the long-term effect of reduced mature forest area (that is, deforestation) and shaded areas show minimum and maximum estimates; the red line represents the mean N2O emission from the post-deforestation pulse effect (that is, crop/pasture N2O emissions from legacy nitrogen of previous forest soil, not accounting for new fertilizer nitrogen added to these crop/pasture lands) and shaded areas show minimum and maximum estimates; the grey line represents the mean net deforestation emission of N2O and shaded areas show minimum and maximum estimates.
Extended Data Fig. 8 Global simulated N2O emission anomaly due to climate effect and global annual land surface temperature anomaly during 1901–2016.
Global N2O emission anomalies are the ensemble of six process-based land biosphere models in NMIP. The temperature data were obtained from the CRU-NCEP v8 climate dataset (https://vesg.ipsl.upmc.fr). a, The correlation between average global annual land surface temperature and simulated N2O emissions (that is, the result of SE6 experiment in NMIP16) considering annual changes in climate but keeping all other factors (that is, nitrogen fertilizer, manure, NDEP, increased CO2 and land cover change) at the level of 1860. b, The correlation between average global annual land surface temperature and simulated N2O emissions (that is, the result of SE1 experiment in NMIP16) considering annual changes in all factors during 1860–2016.
a, The red line shows the ensemble direct N2O emissions from livestock manure based on EDGAR v4.3.2, GAINS and FAOSTAT, the sum of ‘manure left on pasture’ and ‘manure management’. The grey columns show the amount of beef exported by Brazil. b, Orange line shows the ensemble direct N2O emissions from croplands due to nitrogen fertilization based on NMIP and SRNM. The grey columns show the amount of soybeans and corn exported by Brazil. Data regarding beef and cereal product exports were adapted from the ABIEC (beef) and FAOSTAT (soybean and corn) databases. Mmt yr−1 represents millions of metric tons per year.
Extended Data Fig. 10 A comparison of anthropogenic N2O emissions and atmospheric N2O concentrations in the unharmonized SSPs.
An extension of Fig. 4, in which the emission and concentration data are the same as in Fig. 4. a, Global anthropogenic N2O emissions; b, Global N2O concentrations. The unharmonized emissions from the Integrated Assessment Models (IAMs)104 show a large variation due to different input data and model assumptions. Comparison with Fig. 4b, d illustrates the modifications to the IAM scenario data for use in CMIP6. All baseline scenarios (SSP 3−7.0 and SSP 5−8.5; without climate policy applied) are shown in grey regardless of the radiative forcing level they reach in 2100.
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Tian, H., Xu, R., Canadell, J.G. et al. A comprehensive quantification of global nitrous oxide sources and sinks. Nature 586, 248–256 (2020). https://doi.org/10.1038/s41586-020-2780-0
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