A comprehensive quantification of global nitrous oxide sources and sinks (original) (raw)

Data availability

The relevant datasets of 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/). Researchers that are interested in using the results made available in the repository are encouraged to contact the original data providers.

Code availability

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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Acknowledgements

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.

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Authors and Affiliations

1. International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA
   Hanqin Tian, Rongting Xu, Naiqing Pan, Shufen Pan, Hao Shi & Yuanzhi Yao
2. Global Carbon Project, CSIRO Oceans and Atmosphere, Canberra, Australian Capital Territory, Australia
   Josep G. Canadell
3. Norsk Institutt for Luftforskning, NILU, Kjeller, Norway
   Rona L. Thompson
4. International Institute for Applied Systems Analysis, Laxenburg, Austria
   Wilfried Winiwarter
5. Institute of Environmental Engineering, University of Zielona Góra, Zielona Góra, Poland
   Wilfried Winiwarter
6. School of Environmental Sciences, University of East Anglia, Norwich, UK
   Parvadha Suntharalingam & Erik T. Buitenhuis
7. Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD, USA
   Eric A. Davidson
8. Laboratoire des Sciences du Climat et de l’Environnement, LSCE, CEA CNRS, UVSQ UPSACLAY, Gif sur Yvette, France
   Philippe Ciais, Jinfeng Chang, Ronny Lauerwald, Wei Li & Nicolas Vuichard
9. Department of Earth System Science, Stanford University, Stanford, CA, USA
   Robert B. Jackson
10. Woods Institute for the Environment, Stanford University, Stanford, CA, USA
    Robert B. Jackson
11. Precourt Institute for Energy, Stanford University, Stanford, CA, USA
    Robert B. Jackson
12. European Commission, Joint Research Centre (JRC), Ispra, Italy
    Greet Janssens-Maenhout
13. Ghent University, Faculty of Engineering and Architecture, Ghent, Belgium
    Greet Janssens-Maenhout
14. Department of Earth System Science, University of California Irvine, Irvine, CA, USA
    Michael J. Prather & Daniel J. Ruiz
15. Department of Geoscience, Environment & Society, Université Libre de Bruxelles, Brussels, Belgium
    Pierre Regnier, Goulven G. Laruelle & Ronny Lauerwald
16. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
    Naiqing Pan & Bojie Fu
17. CICERO Center for International Climate Research, Oslo, Norway
    Glen P. Peters
18. Statistics Division, Food and Agriculture Organization of the United Nations, Rome, Italy
    Francesco N. Tubiello
19. Max Planck Institute for Biogeochemistry, Jena, Germany
    Sönke Zaehle
20. Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
    Feng Zhou
21. Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany
    Almut Arneth
22. Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
    Gianna Battaglia, Fortunat Joos & Sebastian Lienert
23. Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, Toulouse, France
    Sarah Berthet
24. LMD-IPSL, Ecole Normale Supérieure / PSL Université, CNRS, Ecole Polytechnique, Sorbonne Université, Paris, France
    Laurent Bopp
25. PBL Netherlands Environmental Assessment Agency, The Hague, The Netherlands
    Alexander F. Bouwman
26. Department of Earth Sciences – Geochemistry, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands
    Alexander F. Bouwman
27. Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao, China
    Alexander F. Bouwman & Junjie Wang
28. Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK
    Erik T. Buitenhuis
29. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
    Jinfeng Chang
30. National Centre for Earth Observation, University of Leeds, Leeds, UK
    Martyn P. Chipperfield & Chris Wilson
31. Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK
    Martyn P. Chipperfield & Chris Wilson
32. Woods Hole Research Center, Falmouth, MA, USA
    Shree R. S. Dangal
33. NOAA Global Monitoring Laboratory, Boulder, CO, USA
    Edward Dlugokencky, James W. Elkins & Bradley Hall
34. Centre for Coastal Biogeochemistry, School of Environment Science and Engineering, Southern Cross University, Lismore, New South Wales, Australia
    Bradley D. Eyre
35. Faculty of Geographical Science, Beijing Normal University, Beijing, China
    Bojie Fu
36. Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan
    Akihiko Ito
37. Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia
    Paul B. Krummel
38. GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
    Angela Landolfi
39. Istituto di Scienze Marine, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
    Angela Landolfi
40. Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Thiverval-Grignon, France
    Ronny Lauerwald
41. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
    Wei Li
42. Yale School of Forestry and Environmental Studies, New Haven, CT, USA
    Taylor Maavara & Peter A. Raymond
43. Land Economy, Environment & Society, Scotland’s Rural College (SRUC), Edinburgh, UK
    Michael MacLeod
44. Department of Soil, Water, and Climate, University of Minnesota, St Paul, MN, USA
    Dylan B. Millet & Kelley C. Wells
45. Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
    Stefan Olin
46. Research Institute for Global Change, JAMSTEC, Yokohama, Japan
    Prabir K. Patra
47. Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
    Prabir K. Patra
48. Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, MA, USA
    Ronald G. Prinn
49. Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands
    Guido R. van der Werf
50. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
    Ray F. Weiss
51. Department of Forestry, Mississippi State University, Mississippi State, MS, USA
    Jia Yang

Authors

1. Hanqin Tian
2. Rongting Xu
3. Josep G. Canadell
4. Rona L. Thompson
5. Wilfried Winiwarter
6. Parvadha Suntharalingam
7. Eric A. Davidson
8. Philippe Ciais
9. Robert B. Jackson
10. Greet Janssens-Maenhout
11. Michael J. Prather
12. Pierre Regnier
13. Naiqing Pan
14. Shufen Pan
15. Glen P. Peters
16. Hao Shi
17. Francesco N. Tubiello
18. Sönke Zaehle
19. Feng Zhou
20. Almut Arneth
21. Gianna Battaglia
22. Sarah Berthet
23. Laurent Bopp
24. Alexander F. Bouwman
25. Erik T. Buitenhuis
26. Jinfeng Chang
27. Martyn P. Chipperfield
28. Shree R. S. Dangal
29. Edward Dlugokencky
30. James W. Elkins
31. Bradley D. Eyre
32. Bojie Fu
33. Bradley Hall
34. Akihiko Ito
35. Fortunat Joos
36. Paul B. Krummel
37. Angela Landolfi
38. Goulven G. Laruelle
39. Ronny Lauerwald
40. Wei Li
41. Sebastian Lienert
42. Taylor Maavara
43. Michael MacLeod
44. Dylan B. Millet
45. Stefan Olin
46. Prabir K. Patra
47. Ronald G. Prinn
48. Peter A. Raymond
49. Daniel J. Ruiz
50. Guido R. van der Werf
51. Nicolas Vuichard
52. Junjie Wang
53. Ray F. Weiss
54. Kelley C. Wells
55. Chris Wilson
56. Jia Yang
57. Yuanzhi Yao

Contributions

H.T., R.L.T., J.G.C. and R.B.J. designed and coordinated the study. H.T., R.X., J.G.C., R.L.T., W.W., P.S., E.A.D., P.C., R.B.J., G.J.-M., M.J.P., N.P., S.P., P.R., H.S., F.N.T., S.Z., F.Z., B.F. and G.P.P. conducted data analysis, synthesis and wrote the paper. R.L.T. led atmospheric inversions teaming with M.P.C., D.B.M., P.K.P., K.C.W. and C.W.; H.T. led land biosphere modelling teaming with P.C., H.S., S.Z., A.A., F.J., J.C., S.R.S.D., A.I., W.L., S.L., S.O., N.V., E.A.D. and S.D.; P.S. led ocean biogeochemical modelling teaming with G.B., L.B., S.B., E.T.B., F.J. and A.L.; P.R. led inland water and coastal modelling and synthesis teaming with B.D.E., G.G.L., R.L., T.M., P.A.R., H.T. and Y.Y.; A.F.B., J.W. and M.M. provided data of N2O flux in aquaculture. G.R.v.d.W. and J.Y. provided data of N2O emissions from biomass burning. F.Z. provided cropland N2O flux data from a statistical model and field observations. G.J.M., F.N.T. and W.W. provided N2O inventory data. M.J.P. and D.J.R. provided data of stratospheric and tropospheric sinks. G.P.P. provided RCP and SSP scenarios data and analysis. B.H., E.D. and J.W.E. provided a global N2O monitoring dataset from NOAA/ESRL GMD. R.G.P. and R.F.W. provided a global N2O monitoring dataset from AGAGE stations. P.B.K. provided a global N2O monitoring dataset from CSIRO. All co-authors reviewed and commented on the manuscript.

Corresponding author

Correspondence toHanqin Tian.

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Competing interests

The authors declare no competing interests.

Additional information

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

Extended Data Fig. 1 Global mean growth rates and atmospheric concentration of N2O.

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.

Extended Data Fig. 2 The methodology for data synthesis of the global N2O budget.

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](/articles/s41586-020-2780-0#ref-CR103 "Dentener, F. Global maps of atmospheric nitrogen deposition, 1860, 1993, and 2050. https://doi.org/10.3334/ORNLDAAC/830

             (Oak Ridge National Laboratory, 2006)."), aquaculture[31](/articles/s41586-020-2780-0#ref-CR31 "MacLeod, M., Hasan, M. R., Robb, D. H. F. & Mamun-Ur-Rashid, M. Quantifying and Mitigating Greenhouse Gas Emissions from Global Aquaculture FAO Fisheries and Aquaculture technical paper no. 626 (FAO, 2019)."),[62](/articles/s41586-020-2780-0#ref-CR62 "Hu, Z., Lee, J. W., Chandran, K., Kim, S. & Khanal, S. K. Nitrous oxide (N2O) emission from aquaculture: a review. Environ. Sci. Technol. 46, 6470–6480 (2012).") and model-based tropospheric sink[81](/articles/s41586-020-2780-0#ref-CR81 "Prather, M. J. & Hsu, J. Coupling of nitrous oxide and methane by global atmospheric chemistry. Science 330, 952–954 (2010).") and observed stratospheric sink[1](/articles/s41586-020-2780-0#ref-CR1 "Prather, M. J. et al. Measuring and modeling the lifetime of nitrous oxide including its variability. J. Geophys. Res. D 120, 5693–5705 (2015).") are included in the current synthesis. aRef. [31](/articles/s41586-020-2780-0#ref-CR31 "MacLeod, M., Hasan, M. R., Robb, D. H. F. & Mamun-Ur-Rashid, M. Quantifying and Mitigating Greenhouse Gas Emissions from Global Aquaculture FAO Fisheries and Aquaculture technical paper no. 626 (FAO, 2019).") and ref. [62](/articles/s41586-020-2780-0#ref-CR62 "Hu, Z., Lee, J. W., Chandran, K., Kim, S. & Khanal, S. K. Nitrous oxide (N2O) emission from aquaculture: a review. Environ. Sci. Technol. 46, 6470–6480 (2012).") provide global aquaculture N2O emissions in 2013 and in 2009, respectively; and the nutrient budget model[30](/articles/s41586-020-2780-0#ref-CR30 "Beusen, A. H., Bouwman, A. F., Van Beek, L. P., Mogollón, J. M. & Middelburg, J. J. Global riverine N and P transport to ocean increased during the 20th century despite increased retention along the aquatic continuum. Biogeosciences 13, 2441–2451 (2016)."),[60](/articles/s41586-020-2780-0#ref-CR60 "Bouwman, A. F. et al. Hindcasts and future projections of global inland and coastal nitrogen and phosphorus loads due to finfish aquaculture. Rev. Fish. Sci. 21, 112–156 (2013)."),[61](/articles/s41586-020-2780-0#ref-CR61 "Bouwman, A. F. et al. Global hindcasts and future projections of coastal nitrogen and phosphorus loads due to shellfish and seaweed aquaculture. Rev. Fish. Sci. 19, 331–357 (2011).") 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 reservoirs[35](/articles/s41586-020-2780-0#ref-CR35 "Maavara, T. et al. Nitrous oxide emissions from inland waters: are IPCC estimates too high? Glob. Change Biol. 25, 473–488 (2019)."),[36](/articles/s41586-020-2780-0#ref-CR36 "Yao, Y. et al. Increased global nitrous oxide emissions from streams and rivers in the Anthropocene. Nat. Clim. Change 10, 138–142 (2020)."), lakes[51](/articles/s41586-020-2780-0#ref-CR51 "Lauerwald, R. et al. Natural lakes are a minor global source of N2O to the atmosphere. Glob. Biogeochem. Cycles 33, 1564–1581 (2019)."), estuaries[35](/articles/s41586-020-2780-0#ref-CR35 "Maavara, T. et al. Nitrous oxide emissions from inland waters: are IPCC estimates too high? Glob. Change Biol. 25, 473–488 (2019)."), coastal zones (that is, seagrasses, mangroves, saltmarsh and intertidal saltmarsh)[68](/articles/s41586-020-2780-0#ref-CR68 "Murray, R. H., Erler, D. V. & Eyre, B. D. Nitrous oxide fluxes in estuarine environments: response to global change. Glob. Change Biol. 21, 3219–3245 (2015).") and coastal upwelling[50](/articles/s41586-020-2780-0#ref-CR50 "Nevison, C. D., Lueker, T. J. & Weiss, R. F. Quantifying the nitrous oxide source from coastal upwelling. Glob. Biogeochem. Cycles 18, GB1018 (2004).").

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.

Extended Data Fig. 4 Global agricultural N2O emissions.

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.

Extended Data Fig. 5 Global N2O emission from other direct anthropogenic sources.

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.

Extended Data Fig. 7 Global N2O dynamics due to land cover changes.

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.

Extended Data Fig. 9 Direct soil emissions and agricultural product trades in Brazil.

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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• Received: 28 December 2019
• Accepted: 14 August 2020
• Published: 07 October 2020
• Version of record: 07 October 2020
• Issue date: 08 October 2020
• DOI: https://doi.org/10.1038/s41586-020-2780-0