# Global nitrous oxide budget (1980-2020)

Hanqin Tian, Naiqing Pan, Rona Thompson, Josep Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, et al.

► To cite this version:

Hanqin Tian, Naiqing Pan, Rona Thompson, Josep Canadell, Parvadha Suntharalingam, et al. Global nitrous oxide budget (1980-2020). Earth System Science Data, 2024, 16 (6), pp.2543-2604. 10.5194/essd-16-2543-2024 . hal-04621501

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<figure><img src="image_1.png" /></figure>

Global nitrous oxide budget (1980–2020)

Hanqin Tian<fnref n="1" />,<fnref n="2" />, Naiqing Pan<fnref n="1" />, Rona L. Thompson<fnref n="3" />, Josep G. Canadell<fnref n="4" />, Parvadha Suntharalingam<fnref n="5" />,
Pierre Regnier<fnref n="6" />, Eric A. Davidson<fnref n="7" />, Michael Prather<fnref n="8" />, Philippe Ciais<fnref n="9" />, Marilena Muntean<fnref n="10" />,
Shufen Pan<fnref n="11" />,<fnref n="1" />, Wilfried Winiwarter<fnref n="12" />,<fnref n="13" />, Sönke Zaehle<fnref n="14" />, Feng Zhou<fnref n="15" />, Robert B. Jackson<fnref n="16" />,<fnref n="17" />,
Hermann W. Bange<fnref n="18" />, Sarah Berthet<fnref n="19" />, Zihao Bian<fnref n="20" />, Daniele Bianchi<fnref n="21" />, Alexander F. Bouwman<fnref n="22" />,
Erik T. Buitenhuis<fnref n="5" />, Geoffrey Dutton<fnref n="23" />,³³, Minpeng Hu²⁴, Akihiko Ito²⁵,²⁶, Atul K. Jain²⁷,
Aurich Jeltsch-Thömmes²⁸,²⁹, Fortunat Joos²⁸,²⁹, Sian Kou-Giesbrecht³⁰,³¹, Paul B. Krummel³²,
Xin Lan<fnref n="23" />,³³, Angela Landolfi³⁴,<fnref n="18" />, Ronny Lauerwald³⁵, Ya Li³⁶, Chaoqun Lu³⁷, Taylor Maavara³⁸,
Manfredi Manizza³⁹, Dylan B. Millet⁴⁰, Jens Mühle³⁹, Prabir K. Patra⁴¹,⁴²,⁴³, Glen P. Peters⁴⁴,
Xiaoyu Qin³⁶, Peter Raymond⁴⁵, Laure Resplandy⁴⁶, Judith A. Rosentreter⁴⁷,⁴⁸, Hao Shi³⁶,
Qing Sun²⁸,²⁹, Daniele Tonina⁴⁹, Francesco N. Tubiello⁵⁰, Guido R. van der Werf⁵¹, Nicolas Vuichard<fnref n="9" />,
Junjie Wang<fnref n="22" />, Kelley C. Wells⁴⁰, Luke M. Western<fnref n="23" />,⁵², Chris Wilson⁵³,⁵⁴, Jia Yang⁵⁵, Yuanzhi Yao⁵⁶,
Yongfa You<fnref n="1" />, and Qing Zhu⁵⁷

<footnote n="1">¹Center for Earth System Science and Global Sustainability, Schiller Institute for Integrated Science and Society, Boston College, Chestnut Hill, MA 02467, USA</footnote>

<footnote n="2">²Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467, USA</footnote>

<footnote n="3">³NILU, 2007 Kjeller, Norway</footnote>

<footnote n="4">⁴Global Carbon Project, CSIRO Environment, Canberra, ACT 2101, Australia</footnote>

<footnote n="5">⁵School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK</footnote>

<footnote n="6">⁶Biogeochemistry and Modelling of the Earth System (BGEOSYS), Department of Geoscience, Environment and Society, Université Libre de Bruxelles, 1050 Brussels, Belgium</footnote>

<footnote n="7">⁷Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD 21532, USA</footnote>

<footnote n="8">⁸Department of Earth System Science, University of California, Irvine, CA 92697, USA</footnote>

<footnote n="9">⁹Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CEA CNRS, UVSQ UPSaclay, 91198 Gif-sur-Yvette, France</footnote>

<footnote n="10">¹⁰European Commission, Joint Research Centre (JRC), 21027 Ispra, Varese, Italy</footnote>

<footnote n="11">¹¹Department of Engineering and Environmental Studies Program, Boston College, Chestnut Hill, MA 02467, USA</footnote>

<footnote n="12">¹²International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria</footnote>

<footnote n="13">¹³Institute of Environmental Engineering, University of Zielona Góra, 65-417 Zielona Góra, Poland</footnote>

<footnote n="14">¹⁴Max Planck Institute for Biogeochemistry, 07701 Jena, Germany</footnote>

<footnote n="15">¹⁵Institute for Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China</footnote>

<footnote n="16">¹⁶Earth System Science Department, Stanford University, Stanford, CA 94305, USA</footnote>

<footnote n="17">¹⁷Woods Institute for the Environment and Precourt Institute for Energy, Stanford University, Stanford, CA 94305, USA</footnote>

<footnote n="18">¹⁸GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148 Kiel, Germany</footnote>

<footnote n="19">¹⁹Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, 31100 Toulouse, France</footnote>

<footnote n="20">²⁰School of Geography, Nanjing Normal University, Nanjing 210023, China</footnote>

<footnote n="21">²¹Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA</footnote>

<footnote n="22">²²Department of Earth Sciences, Utrecht University, 3584CB Utrecht, the Netherlands</footnote>

<footnote n="23">²³Global Monitoring Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA</footnote>
<figure><img src="image_1.png" /></figure>

Global nitrous oxide budget (1980–2020)

Hanqin Tian<fnref n="1" />,<fnref n="2" />, Naiqing Pan<fnref n="1" />, Rona L. Thompson<fnref n="3" />, Josep G. Canadell<fnref n="4" />, Parvadha Suntharalingam<fnref n="5" />,
Pierre Regnier<fnref n="6" />, Eric A. Davidson<fnref n="7" />, Michael Prather<fnref n="8" />, Philippe Ciais<fnref n="9" />, Marilena Muntean<fnref n="10" />,
Shufen Pan<fnref n="11" />,<fnref n="1" />, Wilfried Winiwarter<fnref n="12" />,<fnref n="13" />, Sönke Zaehle<fnref n="14" />, Feng Zhou<fnref n="15" />, Robert B. Jackson<fnref n="16" />,<fnref n="17" />,
Hermann W. Bange<fnref n="18" />, Sarah Berthet<fnref n="19" />, Zihao Bian<fnref n="20" />, Daniele Bianchi<fnref n="21" />, Alexander F. Bouwman<fnref n="22" />,
Erik T. Buitenhuis<fnref n="5" />, Geoffrey Dutton<fnref n="23" />,³³, Minpeng Hu²⁴, Akihiko Ito²⁵,²⁶, Atul K. Jain²⁷,
Aurich Jeltsch-Thömmes²⁸,²⁹, Fortunat Joos²⁸,²⁹, Sian Kou-Giesbrecht³⁰,³¹, Paul B. Krummel³²,
Xin Lan<fnref n="23" />,³³, Angela Landolfi³⁴,<fnref n="18" />, Ronny Lauerwald³⁵, Ya Li³⁶, Chaoqun Lu³⁷, Taylor Maavara³⁸,
Manfredi Manizza³⁹, Dylan B. Millet⁴⁰, Jens Mühle³⁹, Prabir K. Patra⁴¹,⁴²,⁴³, Glen P. Peters⁴⁴,
Xiaoyu Qin³⁶, Peter Raymond⁴⁵, Laure Resplandy⁴⁶, Judith A. Rosentreter⁴⁷,⁴⁸, Hao Shi³⁶,
Qing Sun²⁸,²⁹, Daniele Tonina⁴⁹, Francesco N. Tubiello⁵⁰, Guido R. van der Werf⁵¹, Nicolas Vuichard<fnref n="9" />,
Junjie Wang<fnref n="22" />, Kelley C. Wells⁴⁰, Luke M. Western<fnref n="23" />,⁵², Chris Wilson⁵³,⁵⁴, Jia Yang⁵⁵, Yuanzhi Yao⁵⁶,
Yongfa You<fnref n="1" />, and Qing Zhu⁵⁷

<footnote n="1">¹Center for Earth System Science and Global Sustainability, Schiller Institute for Integrated Science and Society, Boston College, Chestnut Hill, MA 02467, USA</footnote>

<footnote n="2">²Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467, USA</footnote>

<footnote n="3">³NILU, 2007 Kjeller, Norway</footnote>

<footnote n="4">⁴Global Carbon Project, CSIRO Environment, Canberra, ACT 2101, Australia</footnote>

<footnote n="5">⁵School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK</footnote>

<footnote n="6">⁶Biogeochemistry and Modelling of the Earth System (BGEOSYS), Department of Geoscience, Environment and Society, Université Libre de Bruxelles, 1050 Brussels, Belgium</footnote>

<footnote n="7">⁷Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD 21532, USA</footnote>

<footnote n="8">⁸Department of Earth System Science, University of California, Irvine, CA 92697, USA</footnote>

<footnote n="9">⁹Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CEA CNRS, UVSQ UPSaclay, 91198 Gif-sur-Yvette, France</footnote>

<footnote n="10">¹⁰European Commission, Joint Research Centre (JRC), 21027 Ispra, Varese, Italy</footnote>

<footnote n="11">¹¹Department of Engineering and Environmental Studies Program, Boston College, Chestnut Hill, MA 02467, USA</footnote>

<footnote n="12">¹²International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria</footnote>

<footnote n="13">¹³Institute of Environmental Engineering, University of Zielona Góra, 65-417 Zielona Góra, Poland</footnote>

<footnote n="14">¹⁴Max Planck Institute for Biogeochemistry, 07701 Jena, Germany</footnote>

<footnote n="15">¹⁵Institute for Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China</footnote>

<footnote n="16">¹⁶Earth System Science Department, Stanford University, Stanford, CA 94305, USA</footnote>

<footnote n="17">¹⁷Woods Institute for the Environment and Precourt Institute for Energy, Stanford University, Stanford, CA 94305, USA</footnote>

<footnote n="18">¹⁸GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148 Kiel, Germany</footnote>

<footnote n="19">¹⁹Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, 31100 Toulouse, France</footnote>

<footnote n="20">²⁰School of Geography, Nanjing Normal University, Nanjing 210023, China</footnote>

<footnote n="21">²¹Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA</footnote>

<footnote n="22">²²Department of Earth Sciences, Utrecht University, 3584CB Utrecht, the Netherlands</footnote>

<footnote n="23">²³Global Monitoring Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA</footnote>
<sup>24</sup>Department of Natural Resources and Environmental Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

<sup>25</sup>Graduate School of Life and Agricultural Sciences, University of Tokyo, 113-8657 Tokyo, Japan

<sup>26</sup>Earth System Division, National Institute for Environmental Studies, 305-8506 Tsukuba, Japan

<sup>27</sup>Department of Climate, Meteorology, and Atmospheric Sciences (CLiMAS), University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

<sup>28</sup>Climate and Environmental Physics, Physics Institute, University of Bern, 3012 Bern, Switzerland

<sup>29</sup>Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland

<sup>30</sup>Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC, V8W 3Z4, Canada

<sup>31</sup>Department of Earth and Environmental Sciences, Dalhousie University, Halifax, NS, B3H 4R2, Canada

<sup>32</sup>CSIRO Environment, Aspendale, 3195 Victoria, Australia

<sup>33</sup>Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA

<sup>34</sup>Institute of Marine Sciences, National Research Council (ISMAR-CNR), Via Fosso del Cavaliere 100, 00133 Rome, Italy

<sup>35</sup>Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 91120 Palaiseau, France

<sup>36</sup>Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

<sup>37</sup>Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA

<sup>38</sup>School of Geography, University of Leeds, Leeds, LS2 9JT, UK

<sup>39</sup>Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093, USA

<sup>40</sup>Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA

<sup>41</sup>Research Institute for Global Change, JAMSTEC, 236 0001 Yokohama, Japan

<sup>42</sup>Research Institute for Humanity and Nature, 603 8047 Kyoto, Japan

<sup>43</sup>Center for Environmental Remote Sensing, Chiba University, 263-8522 Chiba, Japan

<sup>44</sup>CICERO Center for International Climate Research, 0349 Oslo, Norway

<sup>45</sup>School of the Environment, Yale University, New Haven, CT 06511, USA

<sup>46</sup>Department of Geosciences, Princeton University, Princeton, NJ 08544, USA

<sup>47</sup>Centre for Coastal Biogeochemistry, Faculty of Science and Engineering, Southern Cross University, Lismore, NSW 2480, Australia

<sup>48</sup>Yale Institute for Biospheric Studies, Yale University, New Haven, CT 06520, USA

<sup>49</sup>Center for Ecohdraulics Research, University of Idaho, Boise, ID 83702, USA

<sup>50</sup>Statistics Division, Food and Agriculture Organization of the United Nations, Via Terme di Caracalla, 00153 Rome, Italy

<sup>51</sup>Meteorology and Air Quality Group, Wageningen University and Research Centre, 6708 PB Wageningen, the Netherlands

<sup>52</sup>School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK

<sup>53</sup>School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK

<sup>54</sup>National Centre for Earth Observation, University of Leeds, Leeds, LS2 9JT, UK

<sup>55</sup>Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, OK 74078, USA

<sup>56</sup>School of Geographic Sciences, East China Normal University, Shanghai 200241, China

<sup>57</sup>Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

**Correspondence:** Hanqin Tian (hanqin.tian@bc.edu)

Received: 3 October 2023 – Discussion started: 9 October 2023

Revised: 15 April 2024 – Accepted: 27 April 2024 – Published: 11 June 2024

**Abstract.** Nitrous oxide (N₂O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance that has been accumulating in the atmosphere since the preindustrial period. The mole fraction of atmospheric N₂O has increased by nearly 25% from 270 ppb (parts per billion) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr⁻¹ in both 2020 and 2021. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the relative contribution of N₂O to the total enhanced effective radiative forcing of greenhouse gases was 6.4% for 1750–2022. As a
<sup>24</sup>Department of Natural Resources and Environmental Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

<sup>25</sup>Graduate School of Life and Agricultural Sciences, University of Tokyo, 113-8657 Tokyo, Japan

<sup>26</sup>Earth System Division, National Institute for Environmental Studies, 305-8506 Tsukuba, Japan

<sup>27</sup>Department of Climate, Meteorology, and Atmospheric Sciences (CLiMAS), University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

<sup>28</sup>Climate and Environmental Physics, Physics Institute, University of Bern, 3012 Bern, Switzerland

<sup>29</sup>Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland

<sup>30</sup>Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC, V8W 3Z4, Canada

<sup>31</sup>Department of Earth and Environmental Sciences, Dalhousie University, Halifax, NS, B3H 4R2, Canada

<sup>32</sup>CSIRO Environment, Aspendale, 3195 Victoria, Australia

<sup>33</sup>Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA

<sup>34</sup>Institute of Marine Sciences, National Research Council (ISMAR-CNR), Via Fosso del Cavaliere 100, 00133 Rome, Italy

<sup>35</sup>Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 91120 Palaiseau, France

<sup>36</sup>Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

<sup>37</sup>Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA

<sup>38</sup>School of Geography, University of Leeds, Leeds, LS2 9JT, UK

<sup>39</sup>Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093, USA

<sup>40</sup>Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA

<sup>41</sup>Research Institute for Global Change, JAMSTEC, 236 0001 Yokohama, Japan

<sup>42</sup>Research Institute for Humanity and Nature, 603 8047 Kyoto, Japan

<sup>43</sup>Center for Environmental Remote Sensing, Chiba University, 263-8522 Chiba, Japan

<sup>44</sup>CICERO Center for International Climate Research, 0349 Oslo, Norway

<sup>45</sup>School of the Environment, Yale University, New Haven, CT 06511, USA

<sup>46</sup>Department of Geosciences, Princeton University, Princeton, NJ 08544, USA

<sup>47</sup>Centre for Coastal Biogeochemistry, Faculty of Science and Engineering, Southern Cross University, Lismore, NSW 2480, Australia

<sup>48</sup>Yale Institute for Biospheric Studies, Yale University, New Haven, CT 06520, USA

<sup>49</sup>Center for Ecohdraulics Research, University of Idaho, Boise, ID 83702, USA

<sup>50</sup>Statistics Division, Food and Agriculture Organization of the United Nations, Via Terme di Caracalla, 00153 Rome, Italy

<sup>51</sup>Meteorology and Air Quality Group, Wageningen University and Research Centre, 6708 PB Wageningen, the Netherlands

<sup>52</sup>School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK

<sup>53</sup>School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK

<sup>54</sup>National Centre for Earth Observation, University of Leeds, Leeds, LS2 9JT, UK

<sup>55</sup>Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, OK 74078, USA

<sup>56</sup>School of Geographic Sciences, East China Normal University, Shanghai 200241, China

<sup>57</sup>Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

**Correspondence:** Hanqin Tian (hanqin.tian@bc.edu)

Received: 3 October 2023 – Discussion started: 9 October 2023

Revised: 15 April 2024 – Accepted: 27 April 2024 – Published: 11 June 2024

**Abstract.** Nitrous oxide (N₂O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance that has been accumulating in the atmosphere since the preindustrial period. The mole fraction of atmospheric N₂O has increased by nearly 25% from 270 ppb (parts per billion) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr⁻¹ in both 2020 and 2021. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the relative contribution of N₂O to the total enhanced effective radiative forcing of greenhouse gases was 6.4% for 1750–2022. As a
core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), our global N₂O budget incorporates both natural and anthropogenic sources and sinks and accounts for the interactions between nitrogen additions and the biogeochemical processes that control N₂O emissions. We use bottom-up (BU: inventory, statistical extrapolation of flux measurements, and process-based land and ocean modeling) and top-down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N₂O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N₂O emissions have increased 40% (or 1.9 Tg N yr⁻¹) in the past 4 decades (1980–2020). Direct agricultural emissions in 2020 (3.9 Tg N yr⁻¹, best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources, including fossil fuel and industry, waste and wastewater, and biomass burning (2.1 Tg N yr⁻¹), and indirect anthropogenic sources (1.3 Tg N yr⁻¹). For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.5 (lower–upper bounds: 10.6–27.0) Tg N yr⁻¹, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr⁻¹. For the 2010–2019 period, the annual BU decadal-average emissions for both natural and anthropogenic sources were 18.2 (10.6–25.9) Tg N yr⁻¹ and TD emissions were 17.4 (15.8–19.20) Tg N yr⁻¹. The once top emitter Europe has reduced its emissions by 31% since the 1980s, while those of emerging economies have grown, making China the top emitter since the 2010s. The observed atmospheric N₂O concentrations in recent years have exceeded projected levels under all scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6), underscoring the importance of reducing anthropogenic N₂O emissions. To evaluate mitigation efforts and contribute to the Global Stocktake of the United Nations Framework Convention on Climate Change, we propose the establishment of a global network for monitoring and modeling N₂O from the surface through to the stratosphere. The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al., 2023).

# 1 Executive summary

The global N₂O budget has been perturbed through direct and indirect anthropogenic emissions as well as through perturbations to the natural N₂O sources and sinks via climate change, increasing atmospheric CO₂, and land-cover change. Ice core data show a relatively constant tropospheric N₂O mixing ratio over the past 2 millennia (Canadell et al., 2021; MacFarling Meure et al., 2006; Fischer et al., 2019), followed by an increase from about 270 ppb (parts per billion) in 1750 to well above 300 ppb in the 2010s. The tropospheric N₂O mole fractions, precisely measured at a global network of stations, increased from 301 ppb in 1980 to 333 ppb in 2020 and 336 ppb in 2022. The tropospheric N₂O mole fraction in 2022 is higher than at any time in the last 800 000 years. The current growth rate of atmospheric N₂O is unprecedented with respect to the ice core record covering the last deglacial transition (with decadal to centennial resolution) and likely unprecedented relative to the ice core records of the past 800 000 years. The mean annual tropospheric growth rate increased from 0.76 (lower–upper bounds: 0.55–0.95) ppb yr⁻¹ in the decade from 2000 to 2009 to 0.96 (0.79–1.15) ppb yr⁻¹ in the decade from 2010 to 2019. In 2020, the N₂O tropospheric growth rate was 1.33 ppb yr⁻¹ (1.38 ppb yr⁻¹ in 2021), the highest observed rate since 1980 and over 30% higher than the average in the 2010s.

Global N₂O emissions have significantly increased in the last 4 decades. The magnitudes of global N₂O emissions esti-

mated by the bottom-up (BU) and top-down (TD) approaches were comparable during the overlapping period from 1997 to 2020, but TD estimates found a larger interannual variability and a faster rate of increase. BU approaches estimated that global N₂O emissions increased from 17.4 Tg N yr⁻¹ (10.3–24.0 Tg N yr⁻¹) in 1997 to 18.5 Tg N yr⁻¹ (10.6–27.0 Tg N yr⁻¹) in 2020, with an average increase rate of 0.043 Tg N yr⁻² (*p* < 0.05). In contrast, according to TD estimates, global emissions increased from 15.4 Tg N yr⁻¹ (13.9–16.7 Tg N yr⁻¹) in 1997 to 17.0 Tg N yr⁻¹ (16.6–17.4 Tg N yr⁻¹) in 2020, implying a higher increase rate of 0.085 Tg N yr⁻² (*p* < 0.05).

According to BU estimates, the increase in global N₂O emissions was primarily due to a 40% increase in anthropogenic emissions from 4.8 (3.1–7.3) Tg yr⁻¹ in 1980 to 6.7 (3.3–10.9) Tg yr⁻¹ in 2020. Among all anthropogenic sources, direct agricultural emissions made the largest contribution, increasing from 2.2 (1.6–2.8) Tg N yr⁻¹ in 1980 to 3.9 (2.9–5.1) Tg N yr⁻¹ in 2020. The concurrent indirect agricultural N₂O emissions also steadily increased from 0.9 (0.7–1.1) to 1.3 (0.9–1.6) Tg N yr⁻¹. In contrast, other direct anthropogenic emissions (including emissions from fossil fuel and industry, biomass burning, and waste and wastewater) did not show a significant trend, while fluxes induced by perturbations to climate, atmospheric CO₂, and land cover were negative and caused a reduction in N₂O emissions that grew from −0.4 (−0.9 to 1.0) Tg yr⁻¹ in 1980 to −0.6 (−2.2 to 1.8) Tg yr⁻¹ in 2020. Unlike anthropogenic emissions, global natural land and ocean N₂O emissions
core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), our global N₂O budget incorporates both natural and anthropogenic sources and sinks and accounts for the interactions between nitrogen additions and the biogeochemical processes that control N₂O emissions. We use bottom-up (BU: inventory, statistical extrapolation of flux measurements, and process-based land and ocean modeling) and top-down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N₂O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N₂O emissions have increased 40% (or 1.9 Tg N yr⁻¹) in the past 4 decades (1980–2020). Direct agricultural emissions in 2020 (3.9 Tg N yr⁻¹, best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources, including fossil fuel and industry, waste and wastewater, and biomass burning (2.1 Tg N yr⁻¹), and indirect anthropogenic sources (1.3 Tg N yr⁻¹). For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.5 (lower–upper bounds: 10.6–27.0) Tg N yr⁻¹, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr⁻¹. For the 2010–2019 period, the annual BU decadal-average emissions for both natural and anthropogenic sources were 18.2 (10.6–25.9) Tg N yr⁻¹ and TD emissions were 17.4 (15.8–19.20) Tg N yr⁻¹. The once top emitter Europe has reduced its emissions by 31% since the 1980s, while those of emerging economies have grown, making China the top emitter since the 2010s. The observed atmospheric N₂O concentrations in recent years have exceeded projected levels under all scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6), underscoring the importance of reducing anthropogenic N₂O emissions. To evaluate mitigation efforts and contribute to the Global Stocktake of the United Nations Framework Convention on Climate Change, we propose the establishment of a global network for monitoring and modeling N₂O from the surface through to the stratosphere. The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al., 2023).

# 1 Executive summary

The global N₂O budget has been perturbed through direct and indirect anthropogenic emissions as well as through perturbations to the natural N₂O sources and sinks via climate change, increasing atmospheric CO₂, and land-cover change. Ice core data show a relatively constant tropospheric N₂O mixing ratio over the past 2 millennia (Canadell et al., 2021; MacFarling Meure et al., 2006; Fischer et al., 2019), followed by an increase from about 270 ppb (parts per billion) in 1750 to well above 300 ppb in the 2010s. The tropospheric N₂O mole fractions, precisely measured at a global network of stations, increased from 301 ppb in 1980 to 333 ppb in 2020 and 336 ppb in 2022. The tropospheric N₂O mole fraction in 2022 is higher than at any time in the last 800 000 years. The current growth rate of atmospheric N₂O is unprecedented with respect to the ice core record covering the last deglacial transition (with decadal to centennial resolution) and likely unprecedented relative to the ice core records of the past 800 000 years. The mean annual tropospheric growth rate increased from 0.76 (lower–upper bounds: 0.55–0.95) ppb yr⁻¹ in the decade from 2000 to 2009 to 0.96 (0.79–1.15) ppb yr⁻¹ in the decade from 2010 to 2019. In 2020, the N₂O tropospheric growth rate was 1.33 ppb yr⁻¹ (1.38 ppb yr⁻¹ in 2021), the highest observed rate since 1980 and over 30% higher than the average in the 2010s.

Global N₂O emissions have significantly increased in the last 4 decades. The magnitudes of global N₂O emissions esti-

mated by the bottom-up (BU) and top-down (TD) approaches were comparable during the overlapping period from 1997 to 2020, but TD estimates found a larger interannual variability and a faster rate of increase. BU approaches estimated that global N₂O emissions increased from 17.4 Tg N yr⁻¹ (10.3–24.0 Tg N yr⁻¹) in 1997 to 18.5 Tg N yr⁻¹ (10.6–27.0 Tg N yr⁻¹) in 2020, with an average increase rate of 0.043 Tg N yr⁻² (*p* < 0.05). In contrast, according to TD estimates, global emissions increased from 15.4 Tg N yr⁻¹ (13.9–16.7 Tg N yr⁻¹) in 1997 to 17.0 Tg N yr⁻¹ (16.6–17.4 Tg N yr⁻¹) in 2020, implying a higher increase rate of 0.085 Tg N yr⁻² (*p* < 0.05).

According to BU estimates, the increase in global N₂O emissions was primarily due to a 40% increase in anthropogenic emissions from 4.8 (3.1–7.3) Tg yr⁻¹ in 1980 to 6.7 (3.3–10.9) Tg yr⁻¹ in 2020. Among all anthropogenic sources, direct agricultural emissions made the largest contribution, increasing from 2.2 (1.6–2.8) Tg N yr⁻¹ in 1980 to 3.9 (2.9–5.1) Tg N yr⁻¹ in 2020. The concurrent indirect agricultural N₂O emissions also steadily increased from 0.9 (0.7–1.1) to 1.3 (0.9–1.6) Tg N yr⁻¹. In contrast, other direct anthropogenic emissions (including emissions from fossil fuel and industry, biomass burning, and waste and wastewater) did not show a significant trend, while fluxes induced by perturbations to climate, atmospheric CO₂, and land cover were negative and caused a reduction in N₂O emissions that grew from −0.4 (−0.9 to 1.0) Tg yr⁻¹ in 1980 to −0.6 (−2.2 to 1.8) Tg yr⁻¹ in 2020. Unlike anthropogenic emissions, global natural land and ocean N₂O emissions
were relatively stable. According to the BU approaches, the total amount of global natural N₂O emissions fluctuated between 11.7 and 12.1 Tg yr⁻¹ during 1980–2020. Among all sources, natural emissions from shelves, inland waters, and lightning and atmospheric production were assumed to be constant during 1980–2020. According to BU approaches, the total natural emissions from these sources were 1.8 (1.0–3.0) Tg N yr⁻¹.

During 2010–2019, similar estimates of global total N₂O emissions were obtained using both the BU and TD approaches, with decadal mean values of 18.2 (10.6–25.9) Tg N yr⁻¹ and 17.4 (15.8–19.2) Tg N yr⁻¹, respectively (Fig. 1). According to the BU estimates, natural sources contributed 65% to the total emissions (11.8, 7.3–15.9 Tg N yr⁻¹). Specifically, natural soils contributed the most, with a decadal average of 6.4 (3.9–8.6) Tg N yr⁻¹, followed by open oceans (3.5, 2.5–4.7 Tg N yr⁻¹), the natural source from shelves (1.2, 0.6–1.6 Tg N yr⁻¹), lightning and atmospheric production (0.6, 0.3–1.2 Tg N yr⁻¹), and inland waters, estuaries, and coastal vegetation (0.1, 0.0–0.1 Tg N yr⁻¹). Anthropogenic sources contributed 35% to the total N₂O emissions (6.5, 3.2–10.0 Tg N yr⁻¹). Direct agricultural emissions accounted for 56% of the total anthropogenic emissions (3.6, 2.7–4.8 Tg N yr⁻¹), followed by emissions from other direct anthropogenic sources (2.1, 1.8–2.4 Tg N yr⁻¹), including fossil fuel and industry (1.1, 1.0–1.2 Tg N yr⁻¹), waste and wastewater (0.3, 0.3–0.3 Tg N yr⁻¹), and biomass burning (0.8, 0.5–1.0 Tg N yr⁻¹), and indirect anthropogenic emissions (1.2, 0.9–1.6 Tg N yr⁻¹). Perturbed fluxes from climate, CO₂, and land-cover changes had a net negative effect (i.e., reduced) on N₂O emissions (−0.6, −2.1 to 1.2 Tg N yr⁻¹). Increased CO₂ and land conversion from mature forest reduced N₂O emissions, but climate change resulted in N₂O emission of 0.7 (0.2–1.2) Tg N yr⁻¹.

Among the 18 regions considered in this study, only Europe, Russia, Australasia, and Japan and Korea had decreasing N₂O emissions. Europe had the largest rate of decrease, with an average of −13.2 × 10⁻³ Tg N yr⁻² during 1980–2020 (31% reduction), largely resulting from reduced fossil fuel and industry emissions, which changed from 0.49 Tg N yr⁻¹ in 1980 to 0.14 Tg N yr⁻¹ in 2020. In addition to the large reduction in fossil fuel and industry emissions in Europe, direct and indirect agricultural emissions also declined during 1980–2020; however, the decreasing trend in direct agricultural emissions had leveled off by the 2000s.

China and South Asia had the largest increases in N₂O emissions from 1980 to 2020. The rates of increase in anthropogenic emissions from China and South Asia were 18.9 × 10⁻³ Tg N yr⁻² (82% increase) and 14.3 × 10⁻³ Tg N yr⁻² (92% increase), respectively. In these two regions, direct nitrogen additions in agriculture made the largest contribution, while other direct and indirect emissions also steadily increased.

The atmospheric chemistry transport models used in this study show an increase in the atmospheric N₂O burden, from 1527 (1504–1545) Tg N in 2000–2009 to 1606 (1592–1621) Tg N in 2020, and, proportional to this, a small increase in the atmospheric loss, from 12.1 (12.0–12.6) to 12.9 (12.5–13.2) Tg N yr⁻¹. The estimated increase in the atmospheric N₂O burden is comparable to estimates by satellite and photolysis models, showing an increase from 1528 Tg N in the 2000s to 1570 Tg N in the 2010s and 1592 Tg N in 2020. The atmospheric chemistry transport models, however, did not show any significant trend in the lifetime, which is in contrast to results based on satellite observations in the stratosphere; these observations indicate that the atmospheric lifetime of N₂O decreased from 119 years in the 2000s to 117 years in the 2010s. The reason for the discrepancy is not yet known and needs to be further investigated.

The following major uncertainties have been identified:

1. Inversion estimates are the most uncertain in the areas of South America, Africa, central and southern Asia, and Australasia, where the inversions are poorly constrained by observations.

2. Large uncertainties exist in the estimates of soil N₂O emissions from tropical ecosystems in the Amazon Basin, the Congo Basin, and Southeast Asia as well as in regions with high fertilizer application rates and emissions, including eastern China, northern India, and the US Corn Belt.

3. The largest uncertainties in the estimates of ocean emissions are found in the equatorial Pacific, the Benguela upwelling region of the Atlantic, and the eastern equatorial Indian Ocean. The highest uncertainty in the equatorial upwelling and low-oxygen waters is associated with high subsurface N₂O production.

4. The N₂O fluxes from atmospheric CO₂, mature forest conversion, and biomass burning are poorly understood and quantified. The relatively sparse distribution of current N₂O observation sites underscores the necessity to establish more sites and regular aircraft profiles, especially in tropical and subtropical regions, to better constrain emission estimates from inversion models.

Based on this analysis and associated uncertainties, we propose the urgent development of a comprehensive terrestrial and ocean N₂O flux monitoring and analysis network to better resolve spatiotemporal patterns and reduce uncertainties in N₂O emissions. Such a development is a requirement to better constrain the future contribution of N₂O to climate change and guide policy choices to reduce N₂O emissions.
were relatively stable. According to the BU approaches, the total amount of global natural N₂O emissions fluctuated between 11.7 and 12.1 Tg yr⁻¹ during 1980–2020. Among all sources, natural emissions from shelves, inland waters, and lightning and atmospheric production were assumed to be constant during 1980–2020. According to BU approaches, the total natural emissions from these sources were 1.8 (1.0–3.0) Tg N yr⁻¹.

During 2010–2019, similar estimates of global total N₂O emissions were obtained using both the BU and TD approaches, with decadal mean values of 18.2 (10.6–25.9) Tg N yr⁻¹ and 17.4 (15.8–19.2) Tg N yr⁻¹, respectively (Fig. 1). According to the BU estimates, natural sources contributed 65% to the total emissions (11.8, 7.3–15.9 Tg N yr⁻¹). Specifically, natural soils contributed the most, with a decadal average of 6.4 (3.9–8.6) Tg N yr⁻¹, followed by open oceans (3.5, 2.5–4.7 Tg N yr⁻¹), the natural source from shelves (1.2, 0.6–1.6 Tg N yr⁻¹), lightning and atmospheric production (0.6, 0.3–1.2 Tg N yr⁻¹), and inland waters, estuaries, and coastal vegetation (0.1, 0.0–0.1 Tg N yr⁻¹). Anthropogenic sources contributed 35% to the total N₂O emissions (6.5, 3.2–10.0 Tg N yr⁻¹). Direct agricultural emissions accounted for 56% of the total anthropogenic emissions (3.6, 2.7–4.8 Tg N yr⁻¹), followed by emissions from other direct anthropogenic sources (2.1, 1.8–2.4 Tg N yr⁻¹), including fossil fuel and industry (1.1, 1.0–1.2 Tg N yr⁻¹), waste and wastewater (0.3, 0.3–0.3 Tg N yr⁻¹), and biomass burning (0.8, 0.5–1.0 Tg N yr⁻¹), and indirect anthropogenic emissions (1.2, 0.9–1.6 Tg N yr⁻¹). Perturbed fluxes from climate, CO₂, and land-cover changes had a net negative effect (i.e., reduced) on N₂O emissions (−0.6, −2.1 to 1.2 Tg N yr⁻¹). Increased CO₂ and land conversion from mature forest reduced N₂O emissions, but climate change resulted in N₂O emission of 0.7 (0.2–1.2) Tg N yr⁻¹.

Among the 18 regions considered in this study, only Europe, Russia, Australasia, and Japan and Korea had decreasing N₂O emissions. Europe had the largest rate of decrease, with an average of −13.2 × 10⁻³ Tg N yr⁻² during 1980–2020 (31% reduction), largely resulting from reduced fossil fuel and industry emissions, which changed from 0.49 Tg N yr⁻¹ in 1980 to 0.14 Tg N yr⁻¹ in 2020. In addition to the large reduction in fossil fuel and industry emissions in Europe, direct and indirect agricultural emissions also declined during 1980–2020; however, the decreasing trend in direct agricultural emissions had leveled off by the 2000s.

China and South Asia had the largest increases in N₂O emissions from 1980 to 2020. The rates of increase in anthropogenic emissions from China and South Asia were 18.9 × 10⁻³ Tg N yr⁻² (82% increase) and 14.3 × 10⁻³ Tg N yr⁻² (92% increase), respectively. In these two regions, direct nitrogen additions in agriculture made the largest contribution, while other direct and indirect emissions also steadily increased.

The atmospheric chemistry transport models used in this study show an increase in the atmospheric N₂O burden, from 1527 (1504–1545) Tg N in 2000–2009 to 1606 (1592–1621) Tg N in 2020, and, proportional to this, a small increase in the atmospheric loss, from 12.1 (12.0–12.6) to 12.9 (12.5–13.2) Tg N yr⁻¹. The estimated increase in the atmospheric N₂O burden is comparable to estimates by satellite and photolysis models, showing an increase from 1528 Tg N in the 2000s to 1570 Tg N in the 2010s and 1592 Tg N in 2020. The atmospheric chemistry transport models, however, did not show any significant trend in the lifetime, which is in contrast to results based on satellite observations in the stratosphere; these observations indicate that the atmospheric lifetime of N₂O decreased from 119 years in the 2000s to 117 years in the 2010s. The reason for the discrepancy is not yet known and needs to be further investigated.

The following major uncertainties have been identified:

1. Inversion estimates are the most uncertain in the areas of South America, Africa, central and southern Asia, and Australasia, where the inversions are poorly constrained by observations.

2. Large uncertainties exist in the estimates of soil N₂O emissions from tropical ecosystems in the Amazon Basin, the Congo Basin, and Southeast Asia as well as in regions with high fertilizer application rates and emissions, including eastern China, northern India, and the US Corn Belt.

3. The largest uncertainties in the estimates of ocean emissions are found in the equatorial Pacific, the Benguela upwelling region of the Atlantic, and the eastern equatorial Indian Ocean. The highest uncertainty in the equatorial upwelling and low-oxygen waters is associated with high subsurface N₂O production.

4. The N₂O fluxes from atmospheric CO₂, mature forest conversion, and biomass burning are poorly understood and quantified. The relatively sparse distribution of current N₂O observation sites underscores the necessity to establish more sites and regular aircraft profiles, especially in tropical and subtropical regions, to better constrain emission estimates from inversion models.

Based on this analysis and associated uncertainties, we propose the urgent development of a comprehensive terrestrial and ocean N₂O flux monitoring and analysis network to better resolve spatiotemporal patterns and reduce uncertainties in N₂O emissions. Such a development is a requirement to better constrain the future contribution of N₂O to climate change and guide policy choices to reduce N₂O emissions.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Global N₂O budget during 2010–2019. The colored arrows represent N₂O fluxes (in Tg N yr⁻¹ for 2010–2019) as follows: red – direct emissions from nitrogen additions in the agricultural sector (agriculture); orange – emissions from other direct anthropogenic sources; maroon – indirect emissions from anthropogenic nitrogen additions; brown – perturbed fluxes from changes in climate, CO₂, or land cover; and green – emissions from natural sources. The anthropogenic and natural N₂O sources are derived from BU estimates. The blue arrows represent the surface sink and the observed atmospheric chemical sink, about 1 % of which occurs in the troposphere. The total budget (sources + sinks) does not exactly match the observed atmospheric accumulation, as each of the terms has been derived independently and we do not force TD agreement by rescaling the terms. This imbalance falls within the overall uncertainty in closing the N₂O budget, as reflected in each of the terms. The N₂O sources and sinks are given in teragrams of nitrogen per year (Tg N yr⁻¹). © The Global Carbon Project.</figcaption></figure>

## 2 Introduction

Nitrogen (N) is an essential element for the survival of all living organisms and is required by numerous biological molecules such as nucleic acids, proteins, and chlorophyll (Galloway et al., 2021; Scheer et al., 2020). The addition of excess reactive N compounds to terrestrial and oceanic ecosystems stimulates emissions of nitrous oxide (N₂O), which is the most important depleting substance of stratospheric ozone (World Meteorological Organization, 2022) and a long-lived potent greenhouse gas with an atmospheric lifetime of more than 100 years (Myhre et al., 2013; Prather et al., 2015). Atmospheric N₂O mole fractions have increased by nearly 25 % since the preindustrial era, from 270 ppb (parts per billion) in 1750 (MacFarling Meure et al., 2006) to 336 ppb in 2022, and have shown an increase of

35 ppb (10 %) since 1980 (Fig. 2). The current mole fraction is higher than at any time in the last 800 000 years (Schilt et al., 2010). The increase rate of atmospheric N₂O in the 20th century is unprecedented over the past 20 000 years, covering the last glacial–interglacial transition, and likely unprecedented compared to the lower-resolution ice core records of the past 800 000 years (Joos and Spahni, 2008; Schilt et al., 2010; Canadell et al., 2021 AR6, WGI, Chap. 5). The observation networks of the Advanced Global Atmospheric Gases Experiment (AGAGE; Prinn et al., 2018), the National Ocean and Atmospheric Administration (NOAA; Hall et al., 2007), and the Commonwealth Scientific and Industrial Research Organization (CSIRO; Francey et al., 2003) all show an overall increasing trend in the growth rate of atmospheric N₂O: the mean annual growth rate increased from 0.76 (0.55–0.95) ppb yr⁻¹ in the 2000s to 0.96 (0.79–
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Global N₂O budget during 2010–2019. The colored arrows represent N₂O fluxes (in Tg N yr⁻¹ for 2010–2019) as follows: red – direct emissions from nitrogen additions in the agricultural sector (agriculture); orange – emissions from other direct anthropogenic sources; maroon – indirect emissions from anthropogenic nitrogen additions; brown – perturbed fluxes from changes in climate, CO₂, or land cover; and green – emissions from natural sources. The anthropogenic and natural N₂O sources are derived from BU estimates. The blue arrows represent the surface sink and the observed atmospheric chemical sink, about 1 % of which occurs in the troposphere. The total budget (sources + sinks) does not exactly match the observed atmospheric accumulation, as each of the terms has been derived independently and we do not force TD agreement by rescaling the terms. This imbalance falls within the overall uncertainty in closing the N₂O budget, as reflected in each of the terms. The N₂O sources and sinks are given in teragrams of nitrogen per year (Tg N yr⁻¹). © The Global Carbon Project.</figcaption></figure>

## 2 Introduction

Nitrogen (N) is an essential element for the survival of all living organisms and is required by numerous biological molecules such as nucleic acids, proteins, and chlorophyll (Galloway et al., 2021; Scheer et al., 2020). The addition of excess reactive N compounds to terrestrial and oceanic ecosystems stimulates emissions of nitrous oxide (N₂O), which is the most important depleting substance of stratospheric ozone (World Meteorological Organization, 2022) and a long-lived potent greenhouse gas with an atmospheric lifetime of more than 100 years (Myhre et al., 2013; Prather et al., 2015). Atmospheric N₂O mole fractions have increased by nearly 25 % since the preindustrial era, from 270 ppb (parts per billion) in 1750 (MacFarling Meure et al., 2006) to 336 ppb in 2022, and have shown an increase of

35 ppb (10 %) since 1980 (Fig. 2). The current mole fraction is higher than at any time in the last 800 000 years (Schilt et al., 2010). The increase rate of atmospheric N₂O in the 20th century is unprecedented over the past 20 000 years, covering the last glacial–interglacial transition, and likely unprecedented compared to the lower-resolution ice core records of the past 800 000 years (Joos and Spahni, 2008; Schilt et al., 2010; Canadell et al., 2021 AR6, WGI, Chap. 5). The observation networks of the Advanced Global Atmospheric Gases Experiment (AGAGE; Prinn et al., 2018), the National Ocean and Atmospheric Administration (NOAA; Hall et al., 2007), and the Commonwealth Scientific and Industrial Research Organization (CSIRO; Francey et al., 2003) all show an overall increasing trend in the growth rate of atmospheric N₂O: the mean annual growth rate increased from 0.76 (0.55–0.95) ppb yr⁻¹ in the 2000s to 0.96 (0.79–
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Global mean atmospheric N₂O dry mole fraction (atmospheric concentration) (1980–2022) and its annual growth rate (1995–2022) estimated by the AGAGE, NOAA, and CSIRO observing networks. The blue and black dashed lines represent the mean annual growth rate in the 2000s and 2010s, respectively.</figcaption></figure>

1.15) ppb yr⁻¹ in the 2010s, with significant seasonal and interannual variation. In 2020, the N₂O atmospheric growth rate was 1.33 ppb yr⁻¹ (1.38 ppb yr⁻¹ in 2021), higher than any previous observed year, and more than 30% higher than the average value in the 2010s.

Due to the rapid increase in global N₂O emissions, observed atmospheric N₂O mole fractions in recent years have begun to exceed the predicted levels under all scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2021; Gidden et al., 2019; Tian et al., 2020). N₂O emissions are expected to continue increasing in the coming decades due to the growing demand for food, feed, fiber, and energy as well as a rising source from waste generation and industrial processes (Davidson and Kanter, 2014; Reay et al., 2012). Reducing N₂O emissions will contribute to the mitigation of global warming and the recovery of stratospheric ozone (Jackson et al., 2019). It is noted that, although increased stratospheric NOₓ due to rising levels of N₂O can lead to incremental stratospheric O₃ loss, it is unlikely to cause catastrophic ozone loss the way that anthropogenic halogens did, as stratospheric NOₓ from N₂O has offset halogen-catalyzed stratospheric ozone loss through various buffering reactions, e.g., the formation of halogen reservoir species like ClONO₂ (Wennberg et al., 1994; Nevison et al., 1999; Ravishankara et al., 2009). Significant reductions in N₂O emissions are required along with net CO₂ emissions to stabilize the global climate system. For pathways consistent with the remaining carbon budget of 1.5, 1.7, and 2°C stabilization, and assuming that all greenhouse gases (GHGs) should be cut in equal propor-

tion to their contribution to anthropogenic radiative forcing, global N₂O emissions need to be reduced by 22%, 18%, and 11%, respectively, by 2050 (Rogelj and Lamboll, 2024). In addition, N₂O mitigation could reduce ozone loss comparable to the depletion potential of the global chlorofluorocarbons (CFCs) stock in old air conditioners, refrigerators, insulation foams, and other units (UNEP, 2013). All in all, implementing N₂O mitigation will contribute to achieving a set of United Nations Sustainable Development Goals (United Nations, 2016).

Nitration and denitration are the two key microbial processes controlling N₂O production (Butterbach-Bahl et al., 2013; Gruber and Galloway, 2008; Kuypers et al., 2018; Firestone and Davidson, 1989), making the largest contribution to global N₂O emissions (Syakila and Kroeze, 2011; Tian et al., 2020); abiotic processes also play a role in the production of N₂O. We categorize the processes governing N₂O sources and sinks in 23 different categories (Fig. 3): (1) Fossil fuel – N₂O emissions from fossil fuel combustion; (2) Findustry – N₂O emissions from the chemical industry; (3) Fwaste water – N₂O emissions from wastewater treatment and discharge; (4) Fnatural inland waters – natural N₂O emissions from inland waters (rivers, lakes, and reservoirs); (5) Fhuman inland waters – anthropogenic N₂O from inland waters (rivers, lakes, and reservoirs); (6) Fdirect soil emission – direct N₂O emissions from agricultural soils; (7) Fmanure left on pasture – N₂O emissions from manure left on pasture; (8) Fmanure management – N₂O emission from manure management; (9)FAquaculture – N₂O emissions from coastal and freshwater aquaculture; (10) Fland use/land cover changes – N₂O emission/reduction due to land-cover change/deforestation; (11) Fnatural soil emissions – natural soil N₂O emission; (12) Fbiomass burning – N₂O emissions from biomass burning; (13) Fsurface uptake – surface N₂O uptake; (14) FN deposition land – indirect N₂O emissions from anthropogenic nitrogen additions on land; (15) FClimate effect – perturbed N₂O fluxes from climate change; (16) FCO₂ effect – perturbed N₂O fluxes from CO₂ change; (17) Fshelves – N₂O emission from continental shelves; (18) Foceans – N₂O emission from open ocean; (19) FN deposition ocean – N₂O emissions from anthropogenic N₂O deposition on oceans; (20) Flightning and atmospheric production – lightning and atmospheric production of N₂O; (21) Fstratospheric sink – stratospheric N₂O sink; (22) Fnatural coastal water – natural N₂O emissions from estuaries and coastal vegetation; and (23) FN dep and leaching – N₂O emissions from nitrogen deposition and leaching to estuaries and coastal vegetation. There is also a small amount of N₂O emission from termite mounds, but such an N₂O flux is not quantified in the current budget analysis due to limited data.

Biogenic N₂O emissions from land are regulated by multiple environmental factors, including soil moisture, temperature, oxygen status, pH, vegetation type, topography, atmospheric CO₂ concentration, and soil N and C availabil-
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Global mean atmospheric N₂O dry mole fraction (atmospheric concentration) (1980–2022) and its annual growth rate (1995–2022) estimated by the AGAGE, NOAA, and CSIRO observing networks. The blue and black dashed lines represent the mean annual growth rate in the 2000s and 2010s, respectively.</figcaption></figure>

1.15) ppb yr⁻¹ in the 2010s, with significant seasonal and interannual variation. In 2020, the N₂O atmospheric growth rate was 1.33 ppb yr⁻¹ (1.38 ppb yr⁻¹ in 2021), higher than any previous observed year, and more than 30% higher than the average value in the 2010s.

Due to the rapid increase in global N₂O emissions, observed atmospheric N₂O mole fractions in recent years have begun to exceed the predicted levels under all scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2021; Gidden et al., 2019; Tian et al., 2020). N₂O emissions are expected to continue increasing in the coming decades due to the growing demand for food, feed, fiber, and energy as well as a rising source from waste generation and industrial processes (Davidson and Kanter, 2014; Reay et al., 2012). Reducing N₂O emissions will contribute to the mitigation of global warming and the recovery of stratospheric ozone (Jackson et al., 2019). It is noted that, although increased stratospheric NOₓ due to rising levels of N₂O can lead to incremental stratospheric O₃ loss, it is unlikely to cause catastrophic ozone loss the way that anthropogenic halogens did, as stratospheric NOₓ from N₂O has offset halogen-catalyzed stratospheric ozone loss through various buffering reactions, e.g., the formation of halogen reservoir species like ClONO₂ (Wennberg et al., 1994; Nevison et al., 1999; Ravishankara et al., 2009). Significant reductions in N₂O emissions are required along with net CO₂ emissions to stabilize the global climate system. For pathways consistent with the remaining carbon budget of 1.5, 1.7, and 2°C stabilization, and assuming that all greenhouse gases (GHGs) should be cut in equal propor-

tion to their contribution to anthropogenic radiative forcing, global N₂O emissions need to be reduced by 22%, 18%, and 11%, respectively, by 2050 (Rogelj and Lamboll, 2024). In addition, N₂O mitigation could reduce ozone loss comparable to the depletion potential of the global chlorofluorocarbons (CFCs) stock in old air conditioners, refrigerators, insulation foams, and other units (UNEP, 2013). All in all, implementing N₂O mitigation will contribute to achieving a set of United Nations Sustainable Development Goals (United Nations, 2016).

Nitration and denitration are the two key microbial processes controlling N₂O production (Butterbach-Bahl et al., 2013; Gruber and Galloway, 2008; Kuypers et al., 2018; Firestone and Davidson, 1989), making the largest contribution to global N₂O emissions (Syakila and Kroeze, 2011; Tian et al., 2020); abiotic processes also play a role in the production of N₂O. We categorize the processes governing N₂O sources and sinks in 23 different categories (Fig. 3): (1) Fossil fuel – N₂O emissions from fossil fuel combustion; (2) Findustry – N₂O emissions from the chemical industry; (3) Fwaste water – N₂O emissions from wastewater treatment and discharge; (4) Fnatural inland waters – natural N₂O emissions from inland waters (rivers, lakes, and reservoirs); (5) Fhuman inland waters – anthropogenic N₂O from inland waters (rivers, lakes, and reservoirs); (6) Fdirect soil emission – direct N₂O emissions from agricultural soils; (7) Fmanure left on pasture – N₂O emissions from manure left on pasture; (8) Fmanure management – N₂O emission from manure management; (9)FAquaculture – N₂O emissions from coastal and freshwater aquaculture; (10) Fland use/land cover changes – N₂O emission/reduction due to land-cover change/deforestation; (11) Fnatural soil emissions – natural soil N₂O emission; (12) Fbiomass burning – N₂O emissions from biomass burning; (13) Fsurface uptake – surface N₂O uptake; (14) FN deposition land – indirect N₂O emissions from anthropogenic nitrogen additions on land; (15) FClimate effect – perturbed N₂O fluxes from climate change; (16) FCO₂ effect – perturbed N₂O fluxes from CO₂ change; (17) Fshelves – N₂O emission from continental shelves; (18) Foceans – N₂O emission from open ocean; (19) FN deposition ocean – N₂O emissions from anthropogenic N deposition on oceans; (20) Flightning and atmospheric production – lightning and atmospheric production of N₂O; (21) Fstratospheric sink – stratospheric N₂O sink; (22) Fnatural coastal water – natural N₂O emissions from estuaries and coastal vegetation; and (23) FN dep and leaching – N₂O emissions from nitrogen deposition and leaching to estuaries and coastal vegetation. There is also a small amount of N₂O emission from termite mounds, but such an N₂O flux is not quantified in the current budget analysis due to limited data.

Biogenic N₂O emissions from land are regulated by multiple environmental factors, including soil moisture, temperature, oxygen status, pH, vegetation type, topography, atmospheric CO₂ concentration, and soil N and C availabil-
<figure><img src="image_4.png" /><figcaption>**Figure 3.** N₂O sources and sinks and flux partitions contributing to the global N₂O budget. Upward-pointing arrows indicate a source to the atmosphere, whereas downward-pointing arrows represent a sink.</figcaption></figure>

ity (Butterbach-Bahl et al., 2013; Dijkstra et al., 2012; Li et al., 2020; Tian et al., 2016, 2019; Yin et al., 2022; H. Yu et al., 2022). The effects of these environmental factors on N₂O emissions have strong spatial and temporal heterogeneity, making upscaling field N₂O measurements to regional and global scales difficult. Studies using atmospheric N₂O inverse modeling suggest a greater source of N₂O from land and ocean under the colder and wetter La Nina conditions and vice versa in the warmer and drier El Niño conditions (Patra et al., 2022; Thompson et al., 2014). Ongoing environmental changes, such as ocean warming (and associated changes in stratification and ice coverage), decreasing pH (i.e., increasing acidification), loss of dissolved oxygen (i.e., deoxygenation), and eutrophication due to increasing anthropogenic inputs of nutrients via rivers and atmospheric deposition of nitrogen aerosols, might significantly alter the production and consumption of N₂O in the upper ocean, its distribution pattern, and, ultimately, its release to the atmosphere (Bange et al., 2019; Bange, 2022; Wilson et al., 2020), exerting a small but uncertain feedback on global warming in the long term (Battaglia and Joos, 2018; Forster et al., 2021).

In this study, we construct a comprehensive global and regional N₂O budget based on the processes and framework shown in Fig. 3 and following the framework of Tian et al. (2020). The figure summarizes the pathways of N₂O formation, consumption, emission, and absorption, and it helps

to guide consistent estimations and comparisons of N₂O budgets among regions and upscaling of regional budgets to the globe. N₂O fluxes are grouped into two major categories based on the sources.

The first category is natural N₂O fluxes (blue arrows in Fig. 3), which are N₂O fluxes in the absence of climate change and anthropogenic disturbances, including natural soil emissions, soil uptake, N₂O emission from natural disturbances causing wetland loss and degradation, lightning, and atmospheric production. This category also includes natural emissions from inland waters, coastal ecosystems, and the ocean.

The second category is anthropogenic N₂O fluxes (red arrows in Fig. 3). The direct emissions from nitrogen additions in the agricultural sector (“Agroecosystems” box in Fig. 3) include emissions from the direct application of synthetic nitrogen fertilizers and manure (henceforth “Direct soil emissions”), manure left on pasture, manure management, and aquaculture, while other direct anthropogenic sources include fossil fuel combustion and industry, waste and wastewater, and biomass burning. Indirect N₂O emissions derive from anthropogenic nitrogen additions, such as atmospheric nitrogen deposition (NDEP) on land and ocean, and the effects of anthropogenic loads of reactive nitrogen in inland waters, estuaries, and coastal vegetation.
<figure><img src="image_4.png" /><figcaption>**Figure 3.** N₂O sources and sinks and flux partitions contributing to the global N₂O budget. Upward-pointing arrows indicate a source to the atmosphere, whereas downward-pointing arrows represent a sink.</figcaption></figure>

ity (Butterbach-Bahl et al., 2013; Dijkstra et al., 2012; Li et al., 2020; Tian et al., 2016, 2019; Yin et al., 2022; H. Yu et al., 2022). The effects of these environmental factors on N₂O emissions have strong spatial and temporal heterogeneity, making upscaling field N₂O measurements to regional and global scales difficult. Studies using atmospheric N₂O inverse modeling suggest a greater source of N₂O from land and ocean under the colder and wetter La Nina conditions and vice versa in the warmer and drier El Niño conditions (Patra et al., 2022; Thompson et al., 2014). Ongoing environmental changes, such as ocean warming (and associated changes in stratification and ice coverage), decreasing pH (i.e., increasing acidification), loss of dissolved oxygen (i.e., deoxygenation), and eutrophication due to increasing anthropogenic inputs of nutrients via rivers and atmospheric deposition of nitrogen aerosols, might significantly alter the production and consumption of N₂O in the upper ocean, its distribution pattern, and, ultimately, its release to the atmosphere (Bange et al., 2019; Bange, 2022; Wilson et al., 2020), exerting a small but uncertain feedback on global warming in the long term (Battaglia and Joos, 2018; Forster et al., 2021).

In this study, we construct a comprehensive global and regional N₂O budget based on the processes and framework shown in Fig. 3 and following the framework of Tian et al. (2020). The figure summarizes the pathways of N₂O formation, consumption, emission, and absorption, and it helps

to guide consistent estimations and comparisons of N₂O budgets among regions and upscaling of regional budgets to the globe. N₂O fluxes are grouped into two major categories based on the sources.

The first category is natural N₂O fluxes (blue arrows in Fig. 3), which are N₂O fluxes in the absence of climate change and anthropogenic disturbances, including natural soil emissions, soil uptake, N₂O emission from natural disturbances causing wetland loss and degradation, lightning, and atmospheric production. This category also includes natural emissions from inland waters, coastal ecosystems, and the ocean.

The second category is anthropogenic N₂O fluxes (red arrows in Fig. 3). The direct emissions from nitrogen additions in the agricultural sector (“Agroecosystems” box in Fig. 3) include emissions from the direct application of synthetic nitrogen fertilizers and manure (henceforth “Direct soil emissions”), manure left on pasture, manure management, and aquaculture, while other direct anthropogenic sources include fossil fuel combustion and industry, waste and wastewater, and biomass burning. Indirect N₂O emissions derive from anthropogenic nitrogen additions, such as atmospheric nitrogen deposition (NDEP) on land and ocean, and the effects of anthropogenic loads of reactive nitrogen in inland waters, estuaries, and coastal vegetation.
In the anthropogenic N₂O fluxes category, we also consider N₂O fluxes from the anthropogenic perturbations to climate, CO₂, and land use/land cover (hereafter perturbation fluxes). In terrestrial ecosystems, perturbation fluxes can be caused by increasing CO₂ concentration, climate change (e.g., warming-induced thawing of permafrost), and land-use change (e.g., converting natural lands to lands for human uses, such as croplands, mining, logging, and the post-deforestation pulse effect – the long-term effect of reduced mature forest area). N₂O emissions can either increase or decrease during land conversion, depending on the type and phase of the land-use change. For example, when tropical forests are first converted to agriculture, there is often a pulse of N₂O emissions for the first year or for as long as 5 years, depending upon the circumstances; following deforestation, emissions decline below those of the original forest if pastures degrade and if croplands are not fertilized, such as in slash-and-burn agriculture (Davidson and Artaxo, 2004; Meurer et al., 2016). When agriculture is abandoned and a secondary forest is allowed to regrow, N₂O emissions gradually increase but usually remain lower than those of the original mature forest or from fertilized croplands (Davidson et al., 2007; Sullivan et al., 2019).

Numerous efforts have estimated individual sources and sinks of N₂O across global ecosystems. Prominently, anthropogenic N₂O emissions have been annually reported for the past 2 decades by Annex I Parties (developed countries) to the United Nations Framework Convention on Climate Change (UNFCCC) (https://unfccc.int/reports, last access: 1 November 2020). As a result of the Paris Agreement, over 190 signatory countries are now required to report their national GHG inventory biannually, if not already reported annually, with sufficient detail and transparency to track progress towards their nationally determined contributions. However, national GHG inventories only provide a partial picture of the observed changes in atmospheric N₂O. They do not cover natural sources and have large uncertainties in the emission factors and activity data. Additionally, data are limited in many regions of the world, e.g., South America and Africa (Tian et al., 2020).

Tian et al. (2020) built the first comprehensive global N₂O budget using multiple BU and TD methods as part of a partnership between the Global Carbon Project (GCP) and the International Nitrogen Initiative (INI). Based on Tian et al. (2020) and the budget framework established in Fig. 3, our study presents an improved and updated global N₂O budget and its regional attribution to 18 land regions and the global ocean. The budgets cover the decades of 1980–1989, 1990–1999, 2000–2009, and 2010–2019 and includes a complete budget extension to 2020 and atmospheric N₂O changes in 2021 and 2022. The work allows us to explore the relative temporal and spatial importance of multiple sources and sinks that drive the atmospheric burden of N₂O, their uncertainties, and interactions between anthropogenic and natural forcings. This study also consolidates the international

scientific capacity and networks that contribute to this assessment with the aim of providing improved and updated N₂O budgets at regular intervals.

This global effort builds from and contributes to the set of global GHG assessments that the GCP has established, including regular updates of the carbon (CO₂-C), methane (CH₄), and (now) N₂O budgets as well as other biogeochemical budgets of global significance. The budgets have been designed to (a) support global and national scientific assessments (e.g., IPCC and WCRP annual reports), (b) align scientific research and data products to support climate mitigation and sustainability policy needs, and (c) contribute to the Global Stocktake of the Paris Agreement to track progress towards nationally determined contributions and the ultimate goal of achieving net-zero GHG emissions. Integration of all GHGs in robust and shared methodological approaches and data delivery platforms are central goals of the GCP.

# 3 Methodology and data

## 3.1 Definitions, terminology, and the unit of N₂O sources and sinks

This study provides an estimation of the global N₂O budget considering all quantifiable sources, sinks, and perturbations, resulting in a total of 21 N₂O fluxes. To simplify our analysis, we further grouped these fluxes into six major categories: (1) “Natural baseline fluxes”, which is the source in the absence of climate change and anthropogenic disturbances and includes emissions from soils, surface uptake, shelf and ocean emissions, lightning and atmospheric production, and emissions from inland waters, estuaries, and coastal vegetation; (2) direct emissions from nitrogen additions in the agricultural sector (“Agriculture”), which includes emissions from the direct application of nitrogen fertilizers and manure (henceforth direct soil emissions), manure left on pasture, manure management, and aquaculture; (3) “Perturbed fluxes from climate, CO₂, and land-cover change”, which includes the effects of CO₂, climate, the post-deforestation pulse, and the long-term effect of reduced mature forest area; (4) indirect emissions from anthropogenic nitrogen additions, which includes atmospheric nitrogen deposition (NDEP) on the land, atmospheric NDEP on the ocean, and the effects of anthropogenic loads of reactive nitrogen in inland waters, estuaries, and coastal vegetation; (5) other direct anthropogenic sources, which includes fossil fuel and industry, waste and wastewater, and biomass burning; and (6) the atmospheric sink in the stratosphere (via photolysis and oxidation by O¹D). Our anthropogenic N₂O emission categories are aligned with those compiled by the national GHG inventories using IPCC (2006) methodologies and reported to the UNFCCC (Table A1).

In this study, N₂O fluxes are expressed in teragrams of N₂O-N per year, where 1 Tg N₂O-
In the anthropogenic N₂O fluxes category, we also consider N₂O fluxes from the anthropogenic perturbations to climate, CO₂, and land use/land cover (hereafter perturbation fluxes). In terrestrial ecosystems, perturbation fluxes can be caused by increasing CO₂ concentration, climate change (e.g., warming-induced thawing of permafrost), and land-use change (e.g., converting natural lands to lands for human uses, such as croplands, mining, logging, and the post-deforestation pulse effect – the long-term effect of reduced mature forest area). N₂O emissions can either increase or decrease during land conversion, depending on the type and phase of the land-use change. For example, when tropical forests are first converted to agriculture, there is often a pulse of N₂O emissions for the first year or for as long as 5 years, depending upon the circumstances; following deforestation, emissions decline below those of the original forest if pastures degrade and if croplands are not fertilized, such as in slash-and-burn agriculture (Davidson and Artaxo, 2004; Meurer et al., 2016). When agriculture is abandoned and a secondary forest is allowed to regrow, N₂O emissions gradually increase but usually remain lower than those of the original mature forest or from fertilized croplands (Davidson et al., 2007; Sullivan et al., 2019).

Numerous efforts have estimated individual sources and sinks of N₂O across global ecosystems. Prominently, anthropogenic N₂O emissions have been annually reported for the past 2 decades by Annex I Parties (developed countries) to the United Nations Framework Convention on Climate Change (UNFCCC) (https://unfccc.int/reports, last access: 1 November 2020). As a result of the Paris Agreement, over 190 signatory countries are now required to report their national GHG inventory biannually, if not already reported annually, with sufficient detail and transparency to track progress towards their nationally determined contributions. However, national GHG inventories only provide a partial picture of the observed changes in atmospheric N₂O. They do not cover natural sources and have large uncertainties in the emission factors and activity data. Additionally, data are limited in many regions of the world, e.g., South America and Africa (Tian et al., 2020).

Tian et al. (2020) built the first comprehensive global N₂O budget using multiple BU and TD methods as part of a partnership between the Global Carbon Project (GCP) and the International Nitrogen Initiative (INI). Based on Tian et al. (2020) and the budget framework established in Fig. 3, our study presents an improved and updated global N₂O budget and its regional attribution to 18 land regions and the global ocean. The budgets cover the decades of 1980–1989, 1990–1999, 2000–2009, and 2010–2019 and includes a complete budget extension to 2020 and atmospheric N₂O changes in 2021 and 2022. The work allows us to explore the relative temporal and spatial importance of multiple sources and sinks that drive the atmospheric burden of N₂O, their uncertainties, and interactions between anthropogenic and natural forcings. This study also consolidates the international

scientific capacity and networks that contribute to this assessment with the aim of providing improved and updated N₂O budgets at regular intervals.

This global effort builds from and contributes to the set of global GHG assessments that the GCP has established, including regular updates of the carbon (CO₂-C), methane (CH₄), and (now) N₂O budgets as well as other biogeochemical budgets of global significance. The budgets have been designed to (a) support global and national scientific assessments (e.g., IPCC and WCRP annual reports), (b) align scientific research and data products to support climate mitigation and sustainability policy needs, and (c) contribute to the Global Stocktake of the Paris Agreement to track progress towards nationally determined contributions and the ultimate goal of achieving net-zero GHG emissions. Integration of all GHGs in robust and shared methodological approaches and data delivery platforms are central goals of the GCP.

# 3 Methodology and data

## 3.1 Definitions, terminology, and the unit of N₂O sources and sinks

This study provides an estimation of the global N₂O budget considering all quantifiable sources, sinks, and perturbations, resulting in a total of 21 N₂O fluxes. To simplify our analysis, we further grouped these fluxes into six major categories: (1) “Natural baseline fluxes”, which is the source in the absence of climate change and anthropogenic disturbances and includes emissions from soils, surface uptake, shelf and ocean emissions, lightning and atmospheric production, and emissions from inland waters, estuaries, and coastal vegetation; (2) direct emissions from nitrogen additions in the agricultural sector (“Agriculture”), which includes emissions from the direct application of nitrogen fertilizers and manure (henceforth direct soil emissions), manure left on pasture, manure management, and aquaculture; (3) “Perturbed fluxes from climate, CO₂, and land-cover change”, which includes the effects of CO₂, climate, the post-deforestation pulse, and the long-term effect of reduced mature forest area; (4) indirect emissions from anthropogenic nitrogen additions, which includes atmospheric nitrogen deposition (NDEP) on the land, atmospheric NDEP on the ocean, and the effects of anthropogenic loads of reactive nitrogen in inland waters, estuaries, and coastal vegetation; (5) other direct anthropogenic sources, which includes fossil fuel and industry, waste and wastewater, and biomass burning; and (6) the atmospheric sink in the stratosphere (via photolysis and oxidation by O¹D). Our anthropogenic N₂O emission categories are aligned with those compiled by the national GHG inventories using IPCC (2006) methodologies and reported to the UNFCCC (Table A1).

In this study, N₂O fluxes are expressed in teragrams of N₂O-N per year, where 1 Tg N₂O-
$N \text{yr}^{-1}$ (1 Tg N yr$^{-1}$) = $10^{12}$ g N$_2$O-N yr$^{-1}$ = 1.57 × $10^{12}$ g N$_2$O yr$^{-1}$, with change rates in N$_2$O fluxes expressed in teragrams of nitrous oxide-nitrogen per year squared (Tg N yr$^{-2}$), representing the first derivative of annual N$_2$O fluxes calculated by the linear regression method. Atmospheric N$_2$O is expressed as dry air mole fractions, in parts per billion (ppb), with atmospheric N$_2$O annual increases expressed in parts per billion per year (ppb yr$^{-1}$). The conversion factor from the unit “ppb yr$^{-1}$” to the unit “Tg N yr$^{-1}$” is 4.79 Tg N ppb$^{-1}$ (Prather et al., 2012). Unless specified, uncertainties are reported in parentheses as minimum and maximum values of all estimates, following Tian et al. (2020).

We focus on N₂O fluxes and their change rates during three periods: 1997–2020, 1980–2020, and 2010–2019. For the time span from 1980 to 2020, which is the entire study period, we report temporal variations in BU estimates of N₂O emissions from different sources to depict the overall trends of these fluxes. For the time span from 1997 to 2020, which is the overlapping period of BU and TD approaches, we compare BU and TD estimates to exam their consistency. For the time span from 2010 to 2019, which is the most recent decade, we report the magnitudes of emissions from different sources to give best estimates of their latest status and relative importance.

## 3.2 Definition of regions

As anthropogenic emissions are often reported at the country level, we divide global land into 18 regions and define these regions based on a country list (Table A2). This approach is compatible with all TD and BU approaches considered here. The number of regions was close to the widely used TransCom intercomparison map (Gurney et al., 2004) but with subdivisions to separate the contribution of important countries or regions to the global N₂O budget (such as China, South Asia, and the USA). This regionalization is also compatible with the Regional Carbon Cycle Assessment and Processes (Poulter et al., 2022) after aggregation into 10 regions. The 18 regions are the United States (USA), Canada (CAN), Central America (CAM), northern South America (NSA), Brazil (BRA), southwestern South America (SSA), Europe (EU), northern Africa (NAF), equatorial Africa (EQAF), southern Africa (SAF), Russia (RUS), Central Asia (CAS), the Middle East (MIDE), China (CHN), Korea and Japan (KAJ), South Asia (SAS), Southeast Asia (SEAS), and Australasia (AUS). The region definition is the same as that used for the GCP methane and N₂O budgets (Saunois et al., 2020; Stavert et al., 2022; Tian et al., 2019).

## 3.3 Overview of methods used for global N₂O budget synthesis

Four major methods are available to estimate large-scale N₂O emissions: atmospheric inversion models (method 1), activity- and emission-factor-based inventories (method 2), empirically based algorithms and machine learning algorithms (method 3), and process-based ecosystem models (method 4). Atmospheric inversion models (method 1), a TD approach, utilize measurements of atmospheric N₂O mixing ratios combined with atmospheric transport models, driven by meteorological fields, to estimate the emissions of N₂O (Thompson et al., 2014). Atmospheric inversion models usually use Bayesian statistics, which, starting from a prior emission estimate, find the optimal N₂O emissions (i.e., those that best agree with observed atmospheric N₂O mixing ratios) while also being guided by the prior emission and observation uncertainties (Nevison et al., 2018; Thompson et al., 2019).

TD approaches generally only estimate the total N₂O emission, which is spatially and temporally resolved, but do not constrain the contributions from different sources. The other three methods belong to BU approaches, which are capable of quantifying N₂O emissions from different sources. Emission-activity- and emission-factor-based inventories (method 2) use a prescribed emission factor (EF) to calculate N₂O emissions. This approach has been widely used in national emission inventories and global studies (Davidson, 2009; Oreggioni et al., 2021; Crippa et al., 2021; Winiwarter et al., 2018). Nevertheless, the fixed EFs cannot capture the nonlinear response of agricultural soil N₂O emissions to N inputs (Gerber et al., 2016) and also cannot fully reflect the dependence of EFs on climate, management practices, and soil physical and biochemical conditions (e.g., Marzadri et al., 2022). Therefore, a spatially referenced nonlinear model (SRNM), which outperformed the default EF method, was developed to simulate N₂O emissions in response to fertilizer application under various environmental and management conditions (Zhou et al., 2015). In recent years, machine learning algorithms (method 3) have been applied to estimate soil N₂O emissions. A random forest model was used to estimate global terrestrial background N₂O emissions (Yin et al., 2022) and N₂O emissions from intensively managed cropping systems (Saha et al., 2021). Moreover, a machine-learning-based stochastic gradient boosting model was developed to predict global terrestrial nitrification and its fraction in N₂O emissions (Pan et al., 2021).

Compared with the three abovementioned methods, process-based ecosystem models (method 4) have two notable advantages (Xu et al., 2020; Tian et al., 2019): (1) they are capable of modeling the key processes affecting N₂O production and emission, such as autotrophic nitrification, denitrification, plant nitrogen uptake, ammonia volatilization, nitrate leaching, and soil thermal and hydrological processes, although their accuracy in representing these processes needs
$N \text{ yr}^{-1}$ (1 Tg N yr$^{-1}$) = $10^{12}$ g N$_2$O-N yr$^{-1}$ = 1.57 × $10^{12}$ g N$_2$O yr$^{-1}$, with change rates in N$_2$O fluxes expressed in teragrams of nitrous oxide-nitrogen per year squared (Tg N yr$^{-2}$), representing the first derivative of annual N$_2$O fluxes calculated by the linear regression method. Atmospheric N$_2$O is expressed as dry air mole fractions, in parts per billion (ppb), with atmospheric N$_2$O annual increases expressed in parts per billion per year (ppb yr$^{-1}$). The conversion factor from the unit “ppb yr$^{-1}$” to the unit “Tg N yr$^{-1}$” is 4.79 Tg N ppb$^{-1}$ (Prather et al., 2012). Unless specified, uncertainties are reported in parentheses as minimum and maximum values of all estimates, following Tian et al. (2020).

We focus on N₂O fluxes and their change rates during three periods: 1997–2020, 1980–2020, and 2010–2019. For the time span from 1980 to 2020, which is the entire study period, we report temporal variations in BU estimates of N₂O emissions from different sources to depict the overall trends of these fluxes. For the time span from 1997 to 2020, which is the overlapping period of BU and TD approaches, we compare BU and TD estimates to exam their consistency. For the time span from 2010 to 2019, which is the most recent decade, we report the magnitudes of emissions from different sources to give best estimates of their latest status and relative importance.

## 3.2 Definition of regions

As anthropogenic emissions are often reported at the country level, we divide global land into 18 regions and define these regions based on a country list (Table A2). This approach is compatible with all TD and BU approaches considered here. The number of regions was close to the widely used TransCom intercomparison map (Gurney et al., 2004) but with subdivisions to separate the contribution of important countries or regions to the global N₂O budget (such as China, South Asia, and the USA). This regionalization is also compatible with the Regional Carbon Cycle Assessment and Processes (Poulter et al., 2022) after aggregation into 10 regions. The 18 regions are the United States (USA), Canada (CAN), Central America (CAM), northern South America (NSA), Brazil (BRA), southwestern South America (SSA), Europe (EU), northern Africa (NAF), equatorial Africa (EQAF), southern Africa (SAF), Russia (RUS), Central Asia (CAS), the Middle East (MIDE), China (CHN), Korea and Japan (KAJ), South Asia (SAS), Southeast Asia (SEAS), and Australasia (AUS). The region definition is the same as that used for the GCP methane and N₂O budgets (Saunois et al., 2020; Stavert et al., 2022; Tian et al., 2019).

## 3.3 Overview of methods used for global N₂O budget synthesis

Four major methods are available to estimate large-scale N₂O emissions: atmospheric inversion models (method 1), activity- and emission-factor-based inventories (method 2), empirically based algorithms and machine learning algorithms (method 3), and process-based ecosystem models (method 4). Atmospheric inversion models (method 1), a TD approach, utilize measurements of atmospheric N₂O mixing ratios combined with atmospheric transport models, driven by meteorological fields, to estimate the emissions of N₂O (Thompson et al., 2014). Atmospheric inversion models usually use Bayesian statistics, which, starting from a prior emission estimate, find the optimal N₂O emissions (i.e., those that best agree with observed atmospheric N₂O mixing ratios) while also being guided by the prior emission and observation uncertainties (Nevison et al., 2018; Thompson et al., 2019).

TD approaches generally only estimate the total N₂O emission, which is spatially and temporally resolved, but do not constrain the contributions from different sources. The other three methods belong to BU approaches, which are capable of quantifying N₂O emissions from different sources. Emission-activity- and emission-factor-based inventories (method 2) use a prescribed emission factor (EF) to calculate N₂O emissions. This approach has been widely used in national emission inventories and global studies (Davidson, 2009; Oreggioni et al., 2021; Crippa et al., 2021; Winiwarter et al., 2018). Nevertheless, the fixed EFs cannot capture the nonlinear response of agricultural soil N₂O emissions to N inputs (Gerber et al., 2016) and also cannot fully reflect the dependence of EFs on climate, management practices, and soil physical and biochemical conditions (e.g., Marzadri et al., 2022). Therefore, a spatially referenced nonlinear model (SRNM), which outperformed the default EF method, was developed to simulate N₂O emissions in response to fertilizer application under various environmental and management conditions (Zhou et al., 2015). In recent years, machine learning algorithms (method 3) have been applied to estimate soil N₂O emissions. A random forest model was used to estimate global terrestrial background N₂O emissions (Yin et al., 2022) and N₂O emissions from intensively managed cropping systems (Saha et al., 2021). Moreover, a machine-learning-based stochastic gradient boosting model was developed to predict global terrestrial nitrification and its fraction in N₂O emissions (Pan et al., 2021).

Compared with the three abovementioned methods, process-based ecosystem models (method 4) have two notable advantages (Xu et al., 2020; Tian et al., 2019): (1) they are capable of modeling the key processes affecting N₂O production and emission, such as autotrophic nitrification, denitrification, plant nitrogen uptake, ammonia volatilization, nitrate leaching, and soil thermal and hydrological processes, although their accuracy in representing these processes needs
further improvement; and (2) they integrate various driving factors controlling soil N₂O emissions, such as fertilizer use, atmospheric N deposition, land-use change, climate change, and atmospheric CO₂ concentration change and, thus, can disentangle the effects of different driving factors. Although multiple process-based models have estimated global soil N₂O emissions, large discrepancies exist in these estimates due to the diverse parameterizations of biogeochemical processes in different models, our limited understanding of the mechanisms responsible for N₂O emissions, and the uncertainties in input data. The Global N₂O Model Intercomparison Project (NMIP) was launched (Tian et al., 2018, 2019) to develop a multi-model ensemble estimation of global soil N₂O emissions during 1861–2016 and quantify the contributions of different driving factors.

We consider global N₂O emissions from land and ocean including natural fluxes and anthropogenic emissions based on BU and TD approaches (Fig. 4). The BU methods considered include eight process-based terrestrial biosphere models from NMIP2 (global Nitrogen/N₂O Model Intercomparison Project phase 2); six ocean models (Battaglia and Joos, 2018; Berthet et al., 2023; Buitenhuis et al., 2018; Carroll et al., 2020; Landolfi et al., 2017); one machine-learning-based observational shelf product (Yang et al., 2020); a mix of five approaches relying on meta-analysis and statistical and process-based models for inland waters and coastal ecosystems (Hu et al., 2016; Lauerwald et al., 2019; Maavara et al., 2019; Yao et al., 2020; Marzadri et al., 2021; Marzadri et al., 2022; Rosentreter et al., 2023); four GHG emission databases – Emissions Database for Global Atmospheric Research (EDGAR) v7.0 (Crippa et al., 2021, https://edgar.jrc.ec.europa.eu/dataset_ghg70, last access: 10 February 2022), FAOSTAT (Tubiello et al., 2015), UNFCCC (https://unfccc.int/reports, last access: 1 November 2020), and GFED4s (van der Werf et al., 2017) (only for biomass burning); and one statistical model (SRNM) only for cropland soils (Wang et al., 2020). The TD approach consisted of four independent atmospheric inversion frameworks, namely INVICAT (Wilson et al., 2014), PyVAR-CAMS (Thompson et al., 2014), MIROC4-ACTM (Patra et al., 2022), and GEOS-Chem (Wells et al., 2018).

## 3.4 Model and inventory data synthesis

### 3.4.1 Natural N₂O fluxes

“Natural soil baseline” emissions were obtained from the ensemble mean of the eight terrestrial biosphere models that participated in NMIP-2 that run with preindustrial land cover (Table 1): (1) the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC) (Asaadi and Arora, 2021; Melton et al., 2020; Kou-Giesbrecht and Arora, 2022); (2) the Dynamic Land Ecosystem Model (DLEM) (Tian et al., 2015; Xu et al., 2017; You et al., 2022); (3) the E3SM Land Model (ELM) (Zhu et al., 2019); (4) the Integrated Sci-

ence Assessment Model (ISAM) (Shu et al., 2020; X. Xu et al., 2021); (5) the Land Processes and eXchanges model – Bern (LPX-Bern v1.4) (Lienert and Joos, 2018; Joos et al., 2020); (6) O-CN (Zaehle et al., 2011); (7) Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) (Goll et al., 2017); and (8) the Vegetation Integrated Simulator for Trace gases (VISIT) (Ito et al., 2018).

Natural emission from “Inland water, estuaries, and coastal vegetation”, including inland and coastal waters, were obtained from models by Yao et al. (2020), Maavara et al. (2019), Lauerwald et al. (2019), and Marzadri et al. (2021) as well as the meta-analyses by Hu et al. (2016) and Rosentreter et al. (2023). As the data (rivers, lakes, reservoirs, and estuaries) provided by Hu et al. (2016), Maavara et al. (2019), Lauerwald et al. (2019), and Marzadri et al. (2021) are for the year 2000, we assumed that these values are constant during 1980–2020. Yao et al. (2020) provided annual riverine N₂O emissions using DLEM during 1980–2019. Here, we averaged riverine estimates from Yao et al. (2020), Maavara et al. (2019), Hu et al. (2016), and Marzadri et al. (2021), assuming that the estimates of Maavara et al. (2019) and Hu et al. (2016) represent emissions from larger rivers only, while Yao et al. (2020) and Marzadri et al. (2021) also account for emissions from streams and small rivers. Note further that the estimate by Marzadri et al. (2021) is not fully global, as it excludes river systems north of 60° N. Therefore, we did not use this assessment for the regions of Canada, the USA, Russia, and Europe. DLEM also estimated annual N₂O emissions from global reservoirs, and we averaged these estimates with those from Maavara et al. (2019) to represent emissions from reservoirs during 1980–2020. The estimate for global and regional lakes was based on the long-term average values provided by Lauerwald et al. (2019) and an estimate by the DLEM-TAC model (Li et al., 2024). For estuaries, we combined the estimate of Maavara et al. (2019), which relies on a process-based modeling approach, with a new metadata analysis by Rosentreter et al. (2023). The analysis of Rosentreter et al. (2023) is observation-based and includes the contribution of coastal vegetated ecosystems, a contribution not accounted for in Maavara et al. (2019). Estuaries and coastal vegetation data are from studies published between 1975 and 2020, and we assume fluxes are constant during 1980–2020 (Rosentreter et al., 2023). To disentangle natural and anthropogenic fluxes, we considered the emissions in the year 1900 simulated by DLEM (Yao et al., 2020) as equivalent to the natural emission, assuming that the N load from land was negligible in that period (Kroeze et al., 1999). Using this approach, we estimated that N₂O emissions from natural sources of rivers, reservoirs, lakes, and estuaries accounted for 44% (36%–52%) of the total emissions from inland waters, taking into account all N inputs (i.e., inorganic, organic, dissolved, and particulate forms).

N₂O emissions from continental shelves were calculated using one data-driven estimate and three high-resolution model estimates for various time periods (Resplandy et
further improvement; and (2) they integrate various driving factors controlling soil N₂O emissions, such as fertilizer use, atmospheric N deposition, land-use change, climate change, and atmospheric CO₂ concentration change and, thus, can disentangle the effects of different driving factors. Although multiple process-based models have estimated global soil N₂O emissions, large discrepancies exist in these estimates due to the diverse parameterizations of biogeochemical processes in different models, our limited understanding of the mechanisms responsible for N₂O emissions, and the uncertainties in input data. The Global N₂O Model Intercomparison Project (NMIP) was launched (Tian et al., 2018, 2019) to develop a multi-model ensemble estimation of global soil N₂O emissions during 1861–2016 and quantify the contributions of different driving factors.

We consider global N₂O emissions from land and ocean including natural fluxes and anthropogenic emissions based on BU and TD approaches (Fig. 4). The BU methods considered include eight process-based terrestrial biosphere models from NMIP2 (global Nitrogen/N₂O Model Intercomparison Project phase 2); six ocean models (Battaglia and Joos, 2018; Berthet et al., 2023; Buitenhuis et al., 2018; Carroll et al., 2020; Landolfi et al., 2017); one machine-learning-based observational shelf product (Yang et al., 2020); a mix of five approaches relying on meta-analysis and statistical and process-based models for inland waters and coastal ecosystems (Hu et al., 2016; Lauerwald et al., 2019; Maavara et al., 2019; Yao et al., 2020; Marzadri et al., 2021; Marzadri et al., 2022; Rosentreter et al., 2023); four GHG emission databases – Emissions Database for Global Atmospheric Research (EDGAR) v7.0 (Crippa et al., 2021, https://edgar.jrc.ec.europa.eu/dataset_ghg70, last access: 10 February 2022), FAOSTAT (Tubiello et al., 2015), UNFCCC (https://unfccc.int/reports, last access: 1 November 2020), and GFED4s (van der Werf et al., 2017) (only for biomass burning); and one statistical model (SRNM) only for cropland soils (Wang et al., 2020). The TD approach consisted of four independent atmospheric inversion frameworks, namely INVICAT (Wilson et al., 2014), PyVAR-CAMS (Thompson et al., 2014), MIROC4-ACTM (Patra et al., 2022), and GEOS-Chem (Wells et al., 2018).

## 3.4 Model and inventory data synthesis

### 3.4.1 Natural N₂O fluxes

“Natural soil baseline” emissions were obtained from the ensemble mean of the eight terrestrial biosphere models that participated in NMIP-2 that run with preindustrial land cover (Table 1): (1) the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC) (Asaadi and Arora, 2021; Melton et al., 2020; Kou-Giesbrecht and Arora, 2022); (2) the Dynamic Land Ecosystem Model (DLEM) (Tian et al., 2015; Xu et al., 2017; You et al., 2022); (3) the E3SM Land Model (ELM) (Zhu et al., 2019); (4) the Integrated Sci-

ence Assessment Model (ISAM) (Shu et al., 2020; X. Xu et al., 2021); (5) the Land Processes and eXchanges model – Bern (LPX-Bern v1.4) (Lienert and Joos, 2018; Joos et al., 2020); (6) O-CN (Zaehle et al., 2011); (7) Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) (Goll et al., 2017); and (8) the Vegetation Integrated Simulator for Trace gases (VISIT) (Ito et al., 2018).

Natural emission from “Inland water, estuaries, and coastal vegetation”, including inland and coastal waters, were obtained from models by Yao et al. (2020), Maavara et al. (2019), Lauerwald et al. (2019), and Marzadri et al. (2021) as well as the meta-analyses by Hu et al. (2016) and Rosentreter et al. (2023). As the data (rivers, lakes, reservoirs, and estuaries) provided by Hu et al. (2016), Maavara et al. (2019), Lauerwald et al. (2019), and Marzadri et al. (2021) are for the year 2000, we assumed that these values are constant during 1980–2020. Yao et al. (2020) provided annual riverine N₂O emissions using DLEM during 1980–2019. Here, we averaged riverine estimates from Yao et al. (2020), Maavara et al. (2019), Hu et al. (2016), and Marzadri et al. (2021), assuming that the estimates of Maavara et al. (2019) and Hu et al. (2016) represent emissions from larger rivers only, while Yao et al. (2020) and Marzadri et al. (2021) also account for emissions from streams and small rivers. Note further that the estimate by Marzadri et al. (2021) is not fully global, as it excludes river systems north of 60° N. Therefore, we did not use this assessment for the regions of Canada, the USA, Russia, and Europe. DLEM also estimated annual N₂O emissions from global reservoirs, and we averaged these estimates with those from Maavara et al. (2019) to represent emissions from reservoirs during 1980–2020. The estimate for global and regional lakes was based on the long-term average values provided by Lauerwald et al. (2019) and an estimate by the DLEM-TAC model (Li et al., 2024). For estuaries, we combined the estimate of Maavara et al. (2019), which relies on a process-based modeling approach, with a new metadata analysis by Rosentreter et al. (2023). The analysis of Rosentreter et al. (2023) is observation-based and includes the contribution of coastal vegetated ecosystems, a contribution not accounted for in Maavara et al. (2019). Estuaries and coastal vegetation data are from studies published between 1975 and 2020, and we assume fluxes are constant during 1980–2020 (Rosentreter et al., 2023). To disentangle natural and anthropogenic fluxes, we considered the emissions in the year 1900 simulated by DLEM (Yao et al., 2020) as equivalent to the natural emission, assuming that the N load from land was negligible in that period (Kroeze et al., 1999). Using this approach, we estimated that N₂O emissions from natural sources of rivers, reservoirs, lakes, and estuaries accounted for 44% (36%–52%) of the total emissions from inland waters, taking into account all N inputs (i.e., inorganic, organic, dissolved, and particulate forms).

N₂O emissions from continental shelves were calculated using one data-driven estimate and three high-resolution model estimates for various time periods (Resplandy et
<table><caption>**Table 1.** Methods, spatial and temporal resolution, and data sources for the synthesis of the global N₂O budget.</caption><thead><tr><th>Model/data name</th><th>Spatial resolution</th><th>Time period</th><th>References</th></tr></thead><tbody><tr><td colspan="4">Inventories (anthropogenic)</td></tr><tr><td>EDGAR v7.0</td><td>0.1° × 0.1°</td><td>1980–2020</td><td>Crippa et al. (2021)</td></tr><tr><td>GFED4s</td><td>0.25° × 0.25°</td><td>1997–2020</td><td>van der Werf et al. (2017)</td></tr><tr><td>FAOSTAT</td><td>Country level</td><td>1980–2020</td><td>Tubiello et al. (2022)</td></tr><tr><td>UNFCCC</td><td>Country level</td><td>1990–2020</td><td>https://di.unfccc.int/time_series (last access: 1 November 2020)</td></tr><tr><td colspan="4">Terrestrial biosphere models that participated in NMIP2 (both anthropogenic and natural)</td></tr><tr><td>CLASSIC</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Asaadi and Arora (2021), Kou-Giesbrecht and Arora (2022)</td></tr><tr><td>DLEM</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Tian et al. (2015), Xu et al. (2017)</td></tr><tr><td>ELM</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Zhu et al. (2019)</td></tr><tr><td>ISAM</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Shu et al. (2020), X. Xu et al. (2021)</td></tr><tr><td>LPX-Bern</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Lienert and Joos (2018), Joos et al. (2020)</td></tr><tr><td>O-CN</td><td>1° × 1°</td><td>1980–2020</td><td>Zaehle et al. (2011)</td></tr><tr><td>ORCHIDEE</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Vuichard et al. (2019)</td></tr><tr><td>VISIT</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Ito et al. (2018)</td></tr><tr><td colspan="4">Ocean biogeochemical models (natural)</td></tr><tr><td>Bern-3D</td><td>9° × 4.5° × 32 levels</td><td>1980–2019</td><td>Battaglia and Joos (2018)</td></tr><tr><td>NEMOv3.6-PISCESv2-gas</td><td>1° × 1° × 75 levels</td><td>1980–2020</td><td>Berthet et al. (2023), Séférian et al. (2019)</td></tr><tr><td>NEMO-PlankTOM10.2</td><td>2° × (0.5–2°) × 30 levels</td><td>1980–2016</td><td>Buitenhuis et al. (2018)</td></tr><tr><td>UVic2.9</td><td>3.6° × 1.8° × 19 levels</td><td>1980–2020</td><td>Landolfi et al. (2017)</td></tr><tr><td colspan="4">Continental shelf products (natural)</td></tr><tr><td>MEM-RF</td><td>0.25° × 0.25°</td><td>1988–2017 mean</td><td>Yang et al. (2020)</td></tr><tr><td>CNRM-0.25°</td><td>0.25° × 0.25°</td><td>1998–2018 mean</td><td>Berthet et al. (2019)</td></tr><tr><td>ECCO2-Darwin and ECCO-Darwin</td><td>1/3° (ECCO-Darwin),<br/>–1/6° (ECCO2-Darwin)</td><td>1998–2013 mean (ECCO-Darwin),<br/>2006–2013 mean (ECCO2-Darwin)</td><td>Ganesan et al. (2020), Carroll et al. (2020)</td></tr><tr><td colspan="4">Inland waters, estuaries, and coastal vegetation (both anthropogenic and natural)</td></tr><tr><td>DLEM-TAC</td><td>0.5° × 0.5°</td><td>1980–2019</td><td>Yao et al. (2020), Tian et al. (2020)</td></tr><tr><td>Mechanistic stochastic model</td><td>0.5° × 0.5°</td><td>2000</td><td>Lauerwald et al. (2019), Maavara et al. (2019)</td></tr><tr><td>Meta-analysis-based upscaling</td><td>Watershed level, 18 regions</td><td>2000, 1975–2020</td><td>Hu et al. (2016), Rosentreter et al. (2023)</td></tr><tr><td>Integrated ML and physical model</td><td>0.5° × 0.5°</td><td>2000</td><td>Marzadri et al. (2021)</td></tr><tr><td colspan="4">Atmospheric inversion models</td></tr><tr><td>INVICAT</td><td>5.625° × 5.625°</td><td>1997–2020</td><td>Wilson et al. (2014)</td></tr><tr><td>PyVAR-CAMS</td><td>3.75° × 1.875°</td><td>1997–2020</td><td>Thompson et al. (2014)</td></tr><tr><td>MIROC4-ACTM</td><td>~ 2.8° × 2.8°</td><td>1997–2019</td><td>Patra et al. (2018, 2022)</td></tr><tr><td>GEOS-Chem</td><td>5° × 4°</td><td>1997–2019</td><td>Wells et al. (2018)</td></tr><tr><td colspan="4">Other models and datasets (anthropogenic)</td></tr><tr><td>SRNM (direct soil emission)</td><td>1/12° × 1/12°</td><td>1980–2020</td><td>Wang et al. (2020)</td></tr><tr><td>Bookkeeping method (perturbed fluxes from land-cover change)</td><td>0.25° × 0.25°</td><td>1980–2020</td><td>Tian et al. (2020), Keller and Reiners (1994)</td></tr><tr><td>IMAGE-GNM</td><td>Country level</td><td>1980–2020</td><td>Bouwman et al. (2011, 2013a)</td></tr></tbody></table>
<table><caption>**Table 1.** Methods, spatial and temporal resolution, and data sources for the synthesis of the global N₂O budget.</caption><thead><tr><th>Model/data name</th><th>Spatial resolution</th><th>Time period</th><th>References</th></tr></thead><tbody><tr><td colspan="4">Inventories (anthropogenic)</td></tr><tr><td>EDGAR v7.0</td><td>0.1° × 0.1°</td><td>1980–2020</td><td>Crippa et al. (2021)</td></tr><tr><td>GFED4s</td><td>0.25° × 0.25°</td><td>1997–2020</td><td>van der Werf et al. (2017)</td></tr><tr><td>FAOSTAT</td><td>Country level</td><td>1980–2020</td><td>Tubiello et al. (2022)</td></tr><tr><td>UNFCCC</td><td>Country level</td><td>1990–2020</td><td>https://di.unfccc.int/time_series (last access: 1 November 2020)</td></tr><tr><td colspan="4">Terrestrial biosphere models that participated in NMIP2 (both anthropogenic and natural)</td></tr><tr><td>CLASSIC</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Asaadi and Arora (2021), Kou-Giesbrecht and Arora (2022)</td></tr><tr><td>DLEM</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Tian et al. (2015), Xu et al. (2017)</td></tr><tr><td>ELM</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Zhu et al. (2019)</td></tr><tr><td>ISAM</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Shu et al. (2020), X. Xu et al. (2021)</td></tr><tr><td>LPX-Bern</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Lienert and Joos (2018), Joos et al. (2020)</td></tr><tr><td>O-CN</td><td>1° × 1°</td><td>1980–2020</td><td>Zaehle et al. (2011)</td></tr><tr><td>ORCHIDEE</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Vuichard et al. (2019)</td></tr><tr><td>VISIT</td><td>0.5° × 0.5°</td><td>1980–2020</td><td>Ito et al. (2018)</td></tr><tr><td colspan="4">Ocean biogeochemical models (natural)</td></tr><tr><td>Bern-3D</td><td>9° × 4.5° × 32 levels</td><td>1980–2019</td><td>Battaglia and Joos (2018)</td></tr><tr><td>NEMOv3.6-PISCESv2-gas</td><td>1° × 1° × 75 levels</td><td>1980–2020</td><td>Berthet et al. (2023), Séférian et al. (2019)</td></tr><tr><td>NEMO-PlankTOMI10.2</td><td>2° × (0.5–2°) × 30 levels</td><td>1980–2016</td><td>Buitenhuis et al. (2018)</td></tr><tr><td>UVic2.9</td><td>3.6° × 1.8° × 19 levels</td><td>1980–2020</td><td>Landolfi et al. (2017)</td></tr><tr><td colspan="4">Continental shelf products (natural)</td></tr><tr><td>MEM-RF</td><td>0.25° × 0.25°</td><td>1988–2017 mean</td><td>Yang et al. (2020)</td></tr><tr><td>CNRM-0.25°</td><td>0.25° × 0.25°</td><td>1998–2018 mean</td><td>Berthet et al. (2019)</td></tr><tr><td>ECCO2-Darwin and ECCO-Darwin</td><td>1/3° (ECCO-Darwin),<br/>–1/6° (ECCO2-Darwin)</td><td>1998–2013 mean (ECCO-Darwin),<br/>2006–2013 mean (ECCO2-Darwin)</td><td>Ganesan et al. (2020), Carroll et al. (2020)</td></tr><tr><td colspan="4">Inland waters, estuaries, and coastal vegetation (both anthropogenic and natural)</td></tr><tr><td>DLEM-TAC</td><td>0.5° × 0.5°</td><td>1980–2019</td><td>Yao et al. (2020), Tian et al. (2020)</td></tr><tr><td>Mechanistic stochastic model</td><td>0.5° × 0.5°</td><td>2000</td><td>Lauerwald et al. (2019), Maavara et al. (2019)</td></tr><tr><td>Meta-analysis-based upscaling</td><td>Watershed level, 18 regions</td><td>2000, 1975–2020</td><td>Hu et al. (2016), Rosentreter et al. (2023)</td></tr><tr><td>Integrated ML and physical model</td><td>0.5° × 0.5°</td><td>2000</td><td>Marzadri et al. (2021)</td></tr><tr><td colspan="4">Atmospheric inversion models</td></tr><tr><td>INVICAT</td><td>5.625° × 5.625°</td><td>1997–2020</td><td>Wilson et al. (2014)</td></tr><tr><td>PyVAR-CAMS</td><td>3.75° × 1.875°</td><td>1997–2020</td><td>Thompson et al. (2014)</td></tr><tr><td>MIROC4-ACTM</td><td>~ 2.8° × 2.8°</td><td>1997–2019</td><td>Patra et al. (2018, 2022)</td></tr><tr><td>GEOS-Chem</td><td>5° × 4°</td><td>1997–2019</td><td>Wells et al. (2018)</td></tr><tr><td colspan="4">Other models and datasets (anthropogenic)</td></tr><tr><td>SRNM (direct soil emission)</td><td>1/12° × 1/12°</td><td>1980–2020</td><td>Wang et al. (2020)</td></tr><tr><td>Bookkeeping method (perturbed fluxes from land-cover change)</td><td>0.25° × 0.25°</td><td>1980–2020</td><td>Tian et al. (2020), Keller and Reiners (1994)</td></tr><tr><td>IMAGE-GNM</td><td>Country level</td><td>1980–2020</td><td>Bouwman et al. (2011, 2013a)</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Methodologies used to estimate each of the main flux categories contributing to the global N₂O budget. We use both BU and TD approaches, including 20 BU and 4 TD estimates of N₂O fluxes from land and oceans. For sources estimated by the BU approach, we include eight process-based terrestrial biosphere modeling studies; six process-based ocean biogeochemical models and one shelf observational product; one nutrient budget model; five inland and coastal water modeling or meta-analysis studies; one statistical model SRNM based on spatial extrapolation of field measurements; and four greenhouse gas inventories – EDGAR v7.0, FAOSTAT, UNFCCC, and GFED. Previous estimates of the surface sink, lightning and atmospheric production, model-based tropospheric sink, and observed stratospheric sink are included in the current synthesis. The nutrient budget model provides nitrogen flows in global freshwater and marine aquaculture over the 1980–2020 period. Model-based estimates of N₂O emissions from inland and coastal waters include rivers and reservoirs, lakes, estuaries, coastal vegetation (i.e., seagrasses, mangroves, and salt marsh), and coastal upwelling.</figcaption></figure>

al., 2024, also see Sect. S7), namely an observation-based estimate that relied on a random forest (RF) algorithm to interpolate N₂O data (Yang et al., 2020), based on a synthesis of over 158 000 observations of the N₂O mixing ratio, partial pressure, and concentration in the surface ocean from the MEMENTO database (MEM-RF) (Kock and Bange, 2015); an estimate relying on the high-resolution configuration (Berthet et al., 2019) of the global ocean–biogeochemical component of CNRM-ESM2-1 (CNRM-0.25°); and two estimates relying on the ECCO-Darwin model run at 1/3° (ECCO-Darwin1) and 1/6° (ECCO-Darwin2), respectively. Considering that ECCO-Darwin1 and ECCO-Darwin2 relied on the same model, their mean N₂O fluxes were used.

Estimates of natural N₂O emissions from open oceans are derived from four global ocean biogeochemistry models, including Bern-3D (Battaglia and Joos, 2018), NEMOv3.6-PISCESv2-gas (Berthet et al., 2023), NEMO-PlankTOM10

(Buitenhuis et al., 2018), and UVic2.9 (Landolfi et al., 2017). Towards the N₂O budget synthesis, modeling groups reported gridded monthly fluxes at a 1° × 1° resolution for the 1980–2020 period. Specific details on ocean model configurations and N₂O parameterizations are reported in the individual model publications.

We combined the estimate from lightning with that from atmospheric production into an integrated category “Lightning and atmospheric production” (Kohlmann and Poppe, 1999; Dentener and Crutzen, 1994). We simplified the Lightning and atmospheric production category as purely natural, although atmospheric production is affected to some extent by anthropogenic activities, such as enhancement of the concentrations of the reactive species NH₃ and NO₂. This category is, in any case, very small, and the anthropogenic enhancement effect is uncertain. The estimate of “Surface sink” was obtained from Schlesinger (2013) and Syakila et al. (2010).
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Methodologies used to estimate each of the main flux categories contributing to the global N₂O budget. We use both BU and TD approaches, including 20 BU and 4 TD estimates of N₂O fluxes from land and oceans. For sources estimated by the BU approach, we include eight process-based terrestrial biosphere modeling studies; six process-based ocean biogeochemical models and one shelf observational product; one nutrient budget model; five inland and coastal water modeling or meta-analysis studies; one statistical model SRNM based on spatial extrapolation of field measurements; and four greenhouse gas inventories – EDGAR v7.0, FAOSTAT, UNFCCC, and GFED. Previous estimates of the surface sink, lightning and atmospheric production, model-based tropospheric sink, and observed stratospheric sink are included in the current synthesis. The nutrient budget model provides nitrogen flows in global freshwater and marine aquaculture over the 1980–2020 period. Model-based estimates of N₂O emissions from inland and coastal waters include rivers and reservoirs, lakes, estuaries, coastal vegetation (i.e., seagrasses, mangroves, and salt marsh), and coastal upwelling.</figcaption></figure>

al., 2024, also see Sect. S7), namely an observation-based estimate that relied on a random forest (RF) algorithm to interpolate N₂O data (Yang et al., 2020), based on a synthesis of over 158 000 observations of the N₂O mixing ratio, partial pressure, and concentration in the surface ocean from the MEMENTO database (MEM-RF) (Kock and Bange, 2015); an estimate relying on the high-resolution configuration (Berthet et al., 2019) of the global ocean–biogeochemical component of CNRM-ESM2-1 (CNRM-0.25°); and two estimates relying on the ECCO-Darwin model run at 1/3° (ECCO-Darwin1) and 1/6° (ECCO-Darwin2), respectively. Considering that ECCO-Darwin1 and ECCO-Darwin2 relied on the same model, their mean N₂O fluxes were used.

Estimates of natural N₂O emissions from open oceans are derived from four global ocean biogeochemistry models, including Bern-3D (Battaglia and Joos, 2018), NEMOv3.6-PISCESv2-gas (Berthet et al., 2023), NEMO-PlankTOM10

(Buitenhuis et al., 2018), and UVic2.9 (Landolfi et al., 2017). Towards the N₂O budget synthesis, modeling groups reported gridded monthly fluxes at a 1° × 1° resolution for the 1980–2020 period. Specific details on ocean model configurations and N₂O parameterizations are reported in the individual model publications.

We combined the estimate from lightning with that from atmospheric production into an integrated category “Lightning and atmospheric production” (Kohlmann and Poppe, 1999; Dentener and Crutzen, 1994). We simplified the Lightning and atmospheric production category as purely natural, although atmospheric production is affected to some extent by anthropogenic activities, such as enhancement of the concentrations of the reactive species NH₃ and NO₂. This category is, in any case, very small, and the anthropogenic enhancement effect is uncertain. The estimate of “Surface sink” was obtained from Schlesinger (2013) and Syakila et al. (2010).
### 3.4.2 Direct emissions from nitrogen additions (agriculture)

Agriculture N₂O emissions consist of four components: “Direct soil emissions”, “Manure left on pasture”, “Manure management”, and “Aquaculture”. Data for direct soil emissions were obtained as the ensemble mean of N₂O emissions from the average of two inventories (EDGAR v7.0 and FAOSTAT), the SRNM and DLEM models, and the NMIP2 and DLEM models. The statistical model SRNM only covers cropland N₂O emissions. Thus, we added the DLEM-based estimate of pasture N₂O emissions into the two estimates of cropland to represent direct agricultural soil emissions (i.e., SRNM/DLEM or NMIP2/DLEM). Manure left on pasture is the ensemble mean of EDGAR v7.0, FAOSTAT, and DLEM. Manure management emissions are the mean of EDGAR v7.0 and FAOSTAT. FAOSTAT emission factors for N additions are based on the 2006 guidelines. Global N flows (i.e., fish feed intake, fish harvest, and waste) in freshwater and marine aquaculture were obtained from Bouwman et al. (2011, 2013a) and Beusen et al. (2016) and based on the IMAGE-GNM aquaculture nutrient budget model for the 1980–2020 period. We then calculated global aquaculture N₂O emissions as a 1.8% loss of N waste in aquaculture, i.e., the same EF used in Hu et al. (2012) and MacLeod et al. (2019). The uncertainty range of the EF is from 0.5% (Eggleston et al., 2006) to 5% (Williams and Crutzen, 2010), the same range used in the UNEP report (Bouwman et al., 2013b).

### 3.4.3 Emissions from other direct anthropogenic sources

This category includes “Fossil fuel and industry”, “Waste and wastewater”, and “Biomass burning”. Both emissions from fossil fuel and industry and waste and wastewater were calculated as the ensemble means of EDGAR v7.0 and UNFCCC databases. The biomass burning emission is the ensemble mean of FAOSTAT, DLEM, and GFED4s databases. In EDGAR v7.0, the Waste and wastewater category includes “Waste incineration” and “Wastewater handling”. We merged “Transportation”, “Energy”, “Industry”, and “Residential and other sectors” to represent the total emission from fossil fuel and industry. The FAOSTAT emissions database of the Food and Agriculture Organization of the United Nations (FAO) covers emissions of N₂O from agriculture and land use by country and globally, from 1961 to 2020 for agriculture and from 1990 for relevant land-use categories, i.e., cultivation of Histosols, biomass burning, etc., applying only Tier-1 coefficients (Tubiello et al., 2021, 2022; Conchedda and Tubiello, 2020; Prosperi et al., 2020). In addition to the IPCC agriculture burning categories “Burning crop residues” and “Burning savannah”, we included FAOSTAT estimates for N₂O emissions from deforestation fires, forest fires, and peatland fires (Prosperi et al., 2020).

### 3.4.4 Indirect emissions from anthropogenic N additions

This category considers N deposition on land and the ocean (“N deposition on land” and “N deposition on ocean”) as well as the N leaching and runoff from upstream (“Inland and coastal waters”). The emission from N deposition on ocean was provided by Suntharalingam et al. (2012) and includes emission from both open oceans and continental shelves, while emission from N deposition on land was the average of two estimates by NMIP2/EDGAR v7.0 and NMIP2. EDGAR v7.0 provided estimates of indirect emissions from both agricultural and non-agricultural sectors; however, here, we sum the ensemble mean of NMIP2 estimates of indirect emissions from agricultural sectors with indirect emissions from the non-agricultural sector of EDGAR v7.0 (i.e., NMIP2/EDGAR v7.0) to represent N-deposition-induced soil emissions from both agricultural and non-agricultural sectors. The N₂O emissions from inland and coastal waters consist of rivers, reservoirs, lakes, estuaries, and continental shelves, which is the ensemble mean of an average of two inventories (EDGAR v7.0 indirect N₂O emissions – leaching and runoff – and FAOSTAT), and the mean of meta-analysis and models. The anthropogenic emission from inland freshwaters estimated by Yao et al. (2020) considered annual N inputs and other environmental factors (i.e., climate, elevated CO₂, and land-cover change). The results in Yao et al. (2020) suggested that 56% of the total N₂O emissions from rivers, reservoirs, estuaries, and lakes was attributed to anthropogenic N additions. Empirical methods (empirical models and meta-analysis) adopted this ratio to calculate long-term average anthropogenic N₂O emissions from inland waters, consistent with Tian et al. (2020). Seagrass, mangrove, and salt marsh N₂O emissions were updated from Rosentreter et al. (2023).

### 3.4.5 Perturbation of N₂O fluxes from climate, CO₂, and land-cover change

The estimate of climate and CO₂ effects on emissions was based on eight NMIP2 models, and we used SH1–SH7 and SH1–SH8 to model the effects of CO₂ and climate on global terrestrial soil N₂O emissions (Table 2), respectively. The effect of land-cover change on N₂O dynamics includes the reduction due to the “Long-term effect of reduced mature forest area” and the additional emissions due to the “Post-deforestation pulse effect”. The two estimates were based on the bookkeeping approach and the DLEM model simulation. The bookkeeping method has been developed by Houghton et al. (1983) to account for carbon flows due to land use. In the original bookkeeping model developed by Houghton et al. (1983), land conversion and the affected carbon pools are tracked each year. The initial values of carbon pools are set for each type of land use. Annual changes in carbon pools in areas affected by land-use change or some land management practices (like wood harvest and fire man-
### 3.4.2 Direct emissions from nitrogen additions (agriculture)

Agriculture N₂O emissions consist of four components: “Direct soil emissions”, “Manure left on pasture”, “Manure management”, and “Aquaculture”. Data for direct soil emissions were obtained as the ensemble mean of N₂O emissions from the average of two inventories (EDGAR v7.0 and FAOSTAT), the SRNM and DLEM models, and the NMIP2 and DLEM models. The statistical model SRNM only covers cropland N₂O emissions. Thus, we added the DLEM-based estimate of pasture N₂O emissions into the two estimates of cropland to represent direct agricultural soil emissions (i.e., SRNM/DLEM or NMIP2/DLEM). Manure left on pasture is the ensemble mean of EDGAR v7.0, FAOSTAT, and DLEM. Manure management emissions are the mean of EDGAR v7.0 and FAOSTAT. FAOSTAT emission factors for N additions are based on the 2006 guidelines. Global N flows (i.e., fish feed intake, fish harvest, and waste) in freshwater and marine aquaculture were obtained from Bouwman et al. (2011, 2013a) and Beusen et al. (2016) and based on the IMAGE-GNM aquaculture nutrient budget model for the 1980–2020 period. We then calculated global aquaculture N₂O emissions as a 1.8% loss of N waste in aquaculture, i.e., the same EF used in Hu et al. (2012) and MacLeod et al. (2019). The uncertainty range of the EF is from 0.5% (Eggleston et al., 2006) to 5% (Williams and Crutzen, 2010), the same range used in the UNEP report (Bouwman et al., 2013b).

### 3.4.3 Emissions from other direct anthropogenic sources

This category includes “Fossil fuel and industry”, “Waste and wastewater”, and “Biomass burning”. Both emissions from fossil fuel and industry and waste and wastewater were calculated as the ensemble means of EDGAR v7.0 and UNFCCC databases. The biomass burning emission is the ensemble mean of FAOSTAT, DLEM, and GFED4s databases. In EDGAR v7.0, the Waste and wastewater category includes “Waste incineration” and “Wastewater handling”. We merged “Transportation”, “Energy”, “Industry”, and “Residential and other sectors” to represent the total emission from fossil fuel and industry. The FAOSTAT emissions database of the Food and Agriculture Organization of the United Nations (FAO) covers emissions of N₂O from agriculture and land use by country and globally, from 1961 to 2020 for agriculture and from 1990 for relevant land-use categories, i.e., cultivation of Histosols, biomass burning, etc., applying only Tier-1 coefficients (Tubiello et al., 2021, 2022; Conchedda and Tubiello, 2020; Prosperi et al., 2020). In addition to the IPCC agriculture burning categories “Burning crop residues” and “Burning savannah”, we included FAOSTAT estimates for N₂O emissions from deforestation fires, forest fires, and peatland fires (Prosperi et al., 2020).

### 3.4.4 Indirect emissions from anthropogenic N additions

This category considers N deposition on land and the ocean (“N deposition on land” and “N deposition on ocean”) as well as the N leaching and runoff from upstream (“Inland and coastal waters”). The emission from N deposition on ocean was provided by Suntharalingam et al. (2012) and includes emission from both open oceans and continental shelves, while emission from N deposition on land was the average of two estimates by NMIP2/EDGAR v7.0 and NMIP2. EDGAR v7.0 provided estimates of indirect emissions from both agricultural and non-agricultural sectors; however, here, we sum the ensemble mean of NMIP2 estimates of indirect emissions from agricultural sectors with indirect emissions from the non-agricultural sector of EDGAR v7.0 (i.e., NMIP2/EDGAR v7.0) to represent N-deposition-induced soil emissions from both agricultural and non-agricultural sectors. The N₂O emissions from inland and coastal waters consist of rivers, reservoirs, lakes, estuaries, and continental shelves, which is the ensemble mean of an average of two inventories (EDGAR v7.0 indirect N₂O emissions – leaching and runoff – and FAOSTAT), and the mean of meta-analysis and models. The anthropogenic emission from inland freshwaters estimated by Yao et al. (2020) considered annual N inputs and other environmental factors (i.e., climate, elevated CO₂, and land-cover change). The results in Yao et al. (2020) suggested that 56% of the total N₂O emissions from rivers, reservoirs, estuaries, and lakes was attributed to anthropogenic N additions. Empirical methods (empirical models and meta-analysis) adopted this ratio to calculate long-term average anthropogenic N₂O emissions from inland waters, consistent with Tian et al. (2020). Seagrass, mangrove, and salt marsh N₂O emissions were updated from Rosentreter et al. (2023).

### 3.4.5 Perturbation of N₂O fluxes from climate, CO₂, and land-cover change

The estimate of climate and CO₂ effects on emissions was based on eight NMIP2 models, and we used SH1–SH7 and SH1–SH8 to model the effects of CO₂ and climate on global terrestrial soil N₂O emissions (Table 2), respectively. The effect of land-cover change on N₂O dynamics includes the reduction due to the “Long-term effect of reduced mature forest area” and the additional emissions due to the “Post-deforestation pulse effect”. The two estimates were based on the bookkeeping approach and the DLEM model simulation. The bookkeeping method has been developed by Houghton et al. (1983) to account for carbon flows due to land use. In the original bookkeeping model developed by Houghton et al. (1983), land conversion and the affected carbon pools are tracked each year. The initial values of carbon pools are set for each type of land use. Annual changes in carbon pools in areas affected by land-use change or some land management practices (like wood harvest and fire man-
agement) are prescribed in the model using response curves, which are usually a function of the age of the newly converted land use. These response curves are specific for each type of land-cover type and land-use change and do not include the effects of environmental changes (Houghton and Castanho, 2023). For each age cohort, it either gains carbon (afforestation or reforestation) or loses carbon (deforestation) until its carbon pools reach a new stable state (the response curve converges). A similar bookkeeping method was developed to account for N₂O emission due to deforestation. Here, different from the original bookkeeping model calculating carbon fluxes through tracking changes in vegetation or soil pools, response curves directly tracking annual N₂O emissions after deforestation, which are also a function of the age of newly converted land use, were developed in our bookkeeping method (for details, please refer to Sect. S9).

### 3.4.6 Atmospheric production of reactive nitrogen

N₂O production in the atmosphere is a relatively small component of the global budget. N₂O is produced by the gaseous-phase oxidation of NH₃ in the troposphere; however, there are few published estimates of this source, and it remains poorly constrained. In this paper, we refer to the two known published estimates, which are 0.4 Tg N yr⁻¹ (Kohlmann and Poppe, 1999) and 0.6 (0.3–1.1) Tg N yr⁻¹ (Dentener and Crutzen, 1994), that are derived using global models of atmospheric chemistry and transport. As human activities have greatly affected the atmospheric abundance of NH₃, a significant portion of this source may be considered anthropogenic. Lightning production of NOₓ indirectly leads to N₂O emission through its oxidation and subsequent deposition on land and the ocean. A recent study estimated the global lightning production of NOₓ to be 9 Tg N yr⁻¹ (Nault et al., 2017), which is larger than previous estimates of 5 (2–8) Tg N yr⁻¹ (Schumann and Huntrieser, 2007). In this study, we assume an effective emission factor of 1% (De Klein et al., 2006); using the median estimate of 5 Tg N yr⁻¹ of NOₓ, we then estimate a global source of N₂O of 0.05 (0.02–0.09) Tg N yr⁻¹. There is also N₂O production from N₂ + O(¹D), which amounts to about 2% of the atmospheric source in the stratosphere (Estupiñán et al., 2005).

## 3.5 Atmospheric observation data synthesis

### 3.5.1 Atmospheric burden and trends from tropospheric observations

The monthly tropospheric N₂O mole fraction and its growth rate are derived from three different atmospheric observational networks: the Advanced Global Atmospheric Gases Experiment (AGAGE; Prinn et al., 2018), the Commonwealth Scientific and Industrial Research Organisation (CSIRO; Francey et al., 2003), and the National Ocean and Atmospheric Administration (NOAA; Dutton et al.,

2023; Lan et al., 2022). Further information on the three networks' stations, instruments, calibration, and uncertainties as well as access to data are provided in Sect. S12 "Atmospheric N₂O Observation Networks".

The atmospheric burden and its rate of change during 1980–2020 were derived from mean maritime surface abundance (mole fraction) of N₂O (Prather et al., 2023) with a conversion factor of 4.79 Tg N ppb⁻¹ (Prather et al., 2012). Combining uncertainties in measuring the annual mean surface mole fraction, which are < 1 ppb (Dlugokencky et al., 1994), with those of converting surface mole fractions to a global mean abundance, we estimate a ±1.4% uncertainty in the absolute burden (Prather et al., 2012). The uncertainty in the conversion from parts per billion to teragrams does not affect the trend uncertainty. This uncertainty is estimated to be ±0.2 ppb or ±1 Tg N between any 2 years over any recent period, based on the combined NOAA and AGAGE record of surface N₂O taken from Table 2.1 of the IPCC AR5 (Hartmann et al., 2013). Thus, the uncertainty in the burden change between 2 decades (e.g., 2000s to 2010s) is bounded by ±1 Tg N (< 0.1%).

### 3.5.2 Atmospheric loss rates and trends from stratospheric observations

The NASA Aura Microwave Limb Sounder (MLS) satellite instrument has provided consistent global measurements of stratospheric N₂O, O₃, and temperature (T) since August 2004. These have been used with simple stratospheric chemistry models to calculate the monthly mean stratospheric loss of N₂O due to photolysis and oxidation by O(¹D) (Prather et al., 2015, 2023; Minschwaner et al., 1998). Tropospheric chemical loss also occurs, although at a very low rate (< 1% of the total) and is, thus, not included in the calculations.

### 3.5.3 Atmospheric inversion estimates of N₂O emissions and losses

For the TD constraints on both land and ocean N₂O fluxes for the 1998–2020 period, we used estimates from four independent atmospheric inversion frameworks (INVICAT, PyVAR-CAMS, MIROC4-ACTM, and GEOS-Chem), all of which used a Bayesian inversion method (see the Supplement for details on the inversion frameworks).

The inversion frameworks INVICAT and PyVAR-CAMS used the transport models TOMCAT and LMDz5, respectively, which were both driven by ECMWF ERA5 meteorology, while MIROC4-ACTM used the transport model ACTM, which was driven by JRA-55 meteorology, and GEOS-Chem used the transport model of the same name, which was driven by MERRA-2 meteorology. All inversion frameworks assumed that the prior distribution of emissions followed a normal distribution, with the multivariate mean taken from different models and data products, with standard
agement) are prescribed in the model using response curves, which are usually a function of the age of the newly converted land use. These response curves are specific for each type of land-cover type and land-use change and do not include the effects of environmental changes (Houghton and Castanho, 2023). For each age cohort, it either gains carbon (afforestation or reforestation) or loses carbon (deforestation) until its carbon pools reach a new stable state (the response curve converges). A similar bookkeeping method was developed to account for N₂O emission due to deforestation. Here, different from the original bookkeeping model calculating carbon fluxes through tracking changes in vegetation or soil pools, response curves directly tracking annual N₂O emissions after deforestation, which are also a function of the age of newly converted land use, were developed in our bookkeeping method (for details, please refer to Sect. S9).

### 3.4.6 Atmospheric production of reactive nitrogen

N₂O production in the atmosphere is a relatively small component of the global budget. N₂O is produced by the gaseous-phase oxidation of NH₃ in the troposphere; however, there are few published estimates of this source, and it remains poorly constrained. In this paper, we refer to the two known published estimates, which are 0.4 Tg N yr⁻¹ (Kohlmann and Poppe, 1999) and 0.6 (0.3–1.1) Tg N yr⁻¹ (Dentener and Crutzen, 1994), that are derived using global models of atmospheric chemistry and transport. As human activities have greatly affected the atmospheric abundance of NH₃, a significant portion of this source may be considered anthropogenic. Lightning production of NOₓ indirectly leads to N₂O emission through its oxidation and subsequent deposition on land and the ocean. A recent study estimated the global lightning production of NOₓ to be 9 Tg N yr⁻¹ (Nault et al., 2017), which is larger than previous estimates of 5 (2–8) Tg N yr⁻¹ (Schumann and Huntrieser, 2007). In this study, we assume an effective emission factor of 1% (De Klein et al., 2006); using the median estimate of 5 Tg N yr⁻¹ of NOₓ, we then estimate a global source of N₂O of 0.05 (0.02–0.09) Tg N yr⁻¹. There is also N₂O production from N₂ + O(¹D), which amounts to about 2% of the atmospheric source in the stratosphere (Estupiñán et al., 2005).

## 3.5 Atmospheric observation data synthesis

### 3.5.1 Atmospheric burden and trends from tropospheric observations

The monthly tropospheric N₂O mole fraction and its growth rate are derived from three different atmospheric observational networks: the Advanced Global Atmospheric Gases Experiment (AGAGE; Prinn et al., 2018), the Commonwealth Scientific and Industrial Research Organisation (CSIRO; Francey et al., 2003), and the National Ocean and Atmospheric Administration (NOAA; Dutton et al.,

2023; Lan et al., 2022). Further information on the three networks' stations, instruments, calibration, and uncertainties as well as access to data are provided in Sect. S12 "Atmospheric N₂O Observation Networks".

The atmospheric burden and its rate of change during 1980–2020 were derived from mean maritime surface abundance (mole fraction) of N₂O (Prather et al., 2023) with a conversion factor of 4.79 Tg N ppb⁻¹ (Prather et al., 2012). Combining uncertainties in measuring the annual mean surface mole fraction, which are < 1 ppb (Dlugokencky et al., 1994), with those of converting surface mole fractions to a global mean abundance, we estimate a ±1.4% uncertainty in the absolute burden (Prather et al., 2012). The uncertainty in the conversion from parts per billion to teragrams does not affect the trend uncertainty. This uncertainty is estimated to be ±0.2 ppb or ±1 Tg N between any 2 years over any recent period, based on the combined NOAA and AGAGE record of surface N₂O taken from Table 2.1 of the IPCC AR5 (Hartmann et al., 2013). Thus, the uncertainty in the burden change between 2 decades (e.g., 2000s to 2010s) is bounded by ±1 Tg N (< 0.1%).

### 3.5.2 Atmospheric loss rates and trends from stratospheric observations

The NASA Aura Microwave Limb Sounder (MLS) satellite instrument has provided consistent global measurements of stratospheric N₂O, O₃, and temperature (T) since August 2004. These have been used with simple stratospheric chemistry models to calculate the monthly mean stratospheric loss of N₂O due to photolysis and oxidation by O(¹D) (Prather et al., 2015, 2023; Minschwaner et al., 1998). Tropospheric chemical loss also occurs, although at a very low rate (< 1% of the total) and is, thus, not included in the calculations.

### 3.5.3 Atmospheric inversion estimates of N₂O emissions and losses

For the TD constraints on both land and ocean N₂O fluxes for the 1998–2020 period, we used estimates from four independent atmospheric inversion frameworks (INVICAT, PyVAR-CAMS, MIROC4-ACTM, and GEOS-Chem), all of which used a Bayesian inversion method (see the Supplement for details on the inversion frameworks).

The inversion frameworks INVICAT and PyVAR-CAMS used the transport models TOMCAT and LMDz5, respectively, which were both driven by ECMWF ERA5 meteorology, while MIROC4-ACTM used the transport model ACTM, which was driven by JRA-55 meteorology, and GEOS-Chem used the transport model of the same name, which was driven by MERRA-2 meteorology. All inversion frameworks assumed that the prior distribution of emissions followed a normal distribution, with the multivariate mean taken from different models and data products, with standard
<table><caption>**Table 2.** Simulation design of NMIP2.</caption><thead><tr><th>Historical</th><th>Climate</th><th>CO<sub>2</sub></th><th>Land cover</th><th>Irrigation</th><th>NDEP</th><th>Nfer</th><th>Manure N</th></tr></thead><tbody><tr><td>SH0</td><td>1901–1920</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td></tr><tr><td>SH1</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td></tr><tr><td>SH2</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>1850</td></tr><tr><td>SH3</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>1850</td><td>*</td></tr><tr><td>SH4</td><td>*</td><td>*</td><td>*</td><td>*</td><td>1850</td><td>*</td><td>*</td></tr><tr><td>SH5</td><td>*</td><td>*</td><td>*</td><td>1850</td><td>*</td><td>*</td><td>*</td></tr><tr><td>SH6</td><td>*</td><td>*</td><td>1850</td><td>*</td><td>*</td><td>*</td><td>*</td></tr><tr><td>SH7</td><td>*</td><td>1850</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td></tr><tr><td>SH8</td><td>1901–1920</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td></tr><tr><td>SH9</td><td>1901–1920</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td><td>*</td><td>*</td></tr><tr><td>SH10</td><td>*</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td></tr><tr><td>SH11</td><td>*</td><td>*</td><td>1850</td><td>1850</td><td>*</td><td>1850</td><td>1850</td></tr><tr><td>SH12</td><td>*</td><td>*</td><td>*</td><td>1850</td><td>*</td><td>1850</td><td>1850</td></tr></tbody></table>

Note: for historical simulations, “*” indicates that the forcing during 1850–2020 is included in the simulation, “1901–1920” indicates that the 20-year mean climate condition during 1901–1920 will be used over the entire simulation period, and “1850” indicates that the forcing will be fixed in 1850 over the entire period. Climate data are only available from 1901; we assume the 20-year average value between 1901 and 1920 for the years 1850–1900. N deposition (NDEP) is available only from 1850. N fertilizer (Nfer) before 1910 was zero. Manure N is available only from 1860; we assume manure N at the 1860 value for the years 1850–1860.

deviations detailed in the Supplement. Specifically, GEOS-Chem, INVICAT, and PyVAR-CAMS built prior flux distributions for natural soil emissions from the terrestrial biospheric model O-CN (Zaehle et al., 2011) and for biomass burning emissions from GFED-v4s (van der Werf et al., 2017). For anthropogenic emissions from agricultural and non-agricultural sectors (excluding biomass burning), estimates from EDGAR v5 were used to build the prior for the 2005–2020 period (as these estimates were only available up to 2015, the emissions for 2016–2020 were estimated based on those of the year 2015), and the estimates from EDGAR-v4.32 were used for the 1997–2004 period. On the other hand, MIROC4-ACTM used the estimate from the terrestrial biospheric model VISIT for natural soil emissions and EDGAR v4.2 estimates for all anthropogenic emissions.

The inversion frameworks used atmospheric observations from ground-based networks, specifically NOAA, AGAGE, and CSIRO (see the Supplement for details).

The atmospheric transport models also calculate the loss of N₂O in the stratosphere by photolysis and oxidation by O(¹D) radicals (Minschwaner et al., 1998). The TD mean posterior estimates for the 18 land regions were calculated by integrating the gridded fluxes at 1° × 1° over each region (the fluxes were interpolated from the original model resolution to 1° × 1°).

# 4 Results

## 4.1 Trends in atmospheric mole fractions and implied emissions

### 4.1.1 Trends in atmospheric N₂O mole fractions

The three observation networks AGAGE, NOAA, and CSIRO show consistent growth in atmospheric N₂O mole fractions from 315.8 (315.5–316.2) ppb in 2000 to 335.9 (335.6–336.1) ppb in 2022. The mean annual growth rate increased from 0.76 (0.55–0.95) ppb yr⁻¹ in the 2000s to 0.96 (0.79–1.15) ppb yr⁻¹ in 2010s with significant seasonal and interannual variation. In 2020 and 2021, the N₂O atmospheric growth rate was 1.33 and 1.38 ppb yr⁻¹, respectively, both higher than any previous observed year (since 1980), and it was more than 30% higher than the average value in the decade of the 2010s (Fig. 2). As is shown in Fig. 5, the observed N₂O mole fraction in 2020 (333.2, 332.7–333.5 ppb) has exceeded predicted levels across the four illustrative Representative Concentration Pathways (RCPs) (329.2–331.5 ppb) used in CMIP5 (Meinshausen et al., 2011) and the seven illustrative Socioeconomic Pathways (SSPs) (330.5–331.9 ppb) used in CMIP6 (Meinshausen et al., 2020).

## 4.2 N₂O sources and sinks: BU estimates

### 4.2.1 Anthropogenic sources

#### Global anthropogenic emissions during 1980–2020

Global total anthropogenic emissions increased in the last 4 decades, from 4.8 (3.1–7.3) Tg N yr⁻¹ in 1980 to 6.7 (3.3–
<table><caption>**Table 2.** Simulation design of NMIP2.</caption><thead><tr><th>Historical</th><th>Climate</th><th>CO<sub>2</sub></th><th>Land cover</th><th>Irrigation</th><th>NDEP</th><th>Nfer</th><th>Manure N</th></tr></thead><tbody><tr><td>SH0</td><td>1901–1920</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td></tr><tr><td>SH1</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td></tr><tr><td>SH2</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>1850</td></tr><tr><td>SH3</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>1850</td><td>*</td></tr><tr><td>SH4</td><td>*</td><td>*</td><td>*</td><td>*</td><td>1850</td><td>*</td><td>*</td></tr><tr><td>SH5</td><td>*</td><td>*</td><td>*</td><td>1850</td><td>*</td><td>*</td><td>*</td></tr><tr><td>SH6</td><td>*</td><td>*</td><td>1850</td><td>*</td><td>*</td><td>*</td><td>*</td></tr><tr><td>SH7</td><td>*</td><td>1850</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td></tr><tr><td>SH8</td><td>1901–1920</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td></tr><tr><td>SH9</td><td>1901–1920</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td><td>*</td><td>*</td></tr><tr><td>SH10</td><td>*</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td><td>1850</td></tr><tr><td>SH11</td><td>*</td><td>*</td><td>1850</td><td>1850</td><td>*</td><td>1850</td><td>1850</td></tr><tr><td>SH12</td><td>*</td><td>*</td><td>*</td><td>1850</td><td>*</td><td>1850</td><td>1850</td></tr></tbody></table>

Note: for historical simulations, “*” indicates that the forcing during 1850–2020 is included in the simulation, “1901–1920” indicates that the 20-year mean climate condition during 1901–1920 will be used over the entire simulation period, and “1850” indicates that the forcing will be fixed in 1850 over the entire period. Climate data are only available from 1901; we assume the 20-year average value between 1901 and 1920 for the years 1850–1900. N deposition (NDEP) is available only from 1850. N fertilizer (Nfer) before 1910 was zero. Manure N is available only from 1860; we assume manure N at the 1860 value for the years 1850–1860.

deviations detailed in the Supplement. Specifically, GEOS-Chem, INVICAT, and PyVAR-CAMS built prior flux distributions for natural soil emissions from the terrestrial biospheric model O-CN (Zaehle et al., 2011) and for biomass burning emissions from GFED-v4s (van der Werf et al., 2017). For anthropogenic emissions from agricultural and non-agricultural sectors (excluding biomass burning), estimates from EDGAR v5 were used to build the prior for the 2005–2020 period (as these estimates were only available up to 2015, the emissions for 2016–2020 were estimated based on those of the year 2015), and the estimates from EDGAR v4.32 were used for the 1997–2004 period. On the other hand, MIROC4-ACTM used the estimate from the terrestrial biospheric model VISIT for natural soil emissions and EDGAR v4.2 estimates for all anthropogenic emissions.

The inversion frameworks used atmospheric observations from ground-based networks, specifically NOAA, AGAGE, and CSIRO (see the Supplement for details).

The atmospheric transport models also calculate the loss of N₂O in the stratosphere by photolysis and oxidation by O(¹D) radicals (Minschwaner et al., 1998). The TD mean posterior estimates for the 18 land regions were calculated by integrating the gridded fluxes at 1° × 1° over each region (the fluxes were interpolated from the original model resolution to 1° × 1°).

# 4 Results

## 4.1 Trends in atmospheric mole fractions and implied emissions

### 4.1.1 Trends in atmospheric N₂O mole fractions

The three observation networks AGAGE, NOAA, and CSIRO show consistent growth in atmospheric N₂O mole fractions from 315.8 (315.5–316.2) ppb in 2000 to 335.9 (335.6–336.1) ppb in 2022. The mean annual growth rate increased from 0.76 (0.55–0.95) ppb yr⁻¹ in the 2000s to 0.96 (0.79–1.15) ppb yr⁻¹ in 2010s with significant seasonal and interannual variation. In 2020 and 2021, the N₂O atmospheric growth rate was 1.33 and 1.38 ppb yr⁻¹, respectively, both higher than any previous observed year (since 1980), and it was more than 30% higher than the average value in the decade of the 2010s (Fig. 2). As is shown in Fig. 5, the observed N₂O mole fraction in 2020 (333.2, 332.7–333.5 ppb) has exceeded predicted levels across the four illustrative Representative Concentration Pathways (RCPs) (329.2–331.5 ppb) used in CMIP5 (Meinshausen et al., 2011) and the seven illustrative Socioeconomic Pathways (SSPs) (330.5–331.9 ppb) used in CMIP6 (Meinshausen et al., 2020).

## 4.2 N₂O sources and sinks: BU estimates

### 4.2.1 Anthropogenic sources

#### Global anthropogenic emissions during 1980–2020

Global total anthropogenic emissions increased in the last 4 decades, from 4.8 (3.1–7.3) Tg N yr⁻¹ in 1980 to 6.7 (3.3–
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Comparison between the measured global N₂O mole fractions from the three GHG observing networks and the projected mole fractions from (a) the four illustrative Representative Concentration Pathways (RCPs) in the IPCC Fifth Assessment Report and (b) the seven illustrative Socioeconomic Pathways (SSPs) used in CMIP6.</figcaption></figure>

10.9) Tg N yr⁻¹ in 2020 (Fig. 6). Among all anthropogenic sources, direct emissions from nitrogen additions in the agricultural sector made the largest contribution to the increase, which grew from 2.2 (1.6–2.8) Tg N yr⁻¹ in 1980 to 3.9 (2.9–5.1) Tg N yr⁻¹ in 2020. Indirect N₂O emissions also steadily increased during the study period, from 0.9 (0.7–1.1) Tg N yr⁻¹ in 1980 to 1.3 (0.9–1.6) Tg N yr⁻¹ in 2020. In contrast, other direct anthropogenic emissions did not have a trend, and the total amount fluctuated around 2.1 Tg N yr⁻¹. Perturbed fluxes from climate, CO₂, and land-cover change led to a small increase in the N₂O sink, from −0.4 (−0.9 to 1.0) Tg N yr⁻¹ in 1980 to −0.6 (−2.2 to 1.8) Tg N yr⁻¹ in 2020.

### Direct emissions from nitrogen additions in the agricultural sector (agriculture)

In the past 4 decades, N₂O emissions from all four sources within the agricultural sector significantly increased (Fig. 7), with the largest contribution from direct soil emissions (from 1.1 Tg N yr⁻¹ in 1980 to 2.1 Tg N yr⁻¹ in 2020), followed by manure left on pasture (from 0.9 Tg N yr⁻¹ in 1980 to 1.4 Tg N yr⁻¹ in 2020), aquaculture (from 0.01 Tg N yr⁻¹ in 1980 to 0.12 Tg N yr⁻¹ in 2020), and manure management (from 0.24 Tg N yr⁻¹ in 1980 to 0.26 Tg N yr⁻¹ in 2020).

Direct soil emissions accounted for the largest proportion of emissions from the agriculture sector. All four estimates show a steady increase in direct soil emissions since 1980 (Fig. 7a). Among them, NMIP2/DLEM exhibited the largest magnitude and the fastest increase rate, from 1.1 Tg N yr⁻¹ in 1980 to 2.6 Tg N yr⁻¹ in 2020. By contrast, SRNM/DLEM suggested the slowest increase rate, from 1.0 Tg N yr⁻¹ in 1980 to 1.7 Tg N yr⁻¹ in 2020. The estimates of the two inventories (FAOSTAT and EDGARv7.0)

exhibited similar magnitudes and trends, especially after 1990. All three estimates suggested a significant increasing trend for N₂O emissions from manure left on pasture over the 1980–2020 period. Although all methods showed an increasing trend, they had significant differences in magnitude and increase rate (Fig. 7b). FAOSTAT showed the largest magnitude and increase rate, from 1.2 Tg N yr⁻¹ in 1980 to 1.9 Tg N yr⁻¹ in 2020. However, DLEM showed a smaller magnitude and a slower increase rate, from 0.5 Tg N yr⁻¹ in 1980 to 0.9 Tg N yr⁻¹ in 2020. Although the two inventory estimates for emissions from manure management showed similar temporal variations, FAOSTAT has a larger magnitude than EDGARv7.0 (Fig. 7c). According to the IMAGE-GNM aquaculture nutrient budget model, N₂O emissions from aquaculture increased more than 10-fold, from 0.01 Tg N yr⁻¹ in 1980 to 0.12 Tg N yr⁻¹ in 2020 (Fig. 7d).

### Other direct anthropogenic sources

Fossil fuel and industry emissions accounted for the largest proportion of N₂O emissions from other direct anthropogenic sources. Estimates from two approaches showed different trends during their overlapping period: EGDARv7.0 had an increasing trend from 0.9 Tg N yr⁻¹ in 1990 to 1.1 Tg N yr⁻¹ in 2020, while EDGAR/UNFCCC did not show a trend with 1.0 Tg N yr⁻¹ in 1990 and 1.0 Tg N yr⁻¹ in 2020 (Fig. 8a). These inventories, however, do not capture a strong increase in emissions from adipic acid production since 2010 (Davidson and Winiwarter, 2023). Both EDGARv7.0 and EDGAR/UNFCCC show a steady and significant increase in N₂O emissions from waste and wastewater. Although EDGAR/UNFCCC shows a larger magnitude than EGDARv7.0, these two inventory estimates display sim-
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Comparison between the measured global N₂O mole fractions from the three GHG observing networks and the projected mole fractions from (a) the four illustrative Representative Concentration Pathways (RCPs) in the IPCC Fifth Assessment Report and (b) the seven illustrative Socioeconomic Pathways (SSPs) used in CMIP6.</figcaption></figure>

10.9) Tg N yr⁻¹ in 2020 (Fig. 6). Among all anthropogenic sources, direct emissions from nitrogen additions in the agricultural sector made the largest contribution to the increase, which grew from 2.2 (1.6–2.8) Tg N yr⁻¹ in 1980 to 3.9 (2.9–5.1) Tg N yr⁻¹ in 2020. Indirect N₂O emissions also steadily increased during the study period, from 0.9 (0.7–1.1) Tg N yr⁻¹ in 1980 to 1.3 (0.9–1.6) Tg N yr⁻¹ in 2020. In contrast, other direct anthropogenic emissions did not have a trend, and the total amount fluctuated around 2.1 Tg N yr⁻¹. Perturbed fluxes from climate, CO₂, and land-cover change led to a small increase in the N₂O sink, from −0.4 (−0.9 to 1.0) Tg N yr⁻¹ in 1980 to −0.6 (−2.2 to 1.8) Tg N yr⁻¹ in 2020.

## Direct emissions from nitrogen additions in the agricultural sector (agriculture)

In the past 4 decades, N₂O emissions from all four sources within the agricultural sector significantly increased (Fig. 7), with the largest contribution from direct soil emissions (from 1.1 Tg N yr⁻¹ in 1980 to 2.1 Tg N yr⁻¹ in 2020), followed by manure left on pasture (from 0.9 Tg N yr⁻¹ in 1980 to 1.4 Tg N yr⁻¹ in 2020), aquaculture (from 0.01 Tg N yr⁻¹ in 1980 to 0.12 Tg N yr⁻¹ in 2020), and manure management (from 0.24 Tg N yr⁻¹ in 1980 to 0.26 Tg N yr⁻¹ in 2020).

Direct soil emissions accounted for the largest proportion of emissions from the agriculture sector. All four estimates show a steady increase in direct soil emissions since 1980 (Fig. 7a). Among them, NMIP2/DLEM exhibited the largest magnitude and the fastest increase rate, from 1.1 Tg N yr⁻¹ in 1980 to 2.6 Tg N yr⁻¹ in 2020. By contrast, SRNM/DLEM suggested the slowest increase rate, from 1.0 Tg N yr⁻¹ in 1980 to 1.7 Tg N yr⁻¹ in 2020. The estimates of the two inventories (FAOSTAT and EDGARv7.0)

exhibited similar magnitudes and trends, especially after 1990. All three estimates suggested a significant increasing trend for N₂O emissions from manure left on pasture over the 1980–2020 period. Although all methods showed an increasing trend, they had significant differences in magnitude and increase rate (Fig. 7b). FAOSTAT showed the largest magnitude and increase rate, from 1.2 Tg N yr⁻¹ in 1980 to 1.9 Tg N yr⁻¹ in 2020. However, DLEM showed a smaller magnitude and a slower increase rate, from 0.5 Tg N yr⁻¹ in 1980 to 0.9 Tg N yr⁻¹ in 2020. Although the two inventory estimates for emissions from manure management showed similar temporal variations, FAOSTAT has a larger magnitude than EDGARv7.0 (Fig. 7c). According to the IMAGE-GNM aquaculture nutrient budget model, N₂O emissions from aquaculture increased more than 10-fold, from 0.01 Tg N yr⁻¹ in 1980 to 0.12 Tg N yr⁻¹ in 2020 (Fig. 7d).

## Other direct anthropogenic sources

Fossil fuel and industry emissions accounted for the largest proportion of N₂O emissions from other direct anthropogenic sources. Estimates from two approaches showed different trends during their overlapping period: EGDARv7.0 had an increasing trend from 0.9 Tg N yr⁻¹ in 1990 to 1.1 Tg N yr⁻¹ in 2020, while EDGAR/UNFCCC did not show a trend with 1.0 Tg N yr⁻¹ in 1990 and 1.0 Tg N yr⁻¹ in 2020 (Fig. 8a). These inventories, however, do not capture a strong increase in emissions from adipic acid production since 2010 (Davidson and Winiwarter, 2023). Both EDGARv7.0 and EDGAR/UNFCCC show a steady and significant increase in N₂O emissions from waste and wastewater. Although EDGAR/UNFCCC shows a larger magnitude than EGDARv7.0, these two inventory estimates display sim-
<figure><img src="image_7.png" /><figcaption>**Figure 6.** Changes in global anthropogenic N₂O emissions (a) and N₂O emissions from different sectors (b–e) during 1980–2020. In each panel, the line represents the mean N₂O emission of different estimates, and the shaded area shows minimum and maximum estimates.</figcaption></figure>

ilar growth rates (Fig. 8b). There are large uncertainties in the magnitude and temporal trend in N₂O emissions from biomass burning (Fig. 8c). DLEM and GFED show a larger magnitude of emissions than FAOSTAT. Both DLEM and GFED have a decreasing trend over the overlapping period from 1997 to 2020, but FAOSTAT shows no significant trend during this period.

## Indirect emissions from anthropogenic nitrogen additions

Global anthropogenic N₂O emissions from inland waters, estuaries, and coastal vegetation continuously increased during 1980–2020 (Fig. 9a). Although all methods revealed an overall increasing trend in emissions, process-based models show a much smaller magnitude and increase rate than the two inventories. According to meta-analysis and models, anthropogenic emissions from inland and coastal waters increased from 0.1 Tg N yr⁻¹ in 1980 to 0.15 Tg N yr⁻¹ in 2020. In contrast, EGDAR v7.0 and FAOSTAT showed that emissions increased from 0.33 and 0.35 Tg N yr⁻¹ in 1980 to 0.53 and

0.57 Tg N yr⁻¹ in 2020, respectively. Emissions from N deposition on land also continued to increase during 1980–2020 (Fig. 9b). NMIP2 and NMIP2/EDGAR v7.0 show emissions increasing from 0.6 and 0.4 Tg N yr⁻¹ in 1980 to 0.9 and 0.6 Tg N yr⁻¹ in 2020, respectively.

## Perturbation fluxes from climate, CO₂, and land-cover change

The spread between different estimates (DLEM and the bookkeeping method) of the post-deforestation pulse effect increased from the 1980s to the 2010s. The post-deforestation pulse effect was 0.8 (0.6–1.1) Tg N yr⁻¹ in 1980 and 0.8 (0.4–1.3) Tg N yr⁻¹ in 2020 (Fig. 10a). In contrast, DLEM and empirical approaches are comparable in terms of the magnitude and temporal changes in the long-term reduction effect of deforestation, with both approaches suggesting a strong long-term reduction effect, which grew from −1.2 (−1.0, −1.4) Tg N yr⁻¹ in 1980 to −1.4 (−1.3, −1.6) Tg N yr⁻¹ in 2020 (Fig. 10b). In general,
<figure><img src="image_7.png" /><figcaption>**Figure 6.** Changes in global anthropogenic N₂O emissions (a) and N₂O emissions from different sectors (b–e) during 1980–2020. In each panel, the line represents the mean N₂O emission of different estimates, and the shaded area shows minimum and maximum estimates.</figcaption></figure>

ilar growth rates (Fig. 8b). There are large uncertainties in the magnitude and temporal trend in N₂O emissions from biomass burning (Fig. 8c). DLEM and GFED show a larger magnitude of emissions than FAOSTAT. Both DLEM and GFED have a decreasing trend over the overlapping period from 1997 to 2020, but FAOSTAT shows no significant trend during this period.

## Indirect emissions from anthropogenic nitrogen additions

Global anthropogenic N₂O emissions from inland waters, estuaries, and coastal vegetation continuously increased during 1980–2020 (Fig. 9a). Although all methods revealed an overall increasing trend in emissions, process-based models show a much smaller magnitude and increase rate than the two inventories. According to meta-analysis and models, anthropogenic emissions from inland and coastal waters increased from 0.1 Tg N yr⁻¹ in 1980 to 0.15 Tg N yr⁻¹ in 2020. In contrast, EGDARv7.0 and FAOSTAT showed that emissions increased from 0.33 and 0.35 Tg N yr⁻¹ in 1980 to 0.53 and

0.57 Tg N yr⁻¹ in 2020, respectively. Emissions from N deposition on land also continued to increase during 1980–2020 (Fig. 9b). NMIP2 and NMIP2/EDGAR v7.0 show emissions increasing from 0.6 and 0.4 Tg N yr⁻¹ in 1980 to 0.9 and 0.6 Tg N yr⁻¹ in 2020, respectively.

## Perturbation fluxes from climate, CO₂, and land-cover change

The spread between different estimates (DLEM and the bookkeeping method) of the post-deforestation pulse effect increased from the 1980s to the 2010s. The post-deforestation pulse effect was 0.8 (0.6–1.1) Tg N yr⁻¹ in 1980 and 0.8 (0.4–1.3) Tg N yr⁻¹ in 2020 (Fig. 10a). In contrast, DLEM and empirical approaches are comparable in terms of the magnitude and temporal changes in the long-term reduction effect of deforestation, with both approaches suggesting a strong long-term reduction effect, which grew from −1.2 (−1.0, −1.4) Tg N yr⁻¹ in 1980 to −1.4 (−1.3, −1.6) Tg N yr⁻¹ in 2020 (Fig. 10b). In general,
<figure><img src="image_8.png" /><figcaption>**Figure 7.** Changes in global direct N₂O emissions from fertilizer and manure applied on agricultural soils (a), manure left on pasture (b), manure management (c), and aquaculture (d) during 1980–2020.</figcaption></figure>

deforestation had a negative effect on global soil N₂O emissions. However, most NMIP2 models suggested a positive effect of climate change on soil N₂O emissions, although with large uncertainty and significant interannual variation; this positive climate feedback significantly increased during the past 4 decades (Fig. 10c). In contrast to climatic effects, most NMIP2 models suggested a negative effect of rising atmospheric CO₂ concentration on soil N₂O emissions through increasing nitrogen use efficiency and, hence, reducing soil N availability (Fig. 10d). However, NMIP2 models have large discrepancies in the CO₂ fertilization effect on N₂O emissions; ELM and ISAM suggested a positive effect, while all the other models suggest a negative effect.

## 4.2.2 Natural N₂O sources

Emissions from natural soils and open oceans remained relatively steady throughout the study period from 1980 to 2020, with mean estimates fluctuating between 9.9 and 10.3 Tg N yr⁻¹ (minimum estimates: 6.2–7.1 Tg N yr⁻¹; maximum estimates: 12.8–13.6 Tg N yr⁻¹). Natural emissions from all other sources including shelves, inland waters, and lightning and atmospheric production were assumed to be constant during 1980–2020. According to BU approaches, the total natural emissions from these sources were 1.8 (1.0–3.0) Tg N yr⁻¹. The mean value of global N₂O emissions from all of the abovementioned sources fluctuated between 11.7 and 12.1 Tg N yr⁻¹, with an average of 11.9 Tg N yr⁻¹. Global natural N₂O emissions also have a large uncertainty, with the maximum estimates (15.8–16.6 Tg N yr⁻¹) roughly double the minimum estimates (7.1–8.1 Tg N yr⁻¹).

### Natural soil N₂O emission baseline

The natural soil N₂O emission baseline represents the preindustrial soil N₂O emissions derived from NMIP2 simulations, driven by potential vegetation/land cover and other environmental factors in the preindustrial period (1850). Global natural soil N₂O emissions are estimated to be 6.4 Tg N yr⁻¹, and they account for 55% of the total natural emissions. However, N₂O emissions from natural soils estimated by the NMIP2 showed large divergences among eight models. Among the NMIP2 models, ELM had the highest estimate with an average of 8.6 Tg N yr⁻¹, which was more than double the estimate from the CLASSIC model (3.9 Tg N yr⁻¹).

### Natural N₂O emission baseline from the open ocean and continental shelves

We also estimated N₂O emissions from the open oceans and continental shelves. Open ocean is the second largest source of natural N₂O emissions, with a global mean value fluctuating between 3.4 and 3.8 Tg N yr⁻¹ during 1980–2020. Open-ocean N₂O emissions were estimated by four ocean models. Among these models, NEMOv3.6-PISCESv2-gas had the highest estimate, with an average of 4.6 Tg N yr⁻¹, while NEMO-PlankTOM10 had the lowest estimate, with an average of 2.8 Tg N yr⁻¹. The four ocean models show different trends in open-ocean emissions. NEMOv3.6-PISCESv2-gas shows a slight increasing trend, whereas the other three models show consistent decreasing trends. In addition to open oceans, shelves are an important source of N₂O emissions that was not quantified in the previous global N₂O budget (Tian et al., 2020). Global shelf N₂O emissions
<figure><img src="image_8.png" /><figcaption>**Figure 7.** Changes in global direct N₂O emissions from fertilizer and manure applied on agricultural soils (a), manure left on pasture (b), manure management (c), and aquaculture (d) during 1980–2020.</figcaption></figure>

deforestation had a negative effect on global soil N₂O emissions. However, most NMIP2 models suggested a positive effect of climate change on soil N₂O emissions, although with large uncertainty and significant interannual variation; this positive climate feedback significantly increased during the past 4 decades (Fig. 10c). In contrast to climatic effects, most NMIP2 models suggested a negative effect of rising atmospheric CO₂ concentration on soil N₂O emissions through increasing nitrogen use efficiency and, hence, reducing soil N availability (Fig. 10d). However, NMIP2 models have large discrepancies in the CO₂ fertilization effect on N₂O emissions; ELM and ISAM suggested a positive effect, while all the other models suggest a negative effect.

### 4.2.2 Natural N₂O sources

Emissions from natural soils and open oceans remained relatively steady throughout the study period from 1980 to 2020, with mean estimates fluctuating between 9.9 and 10.3 Tg N yr⁻¹ (minimum estimates: 6.2–7.1 Tg N yr⁻¹; maximum estimates: 12.8–13.6 Tg N yr⁻¹). Natural emissions from all other sources including shelves, inland waters, and lightning and atmospheric production were assumed to be constant during 1980–2020. According to BU approaches, the total natural emissions from these sources were 1.8 (1.0–3.0) Tg N yr⁻¹. The mean value of global N₂O emissions from all of the abovementioned sources fluctuated between 11.7 and 12.1 Tg N yr⁻¹, with an average of 11.9 Tg N yr⁻¹. Global natural N₂O emissions also have a large uncertainty, with the maximum estimates (15.8–16.6 Tg N yr⁻¹) roughly double the minimum estimates (7.1–8.1 Tg N yr⁻¹).

#### Natural soil N₂O emission baseline

The natural soil N₂O emission baseline represents the preindustrial soil N₂O emissions derived from NMIP2 simulations, driven by potential vegetation/land cover and other environmental factors in the preindustrial period (1850). Global natural soil N₂O emissions are estimated to be 6.4 Tg N yr⁻¹, and they account for 55% of the total natural emissions. However, N₂O emissions from natural soils estimated by the NMIP2 showed large divergences among eight models. Among the NMIP2 models, ELM had the highest estimate with an average of 8.6 Tg N yr⁻¹, which was more than double the estimate from the CLASSIC model (3.9 Tg N yr⁻¹).

#### Natural N₂O emission baseline from the open ocean and continental shelves

We also estimated N₂O emissions from the open oceans and continental shelves. Open ocean is the second largest source of natural N₂O emissions, with a global mean value fluctuating between 3.4 and 3.8 Tg N yr⁻¹ during 1980–2020. Open-ocean N₂O emissions were estimated by four ocean models. Among these models, NEMOv3.6-PISCESv2-gas had the highest estimate, with an average of 4.6 Tg N yr⁻¹, while NEMO-PlankTOM10 had the lowest estimate, with an average of 2.8 Tg N yr⁻¹. The four ocean models show different trends in open-ocean emissions. NEMOv3.6-PISCESv2-gas shows a slight increasing trend, whereas the other three models show consistent decreasing trends. In addition to open oceans, shelves are an important source of N₂O emissions that was not quantified in the previous global N₂O budget (Tian et al., 2020). Global shelf N₂O emissions
<figure><img src="image_9.png" /><figcaption>**Figure 8.** Changes in N₂O emissions from other direct anthropogenic sources between 1980 and 2020: fossil fuel (a), waste and wastewater (b), and biomass burning (c).</figcaption></figure>

sions were estimated by two high-resolution models (CNRM and ECCO) and one data product (MEM-RF). The average of the three estimates is 1.2 Tg N yr⁻¹, ranging from 0.6 Tg N yr⁻¹ (ECCO) to 1.6 Tg N yr⁻¹ (MEM-RF).

## Natural N₂O emission from inland waters, estuaries, and coastal vegetation

Natural N₂O emissions from inland waters and estuaries were much smaller than emissions from the soils, oceans, and shelves. These emissions have an average value of 0.08 Tg N yr⁻¹, ranging from 0.05 to 0.14 Tg N yr⁻¹. Rivers are the largest source: they emit 0.04 (0.01–0.08) Tg N yr⁻¹ of N₂O and account for 48% of the natural emissions from inland waters and estuaries. The global natural N₂O emissions from lakes and estuaries were 0.02 (0.01–0.03) and 0.02 (0.02–0.03) Tg N yr⁻¹, respectively.

<figure><img src="image_10.png" /><figcaption>**Figure 9.** Changes in indirect N₂O emissions from anthropogenic nitrogen additions to inland waters (rivers, lakes, and reservoirs), estuaries, and coastal vegetation (a) as well as N deposition on land (b) during 1980–2020.</figcaption></figure>

## Lightning, atmospheric production, and natural sinks

The source of reactive N from lightning (and its contribution to N₂O) and the direct production of N₂O from NH₃ in the atmosphere are relatively small, and we have no new estimates in this work. However, synthesizing the available estimates in the scientific literature, we estimate lightning to contribute 0.05 (0.02–0.09) Tg N yr⁻¹ (median and range) (Nault et al., 2017; Schumann and Huntrieser, 2007) and atmospheric production to contribute 0.5 (0.3–1.1) Tg N yr⁻¹ (Kohlmann and Poppe, 1999; Dentener and Crutzen, 1994).

Similarly, the surface sink of N₂O is small. Again, we do not produce a new estimate in this budget and rather only synthesize available estimates from the literature. We estimate the global surface sink to be 0.01 (0.0–0.3) Tg N yr⁻¹.

## 4.3 N₂O sources and sinks: TD estimates

### 4.3.1 TD total source

Ensemble estimates across the four atmospheric inversions show that the long-term average global N₂O emissions during 1997–2020 were 16.6 Tg N yr⁻¹ (minimum: 15.5 Tg N yr⁻¹; maximum: 18.2 Tg N yr⁻¹). All four inversions show a significant increasing trend in global N₂O emissions (p < 0.05) with a mean rate of increase of 0.10 Tg N yr⁻² (0.08–0.12 Tg N yr⁻²) (Fig. 11a).
<figure><img src="image_9.png" /><figcaption>**Figure 8.** Changes in N₂O emissions from other direct anthropogenic sources between 1980 and 2020: fossil fuel (a), waste and wastewater (b), and biomass burning (c).</figcaption></figure>

sions were estimated by two high-resolution models (CNRM and ECCO) and one data product (MEM-RF). The average of the three estimates is 1.2 Tg N yr⁻¹, ranging from 0.6 Tg N yr⁻¹ (ECCO) to 1.6 Tg N yr⁻¹ (MEM-RF).

## Natural N₂O emission from inland waters, estuaries, and coastal vegetation

Natural N₂O emissions from inland waters and estuaries were much smaller than emissions from the soils, oceans, and shelves. These emissions have an average value of 0.08 Tg N yr⁻¹, ranging from 0.05 to 0.14 Tg N yr⁻¹. Rivers are the largest source: they emit 0.04 (0.01–0.08) Tg N yr⁻¹ of N₂O and account for 48% of the natural emissions from inland waters and estuaries. The global natural N₂O emissions from lakes and estuaries were 0.02 (0.01–0.03) and 0.02 (0.02–0.03) Tg N yr⁻¹, respectively.

<figure><img src="image_10.png" /><figcaption>**Figure 9.** Changes in indirect N₂O emissions from anthropogenic nitrogen additions to inland waters (rivers, lakes, and reservoirs), estuaries, and coastal vegetation (a) as well as N deposition on land (b) during 1980–2020.</figcaption></figure>

## Lightning, atmospheric production, and natural sinks

The source of reactive N from lightning (and its contribution to N₂O) and the direct production of N₂O from NH₃ in the atmosphere are relatively small, and we have no new estimates in this work. However, synthesizing the available estimates in the scientific literature, we estimate lightning to contribute 0.05 (0.02–0.09) Tg N yr⁻¹ (median and range) (Nault et al., 2017; Schumann and Huntrieser, 2007) and atmospheric production to contribute 0.5 (0.3–1.1) Tg N yr⁻¹ (Kohlmann and Poppe, 1999; Dentener and Crutzen, 1994).

Similarly, the surface sink of N₂O is small. Again, we do not produce a new estimate in this budget and rather only synthesize available estimates from the literature. We estimate the global surface sink to be 0.01 (0.0–0.3) Tg N yr⁻¹.

## 4.3 N₂O sources and sinks: TD estimates

### 4.3.1 TD total source

Ensemble estimates across the four atmospheric inversions show that the long-term average global N₂O emissions during 1997–2020 were 16.6 Tg N yr⁻¹ (minimum: 15.5 Tg N yr⁻¹; maximum: 18.2 Tg N yr⁻¹). All four inversions show a significant increasing trend in global N₂O emissions (p < 0.05) with a mean rate of increase of 0.10 Tg N yr⁻² (0.08–0.12 Tg N yr⁻²) (Fig. 11a).
<figure><img src="image_11.png" /></figure>

**Figure 10.** Changes in perturbed N₂O fluxes from changes in climate, CO₂, and land cover during 1980–2020. In each panel, the line represents the mean N₂O emission of different estimates, and the shaded area shows minimum and maximum estimates.

<figure><img src="image_12.png" /></figure>

**Figure 11.** Annual global N₂O emissions during 1997–2020 estimated by four atmospheric inversions (TD models): (a) total global emission, (b) land emission, and (c) ocean emission.

TD land emission

The estimates derived from the four inversions show that the land-based emission is the dominant source of N₂O emissions, over ocean sources, and the long-term average land N₂O emission during 1997–2020 was 13.7 Tg N yr⁻¹ (minimum: 12.6 Tg N yr⁻¹; maximum: 15.0 Tg N yr⁻¹), contributing 80%–85% of the global N₂O emissions. Land sources dominated the interannual variability in global N₂O emissions and the trend (Fig. 11b). All TD models suggested a significant increasing trend in land N₂O emissions during the study period from 1997 to 2020 (p < 0.05), with increase rates ranging from 0.09 to 0.13 Tg N yr⁻², which were higher than the increase rates of prior fluxes (mean: 0.04 Tg N yr⁻²; range: 0.00–0.08 Tg N yr⁻²).

TD ocean emission

The magnitude of N₂O emissions from oceans is much smaller than that from land (Fig. 11c). The mean ocean N₂O emission during 1997–2020 derived from four inversion models was 2.9 Tg N yr⁻¹, ranging from a minimum of 2.7 Tg N yr⁻¹ to a maximum of 3.3 Tg N yr⁻¹. The estimates of MIROC4 were much higher than the estimates of other models. The four inversions show divergent interannual variability, and none suggested a significant trend. The TD estimates of ocean N₂O emission are much smaller than the values estimated by four ocean biogeochemical models, with a global mean value fluctuating between 3.4 and 3.8 Tg N yr⁻¹ during 1980–2020.
<figure><img src="image_11.png" /><figcaption>**Figure 10.** Changes in perturbed N₂O fluxes from changes in climate, CO₂, and land cover during 1980–2020. In each panel, the line represents the mean N₂O emission of different estimates, and the shaded area shows minimum and maximum estimates.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 11.** Annual global N₂O emissions during 1997–2020 estimated by four atmospheric inversions (TD models): (a) total global emission, (b) land emission, and (c) ocean emission.</figcaption></figure>

TD land emission

The estimates derived from the four inversions show that the land-based emission is the dominant source of N₂O emissions, over ocean sources, and the long-term average land N₂O emission during 1997–2020 was 13.7 Tg N yr⁻¹ (minimum: 12.6 Tg N yr⁻¹; maximum: 15.0 Tg N yr⁻¹), contributing 80%–85% of the global N₂O emissions. Land sources dominated the interannual variability in global N₂O emissions and the trend (Fig. 11b). All TD models suggested a significant increasing trend in land N₂O emissions during the study period from 1997 to 2020 (p < 0.05), with increase rates ranging from 0.09 to 0.13 Tg N yr⁻², which were higher than the increase rates of prior fluxes (mean: 0.04 Tg N yr⁻²; range: 0.00–0.08 Tg N yr⁻²).

TD ocean emission

The magnitude of N₂O emissions from oceans is much smaller than that from land (Fig. 11c). The mean ocean N₂O emission during 1997–2020 derived from four inversion models was 2.9 Tg N yr⁻¹, ranging from a minimum of 2.7 Tg N yr⁻¹ to a maximum of 3.3 Tg N yr⁻¹. The estimates of MIROC4 were much higher than the estimates of other models. The four inversions show divergent interannual variability, and none suggested a significant trend. The TD estimates of ocean N₂O emission are much smaller than the values estimated by four ocean biogeochemical models, with a global mean value fluctuating between 3.4 and 3.8 Tg N yr⁻¹ during 1980–2020.
<figure><img src="image_13.png" /><figcaption>**Figure 12.** Global stratospheric N₂O sink estimated by atmospheric inversions and a satellite and photolysis model during 1997–2020.</figcaption></figure>

### 4.3.2 TD stratospheric sink

The four inversions have comparable magnitudes of global stratospheric N₂O sink (via photolysis and oxidation by the electronically excited atomic oxygen, O(¹D), in the stratosphere), with an average value of 12.4 Tg N yr⁻¹ (minimum and maximum of 12.2 and 12.7 Tg N yr⁻¹, respectively) for 2000–2020 (Fig. 12). All four inversions found that the global stratospheric N₂O sink increased during 1997–2020 (Fig. 13) in proportion to the growing atmospheric N₂O abundance, with an average rate of increase of 0.05 Tg N yr⁻² (0.03–0.07 Tg N yr⁻²). Differences among the estimates decreased after 2000, likely due to improvements in observation coverage and accuracy but also possibly due to the decreasing influence of the initial mixing ratio fields, which differed among the inversion frameworks. Although the inversions show comparable trends in the sink, they differ with respect to their interannual variability.

We also provide an independent estimate of the stratospheric sink based on satellite observations and a photolysis model. This estimate likewise showed that the sink increased, from 12.8 Tg N yr⁻¹ in the 1990s to 14.0 Tg N yr⁻¹ in the 2010s (Table 3), with higher annual loss rates than estimated by the inversions and an average loss of 13.4 Tg N yr⁻¹ for 2005–2021. This estimate also showed large quasi-biennial interannual variability with an amplitude of 7%. More interestingly, over this time period, the abundance of N₂O in the middle stratosphere, where the greatest loss of N₂O occurs, was increasing at a rate of 5.0 ± 1.2% per decade, which is faster than the increase in the tropospheric abundance of 2.9 ± 0.0% per decade. This resulted in a greater loss of N₂O (i.e., more than proportionate to the mean atmospheric increase) and, thus, a decrease in the mean

atmospheric lifetime (burden divided by loss) of 2.1 ± 0.7% per decade, from 119.3 years in the 2000s to 117 years in the 2010s (Prather et al., 2023; also see Table 3). These changes are thought to be a result of an increase in the intensity of the Brewer–Dobson circulation (BDC), which would transport N₂O more rapidly from the troposphere into the midstratosphere. An increase in the intensity of BDC is predicted by climate models (Oberländer-Hayn et al., 2016). However, we note that none of the atmospheric inversions found a significant trend in the atmospheric lifetime (although the total loss increased; Fig. 12), and more research is needed to identify why there is this discrepancy.

### 4.4 Decadal patterns and trend in the global N₂O budget: comparisons between BU and TD approaches

BU approaches provide estimates of N₂O fluxes for the identified sources and sinks during 1980–2020, while TD approaches only provide the total net flux during 1997–2020. In the following analyses of the decadal global N₂O budget, the comparison between BU and TD approaches is only for total N₂O estimates. We rely on BU approaches to quantify all identified sources and sinks (Table 3, Fig. 1).

#### 4.4.1 Global N₂O budget in recent decade (2010–2019)

The BU and TD approaches give remarkably consistent estimates of global total N₂O emissions in the 2010s, with values of 18.2 (10.6–25.9) Tg N yr⁻¹ and 17.4 (15.8–19.2) Tg N yr⁻¹ (Fig. 1, Table 3), respectively. However, the BU estimate shows a large uncertainty range, in part because of the spread of estimates from process-based models. TD approaches estimate that the stratospheric sink (i.e., N₂O losses via photolysis and reaction with O(¹D) in the stratosphere) for the 2010s was 12.6 (12.3–12.9) Tg N yr⁻¹. However, the atmospheric sink estimate based on satellite observations and a photolysis model for the 2010s was 13.4 (12.3–14.5) Tg N yr⁻¹. The imbalance of sources and sinks of N₂O derived from the averaged BU and TD estimates is 4.7 Tg N yr⁻¹. This imbalance agrees well with the observed increase in atmospheric abundance of N₂O between 2010 and 2019 of 4.6 (4.5–4.7) Tg N yr⁻¹. Based on the BU-based estimates, natural sources contributed 65% of total emissions (11.8, 7.3–15.9 Tg N yr⁻¹) during this period. Specifically, the natural soil flux contributed the most, with the decadal mean of 6.4 (3.9–8.6) Tg N yr⁻¹, followed by the open-ocean emissions (3.5, 2.5–4.7 Tg N yr⁻¹), shelf emissions (1.2, 0.6–1.6 Tg N yr⁻¹), lightning and atmospheric production (0.6, 0.3–1.2 Tg N yr⁻¹), and natural emissions from inland waters and estuaries (0.1, 0.0–0.1 Tg N yr⁻¹) (Fig. 1).

Anthropogenic sources contributed, on average, 35% to the total N₂O emissions (6.5, 3.2–10.0 Tg N yr⁻¹) in the 2010s. Direct emissions from nitrogen additions in agriculture were 3.6 (2.7–4.8) Tg N yr⁻¹, contributing 56% of
<figure><img src="image_13.png" /><figcaption>**Figure 12.** Global stratospheric N₂O sink estimated by atmospheric inversions and a satellite and photolysis model during 1997–2020.</figcaption></figure>

### 4.3.2 TD stratospheric sink

The four inversions have comparable magnitudes of global stratospheric N₂O sink (via photolysis and oxidation by the electronically excited atomic oxygen, O(¹D), in the stratosphere), with an average value of 12.4 Tg N yr⁻¹ (minimum and maximum of 12.2 and 12.7 Tg N yr⁻¹, respectively) for 2000–2020 (Fig. 12). All four inversions found that the global stratospheric N₂O sink increased during 1997–2020 (Fig. 13) in proportion to the growing atmospheric N₂O abundance, with an average rate of increase of 0.05 Tg N yr⁻² (0.03–0.07 Tg N yr⁻²). Differences among the estimates decreased after 2000, likely due to improvements in observation coverage and accuracy but also possibly due to the decreasing influence of the initial mixing ratio fields, which differed among the inversion frameworks. Although the inversions show comparable trends in the sink, they differ with respect to their interannual variability.

We also provide an independent estimate of the stratospheric sink based on satellite observations and a photolysis model. This estimate likewise showed that the sink increased, from 12.8 Tg N yr⁻¹ in the 1990s to 14.0 Tg N yr⁻¹ in the 2010s (Table 3), with higher annual loss rates than estimated by the inversions and an average loss of 13.4 Tg N yr⁻¹ for 2005–2021. This estimate also showed large quasi-biennial interannual variability with an amplitude of 7%. More interestingly, over this time period, the abundance of N₂O in the middle stratosphere, where the greatest loss of N₂O occurs, was increasing at a rate of 5.0 ± 1.2% per decade, which is faster than the increase in the tropospheric abundance of 2.9 ± 0.0% per decade. This resulted in a greater loss of N₂O (i.e., more than proportionate to the mean atmospheric increase) and, thus, a decrease in the mean

atmospheric lifetime (burden divided by loss) of 2.1 ± 0.7% per decade, from 119.3 years in the 2000s to 117 years in the 2010s (Prather et al., 2023; also see Table 3). These changes are thought to be a result of an increase in the intensity of the Brewer–Dobson circulation (BDC), which would transport N₂O more rapidly from the troposphere into the midstratosphere. An increase in the intensity of BDC is predicted by climate models (Oberländer-Hayn et al., 2016). However, we note that none of the atmospheric inversions found a significant trend in the atmospheric lifetime (although the total loss increased; Fig. 12), and more research is needed to identify why there is this discrepancy.

### 4.4 Decadal patterns and trend in the global N₂O budget: comparisons between BU and TD approaches

BU approaches provide estimates of N₂O fluxes for the identified sources and sinks during 1980–2020, while TD approaches only provide the total net flux during 1997–2020. In the following analyses of the decadal global N₂O budget, the comparison between BU and TD approaches is only for total N₂O estimates. We rely on BU approaches to quantify all identified sources and sinks (Table 3, Fig. 1).

#### 4.4.1 Global N₂O budget in recent decade (2010–2019)

The BU and TD approaches give remarkably consistent estimates of global total N₂O emissions in the 2010s, with values of 18.2 (10.6–25.9) Tg N yr⁻¹ and 17.4 (15.8–19.2) Tg N yr⁻¹ (Fig. 1, Table 3), respectively. However, the BU estimate shows a large uncertainty range, in part because of the spread of estimates from process-based models. TD approaches estimate that the stratospheric sink (i.e., N₂O losses via photolysis and reaction with O(¹D) in the stratosphere) for the 2010s was 12.6 (12.3–12.9) Tg N yr⁻¹. However, the atmospheric sink estimate based on satellite observations and a photolysis model for the 2010s was 13.4 (12.3–14.5) Tg N yr⁻¹. The imbalance of sources and sinks of N₂O derived from the averaged BU and TD estimates is 4.7 Tg N yr⁻¹. This imbalance agrees well with the observed increase in atmospheric abundance of N₂O between 2010 and 2019 of 4.6 (4.5–4.7) Tg N yr⁻¹. Based on the BU-based estimates, natural sources contributed 65% of total emissions (11.8, 7.3–15.9 Tg N yr⁻¹) during this period. Specifically, the natural soil flux contributed the most, with the decadal mean of 6.4 (3.9–8.6) Tg N yr⁻¹, followed by the open-ocean emissions (3.5, 2.5–4.7 Tg N yr⁻¹), shelf emissions (1.2, 0.6–1.6 Tg N yr⁻¹), lightning and atmospheric production (0.6, 0.3–1.2 Tg N yr⁻¹), and natural emissions from inland waters and estuaries (0.1, 0.0–0.1 Tg N yr⁻¹) (Fig. 1).

Anthropogenic sources contributed, on average, 35% to the total N₂O emissions (6.5, 3.2–10.0 Tg N yr⁻¹) in the 2010s. Direct emissions from nitrogen additions in agriculture were 3.6 (2.7–4.8) Tg N yr⁻¹, contributing 56% of
<table><caption>Table 3. The global N₂O budget (in Tg N yr⁻¹) for the 1980s, the 1990s, the 2000s, the 2010s, and the year 2020.</caption><thead><tr><th></th><th></th><th>1980–1989</th><th>1990–1999</th><th>2000–2009</th><th>2010–2019</th><th>2020</th></tr></thead><tbody><tr><td rowspan="5">Anthropogenic sources (BU)</td><td></td><td>Mean (min, max)</td><td>Mean (min, max)</td><td>Mean (min, max)</td><td>Mean (min, max)</td><td>Mean (min, max)</td></tr><tr><td rowspan="4">Agricultural</td><td>Direct soil emissions</td><td>1.2 (1.1, 1.3)</td><td>1.5 (1.2, 1.6)</td><td>1.7 (1.4, 2.0)</td><td>2.0 (1.6, 2.4)</td><td>2.1 (1.7, 2.6)</td></tr><tr><td>Manure left on pasture</td><td>0.9 (0.5, 1.3)</td><td>1.1 (0.6, 1.4)</td><td>1.2 (0.7, 1.6)</td><td>1.3 (0.8, 1.8)</td><td>1.4 (0.9, 1.9)</td></tr><tr><td>Manure management</td><td>0.2 (0.2, 0.3)</td><td>0.3 (0.2, 0.3)</td><td>0.2 (0.2, 0.3)</td><td>0.3 (0.2, 0.3)</td><td>0.3 (0.2, 0.3)</td></tr><tr><td>Aquaculture</td><td>0.0 (0.0, 0.0)</td><td>0.0 (0.0, 0.1)</td><td>0.1 (0.0, 0.2)</td><td>0.1 (0.0, 0.3)</td><td>0.1 (0.0, 0.3)</td></tr><tr><td></td><td>Subtotal</td><td>2.4 (1.8, 3.0)</td><td>2.8 (2.1, 3.4)</td><td>3.2 (2.3, 4.0)</td><td>3.6 (2.7, 4.8)</td><td>3.9 (2.9, 5.1)</td></tr><tr><td rowspan="4">Other direct anthropogenic sources</td><td>Fossil fuels and industry</td><td>1.0 (1.0, 1.0)</td><td>1.0 (0.9, 1.1)</td><td>1.1 (1.0, 1.1)</td><td>1.1 (1.0, 1.2)</td><td>1.1 (1.0, 1.1)</td></tr><tr><td>Waste and wastewater</td><td>0.1 (0.1, 0.1)</td><td>0.2 (0.2, 0.2)</td><td>0.2 (0.2, 0.2)</td><td>0.3 (0.3, 0.3)</td><td>0.3 (0.3, 0.3)</td></tr><tr><td>Biomass burning</td><td>0.9 (0.5, 1.2)</td><td>0.9 (0.5, 1.2)</td><td>0.8 (0.5, 1.0)</td><td>0.8 (0.5, 1.0)</td><td>0.8 (0.5, 0.9)</td></tr><tr><td>Subtotal</td><td>2.0 (1.7, 2.4)</td><td>2.1 (1.6, 2.4)</td><td>2.1 (1.8, 2.4)</td><td>2.1 (1.8, 2.4)</td><td>2.1 (1.7, 2.4)</td></tr><tr><td rowspan="4">Indirect emissions from anthropogenic nitrogen additions</td><td>Inland waters, estuaries, and coastal vegetation</td><td>0.3 (0.1, 0.4)</td><td>0.3 (0.1, 0.4)</td><td>0.4 (0.1, 0.5)</td><td>0.4 (0.1, 0.5)</td><td>0.4 (0.1, 0.6)</td></tr><tr><td>Atmospheric nitrogen deposition on land</td><td>0.5 (0.5, 0.6)</td><td>0.6 (0.5, 0.7)</td><td>0.7 (0.6, 0.8)</td><td>0.7 (0.6, 0.8)</td><td>0.8 (0.6, 0.9)</td></tr><tr><td>Atmospheric nitrogen deposition on ocean</td><td>0.1 (0.1, 0.2)</td><td>0.1 (0.1, 0.2)</td><td>0.1 (0.1, 0.2)</td><td>0.1 (0.1, 0.2)</td><td>0.1 (0.1, 0.2)</td></tr><tr><td>Subtotal</td><td>0.9 (0.7, 1.2)</td><td>1.1 (0.8, 1.4)</td><td>1.1 (0.8, 1.4)</td><td>1.2 (0.9, 1.6)</td><td>1.3 (0.9, 1.6)</td></tr><tr><td rowspan="4">Perturbed fluxes from climate, CO₂, and land-cover change</td><td>CO₂ effect</td><td>-0.4 (-0.8, 0.2)</td><td>-0.5 (-1.0, 0.2)</td><td>-0.6 (-1.3, 0.3)</td><td>-0.7 (-1.5, 0.3)</td><td>-0.8 (-1.6, 0.3)</td></tr><tr><td>Climate effect</td><td>0.4 (0.1, 0.8)</td><td>0.5 (0.2, 0.7)</td><td>0.6 (0.1, 0.8)</td><td>0.7 (0.2, 1.2)</td><td>0.9 (0.4, 1.8)</td></tr><tr><td>Post-deforestation pulse effect</td><td>0.8 (0.6, 1.1)</td><td>0.9 (0.6, 1.2)</td><td>0.9 (0.5, 1.3)</td><td>0.9 (0.4, 1.3)</td><td>0.8 (0.4, 1.3)</td></tr><tr><td>Long-term effect of reduced mature forest area</td><td>-1.2 (-1.1, -1.4)</td><td>-1.3 (-1.2, -1.5)</td><td>-1.4 (-1.2, -1.5)</td><td>-1.4 (-1.3, -1.6)</td><td>-1.5 (-1.4, -1.6)</td></tr><tr><td></td><td>Subtotal</td><td>-0.4 (-1.1, 0.7)</td><td>-0.5 (-1.4, 0.6)</td><td>-0.6 (-1.9, 0.8)</td><td>-0.6 (-2.1, 1.2)</td><td>-0.6 (-2.2, 1.8)</td></tr><tr><td colspan="2">Anthropogenic total</td><td>5.0 (3.0, 7.3)</td><td>5.5 (3.1, 7.9)</td><td>5.8 (3.1, 8.6)</td><td>6.5 (3.2, 10.0)</td><td>6.7 (3.3, 10.9)</td></tr><tr><td colspan="2">Natural fluxes (BU)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td colspan="2">Natural soil baseline</td><td>6.4 (3.9, 8.5)</td><td>6.4 (3.8, 8.6)</td><td>6.4 (3.9, 8.5)</td><td>6.4 (3.9, 8.6)</td><td>6.4 (3.8, 8.7)</td></tr><tr><td colspan="2">Open-ocean baseline</td><td>3.7 (3.0, 4.6)</td><td>3.6 (2.8, 4.5)</td><td>3.6 (2.7, 4.7)</td><td>3.5 (2.5, 4.7)</td><td>3.5 (2.5, 4.7)</td></tr><tr><td colspan="2">Continental shelves</td><td>1.2 (0.6, 1.6)</td><td>1.2 (0.6, 1.6)</td><td>1.2 (0.6, 1.6)</td><td>1.2 (0.6, 1.6)</td><td>1.2 (0.6, 1.6)</td></tr><tr><td colspan="2">Natural (inland waters, estuaries, coastal vegetation)</td><td>0.1 (0.0, 0.1)</td><td>0.1 (0.0, 0.1)</td><td>0.1 (0.0, 0.1)</td><td>0.1 (0.0, 0.1)</td><td>0.1 (0.0, 0.1)</td></tr><tr><td colspan="2">Lightning and atmospheric production</td><td>0.6 (0.3, 1.2)</td><td>0.6 (0.3, 1.2)</td><td>0.6 (0.3, 1.2)</td><td>0.6 (0.3, 1.2)</td><td>0.6 (0.3, 1.2)</td></tr><tr><td colspan="2">Surface sink (soils and wetlands)</td><td>0.0 (0.0, -0.3)</td><td>0.0 (0.0, -0.3)</td><td>0.0 (0.0, -0.3)</td><td>0.0 (0.0, -0.3)</td><td>0.0 (0.0, -0.3)</td></tr><tr><td colspan="2">Natural total</td><td>12.0 (7.9, 15.8)</td><td>11.9 (7.7, 15.8)</td><td>11.9 (7.5, 15.9)</td><td>11.8 (7.3, 15.9)</td><td>11.8 (7.4, 16.1)</td></tr><tr><td colspan="2">BU total net flux (source)</td><td>16.9 (10.9, 23.1)</td><td>17.4 (10.7, 23.6)</td><td>17.7 (10.6, 24.5)</td><td>18.2 (10.6, 25.9)</td><td>18.5 (10.6, 27.0)</td></tr><tr><td colspan="2">TD ocean</td><td></td><td></td><td>2.8 (2.6, 3.2)</td><td>3.0 (2.7, 3.3)</td><td>2.7 (2.7, 2.7)</td></tr><tr><td colspan="2">TD land</td><td></td><td></td><td>13.2 (12.1, 14.3)</td><td>14.5 (13.0, 15.9)</td><td>14.3 (13.9, 14.7)</td></tr><tr><td colspan="2">TD total net flux (source)</td><td></td><td></td><td>16.0 (14.9, 17.5)</td><td>17.4 (15.8, 19.2)</td><td>17.0 (16.6, 17.4)</td></tr><tr><td colspan="2">TD stratospheric sink</td><td></td><td></td><td>12.2 (11.7, 12.6)</td><td>12.6 (12.3, 12.9)</td><td>12.9 (12.5, 13.2)</td></tr></tbody></table>
<table><caption>Table 3. The global N₂O budget (in Tg N yr⁻¹) for the 1980s, the 1990s, the 2000s, the 2010s, and the year 2020.</caption><thead><tr><th></th><th></th><th>1980–1989</th><th>1990–1999</th><th>2000–2009</th><th>2010–2019</th><th>2020</th></tr></thead><tbody><tr><td rowspan="5">Anthropogenic sources (BU)</td><td></td><td>Mean (min, max)</td><td>Mean (min, max)</td><td>Mean (min, max)</td><td>Mean (min, max)</td><td>Mean (min, max)</td></tr><tr><td rowspan="4">Agricultural</td><td>Direct soil emissions</td><td>1.2 (1.1, 1.3)</td><td>1.5 (1.2, 1.6)</td><td>1.7 (1.4, 2.0)</td><td>2.0 (1.6, 2.4)</td><td>2.1 (1.7, 2.6)</td></tr><tr><td>Manure left on pasture</td><td>0.9 (0.5, 1.3)</td><td>1.1 (0.6, 1.4)</td><td>1.2 (0.7, 1.6)</td><td>1.3 (0.8, 1.8)</td><td>1.4 (0.9, 1.9)</td></tr><tr><td>Manure management</td><td>0.2 (0.2, 0.3)</td><td>0.3 (0.2, 0.3)</td><td>0.2 (0.2, 0.3)</td><td>0.3 (0.2, 0.3)</td><td>0.3 (0.2, 0.3)</td></tr><tr><td>Aquaculture</td><td>0.0 (0.0, 0.0)</td><td>0.0 (0.0, 0.1)</td><td>0.1 (0.0, 0.2)</td><td>0.1 (0.0, 0.3)</td><td>0.1 (0.0, 0.3)</td></tr><tr><td></td><td>Subtotal</td><td>2.4 (1.8, 3.0)</td><td>2.8 (2.1, 3.4)</td><td>3.2 (2.3, 4.0)</td><td>3.6 (2.7, 4.8)</td><td>3.9 (2.9, 5.1)</td></tr><tr><td rowspan="4">Other direct anthropogenic sources</td><td>Fossil fuels and industry</td><td>1.0 (1.0, 1.0)</td><td>1.0 (0.9, 1.1)</td><td>1.1 (1.0, 1.1)</td><td>1.1 (1.0, 1.2)</td><td>1.1 (1.0, 1.1)</td></tr><tr><td>Waste and wastewater</td><td>0.1 (0.1, 0.1)</td><td>0.2 (0.2, 0.2)</td><td>0.2 (0.2, 0.2)</td><td>0.3 (0.3, 0.3)</td><td>0.3 (0.3, 0.3)</td></tr><tr><td>Biomass burning</td><td>0.9 (0.5, 1.2)</td><td>0.9 (0.5, 1.2)</td><td>0.8 (0.5, 1.0)</td><td>0.8 (0.5, 1.0)</td><td>0.8 (0.5, 0.9)</td></tr><tr><td>Subtotal</td><td>2.0 (1.7, 2.4)</td><td>2.1 (1.6, 2.4)</td><td>2.1 (1.8, 2.4)</td><td>2.1 (1.8, 2.4)</td><td>2.1 (1.7, 2.4)</td></tr><tr><td rowspan="4">Indirect emissions from anthropogenic nitrogen additions</td><td>Inland waters, estuaries, and coastal vegetation</td><td>0.3 (0.1, 0.4)</td><td>0.3 (0.1, 0.4)</td><td>0.4 (0.1, 0.5)</td><td>0.4 (0.1, 0.5)</td><td>0.4 (0.1, 0.6)</td></tr><tr><td>Atmospheric nitrogen deposition on land</td><td>0.5 (0.5, 0.6)</td><td>0.6 (0.5, 0.7)</td><td>0.7 (0.6, 0.8)</td><td>0.7 (0.6, 0.8)</td><td>0.8 (0.6, 0.9)</td></tr><tr><td>Atmospheric nitrogen deposition on ocean</td><td>0.1 (0.1, 0.2)</td><td>0.1 (0.1, 0.2)</td><td>0.1 (0.1, 0.2)</td><td>0.1 (0.1, 0.2)</td><td>0.1 (0.1, 0.2)</td></tr><tr><td>Subtotal</td><td>0.9 (0.7, 1.2)</td><td>1.1 (0.8, 1.4)</td><td>1.1 (0.8, 1.4)</td><td>1.2 (0.9, 1.6)</td><td>1.3 (0.9, 1.6)</td></tr><tr><td rowspan="4">Perturbed fluxes from climate, CO₂, and land-cover change</td><td>CO₂ effect</td><td>-0.4 (-0.8, 0.2)</td><td>-0.5 (-1.0, 0.2)</td><td>-0.6 (-1.3, 0.3)</td><td>-0.7 (-1.5, 0.3)</td><td>-0.8 (-1.6, 0.3)</td></tr><tr><td>Climate effect</td><td>0.4 (0.1, 0.8)</td><td>0.5 (0.2, 0.7)</td><td>0.6 (0.1, 0.8)</td><td>0.7 (0.2, 1.2)</td><td>0.9 (0.4, 1.8)</td></tr><tr><td>Post-deforestation pulse effect</td><td>0.8 (0.6, 1.1)</td><td>0.9 (0.6, 1.2)</td><td>0.9 (0.5, 1.3)</td><td>0.9 (0.4, 1.3)</td><td>0.8 (0.4, 1.3)</td></tr><tr><td>Long-term effect of reduced mature forest area</td><td>-1.2 (-1.1, -1.4)</td><td>-1.3 (-1.2, -1.5)</td><td>-1.4 (-1.2, -1.5)</td><td>-1.4 (-1.3, -1.6)</td><td>-1.5 (-1.4, -1.6)</td></tr><tr><td></td><td>Subtotal</td><td>-0.4 (-1.1, 0.7)</td><td>-0.5 (-1.4, 0.6)</td><td>-0.6 (-1.9, 0.8)</td><td>-0.6 (-2.1, 1.2)</td><td>-0.6 (-2.2, 1.8)</td></tr><tr><td colspan="2">Anthropogenic total</td><td>5.0 (3.0, 7.3)</td><td>5.5 (3.1, 7.9)</td><td>5.8 (3.1, 8.6)</td><td>6.5 (3.2, 10.0)</td><td>6.7 (3.3, 10.9)</td></tr><tr><td colspan="2">Natural fluxes (BU)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td colspan="2">Natural soil baseline</td><td>6.4 (3.9, 8.5)</td><td>6.4 (3.8, 8.6)</td><td>6.4 (3.9, 8.5)</td><td>6.4 (3.9, 8.6)</td><td>6.4 (3.8, 8.7)</td></tr><tr><td colspan="2">Open-ocean baseline</td><td>3.7 (3.0, 4.6)</td><td>3.6 (2.8, 4.5)</td><td>3.6 (2.7, 4.7)</td><td>3.5 (2.5, 4.7)</td><td>3.5 (2.5, 4.7)</td></tr><tr><td colspan="2">Continental shelves</td><td>1.2 (0.6, 1.6)</td><td>1.2 (0.6, 1.6)</td><td>1.2 (0.6, 1.6)</td><td>1.2 (0.6, 1.6)</td><td>1.2 (0.6, 1.6)</td></tr><tr><td colspan="2">Natural (inland waters, estuaries, coastal vegetation)</td><td>0.1 (0.0, 0.1)</td><td>0.1 (0.0, 0.1)</td><td>0.1 (0.0, 0.1)</td><td>0.1 (0.0, 0.1)</td><td>0.1 (0.0, 0.1)</td></tr><tr><td colspan="2">Lightning and atmospheric production</td><td>0.6 (0.3, 1.2)</td><td>0.6 (0.3, 1.2)</td><td>0.6 (0.3, 1.2)</td><td>0.6 (0.3, 1.2)</td><td>0.6 (0.3, 1.2)</td></tr><tr><td colspan="2">Surface sink (soils and wetlands)</td><td>0.0 (0.0, -0.3)</td><td>0.0 (0.0, -0.3)</td><td>0.0 (0.0, -0.3)</td><td>0.0 (0.0, -0.3)</td><td>0.0 (0.0, -0.3)</td></tr><tr><td colspan="2">Natural total</td><td>12.0 (7.9, 15.8)</td><td>11.9 (7.7, 15.8)</td><td>11.9 (7.5, 15.9)</td><td>11.8 (7.3, 15.9)</td><td>11.8 (7.4, 16.1)</td></tr><tr><td colspan="2">BU total net flux (source)</td><td>16.9 (10.9, 23.1)</td><td>17.4 (10.7, 23.6)</td><td>17.7 (10.6, 24.5)</td><td>18.2 (10.6, 25.9)</td><td>18.5 (10.6, 27.0)</td></tr><tr><td colspan="2">TD ocean</td><td></td><td></td><td>2.8 (2.6, 3.2)</td><td>3.0 (2.7, 3.3)</td><td>2.7 (2.7, 2.7)</td></tr><tr><td colspan="2">TD land</td><td></td><td></td><td>13.2 (12.1, 14.3)</td><td>14.5 (13.0, 15.9)</td><td>14.3 (13.9, 14.7)</td></tr><tr><td colspan="2">TD total net flux (source)</td><td></td><td></td><td>16.0 (14.9, 17.5)</td><td>17.4 (15.8, 19.2)</td><td>17.0 (16.6, 17.4)</td></tr><tr><td colspan="2">TD stratospheric sink</td><td></td><td></td><td>12.2 (11.7, 12.6)</td><td>12.6 (12.3, 12.9)</td><td>12.9 (12.5, 13.2)</td></tr></tbody></table>
<table><caption>**Table 3. Continued.**</caption><thead><tr><th></th><th>1980–1989</th><th>1990–1999</th><th>2000–2009</th><th>2010–2019</th><th>2020</th></tr></thead><tbody><tr><td>Atmospheric chemical sink<sup>a</sup></td><td></td><td>12.8 (11.7, 13.8)</td><td>13.4 (12.3, 14.5)</td><td>14.0 (12.8, 15.2)</td><td></td></tr><tr><td>Change in atmospheric abundance<sup>b</sup></td><td></td><td>3.6 (3.6, 3.7)</td><td>4.6 (4.5, 4.7)</td><td>6.4 (6.2, 6.5)</td><td></td></tr><tr><td>Atmospheric burden</td><td></td><td>1528</td><td>1570</td><td>1592</td><td></td></tr><tr><td>Lifetime (“obs” from MLS)</td><td></td><td>119.3</td><td>117</td><td></td><td></td></tr></tbody></table>

Notes: BU estimates include four categories of anthropogenic source and one category for natural sources and sinks. The sources and sinks of N₂O are given in teragrams of nitrogen per year (Tg N yr⁻¹). The atmospheric burden is given in teragrams of nitrogen (Tg N). Detailed information on calculating each subcategory is shown in tables in the Supplement. a Calculated from satellite observations with a photolysis model (about 1% of this sink occurs in the troposphere).

b Calculated from the combined NOAA and AGAGE record of surface N₂O and adopting the uncertainty of the IPCC Fifth Assessment Report (Chap. 6), with a conversion factor of 4.79 Tg N ppb⁻¹.

<figure><img src="image_14.png" /><figcaption>**Figure 13.** Comparison of global and regional N₂O emissions estimated using BU and TD approaches. The 18 regions include the United States (USA), Canada (CAN), Central America (CAM), northern South America (NSA), Brazil (BRA), southwestern South America (SSA), Europe (EU), northern Africa (NAF), equatorial Africa (EQAF), southern Africa (SAF), Russia (RUS), Central Asia (CAS), the Middle East (MIDE), China (CHN), Korea and Japan (KAJ), South Asia (SAS), Southeast Asia (SEAS), and Australasia (AUS). The blue lines represent the mean N₂O emission from BU methods, and the shaded areas show minimum and maximum estimates; the red lines represent the mean N₂O emission from TD methods, and the shaded areas show minimum and maximum estimates.</figcaption></figure>

the total anthropogenic emissions (Table 3). Emissions from other direct anthropogenic sources made the second largest contribution, with a decadal mean of 2.1 (1.8–2.4) Tg N yr⁻¹. Indirect emissions from anthropogenic nitrogen additions contributed 19% of the total anthropogenic emissions, with a decadal mean of 1.2 (0.9–1.6) Tg N yr⁻¹. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions (−0.6, −2.1 to 1.2 Tg N yr⁻¹), mainly be-

cause of the negative effects of reduced mature forest area (−1.4, −1.6 to −1.3 Tg N yr⁻¹) and increasing CO₂ concentration (−0.7, −1.5 to −0.3 Tg N yr⁻¹).

### 4.4.2 Decadal trend in the global N₂O budget

Global N₂O emissions estimated by the BU and TD approaches were comparable in magnitude during the over-