Global nitrous oxide budget (1980–2020)

<jats:p>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).                     </jats:p>

section_content: core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), our global N 2 O budget incorporates both natural and anthropogenic sources and sinks and accounts for the interactions between nitrogen additions and the biogeochemical processes that control N 2 O emissions. We use bottomup (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 N 2 O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N 2 O emissions have increased 40 % (or 1. 9 Tg N yr -1 ) in the past 4 decades. 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 N 2 O 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 N 2 O 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 N 2 O 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).

section_content: The global N 2 O budget has been perturbed through direct and indirect anthropogenic emissions as well as through perturbations to the natural N 2 O sources and sinks via climate change, increasing atmospheric CO 2, and land-cover change. Ice core data show a relatively constant tropospheric N 2 O 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 N 2 O 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 N 2 O mole fraction in 2022 is higher than at any time in the last 800 000 years. The current growth rate of atmospheric N 2 O 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 (lowerupper 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 N 2 O 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 N 2 O emissions have significantly increased in the last 4 decades. The magnitudes of global N 2 O 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 N 2 O 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 N 2 O 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 N 2 O 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 CO 2, and land cover were negative and caused a reduction in N 2 O 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 N 2 O emissions H. Tian et al. : Global nitrous oxide budget (1980 -2020) were relatively stable. According to the BU approaches, the total amount of global natural N 2 O 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 N 2 O 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 N 2 O 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, CO 2, and land-cover changes had a net negative effect (i. e., reduced) on N 2 O emissions (-0. 6, -2. 1 to 1. 2 Tg N yr -1 ). Increased CO 2 and land conversion from mature forest reduced N 2 O emissions, but climate change resulted in N 2 O 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 N 2 O 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 N 2 O 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 N 2 O burden, from 1527 (1504-1545) Tg N in 2000 Tg N in -2009 Tg N in to 1606 Tg N in (1592 Tg N in -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 N 2 O 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 N 2 O 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 N 2 O 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 N 2 O production. 

4. The N 2 O fluxes from atmospheric CO 2, mature forest conversion, and biomass burning are poorly understood and quantified. The relatively sparse distribution of current N 2 O 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 N 2 O flux monitoring and analysis network to better resolve spatiotemporal patterns and reduce uncertainties in N 2 O emissions. Such a development is a requirement to better constrain the future contribution of N 2 O to climate change and guide policy choices to reduce N 2 O emissions. for 2010-2019) as follows: reddirect 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, CO 2, or land cover; and green -emissions from natural sources. The anthropogenic and natural N 2 O 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 N 2 O budget, as reflected in each of the terms. The N 2 O sources and sinks are given in teragrams of nitrogen per year (Tg N yr -1 ). © The Global Carbon Project.
section_name: Executive summary

section_content: 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 (N 2 O), 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 N 2 O 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 N 2 O 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 N 2 O: 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 the 2010s, with significant seasonal and interannual variation. In 2020, the N 2 O 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 N 2 O emissions, observed atmospheric N 2 O 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). N 2 O 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 N 2 O 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 NO x due to rising levels of N 2 O can lead to incremental stratospheric O 3 loss, it is unlikely to cause catastrophic ozone loss the way that anthropogenic halogens did, as stratospheric NO x from N 2 O has offset halogen-catalyzed stratospheric ozone loss through various buffering reactions, e. g., the formation of halogen reservoir species like ClONO 2 (Wennberg et al., 1994; Nevison et al., 1999; Ravishankara et al., 2009). Significant reductions in N 2 O emissions are required along with net CO 2 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 N 2 O emissions need to be reduced by 22 %, 18 %, and 11 %, respectively, by 2050 (Rogelj and Lamboll, 2024). In addition, N 2 O 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 N 2 O mitigation will contribute to achieving a set of United Nations Sustainable Development Goals (United Nations, 2016). 

Nitrification and denitrification are the two key microbial processes controlling N 2 O production (Butterbach-Bahl et al., 2013; Gruber and Galloway, 2008; Kuypers et al., 2018; Firestone and Davidson, 1989), making the largest contribution to global N 2 O emissions (Syakila and Kroeze, 2011; Tian et al., 2020) ; abiotic processes also play a role in the production of N 2 O. We categorize the processes governing N 2 O sources and sinks in 23 different categories (Fig. 3 ): ( 1 There is also a small amount of N 2 O emission from termite mounds, but such an N 2 O flux is not quantified in the current budget analysis due to limited data. 

Biogenic N 2 O emissions from land are regulated by multiple environmental factors, including soil moisture, temperature, oxygen status, pH, vegetation type, topography, atmospheric CO 2 concentration, and soil N and C availabil- ity (Butterbach-Bahl et al., 2013; Dijkstra et al., 2012; Li et al., 2020; Tian et al., 2016 Tian et al.,, 2019;; Yin et al., 2022; H. Yu et al., 2022). The effects of these environmental factors on N 2 O emissions have strong spatial and temporal heterogeneity, making upscaling field N 2 O measurements to regional and global scales difficult. Studies using atmospheric N 2 O inverse modeling suggest a greater source of N 2 O 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 N 2 O 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 N 2 O 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 N 2 O formation, consumption, emission, and absorption, and it helps to guide consistent estimations and comparisons of N 2 O budgets among regions and upscaling of regional budgets to the globe. N 2 O fluxes are grouped into two major categories based on the sources. 

The first category is natural N 2 O fluxes (blue arrows in Fig. 3 ), which are N 2 O fluxes in the absence of climate change and anthropogenic disturbances, including natural soil emissions, soil uptake, N 2 O 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 N 2 O 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 N 2 O 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. 

H. Tian et al. : Global nitrous oxide budget (1980 -2020) In the anthropogenic N 2 O fluxes category, we also consider N 2 O fluxes from the anthropogenic perturbations to climate, CO 2, and land use/land cover (hereafter perturbation fluxes). In terrestrial ecosystems, perturbation fluxes can be caused by increasing CO 2 concentration, climate change (e. g., warming-induced thawing of permafrost), and landuse change (e. g., converting natural lands to lands for human uses, such as croplands, mining, logging, and the postdeforestation pulse effect -the long-term effect of reduced mature forest area). N 2 O 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 N 2 O 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, N 2 O 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 N 2 O across global ecosystems. Prominently, anthropogenic N 2 O 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 N 2 O. 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 N 2 O 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 N 2 O 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 N 2 O 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 N 2 O, 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 N 2 O 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 (CO 2 -C), methane (CH 4 ), and (now) N 2 O 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.
section_name: Introduction

section_content: 3. 1 Definitions, terminology, and the unit of N 2 O sources and sinks

This study provides an estimation of the global N 2 O budget considering all quantifiable sources, sinks, and perturbations, resulting in a total of 21 N 2 O 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, CO 2, and land-cover change", which includes the effects of CO 2, 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 O 1 D). Our anthropogenic N 2 O 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, N 2 O fluxes are expressed in teragrams of N 2 O-N per year, where 1 Tg N 2 O-N 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 N 2 O 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 N 2 O 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.
section_name: Methodology and data

section_content: 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 N 2 O 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 N 2 O budgets (Saunois et al., 2020; Stavert et al., 2022; Tian et al., 2019).
section_name: Definition of regions

section_content: Four major methods are available to estimate large-scale N 2 O 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 N 2 O mixing ratios combined with atmospheric transport models, driven by meteorological fields, to estimate the emissions of N 2 O (Thompson et al., 2014). Atmospheric inversion models usually use Bayesian statistics, which, starting from a prior emission estimate, find the optimal N 2 O emissions (i. e., those that best agree with observed atmospheric N 2 O 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 N 2 O 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 N 2 O emissions from different sources. Emission-activity-and emission-factor-based inventories (method 2) use a prescribed emission factor (EF) to calculate N 2 O 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 N 2 O 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 N 2 O 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 N 2 O emissions. A random forest model was used to estimate global terrestrial background N 2 O emissions (Yin et al., 2022) and N 2 O 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 N 2 O 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 N 2 O 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 H. Tian et al. : Global nitrous oxide budget (1980 -2020) further improvement; and (2) they integrate various driving factors controlling soil N 2 O emissions, such as fertilizer use, atmospheric N deposition, land-use change, climate change, and atmospheric CO 2 concentration change and, thus, can disentangle the effects of different driving factors. Although multiple process-based models have estimated global soil N 2 O 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 N 2 O emissions, and the uncertainties in input data. The Global N 2 O Model Intercomparison Project (NMIP) was launched (Tian et al., 2018 (Tian et al.,, 2019) ) to develop a multi-model ensemble estimation of global soil N 2 O emissions during 1861-2016 and quantify the contributions of different driving factors. 

We consider global N 2 O 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/N 2 O 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 INVI-CAT (Wilson et al., 2014), PyVAR-CAMS (Thompson et al., 2014), MIROC4-ACTM (Patra et al., 2022), and GEOS-Chem (Wells et al., 2018).
section_name: Overview of methods used for global N 2 O budget synthesis

section_content: 3. 4. 1 Natural N 2 O 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 N 2 O 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 N 2 O 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 N 2 O 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). 

N 2 O emissions from continental shelves were calculated using one data-driven estimate and three high-resolution model estimates for various time periods (Resplandy et al., 2024, also see Sect. S7), namely an observation-based estimate that relied on a random forest (RF) algorithm to interpolate N 2 O data (Yang et al., 2020), based on a synthesis of over 158 000 observations of the N 2 O 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 oceanbiogeochemical 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 N 2 O fluxes were used. 

Estimates of natural N 2 O 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 N 2 O 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 N 2 O 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 NH 3 and NO 2. 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).
section_name: Model and inventory data synthesis

section_content: Agriculture N 2 O 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 N 2 O emissions from the average of two inventories (EDGAR v7. 0 and FAOSTAT), the SRNM and DLEM models, and the NMIP2 and DLEM models. 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).
section_name: Direct emissions from nitrogen additions (agriculture)

section_content: 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 UN-FCCC 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 N 2 O 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 (Tubiello et al.,, 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 N 2 O emissions from deforestation fires, forest fires, and peatland fires (Prosperi et al., 2020).
section_name: Emissions from other direct anthropogenic sources

section_content: 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 N 2 O 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 N 2 O 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 CO 2, and land-cover change). The results in Yao et al. (2020) suggested that 56 % of the total N 2 O 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 N 2 O emissions from inland waters, consistent with Tian et al. (2020). Seagrass, mangrove, and salt marsh N 2 O emissions were updated from Rosentreter et al. (2023). 

3. 4. 5 Perturbation of N 2 O fluxes from climate, CO 2, and land-cover change

The estimate of climate and CO 2 effects on emissions was based on eight NMIP2 models, and we used SH1-SH7 and SH1-SH8 to model the effects of CO 2 and climate on global terrestrial soil N 2 O emissions (Table 2 ), respectively. The effect of land-cover change on N 2 O dynamics includes the reduction due to the "Long-term effect of reduced mature forest area" and the additional emissions due to the "Postdeforestation 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 N 2 O 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 N 2 O 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).
section_name: Indirect emissions from anthropogenic N additions

section_content: N 2 O production in the atmosphere is a relatively small component of the global budget. N 2 O is produced by the gaseousphase oxidation of NH 3 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 NH 3, a significant portion of this source may be considered anthropogenic. Lightning production of NO x indirectly leads to N 2 O emission through its oxidation and subsequent deposition on land and the ocean. A recent study estimated the global lightning production of NO x 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 NO x, we then estimate a global source of N 2 O of 0. 05 (0. 02-0. 09) Tg N yr -1. There is also N 2 O production from N 2 + O( 1 D), which amounts to about 2 % of the atmospheric source in the stratosphere (Estupiñán et al., 2005).
section_name: Atmospheric production of reactive nitrogen

section_name: Atmospheric observation data synthesis

section_content: The monthly tropospheric N 2 O 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 Organization (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 N 2 O Observation Networks". 

The atmospheric burden and its rate of change during 1980-2020 were derived from mean maritime surface abundance (mole fraction) of N 2 O (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 N 2 O 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 %).
section_name: Atmospheric burden and trends from tropospheric observations

section_content: The NASA Aura Microwave Limb Sounder (MLS) satellite instrument has provided consistent global measurements of stratospheric N 2 O, O 3, and temperature (T ) since August 2004. These have been used with simple stratospheric chemistry models to calculate the monthly mean stratospheric loss of N 2 O due to photolysis and oxidation by O( 1 D) (Prather et al., 2015 (Prather et al.,, 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.
section_name: Atmospheric loss rates and trends from stratospheric observations

section_content: For the TD constraints on both land and ocean N 2 O 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 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 N 2 O in the stratosphere by photolysis and oxidation by O( 1 D) 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°).
section_name: Atmospheric inversion estimates of N 2 O emissions and losses

section_name: Results

section_name: Trends in atmospheric mole fractions and implied emissions

section_content: The three observation networks AGAGE, NOAA, and CSIRO show consistent growth in atmospheric N 2 O 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 N 2 O 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 N 2 O 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). 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 N 2 O 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, CO 2, and land-cover change led to a small increase in the N 2 O 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, N 2 O 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 N 2 O 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 ). FAO-STAT 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, FAO-STAT has a larger magnitude than EDGARv7. 0 (Fig. 7c ). According to the IMAGE-GNM aquaculture nutrient budget model, N 2 O 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 ).
section_name: Trends in atmospheric N 2 O mole fractions

section_content: Fossil fuel and industry emissions accounted for the largest proportion of N 2 O 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 N 2 O emissions from waste and wastewater. Although EDGAR/UNFCCC shows a larger magnitude than EGDARv7. 0, these two inventory estimates display sim- ilar growth rates (Fig. 8b ). There are large uncertainties in the magnitude and temporal trend in N 2 O 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.
section_name: Other direct anthropogenic sources

section_content: Global anthropogenic N 2 O 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.
section_name: Indirect emissions from anthropogenic nitrogen additions

section_content: The spread between different estimates (DLEM and the bookkeeping method) of the post-deforestation pulse effect increased from the 1980s to the 2010s. The postdeforestation 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, deforestation had a negative effect on global soil N 2 O emissions. However, most NMIP2 models suggested a positive effect of climate change on soil N 2 O 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 CO 2 concentration on soil N 2 O emissions through increasing nitrogen use efficiency and, hence, reducing soil N availability (Fig. 10d ). However, NMIP2 models have large discrepancies in the CO 2 fertilization effect on N 2 O emissions; ELM and ISAM suggested a positive effect, while all the other models suggest a negative effect.
section_name: Perturbation fluxes from climate, CO 2 , and land-cover change

section_content: 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 N 2 O 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 N 2 O 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 ).
section_name: Natural N 2 O sources

section_content: The natural soil N 2 O emission baseline represents the preindustrial soil N 2 O emissions derived from NMIP2 simulations, driven by potential vegetation/land cover and other environmental factors in the preindustrial period (1850). Global natural soil N 2 O emissions are estimated to be 6. 4 Tg N yr -1, and they account for 55 % of the total natural emissions. However, N 2 O 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 ).
section_name: Natural soil N 2 O emission baseline

section_content: We also estimated N 2 O emissions from the open oceans and continental shelves. Open ocean is the second largest source of natural N 2 O emissions, with a global mean value fluctuating between 3. 4 and 3. 8 Tg N yr -1 during 1980-2020. Openocean N 2 O 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-PISCESv2gas 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 N 2 O emissions that was not quantified in the previous global N 2 O budget (Tian et al., 2020). Global shelf N 2 O emis- 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).
section_name: Natural N 2 O emission baseline from the open ocean and continental shelves

section_content: Natural N 2 O 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 N 2 O and account for 48 % of the natural emissions from inland waters and estuaries. The global natural N 2 O emissions from lakes and estuaries were 0. 02 (0. 01-0. 03) and 0. 02 (0. 02-0. 03) Tg N yr -1, respectively.
section_name: Natural N 2 O emission from inland waters, estuaries, and coastal vegetation

section_content: The source of reactive N from lightning (and its contribution to N 2 O) and the direct production of N 2 O from NH 3 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 N 2 O 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.
section_name: Lightning, atmospheric production, and natural sinks

section_name: N 2 O sources and sinks: TD estimates

section_content: Ensemble estimates across the four atmospheric inversions show that the long-term average global N 2 O 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 N 2 O 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 ).
section_name: TD total source

section_content: The estimates derived from the four inversions show that the land-based emission is the dominant source of N 2 O emissions, over ocean sources, and the long-term average land N 2 O 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 N 2 O emissions. Land sources dominated the interannual variability in global N 2 O emissions and the trend (Fig. 11b ). All TD models suggested a significant increasing trend in land N 2 O 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 ).
section_name: TD land emission

section_content: The magnitude of N 2 O emissions from oceans is much smaller than that from land (Fig. 11c ). The mean ocean N 2 O 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 N 2 O 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.
section_name: TD ocean emission

section_content: The four inversions have comparable magnitudes of global stratospheric N 2 O sink (via photolysis and oxidation by the electronically excited atomic oxygen, O( 1 D), 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 N 2 O sink increased during 1997-2020 (Fig. 13 ) in proportion to the growing atmospheric N 2 O 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 N 2 O in the middle stratosphere, where the greatest loss of N 2 O 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 N 2 O (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 N 2 O 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. In the following analyses of the decadal global N 2 O budget, the comparison between BU and TD approaches is only for total N 2 O estimates. We rely on BU approaches to quantify all identified sources and sinks (Table 3, Fig. 1 ). 

The BU and TD approaches give remarkably consistent estimates of global total N 2 O 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., N 2 O losses via photolysis and reaction with O( 1 D) 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 N 2 O 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 N 2 O 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 N 2 O 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 0. 9 (0. 7, 1. 2) 1. 1 (0. 8, 1. 4) 1. 1 (0. 8, 1. 4) 1. 2 (0. 9, 1. 6) 1. 3 (0. 9, 1. 6) Perturbed CO 2 effect -0. 4 (-0. 8, 0. 2) -0. 5 (-1. 0, 0. 2) -0. 6 (-1. 3, 0. 3) -0. 7 (-1. 5, 0. 3) -0. 8 (-1. 6, 0. 3) fluxes from Climate effect 0. 4 (0. 1, 0. 8) 0. 5 (0. 2, 0. 7) 0. 6 (0. 1, 0. 8) 0. 7 (0. 2, 1. 2) 0. 9 (0. 4, 1. 8) climate, CO 2, and Post-deforestation 0. 8 (0. 6, 1. 1) 0. 9 (0. 6, 1. 2) 0. 9 (0. 5, 1. 3) 0. 9 (0. 4, 1. 3) 0. 8 (0. 4, 1. 3) land-cover change pulse effect Long-term effect of -1. 2 (-1. 1, -1. 4) -1. 3 (-1. 2, -1. 5) -1. 4 (-1. 2, -1. 5) -1. 4 (-1. 3, -1. 6) -1. 5 (-1. 4, -1. 6) reduced mature forest area
section_name: TD stratospheric sink

section_content: -0. 4 (-1. 1, 0. 7) -0. 5 (-1. 4, 0. 6) -0. 6 (-1. 9, 0. 8) -0. 6 (-2. 1, 1. 2) -0. 6 (-2. 2, 1. 8) Anthropogenic total 5. 0 (3. 0, 7. 3) 5. 5 (3. 1, 7. 9) 5. 8 (3. 1, 8. 6) 6. 5 (3. 2, 10. 0) 6. 7 (3. 3, 10. 9)

Natural fluxes (BU)

Natural soil baseline 6. 4 (3. 9, 8. 5) 6. 4 (3. 8, 8. 6) 6. 4 (3. 9, 8. 5) 6. 4 (3. 9, 8. 6) 6. 4 (3. Surface sink (soils and wetlands) 0. 0 (0. 0, -0. 3) 0. 0 (0. 0, -0. 3) 0. 0 (0. 0, -0. 3) 0. 0 (0. 0, -0. 3) 0. 0 (0. 0, -0. 3) Natural total 12. 0 (7. 9, 15. 8) 11. 9 (7. 7, 15. 8) 11. 9 (7. 5, 15. 9) 11. 8 (7. 1980-1989 1990-1999 2000-2009 2010-2019 2020 Atmospheric chemical sink a 12. 8 (11. 7, 13. 8) 13. 4 (12. 3, 14. 5) 14. 0 (12. 8, 15. 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, CO 2, and land cover had an overall negative effect on N 2 O 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 CO 2 concentration (-0. 7, -1. 5 to -0. 3 Tg N yr -1 ).
section_name: Subtotal

section_content: Global N 2 O emissions estimated by the BU and TD approaches were comparable in magnitude during the over-H. Tian et al. : Global nitrous oxide budget (1980 -2020) lapping period from 1997 to 2020, but TD estimates implied a larger interannual variability and a faster rate of increase (Fig. 13a ). BU and TD approaches diverge when estimating the magnitude of land emissions compared with ocean emissions, although they are consistent with respect to trends (Fig. 13b ). According to the BU approaches, global N 2 O 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, TD approaches suggested that 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). The BU estimate during 1997-2010 was on average 1. 6 Tg N yr -1 higher than the TD estimate. However, after 2010, the difference in the magnitude of emissions between the two approaches is smaller, due to the rapid increase in the TD estimates. From the year 1980, BU approaches suggested a significant increase in global N 2 O emissions that was primarily driven by anthropogenic sources (Table 3 ). Satellite and photolysis models estimate that the atmospheric N 2 O burden increased from 1528 Tg N in the 2000s to 1570 in the 2010s and 1592 Tg N in 2020, which is comparable to estimates by atmospheric chemistry transport models, showing an increase in the atmospheric N 2 O burden from 1527 (1504-1545) Tg N in the 2000s to 1606 (1592-1621) Tg N in 2020.
section_name: Decadal trend in the global N 2 O budget

section_content: To assess regional N 2 O budgets, we divide the global land into 18 regions (as described in Sect. 3). Our regional analyses include the following: (1) trends and variations in regional total N 2 O emissions from all sources derived from available TD (1997-2020) and BU (1980-2020) estimates (Fig. 13 ), (2) trends and variations in region anthropogenic N 2 O emissions from all identified sources during 1980-2020 derived from a BU approach (Fig. 14 ), and (3) decadal regional N 2 O budget (2010-2019) derived from both BU and TD approaches (Fig. 15 ). The following sections provide detailed estimates for each of the 18 regions.
section_name: Regional BU and TD estimates and their trends

section_content: For the USA, the TD estimates show higher total N 2 O emissions than the BU estimates over the 1997-2020 period (Fig. 13c ), with 1. 00 (0. 69-1. 39) and 0. 82 (0. 31-1. 42) Tg N yr -1, respectively. Both approaches suggest that the total N 2 O emissions from the USA remained relatively stable during 1997-2020. Based on the BU estimates, changes in climate, CO 2, and land cover caused emission decline over 1980-2020. The flux fluctuated between -0. 30 and -0. 12 Tg N yr -1, with an average of -0. 20 Tg N yr -1. Indirect emissions from anthropogenic nitrogen additions increased from 0. 11 Tg N yr -1 in 1980 to 0. 13 Tg N yr -1 in 1995 and then decreased to 0. 10 Tg N yr -1 in 2020. Direct emissions from nitrogen additions in agriculture increased from 0. 25 Tg N yr -1 in 1980 to 0. 30 Tg N yr -1 in 2020. However, the increase in direct agricultural emissions was offset by the trend in emissions from other direct anthropogenic sources, which decreased from 0. 26 Tg N yr -1 in 1980 to 0. 19 Tg N yr -1 in 2020. The total anthropogenic N 2 O emissions slightly increased during 1980-2020, at an average rate of 0. 6 × 10 -3 Tg N yr -2. This increase primarily occurred during 1980-1997 (Fig. 14 ). 

In the 2010s, the BU estimates (0. 81, 0. 29-1. 43 Tg N yr -1 ) were on average 0. 22 Tg N yr -1 lower than the TD estimates (1. 03, 0. 71-1. 45 Tg N yr -1 ) (Fig. 15 ). According to the BU results, natural sources contributed 48 % of total emissions (0. 39, 0. 22-0. 65 Tg N yr -1 ) during this period. Direct emissions from nitrogen additions in agriculture were 0. 30 (0. 18-0. 38) Tg N yr -1, contributing 37 % of the total emissions. Emissions from other direct anthropogenic sources made the second largest contribution to anthropogenic emissions, with a decadal mean of 0. 21 (0. 18-0. 23) Tg N yr -1. Indirect emissions from anthropogenic nitrogen additions contributed 14 % of the total anthropogenic emissions, with a decadal mean of 0. 11 (0. 07-0. 14) Tg N yr -1. Changes in climate, CO 2, and land cover had an overall negative effect on N 2 O emissions, with a mean value of -0. 19 Tg N yr -1 and range of -0. 37 to 0. 03 Tg N yr -1. Recent study has indicated that N 2 O emissions could be increased by freeze-thaw cycles (Del Grosso et al., 2022) and tillage practices (Lu et al., 2022). Our BU estimates did not consider freeze-thaw cycles or tillage practices and, thus, may have underestimated N 2 O emissions.
section_name: United States of America (USA)

section_content: BU approaches suggested a larger magnitude of total N 2 O emissions from Canada than TD approaches over the 1997-2020 period (Fig. 13d ), with values of 0. 29 (0. 05-0. 69) and 0. 12 (0. 06-0. 19) Tg N yr -1, respectively. BU and TD estimates also showed divergent trends: TD estimates decreased at a rate of -1. 5 × 10 -3 Tg N yr -2, whereas BU estimates increased at a rate of 0. 7 × 10 -3 Tg N yr -2. According to the BU results, the increase in total N 2 O emissions from Canada was mainly driven by the direct emissions from nitrogen additions in agriculture, which increased from 0. 02 Tg N yr -1 in 1980 to 0. 05 Tg N yr -1 in 2020. Perturbed fluxes from changes in climate, CO 2, and land cover showed an overall increase from 0. 00 Tg N yr -1 in 1980 to 0. 02 Tg N yr -1 in 2020. Indirect N 2 O emissions from Canada were relatively stable during the study period, while emissions from other direct anthropogenic sources had large interannual variability (Fig. 14 ). 

In the 2010s, the BU estimates of Canada's total N 2 O emissions (0. 29, 0. 07-0. 69 Tg N yr -1 ) were over 2 times higher than the TD estimates (0. 12, 0. 06-0. 20 Tg N yr -1 ) (Fig. 15 ). According to the BU results, natural sources contributed 59 % of total emissions (0. 17, 0. 04-0. 43 Tg N yr -1 ) during this period. Direct emissions from nitrogen additions in agriculture were 0. 05 (0. 03-0. 06) Tg N yr -1, contributing 15 % of the total emissions. Emissions from other direct anthropogenic sources and indirect emissions from anthropogenic nitrogen additions were 0. 04 (0. 02-0. 08) and 0. 02 (0. 02-0. 03) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall positive effect on N 2 O emissions, with a mean value of 0. 01 Tg N yr -1 and a range of -0. 04 to 0. 09 Tg N yr -1.
section_name: Canada (CAN)

section_content: TD and BU estimates are comparable with respect to the magnitudes and trends of N 2 O emissions from Central America (Fig. 13e ), with mean values of 0. 42 (0. 21-0. 64) and 0. 35 (0. 25-0. 47) Tg N yr -1 for the BU and TD approaches, respectively. During 1997-2020, the rate of increase of the BU estimates (4. 7 × 10 -3 Tg N yr -2 ) was higher than that of TD estimates (2. 5 × 10 -3 Tg N yr -2 ). Emissions from other direct anthropogenic sources increased from 0. 03 Tg N yr -1 in 1980 to 0. 15 Tg N yr -1 in 2020 and were the major driver of the increase in N 2 O emissions from Central America. Direct agricultural emissions increased during the study period, from 0. 08 Tg N yr -1 in 1980 to 0. 11 Tg N yr -1 in 2020. Indirect emissions and perturbed fluxes from changes in climate, CO 2, and land cover were relatively stable during this period (Fig. 14 ). 

The BU and TD approaches gave comparable estimates of total N 2 O emissions from Central America in the 2010s, with values of 0. 46 (0. 24-0. 68) and 0. 36 (0. 24-0. 48) Tg N yr -1 for the BU and TD approaches (Fig. 15 ), respectively. Natural sources contributed 37 % of total emissions (0. 17, 0. 07-0. 26 Tg N yr -1 ) during this period. Emissions from other direct anthropogenic sources contributed 39 % of the total emissions (0. 18, 0. 17-0. 18 Tg N yr -1 ). Direct and indirect emissions were 0. 11 (0. 07-0. 14) and 0. 02 (0. 02- Each subplot shows the emissions from five subsectors using BU approaches, followed by the sum of these five categories using BU approaches (coral) and the estimates from TD approaches (lime). Error bars indicate the spread between the minimum and the maximum values. The center map shows the spatial distribution of 10-year average N 2 O emissions from land and the ocean based on the land and ocean models. 0. 03) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall negative effect on N 2 O emissions, with a mean value of -0. 02 Tg N yr -1 and a range of -0. 10 to 0. 07 Tg N yr -1.
section_name: Central America (CAM)

section_content: TD approaches suggested a larger magnitude of total N 2 O emissions from northern South America than BU approaches over the 1997-2020 period (Fig. 13f ), with respective values of 0. 55 (0. 34-0. 98) and 0. 40 (0. 04-1. 08) Tg N yr -1 for each approach. During 1997-2020, the increase rate of the TD estimates (2. 2 × 10 -3 Tg N yr -2 ) was higher than that of BU estimates (0. 8 × 10 -3 Tg N yr -2 ). Direct agricultural emissions made the largest contribution to the increase in N 2 O emissions from northern South America, increasing from 0. 04 Tg N yr -1 in 1980 to 0. 07 Tg N yr -1 in 2020 (Fig. 14 ). N 2 O emissions from the other three anthropogenic sectors did not have a significant trend during 1980-2020. 

The BU estimates in the 2010s (0. 41, 0. 03-1. 09 Tg N yr -1 ) were on average 0. 17 Tg N yr -1 lower than the TD estimates (0. 58, 0. 35-1. 06 Tg N yr -1 ) (Fig. 15 ). The average natural emission was 0. 35 Tg N yr -1 in the 2010s, contributing 85 % of total emissions. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 07 (0. 05-0. 09), 0. 02 (0. 01-0. 02), and 0. 01 (0. 01-0. 02) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall negative effect on N 2 O emissions, with a mean value of -0. 04 Tg N yr -1 and a range of -0. 19 to 0. 10 Tg N yr -1.
section_name: Northern South America (NSA)

section_content: The average total N 2 O emission from Brazil estimated by the BU approaches was 1. 21 Tg N yr -1, ranging from 0. 26 to 2. 32 Tg N yr -1 (Fig. 13g ), which was lower than the TD estimates (1. 42, 1. 18-1. 75 Tg N yr -1 ). Both approaches detected a notable increasing trend in total N 2 O emissions during 1997-2020. TD approaches suggested a higher increase rate (11. 6 × 10 -3 Tg N yr -2 ) than BU approaches (4. 3 × 10 -3 Tg N yr -2 ). Direct agricultural emissions, which increased from 0. 13 Tg N yr -1 in 1980 to 0. 32 Tg N yr -1 in 2020, made the largest contribution to the increase in N 2 O emissions from Brazil (Fig. 14 ). Indirect emissions also showed an increase from 0. 03 Tg N yr -1 in 1980 to 0. 06 Tg N yr -1 in 2020. Emissions from other anthropogenic sources and perturbed fluxes from changes in climate, CO 2, and land cover did not have an obvious trend during the study period. 

The TD estimates in the 2010s (1. 51, 1. 40-1. 79 Tg N yr -1 ) were on average 0. 28 Tg N yr -1 higher than the BU estimates (1. 23, 0. 24-2. 41 Tg N yr -1 ) (Fig. 15 ). According to the BU results, the average natural emission was 0. 95 Tg N yr -1 in the 2010s, contributing 77 % of total emissions. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 28 (0. 22-0. 35), 0. 09 (0. 06-0. 11), and 0. 05 (0. 02-0. 07) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall negative effect on N 2 O emissions, with a mean value of -0. 14 Tg N yr -1 and a range of -0. 48 to 0. 25 Tg N yr -1.
section_name: Brazil (BRA)

section_content: BU and TD estimates are consistent with respect to the magnitude of the total N 2 O emissions from southwestern South America during 1997-2020, with values of 0. 55 (0. 18-1. 03) and 0. 51 (0. 40-0. 63) Tg N yr -1 (Fig. 13h ), respectively. TD estimates increased at a rate of 5. 3 × 10 -3 Tg N yr -2 over 1997-2020, whereas BU estimates did not have an obvious trend during this period. Among the four anthropogenic sectors, direct agricultural emissions had the largest increase, from 0. 10 Tg N yr -1 in 1980 to 0. 15 Tg N yr -1 in 2020 (Fig. 14 ). Indirect emissions also increased from 0. 02 Tg N yr -1 in 1980 to 0. 03 Tg N yr -1 in 2020. Perturbed fluxes from changes in climate, CO 2, and land cover had a decreasing trend, while emissions from other sectors fluctuated over the study period. 

The BU and TD approaches gave similar estimates of total N 2 O emissions from southwestern South America in the 2010s, with values of 0. 55 (0. 19-1. 04) and 0. 55 (0. 44-0. 67) Tg N yr -1 (Fig. 15 ), respectively. The mean natural emission was 0. 39 Tg N yr -1 in the 2010s, accounting for 71 % of total emissions. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 14 (0. 09-0. 19), 0. 05 (0. 03-0. 06), and 0. 03 (0. 01-0. 03) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall negative effect on N 2 O emissions, with a mean value of -0. 05 Tg N yr -1 and a range of -0. 16 to 0. 08 Tg N yr -1.
section_name: Southwest South America (SSA)

section_content: The BU estimates suggest that Europe had the largest decrease rate of regional N 2 O emissions among the 18 regions, and the average decrease rate during 1980-2020 was -13. 3 × 10 -3 Tg N yr -2 (Fig. 13i ). For the 1997-2020 period, this decreasing trend, as estimated by the BU approaches, slowed down (-7. 7 × 10 -3 Tg N yr -2 ), whereas the TD approach suggests a small increase of 1. 6 × 10 -3 Tg N yr -2 (Fig. 13i ). Emissions from other direct anthropogenic sources (including fossil fuel and industry, waste and wastewater, and biomass burning), which decreased from 0. 51 Tg N yr -1 in 1980 to 0. 18 Tg N yr -1 in 2020, made the largest contribution to the decreasing trend in N 2 O emissions from Europe. Direct agricultural emissions and indirect emissions show overall decreasing trends from 0. 46 and 0. 16 Tg N yr -1 in 1980 to 0. 38 and 0. 12 Tg N yr -1 in 2020, respectively, mainly due to a reduction in fertilizer use after the collapse of the Soviet Union (Tian et al., 2022). However, the decreasing trend in direct agricultural emissions has leveled off since the 2000s. Perturbed fluxes from changes in climate, CO 2, and land cover decreased during 1980-1985 and then slowly increased (Fig. 14 ). 

The BU and TD approaches gave comparable estimates of European N 2 O emissions in the 2010s, with values of 1. 00 (0. 45-1. 57) and 0. 86 (0. 49-1. 36) Tg N yr -1 (Fig. 15 ), respectively. According to the BU results, natural sources only contributed 26 % of total emissions (0. 26, 0. 11-0. 52 Tg N yr -1 ) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 38 (0. 32-0. 44), 0. 19 (0. 15-0. 24), and 0. 13 (0. 08-0. 16) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall positive effect on N 2 O emissions, with a mean value of 0. 03 Tg N yr -1 and a range of -0. 21 to 0. 22 Tg N yr -1.
section_name: Europe (EU)

section_content: For northern Africa, TD approaches suggested a larger magnitude of the total N 2 O emissions than BU approaches over the 1997-2020 period (Fig. 13j ), with values of 1. 01 (0. 52-1. 32) and 0. 69 (0. 18-1. 27) Tg N yr -1, respectively. Both approaches suggest that N 2 O emissions from northern Africa significantly increased during 1997-2020, and the increase rates estimated by the BU and TD approaches were 4. 9 × 10 -3 and 4. 7 × 10 -3 Tg N yr -2, respectively. Direct emissions increased from 0. 10 Tg N yr -1 in 1980 to 0. 27 Tg N yr -1 in 2020, making the largest contribution to the increase in N 2 O emissions from northern Africa (Fig. 14 ). Indirect emissions also significantly increased from 0. 02 Tg N yr -1 in 1980 to 0. 04 Tg N yr -1 in 2020. In contrast, other anthropogenic emissions decreased from 0. 12 Tg N yr -1 in 1980 to 0. 11 Tg N yr -1 in 2020. N 2 O fluxes caused by changes in climate, CO 2, and land cover remained relatively stable during 1980-2020. 

In the 2010s, the BU estimates (0. 72, 0. 17-1. 30 Tg N yr -1 ) were on average 0. 32 Tg N yr -1 lower than the TD estimates (1. 04, 0. 54-1. 31 Tg N yr -1 ) (Fig. 15 ). Natural sources accounted for 44 % of total emissions (0. 32, 0. 07-0. 60 Tg N yr -1 ) during this period. Direct emissions from nitrogen additions in agriculture were 0. 23 (0. 09-0. 34) Tg N yr -1, contributing 32 % of the total emissions. Emissions from other direct anthropogenic sources made the second largest contribution to anthropogenic emissions, with a decadal mean of 0. 11 (0. 08-0. 14) Tg N yr -1. Indirect emissions and perturbed fluxes from changes in climate, H. Tian et al. : Global nitrous oxide budget (1980 -2020) CO 2, and land cover were 0. 04 (0. 02-0. 06) Tg N yr -1, and 0. 02 (-0. 10 to 0. 16), respectively.
section_name: Northern Africa (NAF)

section_content: Similar to northern Africa, TD approaches suggested a larger magnitude of total N 2 O emissions from equatorial Africa than BU approaches over the 1997-2020 period (Fig. 13k ), with values of 1. 45 (1. 15-1. 78) and 1. 36 (0. 36-2. 22 ) Tg N yr -1, respectively. Both approaches suggested that N 2 O emissions from equatorial Africa significantly increased during 1997-2020, and the increase rates estimated by the BU and TD approaches were 4. 4 × 10 -3 and 2. 1 × 10 -3 Tg N yr -1, respectively. Direct emissions more than tripled during the study period, from 0. 07 Tg N yr -1 in 1980 to 0. 22 Tg N yr -1 in 2020, dominating the increase in N 2 O emissions from equatorial Africa (Fig. 14 ). Indirect emissions also steadily increased from 0. 04 Tg N yr -1 in 1980 to 0. 06 Tg N yr -1 in 2020. On the contrary, perturbed fluxes from changes in climate, CO 2, and land cover showed an overall decreasing trend with large interannual variability. Emissions from other anthropogenic sources showed relatively stable values. 

The BU and TD approaches gave comparable estimates of N 2 O emissions from equatorial Africa in the 2010s, with values of 1. 38 (0. 38-2. 28) and 1. 50 (1. 15-1. 80) Tg N yr -1 (Fig. 15 ), respectively. According to the BU results, natural emissions were the dominant component, accounting for 71 % of total emissions (0. 98, 0. 42-1. 32 Tg N yr -1 ) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 18 (0. 13-0. 25), 0. 26 (0. 19-0. 34), and 0. 05 (0. 03-0. 08) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall negative effect on N 2 O emissions, with a mean value of -0. 09 Tg N yr -1 and a range of -0. 40 to 0. 29 Tg N yr -1.
section_name: Equatorial Africa (EQAF)

section_content: BU and TD estimates are consistent with respect to the magnitude of the total N 2 O emissions from southern Africa during 1997-2020, at 0. 61 (0. 13-1. 09) and 0. 58 (0. 33-0. 86) Tg N yr -1 (Fig. 13l ), respectively. TD estimates increased at a rate of 4. 5 × 10 -3 Tg N yr -2 over 1997-2020, whereas BU estimates did not show an obvious trend during this period. According to the BU results, direct agricultural emissions increased from 0. 05 Tg N yr -1 in 1980 to 0. 08 Tg N yr -1 in 2020, while emissions from other anthropogenic sources slightly decreased from 0. 19 Tg N yr -1 in 1980 to 0. 17 Tg N yr -1 in 2020. Both indirect emissions and perturbed fluxes from changes in climate, CO 2, and land cover had no significant trend (Fig. 14 ). 

BU and TD approaches gave consistent estimates of total N 2 O emissions from southern Africa in the 2010s, with values of 0. 62 (0. 13-1. 10) and 0. 61 (0. 35-0. 87) Tg N yr -1 for the BU and TD approaches (Fig. 15 ), respectively. Nat-ural emissions were the dominant components, accounting for 61 % of total emissions (0. 38, 0. 13-0. 61 Tg N yr -1 ) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 07 (0. 05-0. 09), 0. 19 (0. 17-0. 23), and 0. 02 (0. 01-0. 03) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall negative effect on N 2 O emissions, with a mean value of -0. 05 Tg N yr -1 and a range of -0. 24 to 0. 14 Tg N yr -1.
section_name: Southern Africa (SAF)

section_content: During 1997-2020, the average total N 2 O emission from Russia estimated by the BU approaches was 0. 74 Tg N yr -1, ranging from 0. 15 to 1. 84 Tg N yr -1 (Fig. 13m ), which was much higher than the estimates of TD approaches (0. 36, 0. 18-0. 52 Tg N yr -1 ). Both approaches suggested that Russia's total N 2 O emissions increased during 1997-2020, and the increase rates estimated by the BU and TD approaches were 1. 2 × 10 -3 and 1. 7 × 10 -3 Tg N yr -2, respectively. Direct agricultural emissions, other direct emissions, and indirect emissions had divergent trends before and after 1997. From 1980 to 1997, N 2 O emissions from all three of these sectors decreased. After 1997, direct agricultural emissions and other direct emissions had an overall increasing trend, while indirect emissions remained relatively stable. Perturbed fluxes from changes in climate, CO 2, and land cover remained relatively stable with large interannual variability (Fig. 14 ). 

In the 2010s, the BU estimates (0. 74, 0. 15-1. 84 Tg N yr -1 ) were on average 0. 36 Tg N yr -1 higher than the TD estimates (0. 38, 0. 18-0. 59 Tg N yr -1 ) (Fig. 15 ). Natural sources accounted for 64 % of total emissions (0. 47, 0. 12-1. 22 Tg N yr -1 ) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 06 (0. 05-0. 07), 0. 10 (0. 04-0. 18), and 0. 05 (0. 03-0. 07) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall positive effect on N 2 O emissions, with a mean value of 0. 05 Tg N yr -1 and a range of -0. 10 to 0. 30 Tg N yr -1.
section_name: Russia (RUS)

section_content: TD approaches suggested a larger magnitude of total N 2 O emissions from Central Asia than BU approaches over the 1997-2020 period (Fig. 13n ), with values of 0. 19 (0. 10-0. 29) and 0. 14 (0. 01-0. 27) Tg N yr -1, respectively. BU and TD estimates were consistent with respect to the trend in total N 2 O emissions during 1997-2020, with increase rates of 1. 9 × 10 -3 and 2. 0 × 10 -3 Tg N yr -2, respectively. Direct emissions increased from 0. 05 Tg N yr -1 in 1980 to 0. 07 Tg N yr -1 in 2020, making the largest contribution to the increase in N 2 O emissions from Central Asia. Other direct emissions and indirect emissions had no significant trend. Fluxes from changes in climate, CO 2, and land cover showed an overall increasing trend with large interannual variability (Fig. 14 ). 

In the 2010s, the TD estimates (0. 20, 0. 10-0. 32 Tg N yr -1 ) were on average 0. 05 Tg N yr -1 higher than the BU estimates (0. 15, 0. 01-0. 30 Tg N yr -1 ) (Fig. 15 ). Natural sources accounted for 30 % of total emissions (0. 04, 0. 01-0. 11 Tg N yr -1 ) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 06 (0. 02-0. 08), 0. 02 (0. 01-0. 02), and 0. 02 (0. 01-0. 03) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall positive effect on N 2 O emissions, with a mean value of 0. 02 Tg N yr -1 and a range of -0. 04 to 0. 07 Tg N yr -1.
section_name: Central Asia (CAS)

section_content: BU and TD estimates are comparable with respect to the magnitude of the total N 2 O emissions from the Middle East during 1997-2020, with values of 0. 27 (0. 11-0. 45) and 0. 30 (0. 25-0. 36) Tg N yr -1 (Fig. 13o ), respectively. BU and TD estimates were consistent regarding the trend in total N 2 O emissions during 1997-2020, with increase rates of 4. 4 × 10 -3 and 3. 9 × 10 -3 Tg N yr -2, respectively. According to the BU results, direct agricultural emissions increased from 0. 07 Tg N yr -1 in 1980 to 0. 13 Tg N yr -1 in 2020. Emissions from other anthropogenic sources (fossil fuel and industry, particularly) had the largest increase, from 0. 03 Tg N yr -1 in 1980 to 0. 10 Tg N yr -1 in 2020. Indirect emissions also continuously increased from 0. 02 Tg N yr -1 in 1980 to 0. 04 Tg N yr -1 in 2020. Perturbed fluxes from changes in climate, CO 2, and land cover had no significant trend (Fig. 14 ). 

BU and TD approaches gave consistent estimates of total N 2 O emissions from the Middle East in the 2010s, with values of 0. 29 (0. 12-0. 49) and 0. 32 (0. 26-0. 39) Tg N yr -1 for the BU and TD approaches (Fig. 15 ), respectively. Natural emissions were 0. 04 (0. 02-0. 08 Tg N yr -1 ), accounting for 15 % of total emissions during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 12 (0. 05-0. 21), 0. 09 (0. 07-0. 10), and 0. 03 (0. 02-0. 04) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall positive effect on N 2 O emissions, with a mean value of 0. 01 Tg N yr -1 and a range of -0. 04 to 0. 05 Tg N yr -1.
section_name: The Middle East (MIDE)

section_content: BU and TD approaches agreed very well regarding the magnitudes and trends of N 2 O emissions from China. Both approaches suggested that China's total N 2 O emissions significantly increased during 1997-2020, and the increase rates estimated by the BU and TD approaches were 12. 6 × 10 -3 and 16. 5 × 10 -3 Tg N yr -1, respectively (Fig. 13p ). According to the BU results, China's total N 2 O emissions increased from 0. 76 Tg N yr -1 in 1980 to 1. 38 Tg N yr -1 in 2020. Direct emissions from N additions in agriculture made the largest contribution to the increase in China's N 2 O emissions, which increased from 0. 29 Tg N yr -1 in 1980 to 0. 71 Tg N yr -1 in 2016 and then decreased to 0. 64 Tg N yr -1 in 2020 due to decreased N fertilizer application (Fig. 14 ). Both indirect emissions and other direct emissions continuously increased, from 0. 09 and 0. 11 Tg N yr -1 in 1980 to 0. 24 and 0. 27 Tg N yr -1 in 2020, respectively. The total anthropogenic N 2 O emissions from China increased at an average rate of 18. 9 × 10 -3 Tg N yr -2 during 1980-2020, which was the largest among the 18 regions and contributed 40 % of the increase in global anthropogenic N 2 O emissions. 

The BU and TD approaches gave consistent estimates of China's total N 2 O emissions in the 2010s, with values of 1. 41 (0. 82-2. 23) and 1. 33 (1. 06-1. 60) Tg N yr -1 for the BU and TD approaches (Fig. 15 ), respectively. According to the BU results, natural sources only contributed 21 % of total emissions (0. 29, 0. 20-0. 51 Tg N yr -1 ) during this period. Nitrogen additions in agriculture were the dominant source of N 2 O emissions, contributing 48 % of the total emissions (0. 68, 0. 48-1. 03 Tg N yr -1 ). Emissions from other direct anthropogenic sources and indirect emissions from anthropogenic nitrogen additions were 0. 23 (0. 23-0. 23) and 0. 24 (0. 17-0. 28) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall negative effect on N 2 O emissions, with a mean value of -0. 03 Tg N yr -1 and a range of -0. 25 to 0. 18 Tg N yr -1.
section_name: China (CHN)

section_content: TD approaches suggested a smaller magnitude of total N 2 O emissions from Korea and Japan than BU approaches over the 1997-2020 period (Fig. 13q ), with values of 0. 06 (0. 03-0. 11) and 0. 11 (0. 06-0. 16) Tg N yr -1, respectively. Both approaches suggested that total N 2 O emissions from Korea and Japan decreased during 1997-2020, and the decrease rates estimated by the BU and TD approaches were -1. 4 × 10 -3 and -0. 5 × 10 -3 Tg N yr -2, respectively. Other direct emissions (fossil fuel and industry, particularly) dominated the temporal variations in N 2 O emissions from Korea and Japan, which increased from 0. 04 Tg N yr -1 in 1980 to 0. 08 Tg N yr -1 in 1997 and then decreased to 0. 04 Tg N yr -1 in 2020. Emissions from agriculture, indirect sources, and perturbed fluxes remained relatively stable during 1997-2020 (Fig. 14 ). 

In the 2010s, BU estimates (0. 10, 0. 05-0. 15 Tg N yr -1 ) of total N 2 O emissions were on average 0. 04 Tg N yr -1 higher than the TD estimate (0. 06, 0. 04-0. 11 Tg N yr -1 ) (Fig. 15 ). Natural sources accounted for 26 % of total emissions (0. 03, 0. 00-0. 05 Tg N yr -1 ) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 03 (0. 02-0. 04), 0. 04 (0. 04-0. 04), and 0. 01 (0. 01-0. 02) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall negative effect on H. Tian et al. : Global nitrous oxide budget (1980 -2020) N 2 O emissions, with a mean value of -0. 01 Tg N yr -1 and a range of -0. 02 to 0. 01 Tg N yr -1.
section_name: Korea and Japan (KAJ)

section_content: BU and TD estimates are comparable in terms of both the magnitude and trend of the total N 2 O emissions from South Asia (Fig. 13r ). During 1997-2020, the magnitudes of total N 2 O emissions estimated by the BU and TD approaches were 1. 04 (0. 35-1. 80) and 1. 21 (0. 96-1. 56) Tg N yr -1, respectively. Both approaches suggested that the total N 2 O emissions from South Asia significantly increased during 1997-2020, and the increase rates estimated by the BU and TD approaches were 17. 7×10 -3 and 20. 2×10 -3 Tg N yr -2, respectively. Direct emissions from nitrogen additions in agriculture made the largest contribution to the increase in N 2 O emissions in South Asia, which increased from 0. 19 Tg N yr -1 in 1980 to 0. 55 Tg N yr -1 in 2020 due to increased N fertilizer application (Fig. 14 ). Other direct emissions and indirect emissions also significantly increased, from 0. 06 and 0. 06 Tg N yr -1 in 1980 to 0. 14 and 0. 17 Tg N yr -1 in 2020, respectively. Fluxes from changes in climate, CO 2, and land cover showed an overall increasing trend with large interannual variability. 

BU estimates (1. 15, 0. 41-2. 06 Tg N yr -1 ) were on average 0. 21 Tg N yr -1 lower than the TD estimate in the 2010s (1. 36, 1. 05-1. 84 Tg N yr -1 ) (Fig. 15 ). Natural sources accounted for 28 % of total emissions (0. 32, 0. 12-0. 56 Tg N yr -1 ) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 49 (0. 25-0. 75), 0. 13 (0. 13-0. 13), and 0. 15 (0. 10-0. 19) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall positive effect on N 2 O emissions, with a mean value of 0. 06 Tg N yr -1 and a range of -0. 19 to 0. 43 Tg N yr -1.
section_name: South Asia (SAS)

section_content: TD approaches suggested a smaller magnitude of the total N 2 O emissions from Southeast Asia than BU approaches over the 1997-2020 period (Fig. 13s ), with values of 0. 69 (0. 50-1. 02) and 0. 92 (0. 24-2. 04) Tg N yr -1, respectively. Both approaches suggested that total N 2 O emissions from Southeast Asia increased during 1997-2020, and the rates of increase estimated by the BU and TD approaches were 5. 1 × 10 -3 and 2. 3 × 10 -3 Tg N yr -2, respectively. Direct agricultural emissions, other direct emissions, and indirect emissions significantly increased during the study period, from 0. 09, 0. 08, and 0. 04 Tg N yr -1 in 1980 to 0. 30, 0. 11, and 0. 12 Tg N yr -1 in 2020, respectively. Meanwhile, perturbed fluxes from changes in climate, CO 2, and land cover significantly decreased from -0. 07 Tg N yr -1 in 1980 to -0. 12 Tg N yr -1 in 2020 (Fig. 14 ). 

The BU and TD approaches gave comparable estimates of the total N 2 O emissions from Southeast Asia in the 2010s, with values of 0. 95 (0. 24-2. 09) and 0. 72 (0. 51-1. 12) Tg N yr -1 for the BU and TD approaches (Fig. 15 ), respectively. Natural sources accounted for 62 % of total emissions (0. 59, 0. 24-1. 30 Tg N yr -1 ) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 26 (0. 20-0. 35), 0. 11 (0. 09-0. 14), and 0. 10 (0. 06-0. 14) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall negative effect on N 2 O emissions, with a mean value of -0. 12 Tg N yr -1 and a range of -0. 35 to 0. 16 Tg N yr -1.
section_name: Southeast Asia (SEAS)

section_content: BU and TD estimates are comparable in terms of the magnitude of the total N 2 O emissions from Australasia during 1997-2020 (Fig. 13t ). The magnitudes of the total N 2 O emissions estimated by the BU and TD approaches were 0. 43 (0. 01-0. 92) and 0. 52 (0. 21-0. 72) Tg N yr -1, respectively. TD estimates increased at a rate of 4. 4 × 10 -3 Tg N yr -2 over 1997-2020, whereas BU estimates did not show a notable trend during this period (Fig. 13t ). According to the BU results, direct agricultural emissions increased from 0. 08 Tg m yr -1 in 1980 to 0. 09 Tg N yr -1 in 2020, while emissions from all the other three anthropogenic sectors remained stable (Fig. 14 ). 

In the 2010s, the magnitudes of total N 2 O emissions estimated by the BU and TD approaches were 0. 42 (0. 01-0. 91) and 0. 53 (0. 20-0. 71) Tg N yr -1, respectively. Natural sources accounted for 59 % of total emissions (0. 25, 0. 05-0. 50 Tg N yr -1 ) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0. 09 (0. 06-0. 11), 0. 08 (0. 06-0. 11), and 0. 01 (0. 01-0. 02) Tg N yr -1, respectively. Changes in climate, CO 2, and land cover had an overall negative effect on N 2 O emissions, with a mean value of -0. 01 Tg N yr -1 and a range of -0. 17 to -0. 17 Tg N yr -1 (Fig. 15 ).
section_name: Australasia (AUS)

section_content: The accompanying database includes two Excel files and 27 txt files. The two Excel files are organized into the following spreadsheets. 

The global N 2 O budget 1980-2020 -global emission data spreadsheet includes the following items: The data presented in this work can be downloaded from https://doi. org/10. 18160/RQ8P-2Z4R (Tian et al., 2023).
section_name: Data availability

section_name: Discussion

section_content: In comparing the global N 2 O budget estimates with previous studies, the definitions and terminology used in this study for N 2 O sources and sinks are consistent with those in Tian et al. (2020). In this new synthesis, we have also included a new emission source, namely "Continental shelves", corresponding to the shallow portion of the ocean overlying continental shelves (Laruelle et al., 2013), which was not explicitly reported in the previous global N 2 O budget (Tian et al., 2020). Thus, a total of 18 sources and 3 sinks are quantified in the global N 2 O budget reported here. We utilized a similar methodology to synthesize multiple TD and BU estimates. 

The TD estimates of global total emissions in this study are consistent with Tian et al. (2020). However, the TD estimates of emissions from the ocean are about 2. 3 Tg N yr -1 lower than the previous estimate in the 2000s, whereas the TD estimates of land emissions are about 2. 4 Tg N yr -1 higher than the previous estimate for the decade 2007-2016 (Tian et al., 2020). Global BU estimates in this study are about 1. 2 Tg N yr -1 higher than the previous estimate, primarily due to the inclusion of emissions from continental shelves (1. 2 Tg N yr -1 ), and 0. 8 Tg N yr -1 higher than the previous estimate for the natural soil baseline. According to our analysis, natural soils contributed more than half of terrestrial N 2 O emissions (Table 3 ), consistent with previous studies (Denman et al., 2007; Tian et al., 2020). The global natural soil emissions derived from this study are estimated to be 6. 4 Tg N yr -1, with a large uncertainty ranging from 3. 9 to 8. 6 Tg N yr -1. Using the emission factor from the 2006 IPCC guidelines, Syakila and Kroeze (2011) estimated that global preindustrial N 2 O emission from natural soils was 7 Tg N yr -1. Xu et al. (2017) suggested that global natural soil N 2 O emissions were about 6. 2 Tg N yr -1, with an uncertainty range of 4. 8 to 8. 1 Tg N yr -1. Tian et al. (2019) estimated global soil N 2 O emissions derived from NMIP using seven process-based terrestrial biosphere models and suggested a global soil N 2 O emission of 6. 3 ± 1. 1 Tg N yr -1 in the 1860s. 

The total of direct agricultural emissions, other direct anthropogenic emissions, and indirect anthropogenic emissions in this study is the same as the previous estimates (Tian et al., 2020). However, the total anthropogenic emission in H. Tian et al. : Global nitrous oxide budget (1980 -2020) this study is lower than our previous estimate (Tian et al., 2020), mainly because of the differences in perturbed fluxes from climate, CO 2, and land-cover change. According to our new estimate derived from NMIP2, the average perturbed flux from climate, CO 2, and land-cover change was -0. 6 (-2. 1 to 1. 2) Tg N yr -1 during 2010-2019 (Table 3 ). By contrast, the average perturbed flux during 2007-2016 reported by Tian et al. (2020) was 0. 2 (-0. 6 to 1. 1) Tg N yr -1, which was based on the first phase of NMIP (Tian et al., 2018). This study suggests a larger negative effect of increased CO 2 concentration and reduced mature forest area on N 2 O emissions than Tian et al. (2020). Much uncertainty exists in estimating the perturbed fluxes of atmospheric CO 2 and mature forest conversion, as discussed in the following section on uncertainties. 

Our estimate indicates that agricultural emissions were the major drivers of the increase in anthropogenic emissions during the past 4 decades, increasing from 3. 0 Tg N yr -1 in 1980 to 5. 0 Tg N yr -1 in 2020 (Fig. 16 ). Direct agricultural emissions had a larger increase than indirect agricultural emissions (2. 2 Tg N yr -1 in 1980 to 3. 9 Tg N yr -1 in 2020 versus 0. 8 Tg N yr -1 in 1980 to 1. 2 Tg N yr -1 in 2020). Agricultural emissions contributed 74 % of total anthropogenic emissions in the 2010s, with 56 % from direct agricultural emissions and 18 % from indirect emissions. Non-agricultural anthropogenic emissions had a slight decreasing trend during 1980-2020 because of a higher estimate of changes in climate, CO 2, and land cover than previous estimate. 

This study divides the global land into 18 regions and provides a more detailed regional budget than a previous study, which had only 10 regions (Tian et al., 2020), thereby enhancing our understanding of the N 2 O budget in subregions of North America, South America, Africa, and East Asia. In the 1980s, Europe made the largest contribution to global anthropogenic N 2 O emissions (23. 6 %), followed by China (11. 6 %), South Asia (8. 0 %), the USA (7. 8 %), and Russia (7. 3 %). During the study period, Europe and Russia had the largest decline in the share of anthropogenic N 2 O emissions, from 23. 6 % and 7. 3 % in the 1980s to 11. 8 % and 4. 3 % in the 2010s, respectively. In contrast, China and South Asia had the largest increase, from 11. 6 % and 8. 0 % in the 1980s to 17. 8 % and 13. 2 % in the 2010s, respectively. In the 2010s, China (17. 8 %), South Asia (13. 2 %), Europe (11. 8 %), the USA (6. 7 %), and equatorial Africa (6. 5 %) were the top five contributors to global anthropogenic N 2 O emissions (Fig. 17 ). 

Among the 18 regions identified in this study, only Europe, Russia, Australasia, and Japan and Korea had decreasing N 2 O 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 emissions from fossil fuel and industry, 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 agricultural emissions and indirect emissions showed overall decreasing trends from 0. 46 and 0. 16 Tg N yr -1 in 1980 to 0. 38 and 0. 12 Tg N yr -1 in 2020, respectively. However, the decreasing trend in agricultural emissions has leveled off since the 2000s. 

China and South Asia had the largest increase in N 2 O emissions during the study period. The rates of increase in the anthropogenic emissions from China and South Asia were 18. 9 × 10 -3 and 14. 3 × 10 -3 Tg N yr -2, respectively. The rates of increase in the anthropogenic emissions from China and South Asia contributed 40 % and 30 % to the global anthropogenic increase rate (0. 05 Tg N yr -2 ), respectively. In these two regions, direct nitrogen additions in agriculture made the largest contribution, while other direct emissions and indirect emissions also steadily increased. Our results show a significant increase in anthropogenic N 2 O emissions from South America, which is consistent with the previous budget (Tian et al., 2020). Moreover, we reveal that Brazil had a higher increase rate in anthropogenic N 2 O emissions (4. 2×10 -3 Tg N yr -2 ) than northern South America (0. 8 × 10 -3 Tg N yr -2 ) and southwestern South America (0. 4 × 10 -3 Tg N yr -2 ) during 1980-2020, and direct emissions from agriculture made the largest contribution. Our results suggest that northern Africa made the largest contribution (58 %) to the increase in anthropogenic N 2 O emissions from Africa, followed by equatorial Africa (38 %) and southern Africa (4 %). Anthropogenic N 2 O emissions from the USA and Canada show similar weak increasing rates of 0. 6 × 10 -3 and 0. 9 × 10 -3 Tg N yr -2 during the 1980-2020 period, respectively. Central America shows a higher anthropogenic N 2 O emission increase rate (5. 9 × 10 -3 Tg N yr -2 ), which is attributed to an increase in emissions from fossil fuels and industry, from 0. 01 Tg N yr -1 in the 1980s to 0. 16 Tg N yr -1 in the 2010s. The data for Mexico from EDGAR have a known problem with their estimates of N 2 O emissions from industry, and this issue requires further exploration. To support countries' N 2 O mitigation, it is essential to accurately estimate sources and sinks of N 2 O at the national level. 

6. 2 Sources of uncertainties and suggestions for improvements
section_name: Emission sources and comparison with previous estimates of the global N 2 O budget

section_content: Four inventories of N 2 O emissions (EDGAR, FAOSTAT, GFED, and UNFCCC) are integrated into the current synthesis of anthropogenic N 2 O emissions. These EF-based inventory datasets (where EF refers to emission factors) used the IPCC default EFs at regional and global scales. Uncertainty in FAOSTAT N 2 O emissions is ∼ 60 % across typologies. 

In fact, it is asymmetrical, following the 2006 guideline values and IPCC uncertainty formulae, with u min ∼ -30 % and u max ∼ 90 % (Tubiello et al., 2013) ; for N 2 O in EDGAR, the global uncertainty ranges defining the 95 % confidence intervals of a lognormal distribution are ±113 % for energy, be- tween -12 % and +16 % for industrial processes and product use, between -225 % and +302 % for agriculture, between -159 % and +203 % for waste, and ±112 % for other sectors (Solazzo et al., 2021). However, the poorly captured dependence of EFs on regional climate, management practices such as tillage, legume effect, and soil physical and biochemical conditions are key causes of the large uncertainty in the estimates of agricultural N 2 O emissions (Shcherbak et al., 2014; Tian et al., 2019; Lu et al., 2022), particularly for croplands where EFs have high spatial heterogeneity (Shang et al., 2019; Wang et al., 2020). There is evidence of a greater-than-linear dependence of emissions on N input where there is an excess of N; however, this is not represented in inventories that assume a linear dependence on N input (Cui et al., 2021). Higher IPCC-tier GHG inventories using the alternative EFs that are disaggregated by environmental factors and management-related factors (Buendia et al., 2019) could provide more accurate estimates, especially for regions where N input surplus is high, such as eastern China, India, and the USA. For example, the US national inventory uses a Tier-3 modeling approach (Del Grosso et al., 2022). Establishing national and regional N 2 O flux measurement networks could improve the accuracy of EF estimates for regions with different vegetation types and management measures. Furthermore, inventory datasets based on EF methods also suffer from large uncertainties induced by the underlying agriculture and rural data and statistics used as input, including statistics on fertilizer applications, livestock manure availability, storage and applications, and nutrient, crop, and soil management. 

According to the ensemble of process-based land model emissions derived from NMIP2, we estimate that the EF of fertilizer and manure applied on global croplands was 1. 9 % (1. 2 %-3. 3 %) in the 2010s, which is significantly larger than the IPCC Tier-1 default for direct emission of 1 %. This higher EF derived from process-based models suggests a strong interactive effect between N additions and other global environmental changes (Perturbed fluxes from climate, atmospheric CO 2, and land-cover change in Table 3 ). Figure 18 shows the spatial pattern of the cropland N 2 O EF during the 2010s and highlights that the EF was high in eastern China, Southeast Asia, western Europe, and the central USA where anthropogenic N inputs were high (Fig. B3 ). Previous field experiments reported a better fit to local observations of soil N 2 O emissions when assuming a nonlinear response to fertilizer N inputs under varied climate and soil conditions (Shcherbak et al., 2014; Wang et al., 2020). The nonlinear response is also likely associated with long-term N accumulation in agricultural soils from N fertilizer use and in aquatic systems from N loads (the legacy effect) (Van Meter et al., 2016), which provides more substrate for microbial processes (Firestone and Davidson, 1989). The increasing N 2 O emissions estimated by process-based models (Tian et al., 2019) also suggest that recent climate change (particularly warming) may have boosted soil nitrification and denitrification processes, contributing to the growing trend in N 2 O emissions with rising N additions to agricultural soils (Griffis et al., 2017; Pärn et al., 2018; Smith, 1997).
section_name: Uncertainties in N 2 O emission factors

section_content: Both process-based land biosphere modeling and measurement-based upscaling approaches have been used to estimate global soil N 2 O emissions (Table 3 ), with large uncertainties in their estimates. As shown in Fig. 19, NMIP2 models exhibit the highest uncertainties in the estimates of soil N 2 O emissions from tropical forests, such as the Amazon Basin, the Congo Basin, and Southeast Asia, as well as in regions with high fertilizer application rates, including eastern China, northern India, and the US Corn Belt. For NMIP2 estimates of direct agricultural emissions, the maximum estimate is about 60 % higher than the ensemble mean, and the minimum estimate is about 40 % lower than the ensemble mean. A large discrepancy in natural soil emissions among NMIP2 models exists, ranging from 3. 9 to 8. 6 Tg N yr -1, which needs to be reconciled in future research.
section_name: Uncertainties in estimates of soil N 2 O emissions

section_content: The uncertainties in process-based models primarily stem from differences in model configuration and process parameterization as well as from the missing processes and critical information (Tian et al., 2019). 

First, the NMIP2 models use divergent schemes to represent the flows of reactive N through ecosystems (biological N fixation, N deposition, N leaching, N volatilization, nitrification, and denitrification), which could result in large discrepancies in soil mineral N that serves as substrates for N 2 O production. Explicit representation of these processes is a critical need with respect to enhancing model simulation accuracy. 

Second, several important processes are missing in most process-based land models. Human management measures like tillage and legume cultivation can alter the physical and chemical characteristics of soil in croplands (Raji and Dörsch, 2020; Z. Yu et al., 2020), but they are not adequately represented in most NMIP2 models. Parameterizing these processes in the models is necessary to reduce uncertainty. Additionally, N addition in pasture and rangeland (e. g., live-stock excreta deposition, manure, and mineral fertilizer application) constitutes an important source of global soil N 2 O emissions (Davidson, 2009), accounting for more than half of the global agricultural N 2 O emissions (Dangal et al., 2019). However, only DLEM considered these processes. The consideration of N addition in managed grasslands is an essential task for process-based models to estimate grassland soil N 2 O emissions accurately. Moreover, most process-based models did not explicitly consider seasonal freeze-thaw processes or the thawing of permafrost, which can emit substantial amounts of N 2 O (Marushchak et al., 2011 (Marushchak et al.,, 2021;; Repo et al., 2009; Voigt et al., 2017; Del Grosso et al., 2022). It is recommended to include explicit representation of permafrost physics and seasonal freeze-thaw processes in process-based models, as this would help better catch the "hotspot" and "hot moment" of soil N 2 O emissions in northern regions (Wagner-Riddle et al., 2017). Current process-based models also face challenges in adequately representing the fine-grained landscape structure of Arctic ecosystems (e. g., landscape elements that act as ultra-emitters of N 2 O, like organic soil nonvegetated fractions); thus, integrating sub-grid information and processes into models may provide a solution for finegrained physical-hydrological modeling. 

Third, microbial nitrification and denitrification processes are regulated by multiple environmental factors, including substrate availability, precipitation, temperature, oxygen status, pH, vegetation type, and atmospheric CO 2 concentration (Butterbach-Bahl et al., 2013; Dijkstra et al., 2012; Li et al., 2020; Tian et al., 2019; Yin et al., 2022; Yu et al., 2022). However, there is significant divergence among NMIP2 models in their response to these factors. For example, simulated soil N 2 O emissions in response to N addition (i. e., fertilizer and manure N applications and N deposition) exhibit large divergence among the participating NMIP2 models, primarily due to differences in model representation of N processes and parameterization schemes. Moreover, in contrast to our findings indicating N fertilizer application and manure additions as dominant drivers, Harris et al. (2022) identified N deposition as the primary contributor to anthropogenic N 2 O emissions, accounting for 41 ± 14 % of all anthropogenic emissions. These different findings highlight the complex nature of N 2 O emissions and the need for further research to better understand the relative contributions of different N sources. For the climatic effects on soil N 2 O emissions, our NMIP2 models indicate enhanced N 2 O emissions due to warming, consistent with findings from experiment-based studies (Smith, 1997; Cui et al., 2018; Voigt et al., 2017; Wang et al., 2017), as the denitrifying bacteria community may adapt to higher temperature (Pärn et al., 2018). Additionally, considering that microbial nitrification and denitrification are also largely controlled by soil moisture (Butterbach-Bahl et al., 2013), it is important to address the discrepancies in NMIP2 models concerning soil moisture representation, such as soil depth, root distribution, root water uptake, and water movement processes (Ostle et al., 2009; Raats, 2007; Raoult et al., 2018). 

At the global scale, although NMIP2 models show large discrepancies in the CO 2 effect on soil N 2 O emissions, most NMIP2 models show a negative effect, suggesting that enhanced plant N uptake caused by a rising CO 2 concentration played a dominant role (Usyskin-Tonne et al., 2020; Tian et al., 2019). Nevertheless, observation-based results of the CO 2 effect diverge among different ecosystem types, with some studies reporting reduced N 2 O emissions in forests under elevated CO 2 (Phillips et al., 2001), while others found increased emissions in grasslands (Moser et al., 2018; Regan et al., 2011). It should be noted that the interactions among environmental factors influencing soil N 2 O emissions are still poorly represented in the NMIP2 models. Further targeted continuous measurements and manipulation experiments are needed to better represent the interactive effects of multiple environmental factors on N 2 O emissions in the models to improve the simulation of complex N 2 O dynamics. Finally, simulations targeted to explain the reconstructed increase in terrestrial N 2 O emissions over the deglaciation and during past abrupt climate events will further help to constrain process-based models (Fischer et al., 2019; Joos et al., 2020).
section_name: Uncertainties associated with NMIP2 models

section_content: The two methods for estimating deforestation-induced N 2 O changes have their limitations. The accuracy of the empirical estimates of post-deforestation pulse N 2 O emissions in tropical forests strongly depends on the availability of paired N 2 O observations in deforested and nearby intact forest sites (Melillo et al., 2001; Verchot et al., 1999), which are extremely scarce. Moreover, a fixed value was adopted as the default reference N 2 O emission rate for tropical forests to simplify computation, but it inevitably ignored the spatiotemporal heterogeneity in tropical forest N 2 O emissions (Barthel et al., 2022). It is also noted that there were no empirical post-deforestation N 2 O emission estimates in extra-tropical areas, as no feasible empirical relationships between N 2 O emissions and years after deforestation were available. The accuracy of process-based estimates (specifically by DLEM here) could be regulated by model-specific configurations for land-use change pathways. For example, in modeling tropical shift cultivation, DLEM assumed that agricultural lands newly converted from forests can only be reforested after at least 15 years to be consistent with the LUHv2 data (Hurtt et al., 2020). Meanwhile, treatments of different nitrogen pools (such as leaf, stem, root, and litter pools) during land conversion would directly influence the nitrogen substrate for nitrification and denitrification. The DLEM model follows the biomass allocation scheme proposed by previous studies (Houghton et al., 1983; McGuire et al., 2001), which may introduce uncertainty in varied land management practices. A bias in the LUHv2 land-use change data in regions experiencing drastic land conversions could also contribute to uncertainty in deforestation-induced GHG emissions, for example, in areas with large-scale plantations (Yu et al., 2022). 

In addition, developing forcing datasets with high quality and high spatiotemporal resolution is also important for reducing uncertainties in simulated N 2 O fluxes. Among various input variables, precise information regarding fertilizer and manure application (including crop-specific application rate, type, timing, and frequency) is pivotal for improving the accuracy of model simulations. However, this crucial information was not unified in NMIP2 simulations, leading to increased modeling uncertainty. To mitigate this issue, it is strongly recommended to use improved fertilizer and manure datasets that provide detailed information on crop-specific application rate, timing, and frequency to drive models in future intercomparison projects. Moreover, with the availability of additional high-precision datasets from manipulation field experiments (e. g., microbial data), we could use these datasets to constrain our models and delve deeper into the underlying mechanisms that regulate N 2 O fluxes (e. g., the role of soil microbes) and further incorporate these mechanisms into models to reduce uncertainties.
section_name: Land-cover change/deforestation

section_content: Measurement-based upscaling estimates are subject to uncertainties due to various factors. One major reason is the limited recording of microscale variables and the incomplete quantification of local EFs related to microbial N 2 O production. Sampling limitations also contribute to uncertainties, as the frequency and repeatability of measurements may not fully capture the high spatiotemporal variability in the N 2 O flux. The lack of the history of control sites further complicates the exclusion of observation data with significant legacy fluxes, thereby biasing our estimates. Additionally, gaps in global agricultural management datasets, particularly regarding fertilization details, enlarge the prediction interval of EFs and introduce uncertainties. We then used a Monte Carlo simulation to estimate three sources of uncertainty for predicting EFs based on a flux upscaling approach: (i) the fixed coefficients, (ii) the random coefficients, and (iii) input data. The uncertainty from sampling frequency and replication is reflected in the first source, while the uncertainty from unquantified sources related to field measurements is reflected in the second source. Each of the cropspecific SRNM models was run by randomly generating the fixed and random coefficients from their fitted multivariate normal distribution as well as climate, soil, and other relevant factors following independent normal distributions with the mean of the value in our dataset and standard deviation of the absolute difference between the dataset used in this study and other global datasets. Fertilizer frequency was randomly selected using a Bernoulli distribution. Predicted values were calculated through 1000 iterations to construct a 95 % prediction interval. The breakdown of uncertainty revealed that the random coefficients contributed the most to the estimation uncertainty, with observations showing that they explained more variance in EFs compared with fixed effects (47 %-74 % versus 19 %-35 %) and contributed to most of estimation uncertainty (Fig. 20 ). 

To address these limitations and reduce uncertainties, concerted efforts should be made to enhance the availability of N 2 O observations representing diverse agroecological conditions. Meanwhile, improving the availability of highprecision datasets (e. g., microbial data) and integrating these datasets and the derived underlying mechanisms to our models could also reduce uncertainties. Currently, most available field N 2 O observations (see the Supplement) are made in Europe, the USA, and China and are scarce in most developing countries (such as sub-Saharan Africa). Therefore, extending the global coverage of direct and indirect N 2 O flux measurements to encompass all major agricultural land-use types and climates, land-use changes, and management practices as well as conducting long-term high-frequency monitoring H. Tian et al. : Global nitrous oxide budget (1980 -2020) are particularly important to increase the reliability of EFs as well as upscale results from site to regional scales.
section_name: Uncertainties associated with the measurement-based upscaling approach

section_content: Global open-ocean N 2 O emissions derived from the ocean biogeochemistry models (Table 1 ) for the 2010-2019 period are estimated to be 3. 5 (2. 5-4. 7) Tg N yr -1. All models show the highest emissions associated with equatorial and coastal upwelling zones as well as with the major oxygen minimum zones (OMZs; e. g., the eastern equatorial Pacific and the Arabian Sea region of the northern Indian Ocean; see Fig. 21 ). These are regions characterized by high levels of biological productivity and higher subsurface organic matter remineralization, resulting in higher N 2 O yields in suboxic waters. 

The four participating models capture these characteristics but also show varying degrees of intensity in regional N 2 O emissions. The models also show good agreement with respect to representing the ocean regions of relatively low N 2 O ocean-atmosphere fluxes (i. e., open-ocean gyres where biological productivity is low). 

The spatial distribution of uncertainty in ocean N 2 O emissions among the models (Fig. 21 ) is similar to that of the net N 2 O ocean-atmosphere flux, with the highest uncertainties observed in the equatorial upwelling and low-oxygen waters associated with high subsurface N 2 O production (Babbin et al., 2020; Ganesan et al., 2020). The largest uncertainties are found in the equatorial Pacific, the Benguela upwelling region of the Atlantic, and the eastern equatorial Indian Ocean. Uncertainties in the ocean models' representation of N 2 O fluxes result from a range of model characteristics (Zamora and Oschlies, 2014; Martinez-Rey et al., 2015; Buitenhuis et al., 2018; Battaglia and Joos, 2018; Landolfi et al., 2017; Berthet et al., 2023), including the following: (i) uncertainties in ocean circulation -particularly the representation of upwelling zones and the ocean circulation features (often sub-grid scale) that control the extent and intensity of OMZs; (ii) simulation of ocean organic matter productivity, export production, and mesopelagic remineralization (a driver of the subsurface source function for N 2 O production in models); (iii) the model biogeochemical parameterizations representing N 2 O production and consumption from marine nitrification and denitrification processes, including their dependence on local dissolved oxygen concentrations and thresholds; and (iv) parameterization of oceanatmosphere gas-exchange fluxes. 

Model simulations of oceanic N 2 O are closely linked to the underlying modeled oxygen distributions, as the embedded biogeochemical parameterizations for N 2 O include the sensitivity of N 2 O cycling processes (e. g., nitrification and denitrification) to the local oxygen level (Ji et al., 2018). Significant uncertainties in modeled N 2 O fluxes result from model biases in the representation of dissolved oxygen, especially in low-oxygen zones such as the eastern equato-rial Pacific (Zamora and Oschlies, 2014; Martinez-Rey et al., 2015). Many ocean model simulations of dissolved oxygen display biases, especially in OMZs critical for N 2 O cycling (Martinez-Rey et al., 2015). To reduce potential sources of error from model-simulated oxygen, one N 2 O model in this analysis employs observation-based oxygen distributions when simulating ocean N 2 O (Buitenhuis et al., 2018). However, this approach also restricts a model's response to climate-related feedback on ocean oxygen. In addition, the models in this analysis include optimization and calibration of N 2 O cycle parameters by incorporating constraints from ocean observations (e. g., surface and interior N 2 O and microbially mediated process rates) (Battaglia and Joos, 2018; Buitenhuis et al., 2018; Berthet et al., 2023). A more detailed error analysis of N 2 O model parameters (including uncertainty in gas-exchange fluxes) in one of the component models (Buitenhuis et al., 2018) suggests estimated uncertainties in global fluxes from biogeochemical parameter specifications of ∼ 33 %. Further, a 1000-member ensemble with 11 parameters varied with one of the models and constrained with both surface and subsurface N 2 O observations yields an observation-constrained standard deviation of ±36 % around the median of 4. 3 Tg N yr -1 (Battaglia and Joos, 2018), consistent with a recent surface pN 2 O-based estimate of 4. 2 ± 1 Tg N yr -1 (Yang et al., 2020). Landolfi et al. (2017) also note that uncertainties arise in current model predictions of marine N 2 O fluxes due to the neglect of feedback from impacts of external nutrient sources and ocean acidification on marine productivity and the ocean nitrogen and oxygen cycles. Reducing uncertainties in model estimates of the evolution of ocean N 2 O fluxes will require accounting for these impacts in the underlying biogeochemical parameterizations. In addition, due to the high sensitivity of modeled N 2 O production and consumption rates to oxygen level in the key ocean OMZ zones, an important priority in reducing modeled ocean N 2 O flux uncertainties is to achieve a more accurate simulation of the ocean circulation and oxygen distribution of these regions.
section_name: Uncertainties in estimates of ocean N 2 O emissions

section_content: Estimates of N 2 O emissions vary by a factor of 2-3 in the continental shelf (one observation-based product and two models). The MEM-RF observational estimate (1. 63 Tg N yr, 2020), in particular along continental margins, is severely limited by the number of N 2 O observations, which is 2 orders of magnitude smaller than for CO 2. Observations tend to be localized in regions of strong air-sea disequilibrium and, thus, might be biased high (e. g., Babbin et al., 2020; Ganesan et al., 2020). 

In addition, many coastal regions remain undersampled, further limiting the performance of MEM-RF. For instance, models point to coastal N 2 O flux hotspots along midlatitude western boundaries (e. g., the US East Coast, the North Pacific east of Japan, the southeast coast of Australia, and the southeastern tip of Africa) that are not diagnosed in the observational product (Resplandy et al., 2024). Furthermore, N 2 O fluxes are highly spatially heterogeneous (scales of 1 to 100 km) due to land-ocean gradients and mesoscale and submesoscale features such as eddies (Arévalo-Martínez et al., 2017, 2019; Yang et al., 2020; Grundle et al., 2017). Eddies are instrumental in setting suboxic conditions favorable for N 2 O production, and it has been suggested that N 2 O production weakens within eddies during their transit across the shelf and further offshore (Arévalo-Martinez et al., 2016). These small-scale circulation features are important controls on N 2 O dynamics but are poorly accounted for in data-based reconstructions and models. This assessment provides the most up-to-date estimate of N 2 O climatological emissions from the global shelves, but the variability in these emissions remains uncertain. Each product covers a different time period and only provides limited or missing information on seasonal fluctuations, interannual variability, and long-term trends. For instance, only a handful of observations per year are available in most regions, providing a limited picture of seasonality and even more limited information on interannual variability (e. g., El Niño-Southern Oscillation and Pacific Decadal Oscillation) and global longer-term trends. Disentangling such influences from limited observations alone remains a major challenge. The effects of extreme events, such as storms and marine heat waves, on N 2 O fluxes are also currently not captured, and the intra-annual variability in hotspot regions, such as coastal upwelling systems, remains poorly constrained. Despite these limitations, data-based reconstructions and models suggest a vigorous seasonal cycle and, potentially, important variability on interannual timescales (Yang et al., 2020; Ganesan et al., 2020). The development of a global N 2 O ocean observation network (N 2 O-ON; Bange et al., 2019; Bange, 2022) is critically needed to better resolve spatiotemporal patterns and reduce uncertainties in N 2 O emissions. Increasing the density of observations in regions of high N 2 O disequilibrium and collecting long time series of N 2 O measurements will allow for better characterization of interannual changes and their dynamics. Meanwhile, algo- Parallel efforts based on the development of mechanistic models are also needed to strengthen our understanding of the dynamics underlying interannual N 2 O flux variability and to detect and attribute long-term anthropogenic effects. However, the representation of N 2 O processes in biogeochemical models remains limited, and very few climate models include marine emissions of N 2 O fluxes (only 4 out of 26 CMIP6 models considered in Séférian et al., 2020). Uncertainty persists regarding the various (micro-)biological processes that drive N 2 O cycling in coastal waters and sediments (Bange, 2022). Current global ocean biogeochemical models typically adopt an indirect representation of N 2 O production, which is diagnosed from environmental conditions (e. g., temperature) and O 2 consumption during remineralization of organic matter, without explicitly representing the bacterial pools and chemical reactions responsible for N 2 O production in suboxic waters (e. g., Aumont et al., 2015; Battaglia and Joos, 2018). In addition, key aspects of air-sea N 2 O exchange, such as the effects of surfactants in the sea surface microlayer (Kock et al., 2012), remain poorly understood. Finally, the interannual variability in N 2 O fluxes and its attribution to climatic and anthropogenic drivers is largely unknown. Disentangling these influences will benefit from (1) interannually varying observational N 2 O flux reconstructions at scales fine enough to capture high emissions along continental margins, (2) statistical methods that address the limited number of observations in space and time, and (3) N 2 O cycle simulations with forward mechanistic models. A blueprint for this work already exists with the approaches developed by the oceanic CO 2 community (Gruber, 2022). Similar approaches would enable attribution of N 2 O flux changes to specific drivers, leading to better predictability.
section_name: Uncertainties in emissions estimates from the continental shelves

section_content: The four atmospheric inversion frameworks show uncertainties in the estimates of N 2 O emissions, especially in hotspot regions such as eastern China, India, Europe, the US Corn Belt, and northern South America (Fig. 22 ). The uncertainties in inversion estimates are mainly from errors in the modeled atmospheric transport, the dependence on the prior information, and the availability of atmospheric observations. Every inversion framework in this study used a different atmospheric transport model with different horizontal and vertical resolutions (Table 1 ). By including estimates from multiple inversion frameworks with different modeled atmospheric transport, the systematic error can be assessed to some extent. The inversion estimates are dependent on the spatial pattern and magnitude of the prior flux estimates to an extent that is determined by the density of the observations. Using the same prior information might reduce the range in the atmospheric inversion estimates but not the uncertainty, as this depends on the spatiotemporal density of the atmospheric observations and the accuracy of the modeled transport. The uncertainty reduction (calculated as 1 minus the ratio of the posterior to prior uncertainty) indicates the degree of constraint on the inversion estimates (Fig. 23 ). It shows that the areas of South America, Africa, central and southern Asia, and Australasia are poorly constrained by observations. The relatively sparse distribution of current N O observation sites underscores the necessity of establishing more sites and regular aircraft profiles, especially in tropical and subtropical regions, to better constrain inversion models and to further reduce the posterior uncertainty.
section_name: Uncertainties in emissions estimates from atmospheric inversions

section_content: We recognize that N 2 O emissions contributed by termites could be a significant natural source in tropical and subtropical ecosystems (Brümmer et al., 2009; Miambi et al., 2022). The metabolic activity of microbial symbionts in the termite gut can maintain steep oxygen gradients, which facilitates nitrification and denitrification processes and the production of N 2 O (Brauman et al., 2015; Brune et al., 1995). Nevertheless, termites have a wide trophic diversity, and their N 2 O emission rates vary significantly, with some species creating emission hotspots (Brümmer et al., 2009), while others function as net sinks (Majeed et al., 2012). Feeding habits and the abundance of nitrifiers and denitrifiers in the gut are reported to be the key factors determining net N 2 O emission of termites (Brauman et al., 2015; Miambi et al., 2022). Termites that consume N-rich material, such as soil organic matter and fungi, exhibit high N 2 O production rates and emit N 2 O into the atmosphere, while those feeding on N-deficient wood can consume atmospheric N 2 O (Brauman et al., 2015). It is difficult to scale up calculations of net N 2 O emission by termites due to the lack of data on their abundance and biomass across global ecosystems; therefore, our understanding of the precise contribution of termites to the atmospheric N 2 O budget on a global scale remains limited, and it is not considered in our analysis.
section_name: Other missing fluxes

section_content: H. Tian et al. : Global nitrous oxide budget (1980 -2020) Competing interests. At least one of the (co-)authors is a member of the editorial board of Earth System Science Data. The peerreview process was guided by an independent editor, and the authors also have no other competing interests to declare. 

Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. 

Acknowledgements. This paper is the result of a collaborative international effort under the umbrella of the Global Carbon Project (a project of Future Earth and a research partner of the World Climate Research Programme) in partnership with the International Nitrogen Initiative (INI). We acknowledge all of the people and institutions who provided the data used in the global nitrous oxide budget as well as the institutions funding parts of this effort (see Table A4 ). We acknowledge the modeling groups for making their simulations available for this analysis. Systems Hub for supporting the GHG budget activities of the Global Carbon Project (GCP), including the global and regional N 2 O budget work. We are grateful to the EDGAR team (Monica Crippa, Diego Guizzardi, Edwin Schaaf, Marilena Muntean, Efisio Solazzo, Federico Pagani, and Manjola Banja) for the work needed to publish the EDGARv7. 0 Global Greenhouse Gas Emissions dataset (https://edgar. jrc. ec. europa. eu/dataset_ghg70, last access: 10 February 2022). Atul K. Jain thanks Shijie Shu and Tzu-Shun Lin for their involvement in developing and analyzing the ISAM model products used here. Giulia Conchedda, Griffiths Obli-Layrea, and Nathan Wanner contributed significant efforts to the generation of fertilizer, livestock, and soil nutrient data that underlie FAO's estimates of N 2 O data. 

Financial support. Please see a full list of funders in the Appendix (Table A4 ). 

Review statement. This paper was edited by Yuyu Zhou and reviewed by Stephen Del Grosso and two anonymous referees.
section_name: 2586

section_name: Appendix A: Additional tables