# Methane and Nitrous Oxide Budgets for Australasia: A Regional Assessment of Natural and Anthropogenic Sources and Sinks

Yohanna Villalobos, Josep G Canadell, Elizabeth D Keller, Peter R Briggs, Phillip Ford, Ian N Harman, Timothy W Hilton, Allison Hogikyan, Ronny Lauerwald, Damien T Maher, et al.

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## ► To cite this version:

Yohanna Villalobos, Josep G Canadell, Elizabeth D Keller, Peter R Briggs, Phillip Ford, et al. Methane and Nitrous Oxide Budgets for Australasia: A Regional Assessment of Natural and Anthropogenic Sources and Sinks. Global Biogeochemical Cycles, 2025, 39 (10), e2024GB008484 (33 p.). 10.1029/2024gb008484. hal-05476992

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Global Biogeochemical Cycles®

## RESEARCH ARTICLE

10.1029/2024GB008484

**Special Collection:**

Regional Carbon Cycle Assessment and Processes - 2

**Key Points:**

* Anthropogenic activities drive 55% of Australasia's CH₄ budget, natural sources 45%, with wetlands contributing 42%, though uncertain

* Natural sources dominate 82% of Australasia's N₂O budget, anthropogenic 18%, with Australia contributing 92% of total N₂O emissions

* Australia's CH₄ and N₂O top-down and bottom-up budgets align within uncertainties, while New Zealand shows major discrepancies

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

Y. Villalobos, yohanna.villalobos@nateko.lu.se

**Citation:**

Villalobos, Y., Canadell, J. G., Keller, E. D., Briggs, P. R., Ford, P., Harman, I. N., et al. (2025). Methane and nitrous oxide budgets for Australasia: A regional assessment of natural and anthropogenic sources and sinks. *Global Biogeochemical Cycles*, *39*, e2024GB008484. https://doi.org/10.1029/2024GB008484

Received 3 JAN 2025

Accepted 8 SEP 2025

**Author Contributions:**

**Conceptualization:** Yohanna Villalobos, Josep G. Canadell, Elizabeth D. Keller

**Formal analysis:** Yohanna Villalobos, Josep G. Canadell, Elizabeth D. Keller, Ian N. Harman, Allison Hogikyan, Ronny Lauerwald, Naiqing Pan, Laure Resplandy, Zhen Zhang

**Investigation:** Yohanna Villalobos, Josep G. Canadell, Elizabeth D. Keller, Peter R. Briggs, Phillip Ford, Ian N. Harman,

© 2025. The Author(s). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

## Methane and Nitrous Oxide Budgets for Australasia: A Regional Assessment of Natural and Anthropogenic Sources and Sinks

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Yohanna Villalobos¹,² ID, Josep G. Canadell² ID, Elizabeth D. Keller³,⁴ ID, Peter R. Briggs², Phillip Ford², Ian N. Harman² ID, Timothy W. Hilton⁴, Allison Hogikyan⁵ ID, Ronny Lauerwald⁶ ID, Damien T. Maher⁷ ID, Adrien Martinez⁸ ID, Naiqing Pan⁹,¹⁰ ID, Benjamin Poulter¹¹ ID, Laure Resplandy¹² ID, Judith A. Rosentreter⁷ ID, Marielle Saunois⁸ ID, Hanqin Tian⁹,¹⁰ ID, Jacob Yeo⁷, and Zhen Zhang¹³

¹Lund University, Lund, Sweden, ²CSIRO Environment, Canberra, ACT, Australia, ³Victoria University of Wellington, Wellington, New Zealand, ⁴Earth Sciences New Zealand, Lower Hutt, New Zealand, ⁵Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ, USA, ⁶Université Paris-Saclay, INRAe/AgroParisTech, UMR EcoSys, Palaiseau, France, ⁷Faculty of Science and Engineering, Southern Cross University, Lismore, NSW, Australia, ⁸Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, Gif-sur-Yvette, France, ⁹Center for Earth System Science and Global Sustaining ability, Schiller Institute for Integrated Science and Society, Boston College, Chestnut Hill, MA, USA, ¹⁰Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA, USA, ¹¹Spark Climate Solutions, San Francisco, CA, USA, ¹²Department of Geosciences and High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA, ¹³National Tibetan Plateau Data Center (TPDC), Chinese Academy of Sciences, Institute of Tibetan Plateau Research, Beijing, China

**Abstract** We present the CH₄ and N₂O budgets for anthropogenic and natural sources and sinks of Australasia (Australia and New Zealand) from 2010 to 2019 using bottom-up and top-down methods, in line with the RECCAP-2 initiative, with extensions to 2022. We show that the bottom-up CH₄ budget for Australasia (2010–2019) was a net source of 14.1 ± 5.5 Tg CH₄ yr⁻¹, with Australia and New Zealand contributing 84% and 16%, respectively. Anthropogenic sources contributed 55% of all CH₄ emissions, the rest coming from natural sources, primarily wetlands. The bottom-up N₂O budget was a net source of 0.5 ± 0.3 Tg N₂O yr⁻¹, with Australia contributing the majority (92%), mainly from natural sources (82%). Australasia top-down CH₄ (10.4 ± 0.5 Tg CH₄ yr⁻¹) and N₂O budgets (0.8 ± 0.5 Tg N₂O yr⁻¹) differ in magnitude from the bottom-up budgets but remain consistent within their uncertainties. Similar consistency is observed for Australia, while New Zealand shows significant discrepancies, particularly for N₂O, where the bottom-up estimate is 71% higher than the top-down estimate. In terms of trends, bottom-up natural wetland CH₄ emissions increased in both countries between 2010 and 2019. CH₄ emissions from enteric fermentation slightly declined in Australia but increased in New Zealand. Soil N₂O emissions from nitrogen additions increased in both countries, with a significant rise in New Zealand driving the overall positive trend in anthropogenic emissions. These findings highlight critical sectors with large mitigation potential and the significance of monitoring natural sources for possible biogeochemical-climate feedback.

**Plain Language Summary** This study assesses the natural- and human-caused sources and sinks of methane (CH₄) and nitrous oxide (N₂O) in Australia and New Zealand, together referred to as the Australasian CH₄ and N₂O budgets, for 2010 to 2019. We estimate that Australasia emitted 14.1 Tg of CH₄ over the past decade, with Australia contributing 84% and New Zealand 16% of the total emissions. However, an independent analysis using satellite and ground-based observations suggests these CH₄ emissions may be 25% lower. For the N₂O budget, Australasia emitted 0.8 Tg of N₂O per year, with 91% of these emissions originating in Australia. In contrast to the CH₄ budget, an independent assessment based on ground-based observations, indicates that actual N₂O emissions may be around 62% higher. Reducing the large uncertainties in CH₄ (e.g., emissions from natural wetlands) and N₂O (e.g., emissions from natural soils) fluxes is essential to understand better the scale of these natural sources and their potential for future impacts on climate change.
Timothy W. Hilton, Ronny Lauerwald, Damien T. Maher, Adrien Martinez, Naiqing Pan, Benjamin Poulter, Judith A. Rosentreter, Marielle Saunois, Hanqin Tian, Jacob Yeo, Zhen Zhang

**Methodology:** Yohanna Villalobos, Josep G. Canadell, Ronny Lauerwald

**Supervision:** Josep G. Canadell

**Visualization:** Yohanna Villalobos

**Writing – original draft:**

Yohanna Villalobos

**Writing – review & editing:**

Yohanna Villalobos, Josep G. Canadell, Elizabeth D. Keller, Peter R. Briggs, Ian N. Harman, Timothy W. Hilton, Allison Hogikyan, Ronny Lauerwald, Naiqing Pan, Benjamin Poulter, Laure Resplandy, Judith A. Rosentreter, Marielle Saunois, Hanqin Tian, Jacob Yeo, Zhen Zhang

# 1. Introduction

Carbon dioxide ($CO_2$), methane ($CH_4$), and nitrous oxide ($N_2O$) are the most important long-lived greenhouse gases (GHGs), contributing approximately 65%, 16%, and 6%, respectively, to the total anthropogenic effective radiative forcing from well-mixed GHGs between 1750 and 2019 (IPCC, 2021; AR6 WGI, Chapter 7). While $CO_2$ is the dominant driver of current anthropogenic warming due to its high atmospheric abundance and long lifetimes, $CH_4$ and $N_2O$ also play substantial roles. This is because, under the assumption of equal mass emissions, $CH_4$ and $N_2O$ have much higher global warming potentials (GWPs) than $CO_2$. GWP is an emission-based metric that compares the cumulative warming impact of a unit mass of a greenhouse gas to that of $CO_2$ over a given time horizon. Methane, in particular, has a relatively short atmospheric lifetime of about 10 years but is 86 times more effective than $CO_2$ at trapping heat over a 20-year horizon, and is estimated to be responsible for approximately 0.5°C of the observed global warming since pre-industrial times (IPCC, 2021). Despite international and national mitigation efforts, atmospheric $CH_4$ concentrations have increased by a factor of 2.6 (from 729 ppb in 1,750–1,916 ppb in 2023), and $N_2O$ by a factor of 1.3 (from 270 to 337 ppb) (WMO, 2024). Their quantification and reduction remain essential for addressing both near- and long-term climate targets.

To effectively assess and monitor the impact of policies aimed at decreasing $CH_4$ and $N_2O$ levels in the atmosphere, countries require accurate estimates of their emissions (Petrescu et al., 2023). However, quantifying these emissions is challenging, as both $CH_4$ and $N_2O$ are produced by natural processes as well as human activities (Canadell et al., 2021; Saunois et al., 2025; Tian et al., 2024). Natural sources of these two GHGs are not included in the national greenhouse gas inventories (NGHGIs), which focus exclusively on direct human-induced fluxes, as required by the United Nations Framework Convention on Climate Change (UNFCCC). However, climate change responds to both direct anthropogenic emissions and indirect emissions through changes in the natural environment (e.g., biogeochemical-climate feedbacks). Examples of the later are the effects of warming and changes in precipitation and atmospheric $CO_2$ on $CH_4$ emissions from natural wetlands (Peng et al., 2022; Zhang et al., 2023) and wildfires (Byrne et al., 2024; Frausto-Vicencio et al., 2023), which have increased this century in response to climate change (Jackson, Saunois, et al., 2020; Saunois et al., 2020).

To support the monitoring of natural and anthropogenic sources and sinks of $CH_4$ and $N_2O$, the Regional Carbon Cycle Assessment and Processes, Phase 2 (RECCAP-2), coordinated by the Global Carbon Project (GCP), supports the establishment of regional GHG budgets of $CO_2$, $CH_4$, and $N_2O$, across the globe. The RECCAP-2 regional delineation encompasses 10 land regions (North America, South America, Europe, Africa, West Asia, South Asia, East Asia, Russia, Southeast Asia, and Australasia), five ocean regions (Pacific Ocean, Atlantic Ocean, Southern Ocean, Artic Ocean, and Indian Ocean), as well as regions of special interest such as the permafrost and polar regions, focusing on the most recent complete decade (2010–2019) (Ciais et al., 2022).

In this study, we focus on the RECCAP-2 land region of Australasia, which includes Australia and New Zealand, to develop the first integrated regional assessment of $CH_4$ and $N_2O$ budgets that separately evaluates anthropogenic and natural sources and sinks for each country. Building on the Australasia $CO_2$ budget by Villalobos et al. (2023), which focused was on carbon dioxide fluxes, this study expands the scope to include both $CH_4$ and nitrous oxide $N_2O$. These budgets integrate both anthropogenic and natural sources and sinks, providing the first complete assessment of their combined contributions at the regional level.

Understanding the relative contributions of anthropogenic and natural sources is essential for identifying effective mitigation strategies. In Australasia, the main anthropogenic sources include enteric fermentation for $CH_4$ and nitrogen fertilizer use for $N_2O$, both linked to the agricultural sector. Natural emissions from wetlands ($CH_4$) and soils ($N_2O$) also contribute substantially to the regional budgets, but remain poorly understood. These natural sources are influenced by human-induced warming and rising atmospheric $CO_2$, however lie outside the scope of direct mitigation efforts. By separately quantifying anthropogenic and natural components, this assessment provides policy-relevant insights to guide emissions reductions, support national reporting under the UNFCCC, and identify research needs to improve understanding of the magnitude and temporal dynamics of natural fluxes.

Following the RECCAP-2 protocol, and similarly to the $CO_2$ budget, we use two complementary approaches to construct the $CH_4$ and $N_2O$ budgets: (a) a bottom-up approach using data from the National GHG Inventories (NGHGIs), primarily the one that governments submitted to the UNFCCC, process-based models (i.e., Dynamic Global Vegetation Models–DGVMs) and data-driven methods, and (b) a top-down approach using atmospheric inversion estimates, which are based on in situ and satellite measurements. We use the most accurate regional data
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Decadal (2010–2019) CH₄ budget for Australasia. The direction of fluxes is shown by the arrows. In the atmosphere box (CH₄ balance), negative numbers indicate that the land is a CH₄ sink, and positive numbers indicate that the land is a source of CH₄ to the atmosphere. Red solid arrows indicate anthropogenic fluxes, green indicates natural fluxes. Water-air continental shelf fluxes (magenta dashed color) are not part of the Australasia budget, but they are included in the diagram because they represent important information about the CH₄ cycle.</figcaption></figure>

available for Australia and New Zealand, as well as estimates extracted from global data products and modeling when regional data is lacking.

## 2. Methods

### 2.1. Bottom-Up CH₄ and N₂O Approach

For constructing the CH₄ (Figures 1–3) and N₂O bottom-up (Figures 7–9) budgets for the Australasia RECCAP-2 region, we followed an accounting framework similar to that proposed by Ciais et al. (2022), adapted for the CH₄ and N₂O budgets as outlined by Saunois et al. (2020) and Tian et al. (2024).

#### 2.1.1. CH₄ Budget

We considered 18 CH₄ flux components to estimate the bottom-up CH₄ budget for Australasia (Figure S1 in Supporting Information S1, Equation 1). Terrestrial CH₄ fluxes were grouped into two categories: (a) anthropogenic (from human activities) and (b) natural sources and sinks. We also included the land-to-ocean aquatic continuum (LOAC) fluxes, which were also separated into natural and anthropogenic sources.

$$ F_{CH_4} = (F_{A,FD} + F_{A,RC} + F_{A,M} + F_{A,EF} + F_{A,FB}) + (F_E + F_I + F_W) + (F_{T,NW} - F_{T,SU} + F_{T,TE} + F_{T,WF} + F_{T,BB}) + (F_{W-A,R} + F_{W-A,LR}) + (F_{ES} + F_{CW}) + F_G \quad (1) $$

#### 2.1.1.1. Anthropogenic CH₄ Sectors

For anthropogenic CH₄ sources, we included emissions from agricultural activities, such as farm dams (Fₐ,FD), rice cultivation (Fₐ,RC), manure fermentation (Fₐ,M), enteric fermentation (Fₐ,EF), and field burning of agricultural residues (Fₐ,FB). For non-agricultural anthropogenic CH₄ sources, we considered emissions from energy (F_E), industry (F_I), waste and landfill (F_W), and from prescribed fires and low-intensity forest fires (F_{T,BB}). All CH₄ emissions from all anthropogenic sources (with some exceptions for certain LOAC fluxes) were obtained from Australia's and New Zealand's NGHGIs (Australian National Inventory Report, 2024; New Zealand Ministry for the Environment, 2024).
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Decadal Australia CH₄ budget (2010–2019). Details as for Figure 1.</figcaption></figure>

CH₄ emissions from Australia and New Zealand's NGHGI are compiled in accordance with the 2006 IPCC Guidelines (IPCC, 2006) and subsequent updates, including the 2013 IPCC Wetlands Supplement and the 2019 IPCC Refinement. Emission estimates are primarily based on activity data, such as livestock numbers, fertilizer application, and waste generation, paired with emissions factors. Both countries predominantly use IPCC Tier 2 and Tier 3 methodologies, which incorporate country-specific emissions factors and advanced process-based models to improve accuracy and align estimates with regional conditions. In cases where detailed data or resources are unavailable, Tier 1 methods, which rely on default emissions factors provided by the IPCC, are used as an alternative. Information on uncertainties for both Australia and New Zealand was obtained from the Australian National Inventory Report, 2024 (Annex 2), and the New Zealand Ministry for the Environment, 2024.

LOAC human-induced fluxes arising from inland systems (streams, rivers, lakes, and reservoirs), estuaries, and coastal vegetated ecosystems (seagrasses, salt marshes, and mangroves) are described in Section 2.1.1.3, including its partition between anthropogenic and natural fluxes.

### 2.1.1.2. Natural CH₄ Sectors

A positive CH₄ flux value indicates CH₄ emission (source), and a negative value indicates CH₄ uptake (sink). We include emissions from wetlands (F<sub>T,NW</sub>), CH₄ soil uptake (F<sub>T,SU</sub>), termites (F<sub>T,TE</sub>), wildfires (F<sub>T,WF</sub>), and geological sources (F<sub>G</sub>). F<sub>T,NW</sub> emissions were calculated considering the ensemble mean of 16 global process-based models compiled in Zhang et al. (2024) (CH4MOD; Li et al., 2010), the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC; Arora et al., 2018; Melton & Arora, 2016), the Dynamic Land Ecosystem Model (DLEM; Tian et al., 2015, 2023), the Energy Exascale Earth System Model–Ecosystem Demography Component (ELM-ECA; Riley et al., 2011), the Integrated Science Assessment Model (ISAM; Shu et al., 2020; Xu et al., 2021), the Jena Scheme for Biosphere-Atmosphere Coupling in Hamburg (JSBACH; Kleinen et al., 2020, 2021, 2023), the Joint UK Land Environment Simulator (JULES; Gedney et al., 2019), the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS; McGuire et al., 2012), LPJ model developed by the Max Planck Institute (LPJ-MPI; Kleinen et al., 2012), the LPJ model version developed by the Swiss Federal Institute for Forest, Snow and Landscape Research-WSL (LPJ-WSL; Zhang et al., 2016), the Land surface Processes and eXchanges–Bern version (LPX-Bern; Spahni et al., 2011; Stocker et al., 2014), the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE; Ringeval et al., 2011), the Sheffield Dynamic Global Vegetation Model (SDGVM; Beerling & Woodward, 2001; Hopcroft et al., 2011, 2020), the Terrestrial Ecosystem Model-Methane Dynamics Module (TEM-MDM; Zhuang et al., 2004), the TRIPLEX Greenhouse
Gas Model (TRIPLEX-GHG; Zhu et al., 2014, 2015), and the Vegetation Integrative Simulator for Trace gases (VISIT; Ito & Inatomi, 2012).

All wetland models were run under a common protocol, using two different climate forcing data sets: (a) CRU/CRU-JRA55 (Climatic Research Unit-Japanese 55-year Reanalysis), and (b) GSWP3-W5E5 (Global Soil Wetness Project Phase 3-WATCH Forcing Data methodology applied to ERA5). We selected diagnostic runs, which use an updated wetland area data set, WAD2Mv2.0 (Wetland Area Dynamics for Methane Modeling, version 2.0) revised by Zhang et al. (2021), which includes floodplain and swamps, but excludes river, stream, lake, and pond areas to prevent overlap with the inland water fluxes as described below. This improvement uses data from the Global River Widths from Landsat (GRWL) database (Allen & Pavelsky, 2018) and HydroLakes v1.0 (Messager et al., 2016), replacing the previous use of the Global Surface Water product in WAD2Mv1.0.

Soil CH₄ uptake (F<sub>T,SU</sub>) (e.g., Consumption by soil methanotrophic bacteria) was obtained a process-based model, Methanotrophy Model (Memo; Murguia-Flores et al., 2021), which simulations are based on a scheme of soil CH₄ uptake fluxes by solving a reaction-diffusion equation. It accounts for (a) CH₄ diffusion into soil and (b) microbial CH₄ oxidation controlled by physical soil structure properties such as soil porosity, soil moisture, clay content, and temperature.

Termite CH₄ emissions (F<sub>T,TE</sub>), produced via microbial decomposition of organic material in the termite gut, were obtained from a global data set produced by Ito (2023). This global study employs an empirical approach, that incorporates data from climate, land use, land cover, termite distribution, and emission factors. It also uses a process-based biogeochemical model that simulates vegetation productivity and the soil carbon cycle, enabling mechanistic inferences about environmental responses. Uncertainties in this flux were calculated as the difference between the Ito (2023) data set and a second global termite data set used in Saunois et al. (2020).

Wildfires (F<sub>T, Wf</sub>) were calculated by subtracting the prescribed fire emissions and low-intensity forest fire emissions, as reported in the Australian and New Zealand NGHGI fire data sets (Australian National Inventory Report, 2024; New Zealand Ministry for the Environment., 2024) from the ensemble mean of two global data sets: the Global Fire Emissions Database (GFED4s) (van der Werf et al., 2017), the Global Fire Assimilation System (GFAS) (Kaiser et al., 2012; Darmenov & da Silva, 2013).

Geological sources (F<sub>G</sub>) were obtained from Etiope et al. (2019) where four main categories were extracted for this study: onshore hydrocarbon seeps (OSs), submarine (offshore) seeps (SSs), diffuse microseepage (MS), and geothermal manifestations (GMs).

### 2.1.1.3. Land-Ocean Aquatic Continuum CH₄ Fluxes

LOAC-related emissions from inland water ecosystems such as streams, rivers (F<sub>W-A,R</sub>), lakes (natural and regulated by dams) and reservoirs (F<sub>W-A,LR</sub>) were obtained from Lauerwald et al. (2023). In their global study, Lauerwald et al. (2023) used existing published global estimates of inland water GHG emissions, which were rescaled to consistent values of inland water surface area. For rivers, for example, the surface area was calculated from the GRWL database (Allen & Pavelsky, 2018), and for lake and reservoir surface area, it was derived from HydroLAKES v.1.1 (Messager et al., 2016). We note that small water bodies such as ponds (<100 m²), and temporary water bodies such as inundated floodplains and swamps were not included in the synthesis by Lauerwald et al. (2023). However, floodplain and swamps are covered by WAD2M and are, therefore, included in wetland process-based model estimates. Uncertainty around this flux component was calculated as the ensemble spread (standard deviation) of these existing global data sets rescaled for Australasia.

To develop a first estimate of the proportion of freshwater CH₄ emissions from natural and human sources, we used phosphorus (P) loading as a proxy, as it often indicates water pollution levels. Using the Global Nutrient Export from WaterSheds 2 (GlobalNEWS2) data set (Mayorga et al., 2010) and an empirical formula from Beaulieu et al. (2019), we separated the anthropogenic and natural P concentrations (dissolved organic and inorganic phosphorus). We found that approximately 50% of CH₄ freshwater emissions in Australia and around 70% in New Zealand are anthropogenic. These estimates are most applicable to natural lakes and those regulated by dams, while emissions from reservoirs were considered entirely anthropogenic. For Australia and New Zealand rivers, we attributed 30% of CH₄ emissions to anthropogenic sources, based on global estimates from Saunois et al. (2025). We assumed that this global 30% applies to the Australasian region in the absence of region-
specific studies making this partition. This percentage reflects the significant impact of eutrophication, largely driven by high P levels from agricultural runoff, wastewater discharge, and other urban sources.

LOAC estuary CH₄ water-air fluxes (FES) and CH₄ fluxes from coastal vegetation (FCW) were obtained from Rosentreter et al. (2023). As described by Rosentreter et al. (2023), FES were calculated using an upscaling method based on global data from peer-reviewed publications collected until the end of 2020. For the Australasia region, 16 sites were compiled. CH₄ flux rates at each site location (within <10 km of each other) were grouped and aggregated by estuary type (tidal systems and deltas, lagoons, fjords) for Australia and New Zealand separately. Australian and New Zealand estuarine surface area was derived from an extrapolation-based regionalized re-evaluation of the global estuarine surface area (Laruelle et al., 2023).

Following a similar approach, CH₄ fluxes from coastal vegetation in Rosentreter et al. (2023) were also derived from an upscaling method using peer-reviewed publications. For CH₄ in coastal wetland vegetation, 15 sites were compiled for the Australasia region. CH₄ flux rates at each site location (>10 km apart) were grouped and aggregated by coastal wetland type (mangroves, salt marshes, and seagrasses) and separated by country (Australia or New Zealand). The CH₄ and N₂O flux rates from sediment-water, sediment-air or water-air interfaces were combined with eddy-covariance measurements where available.

Uncertainty in both FES and FCW estimates were assessed using bootstrapping methods, which produced a complete set of statistics, including the first (Q1) and third (Q3) quartiles (see the uncertainty analysis in Rosentreter et al. (2023). From these data, the associated standard deviation used in this study was calculated.

To separate the natural and anthropogenic contributions of FES and FCW, we used an expert assessment. For Australian estuaries, Yeo, Rosentreter, Oakes, Schulz, and Eyre (2024) quantified a proportion of 75% natural fluxes (estuarine surface area classified as low to moderately disturbed) and 25% anthropogenic fluxes (estuarine surface area classified as high to very highly disturbed), based on the relative occurrence of estuary types and extent of disturbance. Disturbance was quantified qualitatively with four classes (low to very high); low disturbance was classified as >90% natural catchment covers with intact estuarine habitats and ecological processes with minimal impact from pests, and no impediments or alterations to hydrology. Very high disturbance was classified as <35% natural catchment cover, with cleared and degraded estuarine habitats and ecological processes, and significant modifications to hydrology (National Land and Water Resources Audit & Commonwealth Government, 2002) as described in Supplementary Table 6 in Yeo, Rosentreter, Oakes, Schulz, and Eyre (2024).

For FCW (mangrove forests, salt marshes and seagrasses), global estimates indicate that approximately 20%–40% of these ecosystems have been lost since 1980 (Campbell et al., 2022; Polidoro et al., 2010; Waycott et al., 2009). In Australia, approximately 30% of salt marshes have been impacted by human activities such as coastal development and agricultural expansion (DCCEEW, 2024). Here, we assigned a conservative estimate of 70% to natural and 30% to anthropogenic CH₄ sources for both estuaries and coastal wetlands.

Our assessment also considered CH₄ air-sea fluxes from continental shelves (FW-A,CS), for which estimates were obtained from Resplandy et al. (2024). FW-A,CS flux estimates in Resplandy et al. (2024) rely on one CH₄ observation-based product developed by Weber et al. (2019), which uses a machine learning approach to estimate the spatial and temporal patterns of global CH₄ in shallow coastal waters. The coastal ocean boundary definition follows the MARgins and CATchments Segmentation (MARCATS) scheme by Laruelle et al. (2017), which defines the seaward boundary at 300 km (about 186.41 mi) from shore or the 1,000-m isobath, whichever is farther. Such definition excludes estuaries and coastal vegetation described in Rosentreter et al. (2023) to avoid double counting. We made a small modification to the MARCATS boundary for Australasia, excluding some of the coastal shelf areas around small islands in northern Australian, as shown in Figures S11 and S12 in Supporting Information S1.

It is important to note that CH₄ emissions from continental shelves (FW-A,CS) were not incorporated into Equation 1 as part of the total bottom-up CH₄ budget, as these estimates lie beyond Australia's coastline. However, they are represented in Figure 1 (indicated by a dashed-line arrow) to highlight their significance in the CH₄ cycle, connecting the coastal aquatic ecosystem with the open ocean.
<figure><img src="image_6.png" /><figcaption>Figure 3. Decadal New Zealand CH$_{4}$ budget (2010–2019). Details as for Figure 1.</figcaption></figure>

## 2.1.2. N$_{2}$O Budget

Similar to the CH$_{4}$ bottom-up budget, we considered 17 N$_{2}$O flux components to estimate the N$_{2}$O bottom-up budget for Australasia (Figure S2 in Supporting Information S1, Equation 2). These fluxes were also grouped into two categories: (a) anthropogenic N$_{2}$O emissions, (b) natural N$_{2}$O sectors, and LOAC fluxes also separated by anthropogenic and natural sources.

$$F_{N_2O} = (F_{A,S} + F_{A,M} + F_{A,FB} + F_{A,C}) + (F_E + F_I + F_W) + (F_{T,NS} + F_{T,WF} - F_{T,CC} + F_{T,BB} + F_{T,LUC} + F_{T,S}) + (F_{W-A,R} + F_{W-A,LR}) + (F_{ES} + F_{CW}) \quad (2)$$

### 2.1.2.1. Anthropogenic N$_{2}$O Emissions

Among the 17 N$_{2}$O flux components, 11 were considered anthropogenic sources. Under agricultural activities (represented by the agroecosystem, yellow box in Figure S2 in Supporting Information S1), we include emissions related to farming or crop production, such as direct and indirect N$_{2}$O emissions from agricultural soils (F$_{A,S}$), manure management (F$_{A,M}$), field burning of agricultural residues (F$_{A,FB}$), and aquaculture (F$_{A,C}$).

Other non-agricultural N$_{2}$O sources, as shown in the terrestrial ecosystems green box in the Figure S2 in Supporting Information S1 include soil N$_{2}$O emissions due to land use and land use changes (F$_{T,LUC}$) (e.g., direct N$_{2}$O emissions from nitrogen (N) mineralization/immobilization associated with loss/gain of soil organic matter); biomass burning (F$_{T,BB}$), including prescribed fires and low-intensity wildfires in temperate forests that occur under controlled conditions and are neither frequent nor extreme; soil N$_{2}$O emissions indirectly influenced by changes in CO$_{2}$ and climate (F$_{T,CC}$), and other indirect N$_{2}$O emissions resulting from anthropogenic nitrogen additions, such as atmospheric nitrogen (N) deposition on land (F$_{T,S}$).

Additional anthropogenic N$_{2}$O fluxes were classified as sources originating from energy and industrial activities (shown in the gray box in the Figure S2 in Supporting Information S1). These include N$_{2}$O emissions from energy sources (F$_{E}$), such as fuel combustion and fugitive emissions from oil, natural gas, and solid fuels; industrial activities (F$_{I}$), including the chemical and metal industries; and waste sources (F$_{W}$).

The majority of the anthropogenic N$_{2}$O sources and their uncertainties were obtained from Australia and New Zealand National (GHG) inventories (Australian National Inventory Report, 2024; New Zealand Ministry for the
Environment., 2024) with the exception of soil N₂O emissions indirectly influenced by changes in CO₂ and climate (Fₜ,CC), as well as other indirect N₂O emissions resulting from anthropogenic nitrogen (N) additions (Fₜ,S).

Indirect N₂O emissions, such as Fₜ,CC and Fₜ,S were obtained from the GCP N₂O model Inter-Comparison Project Phase 2 (NMIP-2) (Tian et al., 2024), which includes simulation experiments isolating the effects of CO₂ and climate on the terrestrial soil N₂O emissions, and quantifying the impact of indirect N additions on N₂O emissions from soils (see Section 2.4.5 in Tian et al., 2024).

These simulation experiments isolate the impacts of elevated atmospheric CO₂ and climate change on soil N₂O emissions by modifying plant nitrogen uptake and soil microbial processes, including nitrification and denitrification. These mechanisms are supported by previous studies showing that elevated CO₂ can suppress N₂O emissions by enhancing plant N uptake and reducing substrate availability for microbes, while rising temperatures generally enhance microbial activity and increase N₂O fluxes (Butterbach-Bahl et al., 2013).

### 2.1.2.2. Natural N₂O Emissions

For natural N₂O fluxes, we considered two key sources (shown as green vertical arrows in the Figure S2 in Supporting Information S1): emissions from natural soils (Fₜ,NS) and wildfires (Fₜ,WF). N₂O emissions from natural soils were estimated using the ensemble mean of NMIP-2 (Tian et al., 2024). Simulations for these natural N₂O soil emissions, as in Tian et al. (2024), were derived from eight process-based models: the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC; Asaadi & Arora, 2021; Melton et al., 2020; Kou-Giesbrecht & Arora, 2022), the Dynamic Land Ecosystem Model (DLEM; Tian et al., 2015; Xu et al., 2017; You et al., 2022), the Energy Exascale Earth System Model-Land (ELM); Zhu et al., 2019), the Integrated Science Assessment Model (ISAM; Shu et al., 2020; Xu et al., 2021), the Land surface Processes and eXchanges-Bern version (LPX-Bern; Lienert & Joos, 2018; Joos et al., 2020), the Organizing Carbon and Nitrogen Dynamics in Ecosystems (O-CN; Zaehle et al., 2011), the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE; Goll et al., 2017), and the Vegetation Integrative Simulator for Trace gases (VISIT; Ito et al., 2018). All models were driven by potential vegetation/land cover and other environmental factors in the pre-industrial period (1850) (see Section 4.2.2 in Tian et al. (2024).

Wildfire N₂O emissions, Fₜ,WF, were calculated by taking the mean of N₂O emissions from fires reported by GFAS and GFED and subtracting the anthropogenic fire estimates provided by the Australian and New Zealand NGHGI, including prescribed fires (controlled biomass burning), wildfires (excluding anomalous fires), field burning of agricultural residues as well as incineration and open burning of waste.

### 2.1.2.3. Land-Ocean Aquatic Continuum N₂O Fluxes

LOAC N₂O emissions from freshwater systems, including streams, rivers, lakes (both natural and regulated by dams), and reservoirs, were obtained from Lauerwald et al. (2023). These estimates follow the same methodology as described for CH₄ inland water flux estimates in Section 2.1.1.3 N₂O emissions from water sources were calculated using existing published estimates from streams, rivers, lakes, and reservoirs, homogenized with regard to underlying global maps of water surface area using the HydroLakes and GRWL databases. N₂O emissions contributions from natural and anthropogenic sources from inland freshwater systems were estimated using simulations from a process-based coupled terrestrial-aquatic model, the Dynamic Land Ecosystem Model-Terrestrial-Aquatic Coupled version (DLEM-TAC; Li et al., 2024). DLEM-TAC simulations quantify the effects of environmental factors such as climate change, atmospheric CO₂ concentrations, land use change, nitrogen deposition, and agricultural nitrogen application (fertilizer and manure) on stream, rivers, lakes, and reservoirs. Using these simulations, we calculated the percentage increase in N₂O emissions from freshwater systems from 1850 to 1859 to the most recent decade, 2010–2019, to determine the relative proportion of N₂O emissions from inland waters attributable to anthropogenic sources.

In the Australasian region, DLEM-TAC simulations indicated that N₂O emissions were 20% lower in 1850–1859 compared to 2010–2019, suggesting that around 80% of last decade's emissions were natural, while 20% were human-induced. For consistency, these percentages of contribution were applied equally to Australia and New Zealand.
LOAC estuary N₂O water-air fluxes (FES) and N₂O fluxes from coastal vegetation (FCW) were obtained from Rosentreter et al. (2023), and follow the same methodology as described for CH₄ flux estimates in Section 2.1.1.3. Our coastal N₂O estimates are based on 15 estuaries and 24 sites for coastal vegetation (16 sites for mangroves and 8 sites for seagrasses). Since no sites were available for salt marshes, global statistics of salt marsh N₂O fluxes were used for upscaling. Using the same expert assessment as for coastal CH₄ emissions, we assign 70% of the estuary and coastal vegetation N₂O emission to natural sources and 30% to anthropogenic sources.

Air-sea N₂O fluxes from continental shelves were estimated by using the ensemble mean of five process-based ocean biogeochemical models (ECCO-Darwin, ECCO2-Darwin, CNRM-HR, CNRM-LR, and NEMO-Plank-TOM5) (see Section 2.2.3 in Resplandy et al., 2024), and one observation-based estimate (Yang et al., 2020). For these N₂O air-sea fluxes, we applied the same MARCAT delimitation redefinition used for CH₄ air-sea fluxes explained in Section 2.1.1.3.

## 2.2. Top-Down Approaches for CH₄ and N₂O Flux Estimate

In this study, we also analyzed global CH₄ and N₂O atmospheric inverse products, which were compared (reconciled) with the bottom-up budget described in Sections 2.1.1 and 2.1.2. Inverse modeling (referred to as the top-down approach) of CH₄ and N₂O fluxes relies on a Bayesian statistical method. It combines observed atmospheric mixing ratios, obtained from ground-based (in situ) and/or satellite measurements, with atmospheric transport models and a priori estimates of surface fluxes along with their uncertainties. This approach yields an optimized posterior flux estimate that represents best the likely state of surface flux estimates.

### 2.2.1. CH₄ Global Inversions

For CH₄ flux estimates, we used six global CH₄ inverse products: the Community Inversion Framework with the Laboratoire de Météorologie Dynamique Zoom transport model (CIF-LMDz; Thanwerdas et al., 2022), CarbonTracker Europe (CTE; Tsuruta et al., 2017), the Model for Interdisciplinary Research on Climate version 4-Atmospheric Chemistry-Transport Model (MIROC4-ACTM; Patra et al., 2018; Watanabe et al., 2008), the National Institute for Environmental Studies Transport Model (NIES-TM-FLEXPART; Maksyutov et al., 2020; Wang et al., 2019), the NIES Monitoring System for GHGs (NISMON; Niwa et al., 2022, 2024), and the Python Variational Inversion System with the Laboratoire de Météorologie Dynamique Zoom model (PYVAR-LMDz; Zheng, Chevallier, Ciais, Yin, Deeter, et al., 2018; Zheng, Chevallier, Ciais, Yin, & Wang, 2018).

Five of these inversion systems (CIF-LMDz, CTE, MIROC4-ACTM, NIES-TM-FLEXPART, and NISMON) assimilated only surface atmospheric CH₄ observations, as documented in the global CH₄ budget of the GCP (Saunois et al., 2025). Most inverse modeling groups relied on the United States National Oceanic and Atmospheric Administration (NOAA) Observation Package (ObsPack) collections, most commonly the GLOBAL-VIEWplus CH₄ compilations, including the near real-time version. These ObsPack data sets consolidate measurements from institutions such as the Integrated Carbon Observation System, the Max Planck Institute for Biogeochemistry (MPI-BGC), the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and other contributing laboratories in NOAA's cooperative network.

Not all inversion systems relied on the same number or type of assimilated observations. CTE, MIROC4-ACTM, and NIES-TM-FLEXPART used the 2021 release of ObsPack (Schuldt et al., 2021a, Schuldt et al., 2023b), while NISMON used the 2023 release (Schuldt et al., 2023a, Schuldt et al., 2023b). In contrast, CIF-LMDz assimilated CH₄ observations provided through the World Data Center for Greenhouse Gases (WDCGG), part of the WMO Global Atmosphere Watch (GAW) programme, which archives data sets submitted by contributing institutions from across the globe, including NOAA, AGAGE, CSIRO, and many others (WDCGG, 2024a). Details of the ObsPack GLOBALVIEWplus compilations, together with the additional sites, are provided in the (Tables S1 and S2 in Supporting Information S1), with further details available in Table S11 of Saunois et al. (2025).

In the Australian region, CH₄ observations come primarily from three long-term monitoring stations: Kennaook/Cape Grim, Gunn Point, and Cape Ferguson (Francey et al., 1999) (see Figure S13 in Supporting Information S1). Kennaook/Cape Grim is operated by CSIRO and funded primarily by the Australian Bureau of Meteorology (BoM). It contributes to the AGAGE and NOAA networks, and its data are also archived in WDCGG. Gunn Point and Cape Ferguson are also operated by CSIRO, with observations made available through WDCGG.
CSIRO also operates four CH₄ monitoring stations in the Australian Antarctic Territory (Mawson Station, Casey Station, Davis Station), and Macquarie Island, all of which are part of the WMO GAW network. These high-latitude sites complement the tropical and mid-latitude stations across the Australian region. Air samples collected in Antarctica are sent to CSIRO's Global Atmospheric Sampling Laboratory (GASLAB) in Aspendale for flask analysis.

In New Zealand, CH₄ observations are available from two stations (Lauder and Baring Head) both operated by the National Institute of Water and Atmospheric Research (NIWA; now part of the new research organisation Earth Sciences New Zealand following its merger with GNS Science on 5 July 2025). Baring Head also contributes to the NOAA network. Both stations are part of the WMO GAW programme and contribute to the broader international effort and to monitor trace gases, and provide useful information for global and regional studies in inverse modeling (Bukosa et., al 2025).

In addition to the inversions that assimilate only ground-based observations, two global systems (PYVAR-LMDz and NIES-TM-FLEXPART) also assimilate CH₄ total column retrievals from the GHGs Observing Satellite (GOSAT). PYVAR-LMDz uses GOSAT Level 1B radiances processed with the University of Leicester Full-Physics retrieval algorithm and the Proxy method (Parker & Boesch, 2020), whereas NIES-TM-FLEXPART uses the NIES Level 2 retrievals (v02.95; Yoshida et al., 2013). The inclusion of satellite data enhances spatial constraints on flux estimates, particularly over under-sampled regions such as inland Australia and the Southern Ocean. Although retrieval biases and cloud cover remain limiting factors, the global coverage and consistency of GOSAT data provide a valuable complement to surface-based observations. Greenhouse Gases Observing Satellite retrieval products are often bias-corrected using data from the Total Carbon Column Observing Network (TCCON), a process that helps improve the accuracy and reliability of the total CH₄ column retrieved by the satellite. In Australia, there are two TCCON sites: one in northern Australia (Darwin) and another in the southeast (Wollongong). In New Zealand, there is one site located at Lauder.

### 2.2.2. N₂O Global Inversions

For N₂O flux estimates, we analyzed an ensemble of four global inversion systems: the inversion system using the TOMCAT chemical transport model (INVICAT; Wilson et al., 2014), the Python Variational Inversion System with the LMDz5 transport model constrained by CAMS (PyVAR-CAMS; Thompson et al., 2014), MIROC4-ACTM (Patra et al., 2018, 2022), and the GEOS-Chem chemical transport model (GEOS-Chem; Wells et al., 2018), as documented in the Global N₂O Budget of the GCP (Tian et al., 2024). All inversions in Tian et al. (2024) assimilated ground-based atmospheric N₂O measurements only, primarily from NOAA (Lan et al., 2025), AGAGE (Prinn et al., 2025), and CSIRO (Francey et al., 2003; WDCGG, 2024b). Satellite-derived N₂O column retrievals were not used, as the current products, such as the Infrared Atmospheric Sounding Interferometer aboard the MetOp A satellite, are not yet suitable for atmospheric inversion modeling. A recent report by the Copernicus Atmosphere Monitoring Service (Thompson, 2021) indicates that these satellite retrievals lack the precision and accuracy required for robust surface flux estimation.

The Southern Hemisphere observational N₂O ground-based network, particularly over the Australasia region, relies primarily on flask sampling rather than continuous in situ measurements. Most of the flask samples used in the global inversions are collected by NOAA, CSIRO, and AGAGE. In Australia, for example, CSIRO collects flask air samples from nine globally distributed sites (Francey et al., 2003), three of which are located within Australia: Cape Grim, Cape Ferguson, and Gunn Point. The remaining sites are in remote locations, including Macquarie Island, Mawson, Casey, the South Pole, and Mauna Loa (see Figure S13 in Supporting Information S1). These samples are analyzed at CSIRO's GASLAB in Aspendale, Victoria.

Flask N₂O measurements from the CSIRO network are analyzed using procedures consistent with international standards developed by networks such as AGAGE and NOAA, with a long-term measurement precision of approximately 0.1%. The data are standardized using the NOAA-2006A N₂O calibration scale to ensure consistency across sites, and are publicly available through the WDCGG. A more detailed description of the observational network can be found in the Supplementary Information of Tian et al. (2024), Section S12 in Supporting Information S1. Further methodological details are also provided there, and a summary of the inversion system configurations is included in Table S1 in Supporting Information S1.
In New Zealand, NOAA and NIWA collect flask air samples from Baring Head, Lauder, and Arrival Heights stations. Although long-term records exist for both Baring Head and Lauder, these data sets were not included in any of the global N₂O inversions used in Tian et al. (2024). At Baring Head, the data were likely excluded due to limited accessibility at the time the inversions were conducted. For Lauder, the data were only recently incorporated into global observational products and were therefore unavailable when the inversions were performed. Data from Arrival Heights were also excluded due to sparse temporal coverage, particularly before 2010.

NIWA also operates a TCCON site at Lauder, which has been active since June 2004. Although TCCON provides total column-average N₂O measurements, these are generally not well suited for constraining surface fluxes in atmospheric inversion systems. Compared to CO₂ and CH₄, N₂O retrievals have lower precision and are more strongly influenced by the stratosphere (Chevallier, 2022). Given that these column measurements are sensitive to the stratosphere and not precise enough to resolve changes near the surface, they provide limited information about surface fluxes (Wunch et al., 2011).

## 2.3. Global Warming Potentials (GWPs)

We used the global warming potentials over 100-year (GWP100) and 20-year (GWP20) time horizons to estimate the radiative forcing of CH₄ and N₂O in terms of CO₂-equivalents (CO₂-eq) units. GWP is an emission-based metric that compares the cumulative radiative forcing from the release of a unit mass of a greenhouse gas to that of an equal mass of CO₂ over a specified time horizon (IPCC, 2021). By using GWPs, we assess their climate impacts in relation to CO₂ across different time horizons.

We applied 100-year GPWs of 27.0 for CH₄ and 273 for N₂O, and 20-year GWPs of 79.7 for CH₄ and 273 for N₂O, based on the GWPs reported in IPCC, 2021, AR6 (Working Group 1, Chapter 7, Table 7.15). Using Equation 3, we calculated the combined anthropogenic effect of the three main GHGs: CO₂, CH₄ and N₂O. We note that the CO₂ budget was obtained from Villalobos et al. (2023).

$$CO_{2-eq} = CO_2 + CH_4 \times (GWP_{CH_4}) + N_2O \times (GWP_{N_2O}) \quad (3)$$

# 3. Results

## 3.1. Bottom-Up CH₄ Budget in Recent Decade (2010–2019)

As seen in Figure 1, over the past decade, the total bottom-up estimate for the Australasia CH₄ budget was 14.1 ± 5.5 Tg CH₄ yr⁻¹. Of this total, anthropogenic sources contributed 55% (7.8 ± 1.3 Tg CH₄ yr⁻¹), while natural CH₄ sources accounted for 45% (6.3 ± 5.3 Tg CH₄ yr⁻¹).

For Australia, the decadal CH₄ budget was 11.9 ± 5.4 Tg CH₄ yr⁻¹, with 53% (6.3 ± 1.2 Tg CH₄ yr⁻¹) attributed to anthropogenic sources and 47% (5.6 ± 5.3 Tg CH₄ yr⁻¹) to natural sources and sinks (Figure 2). For New Zealand, the decadal CH₄ budget was 2.2 ± 0.6 Tg CH₄ yr⁻¹, with anthropogenic emissions contributing 70% (1.5 ± 0.5 Tg CH₄ yr⁻¹) and natural emissions (0.7 ± 0.2 Tg CH₄ yr⁻¹) accounting for 30% of the total (Figure 3).

Enteric fermentation from ruminants (e.g., cattle, sheep, swines and other livestock) was the dominant source of anthropogenic CH₄ emissions in the agricultural sector in Australasia, accounting for 41.3% (3.2 ± 0.5 Tg CH₄ yr⁻¹) of total anthropogenic emissions. The energy sector was the second-largest source of CH₄ emissions, with a decadal mean of 1.4 ± 0.7 Tg CH₄ yr⁻¹, representing 18.2% of the total. Here, CH₄ emissions from the energy sector come primarily from fugitive emissions associated with extraction, processing, and transportation of oil, and gas, as well as CH₄ releases from soil fuels, particularly from coal mining activities. Minor contributions come from CH₄ released during fuel combustion in energy industries, manufacturing, and transport. However, these emissions are relatively small compared to CO₂ emissions from combustion, as incomplete combustion can release a small amount of CH₄.

Natural and human-made lakes and reservoirs impacted by anthropogenic factors (1.6 ± 0.6 TgCH₄ yr⁻¹) were comparable to those from the energy sector, accounting for 19.8% of the total anthropogenic emissions.

CH₄ emissions from enteric fermentation were also the largest source of anthropogenic CH₄ in Australia and New Zealand. In Australia, these emissions accounted for 32.6% (2.1 ± 0.5 Tg CH₄ yr⁻¹) of the country's total anthropogenic CH₄ emissions, while in New Zealand, they represented 76.2% (1.2 ± 0.2 Tg CH₄ yr⁻¹). In
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Waterfall charts of anthropogenic CH₄ emissions as reported by the national greenhouse gas inventorie for Australia (a) 2010–2019, (b) 2019–2022, and for New Zealand (c) 2010–2019, (d) 2019–2022. Red bars indicate total emissions at the start and end of each period; pale-orange bars represent sectoral contributions to the net change along with their uncertainties.</figcaption></figure>

Australia, the second and third largest sources of anthropogenic emissions were also the energy sector, contributing 22.1% (1.4 ± 0.2 Tg CH₄ yr⁻¹), and lakes and reservoirs, contributing 23.1% (1.4 ± 0.6 Tg CH₄ yr⁻¹). However, in New Zealand, the second-largest source of anthropogenic CH₄ emissions was waste, which accounted for 7.7% (0.1 ± 0.5 Tg CH₄ yr⁻¹) of the total anthropogenic, followed by anthropogenic CH₄ emissions from reservoirs and lakes (0.1 ± 0.04 Tg CH₄ yr⁻¹) (8%), manure management (0.1 ± 0.02 Tg CH₄ yr⁻¹) (3.9%) and energy sector (0.03 ± 0.02 Tg CH₄ yr⁻¹) (2.2%).

Among all-natural CH₄ sources in Australasia (9.4 ± 5.3 Tg CH₄ yr⁻¹), 33% (−3.1 ± 0.03 Tg CH₄ yr⁻¹) was offset by aerobic methanotrophic bacteria in soils, reducing natural CH₄ sources to 6.3 ± 5.3 Tg CH₄ yr⁻¹. From this reduced estimate, inland wetland CH₄ emissions (5.9 ± 5.3 Tg CH₄ yr⁻¹) and coastal wetlands (0.1 ± 0.04 Tg CH₄ yr⁻¹) account for 96.4%, with 94.3% (5.6 ± 5.3 Tg CH₄ yr⁻¹) from Australia and 2.1% (0.3 ± 0.2 Tg CH₄ yr⁻¹) from New Zealand.

Other natural emissions, including those from termites (1.1 ± 0.6 Tg CH₄ yr⁻¹), geological sources (0.8 ± 0.01 Tg CH₄ yr⁻¹), and rivers and lakes (1.3 ± 0.2 Tg CH₄ yr⁻¹), account for 33% (3.2 ± 0.7 Tg CH₄ yr⁻¹) of natural emissions, a percentage comparable to the CH₄ uptake by soils discussed above. The majority of these emissions
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Waterfall charts of natural CH₄ sources and sinks over the 2010–2019 period for Australia (a) and New Zealand (b). Green bars indicate total emissions at the start and end of the period, while pale-green bars represent the contributions of individual components (wetlands, wildfires, termites, and soil uptake) to the net change along with their uncertainties.</figcaption></figure>

originate from Australia (32%), with only 1% attributed to New Zealand. Across Australasia, CH₄ emissions from estuaries account for only 0.18%.

## 3.2. Decadal Change and Trends of Anthropogenic and Natural CH₄ Emissions

In this section, we quantify the absolute changes in anthropogenic CH₄ emissions in Australasia, Australia, and New Zealand over two time intervals: 2010–2019 and 2019–2022. Detailed annual trend statistics, including slopes and p-values for each CH₄ emission source, are provided in the (Figures S14 to S19 in Supporting Information S1).

### 3.2.1. Decadal Change and Trends of Anthropogenic CH₄ Emissions

Total anthropogenic CH₄ emissions in Australasia decreased from 6.3 ± 0.6 Tg CH₄ yr⁻¹ in 2010 to 5.9 ± 0.5 Tg CH₄ yr⁻¹ in 2019, a decline of 0.39 ± 0.06 Tg CH₄ yr⁻¹ (6.3%) over the decade, with a statistically significant decrease mean rate of 0.04 Tg CH₄ yr⁻¹ (*p* < 0.05; Figure S14 in Supporting Information S1). Australia drove the decline, with a decadal reduction of 0.38 ± 0.06 Tg CH₄ yr⁻¹ (7.8%) (Figure 4a). In contrast, New Zealand's reduction was 0.01 ± 0.03 Tg CH₄ yr⁻¹ (0.97%) (Figure 4c), with no statistically significant trend (Figure S16 in Supporting Information S1).

In Australia, enteric fermentation, the largest contributor to anthropogenic CH₄ emissions in the decadal budget (43.1%), declined by 0.05 ± 0.01 Tg CH₄ yr⁻¹ between 2010 and 2019 (2.3%) contributing 11.9% to the national reduction. The largest contribution came from biomass burning from managed fires (prescribed fires) and low-intensity temperate forest fires, which declined by 0.18 ± 0.08 Tg CH₄ yr⁻¹ (26%), representing 46.2% of the total reduction in anthropogenic emissions. This decline was statistically significant, with an average annual reduction of 0.02 Tg CH₄ yr⁻¹ (Figure S16 in Supporting Information S1).

Other notable sectors contributing to the decline of CH₄ emissions in Australia were waste (17.9%; 0.1 ± 0.01 Tg CH₄ yr⁻¹) and energy (3.4%; 0.05 ± 0.003 Tg CH₄ yr⁻¹), together accounting for 38.6% of the total decline. Among these, only waste showed a statistically significant decreasing trend (*p* < 0.05, Figure S16 in Supporting Information S1). Minor contributions came from field burning of agricultural residues, manure management, rice cultivation, and farm dams, which collectively accounted for less than 3.1% of the total reduction. Industrial processes had a negligible impact (0.2%) on the total decline, reflecting their limited role in the overall change.
<figure><img src="image_9.png" /><figcaption>Figure 6.</figcaption></figure>
Between 2019 and 2022, anthropogenic CH₄ emissions in Australasia declined by 0.21 ± 0.03 Tg CH₄ yr⁻¹, indicating a continued but slower reduction compared to the 0.39 ± 0.06 Tg CH₄ yr⁻¹ decrease observed from 2010 to 2019. Over the full period from 2010 to 2022, the average annual rate of decrease was 0.06 Tg CH₄ yr⁻¹ (*p* < 0.05; Figure S14 in Supporting Information S1).

In Australia, the slowdown in emissions reductions was particularly notable, with the total decrease in anthro-
pogenic CH₄ emissions declining from 0.38 ± 0.06 Tg CH₄ yr⁻¹ in 2010–2019 to 0.18 ± 0.03 Tg CH₄ yr⁻¹ in
2019–2022. Despite the overall slowdown, some sectors continued to reduce their emissions significantly. For
example, between 2019 and 2022, the energy sector achieved a reduction of 0.19 ± 0.01 Tg CH₄ yr⁻¹ (13.7%),
substantially stronger reduction than the previous decade (3.4%). This sharp decline was primarily driven by a
significant reduction in fugitive CH₄ emissions from the oil and gas sector, which decreased by 0.18 ± 0.09 Tg
CH₄ yr⁻¹ from 2019 to 2022, compared to 0.05 ± 0.03 Tg CH₄ yr⁻¹ between 2010 and 2019.

Biomass burning from prescribed fires and low-intensity wildfires in temperate forests, which remained under control (e.g., manageable by firefighters) also contributed to the reduction (0.07 ± 0.04 Tg CH₄ yr⁻¹, 13.7%) between 2019 and 2022. However, this decline was lower than in the previous decade. On the contrary, several agricultural sources such as manure management, rice cultivation, and field burning of agricultural residues shifted from small declines in 2010–2019 to slight increases in 2019–2022. Enteric fermentation also reversed from a decline to an increase of 0.03 ± 0.01 Tg CH₄ yr⁻¹ in 2019–2022. Such increase was primarily driven by increased CH₄ emissions from ruminant animals such cattle (0.04 ± 0.01 Tg CH₄ yr⁻¹) and sheep (0.03 ± 0.01 Tg CH₄ yr⁻¹).

In New Zealand, anthropogenic CH₄ emissions decreased slightly from 1.42 ± 0.2 Tg CH₄ yr⁻¹ in 2010 to 1.40 ± 0.02 Tg CH₄ yr⁻¹ in 2019 (Figure 4c). This modest reduction was primarily driven by the energy sector, which experienced a 173% decrease in emissions (0.03 ± 0.02 Tg CH₄ yr⁻¹), with an average annual reduction of 0.04 Tg CH₄ yr⁻¹ (*p* < 0.005; Figure S16 in Supporting Information S1), contributing 254.4% to the total reduction.

In contrast, emissions from enteric fermentation (2.3%), manure management (22.9%), and industrial processes (85.4%) increased, together accounting for 325.3% of the total increase in emissions, offsetting reductions from the energy sector by 0.04 Tg CH₄ yr⁻¹. Reductions in the waste sector (19.6%) and biomass burning (12.1%) further supported the decline, together contributing 167.2% to the total reduction and decreasing emissions by 0.006 Tg CH₄ yr⁻¹. Meanwhile, declines in field burning of agricultural residues (24.7%) and biomass burning (12.1%) had relatively minor impacts, contributing only 3.8% to the total decline.

Between 2019 and 2022, New Zealand's anthropogenic CH₄ emissions declined by 0.03 Tg CH₄ yr⁻¹ (2.1%), a larger reduction than the observed previus decade. This more pronounced decline was primarily driven by a reversal in agricultural trends. Emissions from enteric fermentation and manure management, which had increased from 2010 to 2019, decreased by 0.02 Tg CH₄ yr⁻¹ (1.7%) and 0.001 Tg CH₄ yr⁻¹ (0.7%), respectively. These reductions likely reflect a decrease in beef cattle and sheep populations, as farmers increasingly favor dairy production, which is more economically viable in New Zealand. In contrast, the energy sector, previously the largest contributor to the decline, showed only a marginal decrease of 0.002 Tg CH₄ yr⁻¹ (8.1%) during this period.

3.2.2. Decadal Change and Trends of Natural CH₄ Sources and Sinks

Total CH₄ emissions from natural sources and sinks in Australasia increased by 2.49 ± 3.79 Tg CH₄ yr⁻¹ (53.4%) between 2010 and 2019, with an average annual increase of 0.18 Tg CH₄ yr⁻¹ that was not statistically significant (Figure S17 in Supporting Information S1). Despite the absence of a statistically significant trend, the increase in natural CH₄ emissions was primarily driven by rising wetland emissions, which contributed 1.5 Tg CH₄ yr⁻¹

**Figure 6.** Water-air CH₄ emissions from rivers, natural lakes, reservoirs, and lakes regulated by dams in Australia (a) and New Zealand (b); black dots represent CH₄ from peer-reviewed published studies (as reviewed in Lauerwald et al. (2023); Table S1 in Supporting Information S1). Water-air fluxes from tidal systems and deltas, lagoons, and fjords in Australia (c) and New Zealand (d) (Rosentreter et al., 2023). Water-sediment-air fluxes from coastal vegetation (mangroves, salt marshes, seagrasses) in (e) Australia and (f) New Zealand (Rosentreter et al., 2023). Boxplots in c-f show the median (black line), mean (dark black circle), and interquartile (Q1–Q3) range of estuaries and coastal vegetation.
<figure><img src="image_10.png" /><figcaption>Figure 7. Decadal N₂O budget for Australasia (2010–2019). Details as in Figure 1, however, units here are in Gg N₂O yr⁻¹.</figcaption></figure>

(24.6%) and accounted for 61.8% of the total increase. This change was predominantly associated with wetland emissions in Australia (60%) (Figure 5a), while New Zealand accounted for only 2% (Figure 5b).

In Australia, natural CH₄ sources and sinks increased by 2.44 ± 4.19 Tg CH₄ yr⁻¹ between 2010 and 2019, driven primarily by wetlands and wildfires (e.g., primarily forest fires). Wetland emissions grew by 1.49 ± 1.99 Tg CH₄ yr⁻¹ (24.8%), accounting for 60.9% of the total increase. As shown in Figure S3 in Supporting Information S1, 2019 was an exceptional year in the last decade, with CH₄ emissions from wetlands reaching their highest levels compared to any other year between 2010 and 2019. This substantial increase was likely linked to record temperatures in 2019, which may have intensified CH₄ production.

Wildfires in Australia also contributed significantly to the rise in natural CH₄ emissions, with an increase of 1.0 ± 0.63 Tg CH₄ yr⁻¹ (201.2%), representing 41.1% of the total increase. This notable contribution was primarily due to the anomalously extensive fires recorded in 2019, driven by extreme drought conditions and record high temperatures that year.

CH₄ uptake by Australian natural soils, an important sink in the country's CH₄ budget (offsetting 22% of CH₄ natural sources), only counteracted the growth in emissions by -0.04 ± 0.001 Tg CH₄ yr⁻¹ (-1.5%) between 2010 and 2019, and accounted for 1.8% of the total decrease. Most CH₄ uptake by soils in Australia occurs in the tropical rainforests and savanna soils of Queensland (northeastern Australia) (Figure S8 in Supporting Information S1).

Termite CH₄ emissions in Australia decreased slightly by 0.004 Tg CH₄ yr⁻¹ (-0.2%) between 2010 and 2019, contributing only 0.15% to the total decrease. While termites play a minor role in offsetting the overall increase in natural CH₄ emissions, Australia hosts a substantial termite population, as shown in Figure S9 in Supporting Information S1. The high termite CH₄ emissions in Australia predominantly originate from the coastal northern region of Queensland, likely driven by the warm climate that fosters the proliferation of mound-building termite colonies.

### 3.2.3. LOAC CH₄ Emissions

LOAC CH₄ fluxes estimates, based on Lauerwald et al. (2023) and Rosentreter et al. (2023), are annual average fluxes from climatology estimates, and therefore considered constant for this study.
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Decadal Australia N₂O budget (2010–2019). Details as in Figure 7.</figcaption></figure>

For Australasia, inland CH₄ emissions from freshwater ecosystems (as shown in Figures 6a and 6b) are predominantly from natural lakes, which account for 87% of total freshwater emissions, with an average release of 2.1 ± 1.2 Tg CH₄ yr⁻¹. Of these emissions, 44% come from lakes in Australia (e.g., permanent, temporary, and ephemeral lakes), while New Zealand contributes 42%.

Rivers represent the second-largest portion, contributing approximately 5% of inland emissions, averaging around 0.3 Tg CH₄ yr⁻¹. Reservoirs, which are anthropogenic sources, account for 7% of the inland emissions. Notably, nearly all river and reservoir CH₄ emissions originate from Australia, as New Zealand only contributes 0.2% and 1%, respectively.

Estuarine median (Q1–Q3) CH₄ emissions from tidal systems and deltas in Australia (0.01 (0.008–0.013) Tg CH₄ yr⁻¹, are slightly higher but comparable to those from lagoons (0.01 (0.005–0.011) Tg CH₄ yr⁻¹. Numerous fjords are located in New Zealand's South Island, but they make up a relatively small area (779 km²); there are no fjords in Australia. Regional estuary emissions are largely driven by Australian estuaries, with New Zealand contributing 14%.

Coastal vegetation median (Q1–Q3) CH₄ emissions in Australia are higher from salt marshes (0.051 (0.030–0.074) Tg CH₄ yr⁻¹), compared to seagrasses (0.045 (0.003–0.045) Tg CH₄ yr⁻¹) and mangroves (0.018 (0.012–0.025) Tg CH₄ yr⁻¹) based on the analysis by Rosentreter et al. (2023). While CH₄ emissions from coastal vegetation are generally higher than from estuaries, ranges and uncertainties remain relatively high in this region. Australia, with its significantly larger coastal vegetation area (>64,000 km²) compared to New Zealand (>500 km²), contributes to ~99% of the region's total coastal vegetation CH₄ emissions.

### 3.2.4. CH₄ Emissions From Australasia Continental Shelf Areas

Based on the Weber-CH₄ database (bounded to the MARCAT-CH₄ region), we estimated that CH₄ emissions from the Australasia continental shelves, including both diffusive and ebullitive (i.e., bubble-induced) components, for the recent decade were 1.0 ± 0.4 Tg CH₄ yr⁻¹. Australia contributes 97% of these emissions, with a large portion coming from tropical shallow water regions (e.g., Gulf of Carpentaria, shallow rectangular sea in the northern part of Australia; see Figures S11 and S12 in Supporting Information S1). We found that, overall, ebullitive CH₄ emissions are the dominant flux, and these are larger near the coast for the majority of Australia, with the exception of the southeast and west corners of the country (Figure S12 in Supporting Information S1).
<figure><img src="image_12.png" /><figcaption>Figure 9. Decadal New Zealand N₂O budget (2010–2019). Details as in Figure 7.</figcaption></figure>

3.3. Bottom-Up N₂O Budget in Recent Decade (2010–2019)

Figure 7 shows the bottom-up N₂O balance for Australasia was 543.3 ± 290.3 Gg N₂O yr⁻¹. Of this total, anthropogenic sources contributed 18% (101.5 ± 126.8 Gg N₂O yr⁻¹), while natural N₂O sources accounted for 82% (441.8 ± 261.6 Gg N₂O yr⁻¹).

Australia accounted for 91.5% (497.1 ± 289.9 Gg N₂O yr⁻¹) of the total N₂O budget (Figure 8), while New Zealand contributed 8.5% (46.1 ± 15.4 Gg N₂O yr⁻¹) (Figure 9). In Australia, anthropogenic N₂O sources contributed 14% (71.1 ± 125.4 Gg N₂O yr⁻¹), whereas natural sources and sinks made up 86% (425.7 ± 261.4 Gg N₂O yr⁻¹). In New Zealand, anthropogenic N₂O emissions contributed 63% (28.9 ± 11.3 Gg N₂O yr⁻¹), with natural sources accounting for 37% (17.3 ± 10 Gg N₂O yr⁻¹).

Direct N₂O emissions from agricultural soils due to fertilizer use were the largest anthropogenic source in Australasia, accounting for 8.8% (47.7 ± 19.1 Gg N₂O yr⁻¹) of the total. From these emissions, 5.6% (28.1 ± 15.7 Gg N₂O yr⁻¹) came from Australia, and 3.6% (19.7 ± 10.9 Gg N₂O yr⁻¹) from New Zealand. Direct N₂O emissions from N mineralization/immobilization associated with loss/gain of soil organic matter resulting from change of land use or management, contributed 0.7% (3.6 ± 0.7 Gg N₂O yr⁻¹). 0.5% of these emissions originated in Australia (2.7 ± 0.7 Gg N₂O yr⁻¹), while 0.2% came from New Zealand (0.9 ± 0.3 Gg N₂O yr⁻¹).

Indirect N₂O emissions from atmospheric nitrogen deposition on agricultural soils (15.5 ± 6.5 Gg N₂O yr⁻¹) were the second largest contributor of all anthropogenic source, accounting for 2.9%. Indirect N₂O emissions from other non-agricultural soils contributed 1.7% (9.3 ± 6.2 Gg N₂O yr⁻¹).

Changes in climate and CO₂ concentrations are additional indirect drivers that influence N₂O emissions from soils. The combined effect of these two drivers was negative, during 2010–2019, leading to a reduction in Australasia soil N₂O emissions of –15 ± 123.8 Gg N₂O yr⁻¹. The reduction was mainly driven by increased CO₂ concentrations, which alone reduced N₂O emissions by –27.4 ± 51.3 Gg N₂O yr⁻¹. In contrast, rising temperatures due to climate change had the opposite effect, increasing N₂O emissions by 13.4 ± 112.7 Gg N₂O yr⁻¹. Australia accounted for 96% (–27.4 ± 51.2 Gg N₂O yr⁻¹) of the reduction in N₂O emissions driven by elevated CO₂, and 89% (11.9 ± 112.7 Gg N₂O yr⁻¹) of the increase in emissions due to climate-related temperature changes. New Zealand contributed only 4% (–1.1 ± 3.1 Gg N₂O yr⁻¹) and 11% (1.5 ± 2.2 Gg N₂O yr⁻¹) to these respective changes.
<figure><img src="image_13.png" /><figcaption>**Figure 10.** Waterfall charts of anthropogenic N₂O emissions as reported by the national greenhouse gas inventorie for Australia (a) 2010–2019, (b) 2019–2022, and for New Zealand (c) 2010–2019, (d) 2019–2022. Red bars indicate total emissions at the start and end of each period; pale-orange bars represent sectoral contributions to the net change along with their uncertainties.</figcaption></figure>

N₂O emissions from energy and industrial processes contributed to 2.3% (12.3 ± 5.7 Gg N₂O yr⁻¹) and 1.3% (6.8 ± 3.3 Gg N₂O yr⁻¹), respectively. Of these emissions, 2.1% and 1.2% came from Australia while New Zealand contributed only 0.2%. N₂O emissions due to biomass burning (that is, prescribed fires and low intensity wildfires accounted for 2.3% of the totals.

Other anthropogenic N₂O sources, including field burning of agricultural residues, manure management, and aquaculture, and waste collectively made up only 0.5% of the total.

Among all-natural N₂O sources in Australasia, emissions from natural soils were the largest contributor, accounting for 71.5% with a decadal mean of 388.8 ± 260.2 Gg N₂O yr⁻¹, followed by wildfire emissions at 8.4% (45.8 ± 26.9 Gg N₂O yr⁻¹). Inland water N₂O emissions contributed 0.4%, while estuaries and coastal wetlands together accounted for another 0.4% of the totals.

Natural soil N₂O emissions were the largest contributor in both Australia (373.5 ± 260 Gg N₂O yr⁻¹) and New Zealand (15.3 ± 9.7 Gg N₂O yr⁻¹), accounting for 68.8% and 2.8% of the total, respectively. In New Zealand, streams and rivers and natural lakes were the second-largest natural N₂O source, contributing together 0.3% to the
<figure><img src="image_14.png" /><figcaption>**Figure 11.** Waterfall charts of natural N₂O sources and sinks over the 2010–2019 period for Australia (a) and New Zealand (b). Green bars indicate total emissions at the start and end of the period, while pale-green bars represent the contributions of individual components (wildfires and natural NO soil emissions) to the net change along with their uncertainties.</figcaption></figure>

total budget. In Australia, on the contrary, wildfires were the second-largest contributor, with a decadal mean of 45.8 ± 9.7 Gg N₂O yr⁻¹, representing 9.2% of the total budget.

## 3.4. Decadal Change and Trends of Anthropogenic and Natural N₂O Emissions

In this section, we quantify the absolute changes and trends in anthropogenic N₂O emissions in Australasia, Australia, and New Zealand over two time intervals: 2010–2019 and 2019–2022. Detailed annual trend statistics, including slopes and p-values for each N₂O emission source, are provided in the (Figures S20 to S25 in Supporting Information S1). We note that N₂O emissions from anthropogenic indirect effects, such as atmospheric nitrogen deposition, climate change, and elevated CO₂ concentrations, are excluded from the analysis below.

### 3.4.1. Decadal Change and Trends of Anthropogenic N₂O Emissions

In Australasia, total anthropogenic N₂O emissions declined by 5.2 ± 1.76 Gg N₂O yr⁻¹ (5%) from 2010 to 2019, with an average annual decrease of 0.6 Gg N₂O yr⁻¹, which was statistically significant (*p* < 0.05, Figure S20 in Supporting Information S1). Despite this overall reduction, Australia (Figure 10a) and New Zealand (Figure 10c) showed opposing trends. Emissions in Australia decreased by 7.3 ± 1.33 Gg N₂O yr⁻¹ (9.4%), with an average annual decline of 0.8 Gg N₂O yr⁻¹ (*p* < 0.05, Figure S21 in Supporting Information S1), whereas New Zealand experienced an increase of 2.03 ± 1.16 Gg N₂O yr⁻¹ (7.7%), corresponding to an average annual rise of 0.2 Gg N₂O yr⁻¹ (*p* < 0.05, Figure S22 in Supporting Information S1).

In Australia, biomass burning (prescribed fires and low-intensity wildfires fires) and industrial processes declined by 3.25 ± 1.83 Gg N₂O yr⁻¹ (24.4%) and 3.05 ± 2.16 Gg N₂O yr⁻¹ (28.9%), respectively, together contributing 86.9% to the total decline. Biomass burning was the only sector that showed a statistically significant decrease, with an average rate of -0.39 Gg N₂O yr⁻¹ (*p* < 0.05, Figure S21 in Supporting Information S1).

Emissions reductions from land-use change (driven by nitrogen mineralization, leaching, and runoff) decreased by 1.94 ± 0.77 Gg N₂O yr⁻¹ (53.3%), accounting for 26.7% of the total reduction. The energy sector, the second-largest contributor to the N₂O budget (16%), recorded a reduction of 2.10 ± 1.48 Gg N₂O yr⁻¹ (17.3%), accounting for 28.9% of the total decline. Smaller reductions were recorded in field burning of agricultural residues (0.05 ± 0.04 Gg N₂O yr⁻¹, 19.3%) and waste (0.004 ± 0.01 Gg N₂O yr⁻¹, 0.3%), contributing a combined total of just 0.8% to the overall decline. All sectors showed a statistically significant downward trend (*p* < 0.05), with the exception of waste, which did not exhibit a significant change (Figure S21 in Supporting Information S1).
<figure><img src="image_15.png" /><figcaption>**Figure 12.** Same as Figure 6 but for water-air N₂O emissions.</figcaption></figure>
By contrast, emissions from agricultural sources, particularly soils, showed an upward trend. Australia's N₂O emissions from agricultural soils (direct and indirect), the dominant source in the N₂O budget (54.1%), increased by 2.5 ± 0.31 Gg N₂O yr⁻¹ (17.2%) over the last decade. This rise was primarily driven by an increase of 1.37 ± 0.29 Gg N₂O yr⁻¹ (5.5%) from direct soil emissions and 1.09 ± 0.12 Gg N₂O yr⁻¹ (11.7%) from indirect soil emissions associated with nitrogen deposition and leaching, together contributing 152.8% of the total increase. Additional increases were observed in manure management (0.60 ± 0.21 Gg N₂O yr⁻¹, 34.6%) and aquaculture (0.07 ± 0.1 Gg N₂O yr⁻¹, 54.5%), which collectively accounted for only 9.3% of the total increase. Among all sectors, only emissions from indirect soils, manure management, and aquaculture showed statistically significant increasing trends (p < 0.05; Figure S21 in Supporting Information S1).

This declining trend in total emissions, however, did not persist beyond 2019. Between 2019 and 2022, anthropogenic N₂O emissions in Australasia increased by 2.9 ± 1.1 Gg N₂O yr⁻¹ (3.0%), reversing the decline observed in the previous decade. This shift was primarily driven by Australia, where emissions rose by 4.4 ± 0.74 Gg N₂O yr⁻¹ during 2019–2022 (Figure 10b), compared to the previous decade.

Most of the growth in Australian emissions was dominated by a sharp increase in direct soil emissions, which rose by 5.87 ± 1.25 Gg N₂O yr⁻¹ (22.2%), with the largest increase occurring in 2022 (Figure S21 in Supporting Information S1). The rise in that year was likely driven by exceptionally wet conditions, which likely enhanced nitrogen mineralization and soil moisture levels, creating favorable conditions for microbial processes that emit N₂O. Smaller contributions also came from soil emissions associated with land-use change (0.90 ± 0.38 Gg N₂O yr⁻¹) and field burning of agricultural residues (0.33 ± 0.22 Gg N₂O yr⁻¹). While reductions in biomass burning (−1.21 ± 0.68 Gg N₂O yr⁻¹) and industrial processes (−1.88 ± 1.33 Gg N₂O yr⁻¹) continued during this period, their magnitude was smaller compared to reductions observed in 2010–2019.

In New Zealand, the increase in anthropogenic N₂O emissions of 2.03 ± 1.16 Gg N₂O yr⁻¹ (7.7%) between 2010 and 2019 was predominantly driven by soil-related sources, which accounted for the majority of the growing N₂O budget. Direct soil emissions increased by 1.85 ± 1.44 Gg N₂O yr⁻¹ (10.0%), while indirect emissions from nitrogen deposition and leaching rose by 0.26 ± 0.20 Gg N₂O yr⁻¹ (5.5%), together contributing 104.1% of the total increase. Additional increases came from waste (0.11 ± 0.1 Gg N₂O yr⁻¹, 25.6%), industrial processes (0.10 ± 0.07 Gg N₂O yr⁻¹, 54.3%), and manure management (0.08 ± 0.16 Gg N₂O yr⁻¹, 26.7%), accounting for 14.2% of the total increase.

In contrast, New Zealand's anthropogenic N₂O reductions were primarily observed in the land-use change sector (0.27 ± 0.2 Gg N₂O yr⁻¹, 13.5%) and the energy sector (0.12 ± 0.02 Gg N₂O yr⁻¹, 6.1%), together accounting for 19.6% of the total decline. Biomass burning (prescribed fires and low-intensity wildfire from forest) increased by 0.03 ± 0.02 Gg N₂O yr⁻¹ (23.1%), contributing 1.5% to the total increase, while field burning of agricultural residues declined by 0.003 ± 0.001 Gg N₂O yr⁻¹ (16.3%), accounting for 0.1% of the total reduction.

During 2019–2022, New Zealand total N₂O emissions (Figure 10d) reversed the increasing trend observed during 2010–2019. Between 2010 and 2019, the total change in emissions shifted from an increase of 2.03 ± 1.16 Gg N₂O yr⁻¹ to a decline of −1.46 ± 0.84 Gg N₂O yr⁻¹. This reduction was primarily driven by a significant decrease in direct soil emissions (−1.22 ± 0.95 Gg N₂O yr⁻¹), likely linked to a decline in non-dairy cattle and sheep numbers, leading to reduced nitrogen inputs to soils from manure and urine deposition.

Other sectors also contributed to reduction in New Zealand N₂O emissions. Indirect soil emissions (0.37 ± 0.29 Gg N₂O yr⁻¹) and energy-related emissions (0.09 ± 0.02 Gg N₂O yr⁻¹) both declined during 2019–2022, while emissions from land use change increased slightly by 0.12 ± 0.09 Gg N₂O yr⁻¹, reflecting potential shifts in nitrogen mineralization and leaching under wetter conditions. Biomass burning and field burning of agricultural residues showed negligible or stable contributions during this period, with minimal impact on the overall trend.

### 3.4.2. Decadal Change and Trends of Natural N₂O Sources

Natural N₂O emissions in Australasia increased by 34.87 ± 29.32 Gg N₂O yr⁻¹ from 2010 to 2019, primarily driven by wildfire emissions in Australia, which contributed 74.5 ± 49.13 Gg N₂O yr⁻¹, accounting for 209.9% of the total increase (Figure 11a). However, no significant trend was observed over this period (Figure S23 in Supporting Information S1). In contrast, wildfire emissions in New Zealand decreased during the same period by 0.11 ± 15.83 Gg N₂O yr⁻¹ (−102.2%), contributing −14.1% to the total decline in the country (Figure 11b).
<figure><img src="image_16.png" /><figcaption>**Figure 13.** Decadal mean (2010–2019) CH₄ emissions for (a) Australia and (c) New Zealand, and N₂O emissions for (b) Australia and (d) New Zealand, categorized as anthropogenic, natural, and total emissions. Total bottom-up emissions are the sum of anthropogenic and natural sources (left section of each panel), while top-down estimates provide an independent assessment based on the ensemble mean of global inversion-based flux estimates.</figcaption></figure>

Natural soil N₂O emissions in Australia declined by 38.97 ± 36.11 Gg N₂O yr⁻¹ (10.0%), offsetting 52.2% of the wildfire-driven increase. In New Zealand, natural soil emissions also decreased during the last decade by 0.65 ± 14.51 Gg N₂O yr⁻¹ (4.2%), contributing an additional 85.9% to the reduction in N₂O emissions.

### 3.4.3. LOAC N₂O Emissions

For Australasia, inland N₂O emissions from freshwater ecosystems (as shown in Figures 12a and 12b) are predominantly from natural lakes, which account for 67% of total freshwater emissions, with an average release of 5.3 ± 4.1 Gg N₂O yr⁻¹. Of these emissions, 44% come from natural lakes in Australia (e.g., permanent, temporary, and ephemeral lakes), while New Zealand contributes 23%.

In Australia, natural lakes (Figure 12a) have a median N₂O flux of 3.8 Gg N₂O yr⁻¹ (Q1–Q3: 2.1–5.0). In comparison, New Zealand's natural lakes have a median flux of 1.5 Gg N₂O yr⁻¹ (Q1–Q3: 0.2–2.7) (Figure 12b). Lakes regulated by dams in Australia contribute only 0.5% to the total, with a median flux of 0.03 Gg N₂O yr⁻¹ (Q1–Q3: 0.3–0.3). In New Zealand, regulated lakes are insignificant, while natural lakes exhibit a median flux of 0.3 Gg N₂O yr⁻¹ (Q1–Q3: 0.2–2.7).

Australian rivers have a median flux rate of 1.6 Gg N₂O yr⁻¹ (Q1–Q3: 0.7–3.21), which is three times higher than reservoirs, at 0.5 Gg N₂O yr⁻¹ (Q1–Q3: 0.4–0.6). In New Zealand, on the contrary, emissions from reservoirs are negligible.

Australia contributes 84% of estuary N₂O emissions with the majority of emissions coming from tidal systems and deltas (median (Q1–Q3) of 1.32 (1.25–1.44) Gg N₂O yr⁻¹) (Figure 12c). Estuary N₂O emissions from tidal systems and deltas were higher than those from lagoons, both in Australia (Figure 12c) and New Zealand (Figure 12d). Because the total area of fjords in New Zealand is relatively small, their overall contributions to estuary N₂O emissions in Australasia is minor. In contrast to CH₄, estuary N₂O emissions are generally higher than N₂O emissions from coastal vegetation.
<figure><img src="image_17.png" /><figcaption>**Figure 14.** Anthropogenic contributions of individual bottom-up greenhouse gas (GHG) budgets ($CO_2$, $CH_4$, and $N_2O$) for Australia (a) and New Zealand (b), expressed in $CO_2$ equivalents (GWP 20- and 100-year time horizons), along with their percentages and the combined total of the three GHGs.</figcaption></figure>

Australia's coastal wetlands contribute 84% to the total coastal vegetation emissions of the Australasia region with the majority of emissions coming from salt marshes (median (Q1-Q3) 0.54 (0.04-0.95) Gg $N_2O$ yr$^{-1}$) (Figure 12e). Australia's coastline contains >40,000 km$^2$ seagrass meadow (Mcowen et al., 2017), which are generally a sink of $N_2O$ (median (Q1-Q3) -0.60 (-0.80-0.55) $N_2O$ Gg yr$^{-1}$) and largely counteract the $N_2O$ emissions from mangrove forests and salt marshes (Figure 12e). Therefore, the median total $N_2O$ flux from coastal vegetation in Australasia is minuscule (0.075 Gg $N_2O$ yr$^{-1}$).

# 4. Bottom-Up and Top-Down Reconciliation

## 4.1. $CH_4$ Bottom-Up and Top-Down Reconciliation

Australasia's top-down $CH_4$ estimate for the last decade indicates that this region released $10.5 \pm 0.5$ Tg $CH_4$ yr$^{-1}$. Australia accounted for 84% of these emissions ($8.8 \pm 0.4$ Tg $CH_4$ yr$^{-1}$), while New Zealand contributed 16% ($1.6 \pm 0.3$ Tg $CH_4$ yr$^{-1}$).

In terms of decadal reconciliation, Figure 13a shows that the Australian bottom-up $CH_4$ estimate ($11.9 \pm 5.5$ Tg $CH_4$ yr$^{-1}$) is 35% larger than the top-down estimate ($8.8 \pm 0.4$ Tg $CH_4$ yr$^{-1}$). Similar results are observed for New Zealand (Figure 13c), where the bottom-up estimate ($2.2 \pm 0.6$ Tg $CH_4$ yr$^{-1}$) is 35% larger than the top-down estimate ($1.64 \pm 0.29$ Tg $CH_4$ yr$^{-1}$). Several possible explanations might be considered for the differences between the bottom-up and top-down estimates. Lower top-down estimates compared to bottom-up estimates may
be related to the limited spatial coverage provided by GOSAT across Australia and New Zealand. Besides, the number of in situ observations used in the global inversion is limited to only a few stations (Kennaook/Cape Grim and Gunn Point in Australia, and Baring Head in New Zealand). On the other hand, a higher bottom-up estimate may be associated with potential overestimation of wetland emissions derived from global wetland models. These models rely on the dynamically mapped wetland product (WAD2M), based on the satellite-derived data set SWAMPS (Surface Water Microwave Product Series) (Bernard et al., 2024, 2025). Misclassification artifacts, such as oceans or deserts being incorrectly identified as wetlands, could lead to overestimations of wetland extent and the associated CH₄ emissions.

Beyond the decadal scale, differences are also evident in inter-annual variability (Figures S5a and S5b in Supporting Information S1). Australia's bottom-up estimates show greater inter-annual variability compared to the relatively stable annual trends observed in the top-down estimates. For New Zealand, the differences are even more pronounced, with some years showing substantial discrepancies between the two approaches in reconciling the annual budget. These discrepancies are partly due to limited observational constraints and the fact that global inversions operate at very coarse spatial resolutions (ranging from 2.8° to 5.6°), making it challenging to accurately resolve fluxes in a small country with complex topography, such as New Zealand.

## 4.2. N₂O Bottom-Up and Top-Down Reconciliation

Australasia's N₂O top-down estimate for the last decade indicates that this region released 0.8 ± 0.3 Tg N₂O yr⁻¹. Here, Australia dominates the emissions profile, accounting for 98%, while New Zealand contributes only 2% (0.01 ± 0.01 Tg N₂O yr⁻¹).

For the decadal N₂O reconciliation, Figure 13b shows that Australia's decadal mean annual bottom-up estimate (0.5 ± 0.3 Tg N₂O yr⁻¹) is 38% lower than the top-down estimate (0.8 ± 0.3 Tg N₂O yr⁻¹). In contrast, New Zealand's bottom-up estimate (0.05 ± 0.02 Tg N₂O yr⁻¹) is 71% higher than the top-down estimate (0.01 ± 0.01 Tg N₂O yr⁻¹) (Figure 13d). Inter-annual variability between bottom-up and top-down estimates also shows large discrepancies, primarily over Zealand (Figures S6a and S6b in Supporting Information S1).

The large discrepancy in New Zealand's N₂O top-down budget is primarily due to the fact that most global N₂O inversions rely on flask air samples collected approximately every 2 weeks from Australia. In Australia, the network is also constrained: some inversion systems use air samples only from Cape Grim and Gunn Point, without incorporating any observations from New Zealand. Without local observations, inversion systems struggle to detect and quantify emissions from specific regions, such as New Zealand, because the N₂O signal becomes less distinguishable as it disperses in the atmosphere.

Unlike N₂O inversions, as shown in Section 4.1, CH₄ inversions benefit from a few high-frequency continuous observation sites, including two in New Zealand (Baring Head and Lauder), which provide stronger constraints on CH₄ flux estimates. The absence of equivalent N₂O data likely contributes to the underestimation of New Zealand's N₂O emissions in top-down models.

# 5. Radiative Forcing Impacts From the Three Main GHGs

To assess the relative contributions to changes in the radiative forcing from the three main GHGs (CO₂, CH₄, and N₂O) for 2010–2019, we calculated the CO₂ equivalents of anthropogenic CH₄ and N₂O emissions using the Global Warming Potentials (GWP) for 20-year (GWP20) and 100-year (GWP100) timeframes. Then, we combined them with the net anthropogenic CO₂ emissions from Villalobos et al. (2023). In this section, we only used the estimates from the bottom-up approach, which enables us to extract anthropogenic fluxes only.

For Australia, using GWP20, anthropogenic CH₄ emissions were 502.3 ± 93.5 Tg CO₂-eq (51.4%), which has an integrated radiative forcing comparable to CO₂ emissions (454.9 ± 34.5 Tg CO₂-eq, 46.6%) (Figure 14a). In contrast, anthropogenic N₂O emissions contributed only 2% (19.5 ± 34.5 Tg CO₂-eq) to the total radiative forcing impact with GWP20.

In New Zealand, anthropogenic CH₄ emissions based on GWP20 were 123.4 ± 42.8 Tg CO₂-eq, accounting for 78.2% of the total changes in radiative forcing from anthropogenic emissions (Figure 14b). At GWP20, CH₄ emissions exceeded the contributions from anthropogenic CO₂ emissions (26.5 ± 21.2 Tg CO₂-eq, 16.8%) and N₂O emissions (7.9 ± 3.1 Tg CO₂-eq, 5%). For New Zealand, the significant impact of CH₄ emissions under
GWP20 on the net GHG climate balance was evident. Over the last decade (2010–2019) shown that net carbon sink (–141.4 ± 49.2 Tg CO₂) can be offset largely by anthropogenic CH₄ emissions, which accounted for 87% of the sink.

In contrast, based on GWP100 (Figure 14b), CH₄ emissions only reduce New Zealand's net carbon sink by 35%. GWP20 is the integrated contribution to the radiative forcing g over the first 20 years after CH₄'s release into the atmosphere and underscores the potential large and near-term impact of implementing CH₄ mitigation policies.

# 6. Discussion

## 6.1. Uncertainties in Bottom-Up CH₄ and N₂O Budget

### 6.1.1. Natural CH₄ and N₂O Estimates

The large uncertainty in the bottom-up CH₄ budget primarily arises from the natural CH₄ wetland emission estimate, which contributes 95% of the overall CH₄ budget uncertainty. This uncertainty does not reflect model error per se but rather the large spread across the 16 global wetland models used in this study (Figures S3 and S4 in Supporting Information S1). Given that all models were run based on the same climate forcing and same wetland dynamic scheme (remote-sensing-based wetland area and dynamics data set WAD2M), a large proportion of the uncertainties is likely to stem from the lack of explicit representation of key mechanisms and processes controlling CH₄ production, release, and absorption.

Similarly, large uncertainties remain in natural soil N₂O emissions, which were also derived from global models. While the multi-model ensemble mean of CH₄ from wetlands and soil N₂O emissions provides a valuable and robust global estimate, significant uncertainties across the Australasian region limit our understanding of the contribution of natural sources to the total budgets. We believe that field data, combined with the development of locally adapted simulation models, can improve the accuracy of these estimates. More realistic national-level estimates based on regional drivers would also help better isolate the effects of climate changes, such as rising temperatures and precipitation, on the increase in wetland CH₄ emissions.

### 6.1.2. Coastal and Freshwater Aquatic CH₄ and N₂O Emissions

The distinction between natural and anthropogenic of CH₄ and N₂O emissions from coastal wetlands (mainly from coastal sediments and surrounding waters) also remains highly uncertain in the bottom-up budgets. Neither Australia nor New Zealand has detailed information on the extent to which these ecosystems have been altered by human activities. In Australia, it is estimated that over 30% of salt marshes have been modified (DCCEEW, 2024), but there is no continental-wide information on the degradation of seagrasses and mangroves. Most available information focuses on the reduction in area and not in the degree of disturbance of the remaining ecosystems.

For estuaries in Australia, Yeo, Rosentreter, Oakes, Schulz, and Eyre (2024) present an Australia-wide CH₄ emissions estimate based on geomorphic estuary types and disturbance classes. When comparing this study with Rosentreter et al. (2023), we found that the median estuary CH₄ emissions from Yeo, Rosentreter, Oakes, Schulz, and Eyre (2024) were nearly identical to those reported in Rosentreter et al. (2023) for Australia, both at 0.02 Tg CH₄ yr⁻¹. However, the mean estimate from Yeo, Rosentreter, Oakes, and Eyre (2024) is slightly higher (0.03 Tg CH₄ yr⁻¹) compared to Rosentreter et al. (2023) (0.02 Tg CH₄ yr⁻¹), due to differences in data analysis. While total estuary CH₄ emissions are similar from the two syntheses, differences exist regarding surface area of estuary types and areal flux rates.

### 6.1.3. Soil Emissions From Anthropogenic Indirect N₂O Drivers (CO₂/Climate)

A distinct source of uncertainty in the bottom-up N₂O budget arises from indirect anthropogenic drivers, particularly elevated atmospheric CO₂ concentrations and climate change. In this study, we included NMIP-2 simulations that isolate the impacts of these drivers on soil N₂O fluxes. While the model ensemble indicates that elevated CO₂ tends to reduce N₂O soil emissions and that warming tends to increase them, the associated uncertainty remains large. This uncertainty does not stem from individual model error, but rather from the divergence in responses across the ensemble of global models participating in NMIP-2, each representing soil–plant–microbe interactions differently. Despite this, the direction of the modeled responses is consistent with
established biogeochemical mechanisms. Elevated CO₂ can reduce N₂O emissions by enhancing plant nitrogen uptake and reducing nitrogen availability for microbial transformation, mainly via denitrification (Tian et al., 2024; Van Groenigen et al., 2011). However, increasing temperature stimulates microbial activity and accelerates both nitrification and denitrification, which are key microbial pathways of N₂O production (Bhat-tacharyya et al., 2013; Butterbach-Bahl et al., 2013). These mechanisms are well represented in the NMIP-2 experiment protocol, which provides a basis for quantifying the potential indirect effects of global change drivers on soil N₂O emissions.

## 6.2. Uncertainties in Top-Down CH₄ and N₂O Budget

### 6.2.1. Limited Ground-Based Observational CH₄ and N₂O Network in Australasia

One of the key limitations of current N₂O top-down estimates in Australasia is the limited observational coverage (Ziehn et al., 2016), rather than the quality of the measurements. Most N₂O data used in global inversions are derived from flask air samples, collected approximately every 2 weeks at Cape Grim and Gunn Point in Australia, with no observation selected from New Zealand. Although flask measurements are highly precise and stable, their low temporal resolution limits the ability of inverse systems to resolve short-term variations in N₂O emissions and atmospheric transport. As a result, top-down N₂O inversions over Australasia often exhibit higher posterior uncertainties and tend to underestimate emissions (Tian et al., 2022).

By contrast, global CH₄ inversions benefit from a more extensive observational network, combining high-frequency in situ measurements with flask data. This allows better temporal coverage and improves the ability to constrain synoptic and seasonal variability in CH₄ emissions. Furthermore, some global CH₄ inverse systems assimilate satellite retrievals, which provide broader spatial coverage, particularly over Australia and New Zealand, where only four and two ground-based sites are available, respectively. This combination of ground-based and satellite observations leads to stronger constraints and lower relative uncertainties in CH₄ top-down estimates compared to those for N₂O (Bruhwiler et al., 2014; Saunois et al., 2020).

Although the inversion systems used in this study have undergone extensive global evaluation as part of the GCP CH₄ and N₂O budgets, their performance over Australasia is less well documented. The limited number of measurement sites in the region hinders robust regional validation of the posterior modeled concentrations, and no systematic benchmarking of inversion performance for CH₄ or N₂O has yet been published for this domain. We acknowledge this limitation and refer the reader to regional inverse studies for CO₂ (e.g., Bukosa et al., 2025; Villalobos et al., 2022), which demonstrate the value of regionally optimized systems and observational enhancements for improving confidence in top-down flux estimates.

### 6.2.2. Model Transport Errors in Global Inversion

Another important source of uncertainty in top-down N₂O estimates is the representation of atmospheric transport in inversion models. Many global inverse systems operate at relatively coarse spatial resolutions (e.g., 1°–5°), which limits their ability to capture sub-grid heterogeneity in N₂O sources and the complex meteorological processes affecting regions like Australia, and particularly New Zealand that strongly influenced by Southern Ocean weather systems and strong frontal activity. Inaccuracies in vertical mixing, boundary-layer dynamics, and convective transport can result in errors in the simulated movement of N₂O, leading to biases in inferred surface fluxes. These transport-related uncertainties become especially critical when observational constraints are sparse, as is the case in Australasia.

Emerging regional inverse modeling systems, such as Bukosa et al. (2025) and Villalobos et al. (2022) for CO₂ in New Zealand and Australia, offer higher-resolution systems that better represent local transport processes. While these systems were originally designed for CO₂, they could be adapted for use with CH₄ and N₂O, particularly as new regional observations or improved satellite products become available. Their application could help reduce transport-related uncertainties and improve regional source attribution for non-CO₂ GHGs.

## 6.3. Implications for Climate Change Mitigation

Natural sources of CH₄ and N₂O, and the way they are changing due to human-induced warming and rising atmospheric CO₂, are beyond the means of direct human management. Unlike CO₂, currently, there are no readily
available technologies capable of removing CH₄ or N₂O from the atmosphere, with their very low atmospheric concentrations being the most important constraint (Jackson, Solomon, et al., 2020; Jackson et al., 2021).

In Australasia, natural sources dominate the N₂O budget, accounting for 82% of total emissions compared to just 16% from anthropogenic activities. A similarly large contribution from natural sources is observed for CH₄, particularly from wetlands, which represent 45% of the total CH₄ budget for the region. In Australia, wetland emissions alone account for 42% of the CH₄ budget, and recent increases, suggested by global wetland models in response to climate drivers, underscore the need to accelerate the reduction of anthropogenic CH₄ emissions and other GHGs. In Australasia, anthropogenic CH₄ emissions were as high (55%) as natural emissions (45%), with Australia accounting for 44% of the region's total CH₄ budget. Although New Zealand represents only 11% of total anthropogenic emissions in the area (due to its smaller size) its local anthropogenic emissions (primarily from agriculture) constitute nearly 70% of its national CH₄ budget.

Historically, both land use change through the expansion of agriculture and the fossil fuel industry have been the major drivers of anthropogenic CH₄ and N₂O emissions in Australasia. In the land sector, technologies aiming at reducing emissions from enteric fermentation will have the highest impact on reducing emissions from the sector while maintaining productivity. The reduction of fugitive emissions in the fossil fuel sector, including liquified natural gas and coal mining, offers the largest opportunities for emission reductions.

Although the drainage of wetlands might reduce CH₄ emissions depending on the new land use, wetland restoration has a cooling effect on the atmosphere as CH₄ emissions are offset by increased carbon sequestration by vegetation and sediment accumulation, even under future warming scenarios where CH₄ emissions are projected to rise (Li et al., 2022). In this context, conservation and restoration of wetlands is an important climate mitigation strategy, while bringing other environmental benefits such as enhanced biodiversity, pollution filtration services, and regulation of water flows.

In addition, improved land management in agriculture is essential for mitigating N₂O emissions, which remain the dominant source of emissions in the N₂O budget by direct N₂O emissions through direct emissions from soils. Practices such as precision fertilizer application, improved timing, and manure management can reduce N₂O losses while maintaining productivity (IPCC, 2019; Reay et al., 2012). Integrating these practices into national mitigation strategies, through incentives, training programs, and regulation, would directly address the dominant anthropogenic drivers of N₂O emissions in the region and support long-term emission reduction goals.

## 7. Conclusion

Using bottom-up and top-down approaches, we conducted a comprehensive assessment of the CH₄ and N₂O budgets for Australasia, including all major anthropogenic and natural sources and sinks, for Australia and New Zealand, from 2010 to 2019. Our study discusses the 2010–2019 trends, with an extension of the NGHG inventory data set to 2022. CO₂-equivalents of anthropogenic CH₄ and N₂O emissions, along with the net anthropogenic CO₂ emissions from Villalobos et al., 2023, were also calculated and assessed for their Global Warming Potential (CO₂, CH₄ and N₂O) budget based on their relative radiative forcing changes.

Over the past decade (2010–2019), the Australasia's bottom-up CH₄ estimate (14.1 ± 5.5 Tg CH₄ yr⁻¹) was notably higher than the top-down budget (10.4 ± 0.5 Tg CH₄ yr⁻¹), but still consistent within their uncertainties. Based on the bottom-up budget, Australia contributed 84% (11.9 ± 5.6 Tg CH₄ yr⁻¹) and New Zealand 16% (2.2 ± 0.6 Tg CH₄ yr⁻¹). A substantial portion of the Australasia CH₄ budget came from natural sources (45%), primarily wetland emissions, which accounted for 42% of the total. Wetlands also represent the most uncertain component of the budget, as this estimate was based on the ensemble mean of 16 wetland models, displaying considerable variability across Australasia. These uncertainties highlight the need for more accurate national and local estimates to better understand this important natural CH₄ emissions source.

For the N₂O budget, top-down estimates (0.8 ± 0.3 Tg N₂O yr⁻¹) were higher than bottom-up estimates (0.5 ± 0.3 Tg N₂O yr⁻¹), with natural N₂O soil emissions being the most uncertain and largest component of the bottom-up budget. Here, the bottom-up estimate is likely a better estimate than the top-down one, because the global atmospheric N₂O inversions are not well constrained by observations, given the small number of sites found across Australia and New Zealand.
The assessment of CO₂-eq (GWP 20) of anthropogenic CH₄ and N₂O emissions underscores the dominant short-term warming impact of CH₄ in Australasia. Over a 20-year timeframe, CH₄ emissions elevate Australia's warming contribution (54%) above that of CO₂ (49%) and nearly offset New Zealand's CO₂ sink. This highlights the critical role of CH₄ mitigation in near-term climate strategies, in addition to the need to bring anthropogenic net CO₂ emissions to zero in order to stop further warming. Reductions in N₂O emissions are also necessary to reach the net zero GHG target of the Paris Agreement (Rogelj & Lamboll, 2024).

Our trend analysis shows that CH₄ emissions from the energy sector (e.g., fugitive emissions from fuels such as oil, natural gas, and coal) and industrial sectors gradually declined over the last decade (2010–2019) and continued to decrease between 2019 and 2022, indicating progress toward emission reduction targets in these sectors. In contrast, N₂O emissions showed different patterns: in Australia, unlike New Zealand, reductions were noticeable during 2010–2019. However, an increase in direct soil emissions in Australia drove a rise in N₂O emissions between 2019 and 2022, likely influenced by significant wet extreme conditions spanning 2020 to 2022.

Despite progress in reducing anthropogenic CH₄ and N₂O emissions, the pronounced upward trends in natural sources, such as wetlands and wildfires (e.g., forest fires), underscore the need for greater efforts to achieve meaningful overall emission reductions. This is because the increasing trends in natural emissions are, to some extent, indirectly influenced by the slow progress in mitigating human-made emissions.

## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

## Data Availability Statement

Data supporting the CH₄ and N₂O budgets for the RECCAP-2 Australasia region, including the breakdowns for Australia and New Zealand, are publicly available from Villalobos (2025).

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Yohanna Villalobos acknowledges support from the CSIRO Early Research Career (CERC) Postdoctoral Fellowship program, during which this research was initiated, and from Lund University which supported its completion. Josep G. Canadell, Ian Harman and Peter R. Briggs acknowledge funding from the National Environment Science Program - Climate Systems Hub of Australia. Elizabeth Keller and Timothy Hilton received funding from the Government of New Zealand under the CarbonWatch-NZ Endeavour Research Programme (#C01X1817). Ronny Lauerwald acknowledges funding from French state aid, managed by ANR under the "Investissements d'avenir" programme (ANR-16-CONV-0003). Judith Rosentreter acknowledges funding from the Australian Research Council through the Discovery Early Career Award (DE240100305). We thank Christopher G. R. Caldow (CSIRO, Aspendale, Australia) for his constructive feedback as part of CSIRO's internal review process, which helped improve the manuscript. We also acknowledge and thanks the many scientists who contributed data and model runs for the GCP - Global GHG Budgets and the REgional Carbon Cycle Assessment and Process, many of which were made available for this assessment. Finally, we acknowledge the observation networks and data centers whose measurements underpin the inversion systems of Tian et al. (2024) and Saunois et al. (2020), including NOAA, AGAGE, CSIRO, WDCGG, and the ObsPack team.
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