<table><caption>**Table 3. Continued.**</caption><thead><tr><th></th><th>1980–1989</th><th>1990–1999</th><th>2000–2009</th><th>2010–2019</th><th>2020</th></tr></thead><tbody><tr><td>Atmospheric chemical sink<sup>a</sup></td><td></td><td>12.8 (11.7, 13.8)</td><td>13.4 (12.3, 14.5)</td><td>14.0 (12.8, 15.2)</td><td></td></tr><tr><td>Change in atmospheric abundance<sup>b</sup></td><td></td><td>3.6 (3.6, 3.7)</td><td>4.6 (4.5, 4.7)</td><td>6.4 (6.2, 6.5)</td><td></td></tr><tr><td>Atmospheric burden</td><td></td><td>1528</td><td>1570</td><td>1592</td><td></td></tr><tr><td>Lifetime (“obs” from MLS)</td><td></td><td>119.3</td><td>117</td><td></td><td></td></tr></tbody></table>

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

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

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

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

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

### 4.4.2 Decadal trend in the global N₂O budget

Global N₂O emissions estimated by the BU and TD approaches were comparable in magnitude during the over-
lapping period from 1997 to 2020, but TD estimates im-
plied a larger interannual variability and a faster rate of in-
crease (Fig. 13a). BU and TD approaches diverge when es-
timating the magnitude of land emissions compared with
ocean emissions, although they are consistent with respect
to trends (Fig. 13b). According to the BU approaches,
global N₂O emissions increased from 17.4 Tg N yr⁻¹
(10.3–24.0 Tg N yr⁻¹) in 1997 to 18.5 Tg N yr⁻¹ (10.6–
27.0 Tg N yr⁻¹) in 2020, with an average increase rate of
0.043 Tg N yr⁻² (*p* < 0.05). In contrast, TD approaches sug-
gested that global emissions increased from 15.4 Tg N yr⁻¹
(13.9–16.7 Tg N yr⁻¹) in 1997 to 17.0 Tg N yr⁻¹ (16.6–
17.4 Tg N yr⁻¹) in 2020, implying a higher increase rate of
0.085 Tg N yr⁻² (*p* < 0.05). The BU estimate during 1997–
2010 was on average 1.6 Tg N yr⁻¹ higher than the TD esti-
mate. However, after 2010, the difference in the magnitude
of emissions between the two approaches is smaller, due to
the rapid increase in the TD estimates. From the year 1980,
BU approaches suggested a significant increase in global
N₂O emissions that was primarily driven by anthropogenic
sources (Table 3). Satellite and photolysis models estimate
that the atmospheric N₂O burden increased from 1528 Tg N
in the 2000s to 1570 in the 2010s and 1592 Tg N in 2020,
which is comparable to estimates by atmospheric chemistry
transport models, showing an increase in the atmospheric
N₂O burden from 1527 (1504–1545) Tg N in the 2000s to
1606 (1592–1621) Tg N in 2020.

4.5 Regional BU and TD estimates and their trends

To assess regional N₂O budgets, we divide the global land
into 18 regions (as described in Sect. 3). Our regional anal-
yses include the following: (1) trends and variations in re-
gional total N₂O emissions from all sources derived from
available TD (1997–2020) and BU (1980–2020) estimates
(Fig. 13), (2) trends and variations in region anthropogenic
N₂O emissions from all identified sources during 1980–2020
derived from a BU approach (Fig. 14), and (3) decadal re-
gional N₂O budget (2010–2019) derived from both BU and
TD approaches (Fig. 15). The following sections provide de-
tailed estimates for each of the 18 regions.

4.5.1 United States of America (USA)

For the USA, the TD estimates show higher total N₂O
emissions than the BU estimates over the 1997–2020 pe-
riod (Fig. 13c), with 1.00 (0.69–1.39) and 0.82 (0.31–
1.42) Tg N yr⁻¹, respectively. Both approaches suggest that
the total N₂O emissions from the USA remained rela-
tively stable during 1997–2020. Based on the BU esti-
mates, changes in climate, CO₂, and land cover caused
emission decline over 1980–2020. The flux fluctuated be-
tween −0.30 and −0.12 Tg N yr⁻¹, with an average of
−0.20 Tg N yr⁻¹. Indirect emissions from anthropogenic ni-
trogen additions increased from 0.11 Tg N yr⁻¹ in 1980 to

0.13 Tg N yr⁻¹ in 1995 and then decreased to 0.10 Tg N yr⁻¹
in 2020. Direct emissions from nitrogen additions in agricul-
ture increased from 0.25 Tg N yr⁻¹ in 1980 to 0.30 Tg N yr⁻¹
in 2020. However, the increase in direct agricultural
emissions was offset by the trend in emissions from
other direct anthropogenic sources, which decreased from
0.26 Tg N yr⁻¹ in 1980 to 0.19 Tg N yr⁻¹ in 2020. The to-
tal anthropogenic N₂O emissions slightly increased during
1980–2020, at an average rate of 0.6 × 10⁻³ Tg N yr⁻². This
increase primarily occurred during 1980–1997 (Fig. 14).

In the 2010s, the BU estimates (0.81, 0.29–1.43 Tg N yr⁻¹) were on average 0.22 Tg N yr⁻¹ lower than the TD estimates (1.03, 0.71–1.45 Tg N yr⁻¹) (Fig. 15). According to the BU results, natural sources contributed 48% of total emissions (0.39, 0.22–0.65 Tg N yr⁻¹) during this period. Direct emissions from nitrogen additions in agriculture were 0.30 (0.18–0.38) Tg N yr⁻¹, contributing 37% of the total emissions. Emissions from other direct anthropogenic sources made the second largest contribution to anthropogenic emissions, with a decadal mean of 0.21 (0.18–0.23) Tg N yr⁻¹. Indirect emissions from anthropogenic nitrogen additions contributed 14% of the total anthropogenic emissions, with a decadal mean of 0.11 (0.07–0.14) Tg N yr⁻¹. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.19 Tg N yr⁻¹ and range of −0.37 to 0.03 Tg N yr⁻¹. Recent study has indicated that N₂O emissions could be increased by freeze–thaw cycles (Del Grosso et al., 2022) and tillage practices (Lu et al., 2022). Our BU estimates did not consider freeze–thaw cycles or tillage practices and, thus, may have underestimated N₂O emissions.

4.5.2 Canada (CAN)

BU approaches suggested a larger magnitude of total N₂O
emissions from Canada than TD approaches over the 1997–
2020 period (Fig. 13d), with values of 0.29 (0.05–0.69) and
0.12 (0.06–0.19) Tg N yr⁻¹, respectively. BU and TD esti-
mates also showed divergent trends: TD estimates decreased
at a rate of −1.5 × 10⁻³ Tg N yr⁻², whereas BU estimates
increased at a rate of 0.7 × 10⁻³ Tg N yr⁻². According to the
BU results, the increase in total N₂O emissions from Canada
was mainly driven by the direct emissions from nitrogen ad-
ditions in agriculture, which increased from 0.02 Tg N yr⁻¹
in 1980 to 0.05 Tg N yr⁻¹ in 2020. Perturbed fluxes from
changes in climate, CO₂, and land cover showed an over-
all increase from 0.00 Tg N yr⁻¹ in 1980 to 0.02 Tg N yr⁻¹
in 2020. Indirect N₂O emissions from Canada were relatively
stable during the study period, while emissions from other di-
rect anthropogenic sources had large interannual variability
(Fig. 14).

In the 2010s, the BU estimates of Canada's total N₂O
emissions (0.29, 0.07–0.69 Tg N yr⁻¹) were over 2 times
higher than the TD estimates (0.12, 0.06–0.20 Tg N yr⁻¹)
lapping period from 1997 to 2020, but TD estimates im-
plied a larger interannual variability and a faster rate of in-
crease (Fig. 13a). BU and TD approaches diverge when es-
timating the magnitude of land emissions compared with
ocean emissions, although they are consistent with respect
to trends (Fig. 13b). According to the BU approaches,
global N₂O emissions increased from 17.4 Tg N yr⁻¹
(10.3–24.0 Tg N yr⁻¹) in 1997 to 18.5 Tg N yr⁻¹ (10.6–
27.0 Tg N yr⁻¹) in 2020, with an average increase rate of
0.043 Tg N yr⁻² (*p* < 0.05). In contrast, TD approaches sug-
gested that global emissions increased from 15.4 Tg N yr⁻¹
(13.9–16.7 Tg N yr⁻¹) in 1997 to 17.0 Tg N yr⁻¹ (16.6–
17.4 Tg N yr⁻¹) in 2020, implying a higher increase rate of
0.085 Tg N yr⁻² (*p* < 0.05). The BU estimate during 1997–
2010 was on average 1.6 Tg N yr⁻¹ higher than the TD esti-
mate. However, after 2010, the difference in the magnitude
of emissions between the two approaches is smaller, due to
the rapid increase in the TD estimates. From the year 1980,
BU approaches suggested a significant increase in global
N₂O emissions that was primarily driven by anthropogenic
sources (Table 3). Satellite and photolysis models estimate
that the atmospheric N₂O burden increased from 1528 Tg N
in the 2000s to 1570 in the 2010s and 1592 Tg N in 2020,
which is comparable to estimates by atmospheric chemistry
transport models, showing an increase in the atmospheric
N₂O burden from 1527 (1504–1545) Tg N in the 2000s to
1606 (1592–1621) Tg N in 2020.

4.5 Regional BU and TD estimates and their trends

To assess regional N₂O budgets, we divide the global land
into 18 regions (as described in Sect. 3). Our regional anal-
yses include the following: (1) trends and variations in re-
gional total N₂O emissions from all sources derived from
available TD (1997–2020) and BU (1980–2020) estimates
(Fig. 13), (2) trends and variations in region anthropogenic
N₂O emissions from all identified sources during 1980–2020
derived from a BU approach (Fig. 14), and (3) decadal re-
gional N₂O budget (2010–2019) derived from both BU and
TD approaches (Fig. 15). The following sections provide de-
tailed estimates for each of the 18 regions.

4.5.1 United States of America (USA)

For the USA, the TD estimates show higher total N₂O
emissions than the BU estimates over the 1997–2020 pe-
riod (Fig. 13c), with 1.00 (0.69–1.39) and 0.82 (0.31–
1.42) Tg N yr⁻¹, respectively. Both approaches suggest that
the total N₂O emissions from the USA remained rela-
tively stable during 1997–2020. Based on the BU esti-
mates, changes in climate, CO₂, and land cover caused
emission decline over 1980–2020. The flux fluctuated be-
tween −0.30 and −0.12 Tg N yr⁻¹, with an average of
−0.20 Tg N yr⁻¹. Indirect emissions from anthropogenic ni-
trogen additions increased from 0.11 Tg N yr⁻¹ in 1980 to

0.13 Tg N yr⁻¹ in 1995 and then decreased to 0.10 Tg N yr⁻¹
in 2020. Direct emissions from nitrogen additions in agricul-
ture increased from 0.25 Tg N yr⁻¹ in 1980 to 0.30 Tg N yr⁻¹
in 2020. However, the increase in direct agricultural
emissions was offset by the trend in emissions from
other direct anthropogenic sources, which decreased from
0.26 Tg N yr⁻¹ in 1980 to 0.19 Tg N yr⁻¹ in 2020. The to-
tal anthropogenic N₂O emissions slightly increased during
1980–2020, at an average rate of 0.6 × 10⁻³ Tg N yr⁻². This
increase primarily occurred during 1980–1997 (Fig. 14).

In the 2010s, the BU estimates (0.81, 0.29–1.43 Tg N yr⁻¹) were on average 0.22 Tg N yr⁻¹ lower than the TD estimates (1.03, 0.71–1.45 Tg N yr⁻¹) (Fig. 15). According to the BU results, natural sources contributed 48% of total emissions (0.39, 0.22–0.65 Tg N yr⁻¹) during this period. Direct emissions from nitrogen additions in agriculture were 0.30 (0.18–0.38) Tg N yr⁻¹, contributing 37% of the total emissions. Emissions from other direct anthropogenic sources made the second largest contribution to anthropogenic emissions, with a decadal mean of 0.21 (0.18–0.23) Tg N yr⁻¹. Indirect emissions from anthropogenic nitrogen additions contributed 14% of the total anthropogenic emissions, with a decadal mean of 0.11 (0.07–0.14) Tg N yr⁻¹. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.19 Tg N yr⁻¹ and range of −0.37 to 0.03 Tg N yr⁻¹. Recent study has indicated that N₂O emissions could be increased by freeze–thaw cycles (Del Grosso et al., 2022) and tillage practices (Lu et al., 2022). Our BU estimates did not consider freeze–thaw cycles or tillage practices and, thus, may have underestimated N₂O emissions.

4.5.2 Canada (CAN)

BU approaches suggested a larger magnitude of total N₂O
emissions from Canada than TD approaches over the 1997–
2020 period (Fig. 13d), with values of 0.29 (0.05–0.69) and
0.12 (0.06–0.19) Tg N yr⁻¹, respectively. BU and TD esti-
mates also showed divergent trends: TD estimates decreased
at a rate of −1.5 × 10⁻³ Tg N yr⁻², whereas BU estimates
increased at a rate of 0.7 × 10⁻³ Tg N yr⁻². According to the
BU results, the increase in total N₂O emissions from Canada
was mainly driven by the direct emissions from nitrogen ad-
ditions in agriculture, which increased from 0.02 Tg N yr⁻¹
in 1980 to 0.05 Tg N yr⁻¹ in 2020. Perturbed fluxes from
changes in climate, CO₂, and land cover showed an over-
all increase from 0.00 Tg N yr⁻¹ in 1980 to 0.02 Tg N yr⁻¹
in 2020. Indirect N₂O emissions from Canada were relatively
stable during the study period, while emissions from other di-
rect anthropogenic sources had large interannual variability
(Fig. 14).

In the 2010s, the BU estimates of Canada's total N₂O
emissions (0.29, 0.07–0.69 Tg N yr⁻¹) were over 2 times
higher than the TD estimates (0.12, 0.06–0.20 Tg N yr⁻¹)
<figure><img src="image_15.png" /><figcaption>**Figure 14.** Ensembles of regional anthropogenic N₂O emissions over the 1980–2020 period. The bar chart in the center shows the total changes in regional and global N₂O emissions during the study period from 1980 to 2020. Error bars indicate the 95% confidence interval for the average of the changes. A Mann–Kendall test was performed to establish any trends globally and for each region over the 1980–2020 period. The changes were calculated from the annual change rate (Tg N yr⁻²), determined from a linear regression, multiplied by 40 years. All regions except Australasia and the USA show a significant increasing or decreasing trend in the estimated ensemble N₂O emissions during 1980–2020. An asterisk (*) denotes significance at the *P* < 0.05 level.</figcaption></figure>

(Fig. 15). According to the BU results, natural sources contributed 59% of total emissions (0.17, 0.04–0.43 Tg N yr⁻¹) during this period. Direct emissions from nitrogen additions in agriculture were 0.05 (0.03–0.06) Tg N yr⁻¹, contributing 15% of the total emissions. Emissions from other direct anthropogenic sources and indirect emissions from anthropogenic nitrogen additions were 0.04 (0.02–0.08) and 0.02 (0.02–0.03) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.01 Tg N yr⁻¹ and a range of −0.04 to 0.09 Tg N yr⁻¹.

### 4.5.3 Central America (CAM)

TD and BU estimates are comparable with respect to the magnitudes and trends of N₂O emissions from Central America (Fig. 13e), with mean values of 0.42 (0.21–0.64) and 0.35 (0.25–0.47) Tg N yr⁻¹ for the BU and TD approaches, respectively. During 1997–2020, the rate of increase of the

BU estimates (4.7 × 10⁻³ Tg N yr⁻²) was higher than that of TD estimates (2.5 × 10⁻³ Tg N yr⁻²). Emissions from other direct anthropogenic sources increased from 0.03 Tg N yr⁻¹ in 1980 to 0.15 Tg N yr⁻¹ in 2020 and were the major driver of the increase in N₂O emissions from Central America. Direct agricultural emissions increased during the study period, from 0.08 Tg N yr⁻¹ in 1980 to 0.11 Tg N yr⁻¹ in 2020. Direct emissions and perturbed fluxes from changes in climate, CO₂, and land cover were relatively stable during this period (Fig. 14).

The BU and TD approaches gave comparable estimates of total N₂O emissions from Central America in the 2010s, with values of 0.46 (0.24–0.68) and 0.36 (0.24–0.48) Tg N yr⁻¹ for the BU and TD approaches (Fig. 15), respectively. Natural sources contributed 37% of total emissions (0.17, 0.07–0.26 Tg N yr⁻¹) during this period. Emissions from other direct anthropogenic sources contributed 39% of the total emissions (0.18, 0.17–0.18 Tg N yr⁻¹). Direct and indirect emissions were 0.11 (0.07–0.14) and 0.02 (0.02–
<figure><img src="image_15.png" /><figcaption>**Figure 14.** Ensembles of regional anthropogenic N₂O emissions over the 1980–2020 period. The bar chart in the center shows the total changes in regional and global N₂O emissions during the study period from 1980 to 2020. Error bars indicate the 95% confidence interval for the average of the changes. A Mann–Kendall test was performed to establish any trends globally and for each region over the 1980–2020 period. The changes were calculated from the annual change rate (Tg N yr⁻²), determined from a linear regression, multiplied by 40 years. All regions except Australasia and the USA show a significant increasing or decreasing trend in the estimated ensemble N₂O emissions during 1980–2020. An asterisk (*) denotes significance at the *P* < 0.05 level.</figcaption></figure>

(Fig. 15). According to the BU results, natural sources contributed 59% of total emissions (0.17, 0.04–0.43 Tg N yr⁻¹) during this period. Direct emissions from nitrogen additions in agriculture were 0.05 (0.03–0.06) Tg N yr⁻¹, contributing 15% of the total emissions. Emissions from other direct anthropogenic sources and indirect emissions from anthropogenic nitrogen additions were 0.04 (0.02–0.08) and 0.02 (0.02–0.03) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.01 Tg N yr⁻¹ and a range of −0.04 to 0.09 Tg N yr⁻¹.

### 4.5.3 Central America (CAM)

TD and BU estimates are comparable with respect to the magnitudes and trends of N₂O emissions from Central America (Fig. 13e), with mean values of 0.42 (0.21–0.64) and 0.35 (0.25–0.47) Tg N yr⁻¹ for the BU and TD approaches, respectively. During 1997–2020, the rate of increase of the

BU estimates (4.7 × 10⁻³ Tg N yr⁻²) was higher than that of TD estimates (2.5 × 10⁻³ Tg N yr⁻²). Emissions from other direct anthropogenic sources increased from 0.03 Tg N yr⁻¹ in 1980 to 0.15 Tg N yr⁻¹ in 2020 and were the major driver of the increase in N₂O emissions from Central America. Direct agricultural emissions increased during the study period, from 0.08 Tg N yr⁻¹ in 1980 to 0.11 Tg N yr⁻¹ in 2020. Direct emissions and perturbed fluxes from changes in climate, CO₂, and land cover were relatively stable during this period (Fig. 14).

The BU and TD approaches gave comparable estimates of total N₂O emissions from Central America in the 2010s, with values of 0.46 (0.24–0.68) and 0.36 (0.24–0.48) Tg N yr⁻¹ for the BU and TD approaches (Fig. 15), respectively. Natural sources contributed 37% of total emissions (0.17, 0.07–0.26 Tg N yr⁻¹) during this period. Emissions from other direct anthropogenic sources contributed 39% of the total emissions (0.18, 0.17–0.18 Tg N yr⁻¹). Direct and indirect emissions were 0.11 (0.07–0.14) and 0.02 (0.02–
<figure><img src="image_16.png" /><figcaption>**Figure 15.** Regional N₂O emissions during 2010–2019. Each subplot shows the emissions from five subsectors using BU approaches, followed by the sum of these five categories using BU approaches (coral) and the estimates from TD approaches (lime). Error bars indicate the spread between the minimum and the maximum values. The center map shows the spatial distribution of 10-year average N₂O emissions from land and the ocean based on the land and ocean models.</figcaption></figure>

0.03) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of -0.02 Tg N yr⁻¹ and a range of -0.10 to 0.07 Tg N yr⁻¹.

### 4.5.4 Northern South America (NSA)

TD approaches suggested a larger magnitude of total N₂O emissions from northern South America than BU approaches over the 1997–2020 period (Fig. 13f), with respective values of 0.55 (0.34–0.98) and 0.40 (0.04–1.08) Tg N yr⁻¹ for each approach. During 1997–2020, the increase rate of the TD estimates (2.2 × 10⁻³ Tg N yr⁻²) was higher than that of BU estimates (0.8 × 10⁻³ Tg N yr⁻²). Direct agricultural emissions made the largest contribution to the increase in N₂O emissions from northern South America, increasing from 0.04 Tg N yr⁻¹ in 1980 to 0.07 Tg N yr⁻¹ in 2020 (Fig. 14). N₂O emissions from the other three anthropogenic sectors did not have a significant trend during 1980–2020.

The BU estimates in the 2010s (0.41, 0.03–1.09 Tg N yr⁻¹) were on average 0.17 Tg N yr⁻¹ lower than the TD estimates (0.58, 0.35–1.06 Tg N yr⁻¹) (Fig. 15). The average natural emission was 0.35 Tg N yr⁻¹ in

the 2010s, contributing 85% of total emissions. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.07 (0.05–0.09), 0.02 (0.01–0.02), and 0.01 (0.01–0.02) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of -0.04 Tg N yr⁻¹ and a range of -0.19 to 0.10 Tg N yr⁻¹.

### 4.5.5 Brazil (BRA)

The average total N₂O emission from Brazil estimated by the BU approaches was 1.21 Tg N yr⁻¹, ranging from 0.26 to 2.32 Tg N yr⁻¹ (Fig. 13g), which was lower than the TD estimates (1.42, 1.18–1.75 Tg N yr⁻¹). Both approaches detected a notable increasing trend in total N₂O emissions during 1997–2020. TD approaches suggested a higher increase rate (11.6 × 10⁻³ Tg N yr⁻²) than BU approaches (4.3 × 10⁻³ Tg N yr⁻²). Direct agricultural emissions, which increased from 0.13 Tg N yr⁻¹ in 1980 to 0.32 Tg N yr⁻¹ in 2020, made the largest contribution to the increase in N₂O emissions from Brazil (Fig. 14). Indirect emissions also showed an increase from 0.03 Tg N yr⁻¹ in 1980 to 0.06 Tg N yr⁻¹ in 2020. Emissions from other anthropogenic
<figure><img src="image_16.png" /><figcaption>**Figure 15.** Regional N₂O emissions during 2010–2019. Each subplot shows the emissions from five subsectors using BU approaches, followed by the sum of these five categories using BU approaches (coral) and the estimates from TD approaches (lime). Error bars indicate the spread between the minimum and the maximum values. The center map shows the spatial distribution of 10-year average N₂O emissions from land and the ocean based on the land and ocean models.</figcaption></figure>

0.03) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of -0.02 Tg N yr⁻¹ and a range of -0.10 to 0.07 Tg N yr⁻¹.

### 4.5.4 Northern South America (NSA)

TD approaches suggested a larger magnitude of total N₂O emissions from northern South America than BU approaches over the 1997–2020 period (Fig. 13f), with respective values of 0.55 (0.34–0.98) and 0.40 (0.04–1.08) Tg N yr⁻¹ for each approach. During 1997–2020, the increase rate of the TD estimates (2.2 × 10⁻³ Tg N yr⁻²) was higher than that of BU estimates (0.8 × 10⁻³ Tg N yr⁻²). Direct agricultural emissions made the largest contribution to the increase in N₂O emissions from northern South America, increasing from 0.04 Tg N yr⁻¹ in 1980 to 0.07 Tg N yr⁻¹ in 2020 (Fig. 14). N₂O emissions from the other three anthropogenic sectors did not have a significant trend during 1980–2020.

The BU estimates in the 2010s (0.41, 0.03–1.09 Tg N yr⁻¹) were on average 0.17 Tg N yr⁻¹ lower than the TD estimates (0.58, 0.35–1.06 Tg N yr⁻¹) (Fig. 15). The average natural emission was 0.35 Tg N yr⁻¹ in

the 2010s, contributing 85% of total emissions. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.07 (0.05–0.09), 0.02 (0.01–0.02), and 0.01 (0.01–0.02) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of -0.04 Tg N yr⁻¹ and a range of -0.19 to 0.10 Tg N yr⁻¹.

### 4.5.5 Brazil (BRA)

The average total N₂O emission from Brazil estimated by the BU approaches was 1.21 Tg N yr⁻¹, ranging from 0.26 to 2.32 Tg N yr⁻¹ (Fig. 13g), which was lower than the TD estimates (1.42, 1.18–1.75 Tg N yr⁻¹). Both approaches detected a notable increasing trend in total N₂O emissions during 1997–2020. TD approaches suggested a higher increase rate (11.6 × 10⁻³ Tg N yr⁻²) than BU approaches (4.3 × 10⁻³ Tg N yr⁻²). Direct agricultural emissions, which increased from 0.13 Tg N yr⁻¹ in 1980 to 0.32 Tg N yr⁻¹ in 2020, made the largest contribution to the increase in N₂O emissions from Brazil (Fig. 14). Indirect emissions also showed an increase from 0.03 Tg N yr⁻¹ in 1980 to 0.06 Tg N yr⁻¹ in 2020. Emissions from other anthropogenic
sources and perturbed fluxes from changes in climate, CO₂, and land cover did not have an obvious trend during the study period.

The TD estimates in the 2010s (1.51, 1.40–1.79 Tg N yr⁻¹) were on average 0.28 Tg N yr⁻¹ higher than the BU estimates (1.23, 0.24–2.41 Tg N yr⁻¹) (Fig. 15). According to the BU results, the average natural emission was 0.95 Tg N yr⁻¹ in the 2010s, contributing 77% of total emissions. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.28 (0.22–0.35), 0.09 (0.06–0.11), and 0.05 (0.02–0.07) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.14 Tg N yr⁻¹ and a range of −0.48 to 0.25 Tg N yr⁻¹.

### 4.5.6 Southwest South America (SSA)

BU and TD estimates are consistent with respect to the magnitude of the total N₂O emissions from southwestern South America during 1997–2020, with values of 0.55 (0.18–1.03) and 0.51 (0.40–0.63) Tg N yr⁻¹ (Fig. 13h), respectively. TD estimates increased at a rate of 5.3 × 10⁻³ Tg N yr⁻² over 1997–2020, whereas BU estimates did not have an obvious trend during this period. Among the four anthropogenic sectors, direct agricultural emissions had the largest increase, from 0.10 Tg N yr⁻¹ in 1980 to 0.15 Tg N yr⁻¹ in 2020 (Fig. 14). Indirect emissions also increased from 0.02 Tg N yr⁻¹ in 1980 to 0.03 Tg N yr⁻¹ in 2020. Perturbed fluxes from changes in climate, CO₂, and land cover had a decreasing trend, while emissions from other sectors fluctuated over the study period.

The BU and TD approaches gave similar estimates of total N₂O emissions from southwestern South America in the 2010s, with values of 0.55 (0.19–1.04) and 0.55 (0.44–0.67) Tg N yr⁻¹ (Fig. 15), respectively. The mean natural emission was 0.39 Tg N yr⁻¹ in the 2010s, accounting for 71% of total emissions. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.14 (0.09–0.19), 0.05 (0.03–0.06), and 0.03 (0.01–0.03) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.05 Tg N yr⁻¹ and a range of −0.16 to 0.08 Tg N yr⁻¹.

### 4.5.7 Europe (EU)

The BU estimates suggest that Europe had the largest decrease rate of regional N₂O emissions among the 18 regions, and the average decrease rate during 1980–2020 was −13.3 × 10⁻³ Tg N yr⁻² (Fig. 13i). For the 1997–2020 period, this decreasing trend, as estimated by the BU approaches, slowed down (−7.7 × 10⁻³ Tg N yr⁻²), whereas the TD approach suggests a small increase of 1.6 × 10⁻³ Tg N yr⁻² (Fig. 13i). Emissions from other direct anthropogenic sources (including fossil fuel and industry, waste and wastewater, and biomass burning), which decreased from

0.51 Tg N yr⁻¹ in 1980 to 0.18 Tg N yr⁻¹ in 2020, made the largest contribution to the decreasing trend in N₂O emissions from Europe. Direct agricultural emissions and indirect emissions show overall decreasing trends from 0.46 and 0.16 Tg N yr⁻¹ in 1980 to 0.38 and 0.12 Tg N yr⁻¹ in 2020, respectively, mainly due to a reduction in fertilizer use after the collapse of the Soviet Union (Tian et al., 2022). However, the decreasing trend in direct agricultural emissions has leveled off since the 2000s. Perturbed fluxes from changes in climate, CO₂, and land cover decreased during 1980–1985 and then slowly increased (Fig. 14).

The BU and TD approaches gave comparable estimates of European N₂O emissions in the 2010s, with values of 1.00 (0.45–1.57) and 0.86 (0.49–1.36) Tg N yr⁻¹ (Fig. 15), respectively. According to the BU results, natural sources only contributed 26% of total emissions (0.26, 0.11–0.52 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.38 (0.32–0.44), 0.19 (0.15–0.24), and 0.13 (0.08–0.16) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.03 Tg N yr⁻¹ and a range of −0.21 to 0.22 Tg N yr⁻¹.

### 4.5.8 Northern Africa (NAF)

For northern Africa, TD approaches suggested a larger magnitude of the total N₂O emissions than BU approaches over the 1997–2020 period (Fig. 13j), with values of 1.01 (0.52–1.32) and 0.69 (0.18–1.27) Tg N yr⁻¹, respectively. Both approaches suggest that N₂O emissions from northern Africa significantly increased during 1997–2020, and the increase rates estimated by the BU and TD approaches were 4.9 × 10⁻³ and 4.7 × 10⁻³ Tg N yr⁻², respectively. Direct emissions increased from 0.10 Tg N yr⁻¹ in 1980 to 0.27 Tg N yr⁻¹ in 2020, making the largest contribution to the increase in N₂O emissions from northern Africa (Fig. 14). Indirect emissions also significantly increased from 0.02 Tg N yr⁻¹ in 1980 to 0.04 Tg N yr⁻¹ in 2020. In contrast, other anthropogenic emissions decreased from 0.12 Tg N yr⁻¹ in 1980 to 0.11 Tg N yr⁻¹ in 2020. N₂O fluxes caused by changes in climate, CO₂, and land cover remained relatively stable during 1980–2020.

In the 2010s, the BU estimates (0.72, 0.17–1.30 Tg N yr⁻¹) were on average 0.32 Tg N yr⁻¹ lower than the TD estimates (1.04, 0.54–1.31 Tg N yr⁻¹) (Fig. 15). Natural sources accounted for 44% of total emissions (0.32, 0.07–0.60 Tg N yr⁻¹) during this period. Direct emissions from nitrogen additions in agriculture were 0.23 (0.09–0.34) Tg N yr⁻¹, contributing 32% of the total emissions. Emissions from other direct anthropogenic sources made the second largest contribution to anthropogenic emissions, with a decadal mean of 0.11 (0.08–0.14) Tg N yr⁻¹. Indirect emissions and perturbed fluxes from changes in climate,
sources and perturbed fluxes from changes in climate, CO₂, and land cover did not have an obvious trend during the study period.

The TD estimates in the 2010s (1.51, 1.40–1.79 Tg N yr⁻¹) were on average 0.28 Tg N yr⁻¹ higher than the BU estimates (1.23, 0.24–2.41 Tg N yr⁻¹) (Fig. 15). According to the BU results, the average natural emission was 0.95 Tg N yr⁻¹ in the 2010s, contributing 77% of total emissions. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.28 (0.22–0.35), 0.09 (0.06–0.11), and 0.05 (0.02–0.07) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.14 Tg N yr⁻¹ and a range of −0.48 to 0.25 Tg N yr⁻¹.

### 4.5.6 Southwest South America (SSA)

BU and TD estimates are consistent with respect to the magnitude of the total N₂O emissions from southwestern South America during 1997–2020, with values of 0.55 (0.18–1.03) and 0.51 (0.40–0.63) Tg N yr⁻¹ (Fig. 13h), respectively. TD estimates increased at a rate of 5.3 × 10⁻³ Tg N yr⁻² over 1997–2020, whereas BU estimates did not have an obvious trend during this period. Among the four anthropogenic sectors, direct agricultural emissions had the largest increase, from 0.10 Tg N yr⁻¹ in 1980 to 0.15 Tg N yr⁻¹ in 2020 (Fig. 14). Indirect emissions also increased from 0.02 Tg N yr⁻¹ in 1980 to 0.03 Tg N yr⁻¹ in 2020. Perturbed fluxes from changes in climate, CO₂, and land cover had a decreasing trend, while emissions from other sectors fluctuated over the study period.

The BU and TD approaches gave similar estimates of total N₂O emissions from southwestern South America in the 2010s, with values of 0.55 (0.19–1.04) and 0.55 (0.44–0.67) Tg N yr⁻¹ (Fig. 15), respectively. The mean natural emission was 0.39 Tg N yr⁻¹ in the 2010s, accounting for 71% of total emissions. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.14 (0.09–0.19), 0.05 (0.03–0.06), and 0.03 (0.01–0.03) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.05 Tg N yr⁻¹ and a range of −0.16 to 0.08 Tg N yr⁻¹.

### 4.5.7 Europe (EU)

The BU estimates suggest that Europe had the largest decrease rate of regional N₂O emissions among the 18 regions, and the average decrease rate during 1980–2020 was −13.3 × 10⁻³ Tg N yr⁻² (Fig. 13i). For the 1997–2020 period, this decreasing trend, as estimated by the BU approaches, slowed down (−7.7 × 10⁻³ Tg N yr⁻²), whereas the TD approach suggests a small increase of 1.6 × 10⁻³ Tg N yr⁻² (Fig. 13i). Emissions from other direct anthropogenic sources (including fossil fuel and industry, waste and wastewater, and biomass burning), which decreased from

0.51 Tg N yr⁻¹ in 1980 to 0.18 Tg N yr⁻¹ in 2020, made the largest contribution to the decreasing trend in N₂O emissions from Europe. Direct agricultural emissions and indirect emissions show overall decreasing trends from 0.46 and 0.16 Tg N yr⁻¹ in 1980 to 0.38 and 0.12 Tg N yr⁻¹ in 2020, respectively, mainly due to a reduction in fertilizer use after the collapse of the Soviet Union (Tian et al., 2022). However, the decreasing trend in direct agricultural emissions has leveled off since the 2000s. Perturbed fluxes from changes in climate, CO₂, and land cover decreased during 1980–1985 and then slowly increased (Fig. 14).

The BU and TD approaches gave comparable estimates of European N₂O emissions in the 2010s, with values of 1.00 (0.45–1.57) and 0.86 (0.49–1.36) Tg N yr⁻¹ (Fig. 15), respectively. According to the BU results, natural sources only contributed 26% of total emissions (0.26, 0.11–0.52 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.38 (0.32–0.44), 0.19 (0.15–0.24), and 0.13 (0.08–0.16) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.03 Tg N yr⁻¹ and a range of −0.21 to 0.22 Tg N yr⁻¹.

### 4.5.8 Northern Africa (NAF)

For northern Africa, TD approaches suggested a larger magnitude of the total N₂O emissions than BU approaches over the 1997–2020 period (Fig. 13j), with values of 1.01 (0.52–1.32) and 0.69 (0.18–1.27) Tg N yr⁻¹, respectively. Both approaches suggest that N₂O emissions from northern Africa significantly increased during 1997–2020, and the increase rates estimated by the BU and TD approaches were 4.9 × 10⁻³ and 4.7 × 10⁻³ Tg N yr⁻², respectively. Direct emissions increased from 0.10 Tg N yr⁻¹ in 1980 to 0.27 Tg N yr⁻¹ in 2020, making the largest contribution to the increase in N₂O emissions from northern Africa (Fig. 14). Indirect emissions also significantly increased from 0.02 Tg N yr⁻¹ in 1980 to 0.04 Tg N yr⁻¹ in 2020. In contrast, other anthropogenic emissions decreased from 0.12 Tg N yr⁻¹ in 1980 to 0.11 Tg N yr⁻¹ in 2020. N₂O fluxes caused by changes in climate, CO₂, and land cover remained relatively stable during 1980–2020.

In the 2010s, the BU estimates (0.72, 0.17–1.30 Tg N yr⁻¹) were on average 0.32 Tg N yr⁻¹ lower than the TD estimates (1.04, 0.54–1.31 Tg N yr⁻¹) (Fig. 15). Natural sources accounted for 44% of total emissions (0.32, 0.07–0.60 Tg N yr⁻¹) during this period. Direct emissions from nitrogen additions in agriculture were 0.23 (0.09–0.34) Tg N yr⁻¹, contributing 32% of the total emissions. Emissions from other direct anthropogenic sources made the second largest contribution to anthropogenic emissions, with a decadal mean of 0.11 (0.08–0.14) Tg N yr⁻¹. Indirect emissions and perturbed fluxes from changes in climate,
CO₂, and land cover were 0.04 (0.02–0.06) Tg N yr⁻¹, and 0.02 (−0.10 to 0.16), respectively.

### 4.5.9 Equatorial Africa (EQAF)

Similar to northern Africa, TD approaches suggested a larger magnitude of total N₂O emissions from equatorial Africa than BU approaches over the 1997–2020 period (Fig. 13k), with values of 1.45 (1.15–1.78) and 1.36 (0.36–2.22) Tg N yr⁻¹, respectively. Both approaches suggested that N₂O emissions from equatorial Africa significantly increased during 1997–2020, and the increase rates estimated by the BU and TD approaches were 4.4 × 10⁻³ and 2.1 × 10⁻³ Tg N yr⁻¹, respectively. Direct emissions more than tripled during the study period, from 0.07 Tg N yr⁻¹ in 1980 to 0.22 Tg N yr⁻¹ in 2020, dominating the increase in N₂O emissions from equatorial Africa (Fig. 14). Indirect emissions also steadily increased from 0.04 Tg N yr⁻¹ in 1980 to 0.06 Tg N yr⁻¹ in 2020. On the contrary, perturbed fluxes from changes in climate, CO₂, and land cover showed an overall decreasing trend with large interannual variability. Emissions from other anthropogenic sources showed relatively stable values.

The BU and TD approaches gave comparable estimates of N₂O emissions from equatorial Africa in the 2010s, with values of 1.38 (0.38–2.28) and 1.50 (1.15–1.80) Tg N yr⁻¹ (Fig. 15), respectively. According to the BU results, natural emissions were the dominant component, accounting for 71% of total emissions (0.98, 0.42–1.32 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.18 (0.13–0.25), 0.26 (0.19–0.34), and 0.05 (0.03–0.08) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.09 Tg N yr⁻¹ and a range of −0.40 to 0.29 Tg N yr⁻¹.

### 4.5.10 Southern Africa (SAF)

BU and TD estimates are consistent with respect to the magnitude of the total N₂O emissions from southern Africa during 1997–2020, at 0.61 (0.13–1.09) and 0.58 (0.33–0.86) Tg N yr⁻¹ (Fig. 13l), respectively. TD estimates increased at a rate of 4.5 × 10⁻³ Tg N yr⁻² over 1997–2020, whereas BU estimates did not show an obvious trend during this period. According to the BU results, direct agricultural emissions increased from 0.05 Tg N yr⁻¹ in 1980 to 0.08 Tg N yr⁻¹ in 2020, while emissions from other anthropogenic sources slightly decreased from 0.19 Tg N yr⁻¹ in 1980 to 0.17 Tg N yr⁻¹ in 2020. Both indirect emissions and perturbed fluxes from changes in climate, CO₂, and land cover had no significant trend (Fig. 14).

BU and TD approaches gave consistent estimates of total N₂O emissions from southern Africa in the 2010s, with values of 0.62 (0.13–1.10) and 0.61 (0.35–0.87) Tg N yr⁻¹ for the BU and TD approaches (Fig. 15), respectively. Natural

emissions were the dominant components, accounting for 61% of total emissions (0.38, 0.13–0.61 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.07 (0.05–0.09), 0.19 (0.17–0.23), and 0.02 (0.01–0.03) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.05 Tg N yr⁻¹ and a range of −0.24 to 0.14 Tg N yr⁻¹.

### 4.5.11 Russia (RUS)

During 1997–2020, the average total N₂O emission from Russia estimated by the BU approaches was 0.74 Tg N yr⁻¹, ranging from 0.15 to 1.84 Tg N yr⁻¹ (Fig. 13m), which was much higher than the estimates of TD approaches (0.36, 0.18–0.52 Tg N yr⁻¹). Both approaches suggested that Russia's total N₂O emissions increased during 1997–2020, and the increase rates estimated by the BU and TD approaches were 1.2 × 10⁻³ and 1.7 × 10⁻³ Tg N yr⁻², respectively. Direct agricultural emissions, other direct emissions, and indirect emissions had divergent trends before and after 1997. From 1980 to 1997, N₂O emissions from all three of these sectors decreased. After 1997, direct agricultural emissions and other direct emissions had an overall increasing trend, while indirect emissions remained relatively stable. Perturbed fluxes from changes in climate, CO₂, and land cover remained relatively stable with large interannual variability (Fig. 14).

In the 2010s, the BU estimates (0.74, 0.15–1.84 Tg N yr⁻¹) were on average 0.36 Tg N yr⁻¹ higher than the TD estimates (0.38, 0.18–0.59 Tg N yr⁻¹) (Fig. 15). Natural sources accounted for 64% of total emissions (0.47, 0.12–1.22 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.06 (0.05–0.07), 0.10 (0.04–0.18), and 0.05 (0.03–0.07) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.05 Tg N yr⁻¹ and a range of −0.10 to 0.30 Tg N yr⁻¹.

### 4.5.12 Central Asia (CAS)

TD approaches suggested a larger magnitude of total N₂O emissions from Central Asia than BU approaches over the 1997–2020 period (Fig. 13n), with values of 0.19 (0.10–0.29) and 0.14 (0.01–0.27) Tg N yr⁻¹, respectively. BU and TD estimates were consistent with respect to the trend in total N₂O emissions during 1997–2020, with increase rates of 1.9 × 10⁻³ and 2.0 × 10⁻³ Tg N yr⁻², respectively. Direct emissions increased from 0.05 Tg N yr⁻¹ in 1980 to 0.07 Tg N yr⁻¹ in 2020, making the largest contribution to the increase in N₂O emissions from Central Asia. Other direct emissions and indirect emissions had no significant trend. Fluxes from changes in climate, CO₂, and land cover
CO₂, and land cover were 0.04 (0.02–0.06) Tg N yr⁻¹, and 0.02 (−0.10 to 0.16), respectively.

### 4.5.9 Equatorial Africa (EQAF)

Similar to northern Africa, TD approaches suggested a larger magnitude of total N₂O emissions from equatorial Africa than BU approaches over the 1997–2020 period (Fig. 13k), with values of 1.45 (1.15–1.78) and 1.36 (0.36–2.22) Tg N yr⁻¹, respectively. Both approaches suggested that N₂O emissions from equatorial Africa significantly increased during 1997–2020, and the increase rates estimated by the BU and TD approaches were 4.4 × 10⁻³ and 2.1 × 10⁻³ Tg N yr⁻¹, respectively. Direct emissions more than tripled during the study period, from 0.07 Tg N yr⁻¹ in 1980 to 0.22 Tg N yr⁻¹ in 2020, dominating the increase in N₂O emissions from equatorial Africa (Fig. 14). Indirect emissions also steadily increased from 0.04 Tg N yr⁻¹ in 1980 to 0.06 Tg N yr⁻¹ in 2020. On the contrary, perturbed fluxes from changes in climate, CO₂, and land cover showed an overall decreasing trend with large interannual variability. Emissions from other anthropogenic sources showed relatively stable values.

The BU and TD approaches gave comparable estimates of N₂O emissions from equatorial Africa in the 2010s, with values of 1.38 (0.38–2.28) and 1.50 (1.15–1.80) Tg N yr⁻¹ (Fig. 15), respectively. According to the BU results, natural emissions were the dominant component, accounting for 71% of total emissions (0.98, 0.42–1.32 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.18 (0.13–0.25), 0.26 (0.19–0.34), and 0.05 (0.03–0.08) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.09 Tg N yr⁻¹ and a range of −0.40 to 0.29 Tg N yr⁻¹.

### 4.5.10 Southern Africa (SAF)

BU and TD estimates are consistent with respect to the magnitude of the total N₂O emissions from southern Africa during 1997–2020, at 0.61 (0.13–1.09) and 0.58 (0.33–0.86) Tg N yr⁻¹ (Fig. 13l), respectively. TD estimates increased at a rate of 4.5 × 10⁻³ Tg N yr⁻² over 1997–2020, whereas BU estimates did not show an obvious trend during this period. According to the BU results, direct agricultural emissions increased from 0.05 Tg N yr⁻¹ in 1980 to 0.08 Tg N yr⁻¹ in 2020, while emissions from other anthropogenic sources slightly decreased from 0.19 Tg N yr⁻¹ in 1980 to 0.17 Tg N yr⁻¹ in 2020. Both indirect emissions and perturbed fluxes from changes in climate, CO₂, and land cover had no significant trend (Fig. 14).

BU and TD approaches gave consistent estimates of total N₂O emissions from southern Africa in the 2010s, with values of 0.62 (0.13–1.10) and 0.61 (0.35–0.87) Tg N yr⁻¹ for the BU and TD approaches (Fig. 15), respectively. Natural

emissions were the dominant components, accounting for 61% of total emissions (0.38, 0.13–0.61 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.07 (0.05–0.09), 0.19 (0.17–0.23), and 0.02 (0.01–0.03) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.05 Tg N yr⁻¹ and a range of −0.24 to 0.14 Tg N yr⁻¹.

### 4.5.11 Russia (RUS)

During 1997–2020, the average total N₂O emission from Russia estimated by the BU approaches was 0.74 Tg N yr⁻¹, ranging from 0.15 to 1.84 Tg N yr⁻¹ (Fig. 13m), which was much higher than the estimates of TD approaches (0.36, 0.18–0.52 Tg N yr⁻¹). Both approaches suggested that Russia's total N₂O emissions increased during 1997–2020, and the increase rates estimated by the BU and TD approaches were 1.2 × 10⁻³ and 1.7 × 10⁻³ Tg N yr⁻², respectively. Direct agricultural emissions, other direct emissions, and indirect emissions had divergent trends before and after 1997. From 1980 to 1997, N₂O emissions from all three of these sectors decreased. After 1997, direct agricultural emissions and other direct emissions had an overall increasing trend, while indirect emissions remained relatively stable. Perturbed fluxes from changes in climate, CO₂, and land cover remained relatively stable with large interannual variability (Fig. 14).

In the 2010s, the BU estimates (0.74, 0.15–1.84 Tg N yr⁻¹) were on average 0.36 Tg N yr⁻¹ higher than the TD estimates (0.38, 0.18–0.59 Tg N yr⁻¹) (Fig. 15). Natural sources accounted for 64% of total emissions (0.47, 0.12–1.22 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.06 (0.05–0.07), 0.10 (0.04–0.18), and 0.05 (0.03–0.07) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.05 Tg N yr⁻¹ and a range of −0.10 to 0.30 Tg N yr⁻¹.

### 4.5.12 Central Asia (CAS)

TD approaches suggested a larger magnitude of total N₂O emissions from Central Asia than BU approaches over the 1997–2020 period (Fig. 13n), with values of 0.19 (0.10–0.29) and 0.14 (0.01–0.27) Tg N yr⁻¹, respectively. BU and TD estimates were consistent with respect to the trend in total N₂O emissions during 1997–2020, with increase rates of 1.9 × 10⁻³ and 2.0 × 10⁻³ Tg N yr⁻², respectively. Direct emissions increased from 0.05 Tg N yr⁻¹ in 1980 to 0.07 Tg N yr⁻¹ in 2020, making the largest contribution to the increase in N₂O emissions from Central Asia. Other direct emissions and indirect emissions had no significant trend. Fluxes from changes in climate, CO₂, and land cover
showed an overall increasing trend with large interannual variability (Fig. 14).

In the 2010s, the TD estimates (0.20, 0.10–0.32 Tg N yr⁻¹) were on average 0.05 Tg N yr⁻¹ higher than the BU estimates (0.15, 0.01–0.30 Tg N yr⁻¹) (Fig. 15). Natural sources accounted for 30% of total emissions (0.04, 0.01–0.11 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.06 (0.02–0.08), 0.02 (0.01–0.02), and 0.02 (0.01–0.03) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.02 Tg N yr⁻¹ and a range of -0.04 to 0.07 Tg N yr⁻¹.

### 4.5.13 The Middle East (MIDE)

BU and TD estimates are comparable with respect to the magnitude of the total N₂O emissions from the Middle East during 1997–2020, with values of 0.27 (0.11–0.45) and 0.30 (0.25–0.36) Tg N yr⁻¹ (Fig. 13o), respectively. BU and TD estimates were consistent regarding the trend in total N₂O emissions during 1997–2020, with increase rates of 4.4 × 10⁻³ and 3.9 × 10⁻³ Tg N yr⁻², respectively. According to the BU results, direct agricultural emissions increased from 0.07 Tg N yr⁻¹ in 1980 to 0.13 Tg N yr⁻¹ in 2020. Emissions from other anthropogenic sources (fossil fuel and industry, particularly) had the largest increase, from 0.03 Tg N yr⁻¹ in 1980 to 0.10 Tg N yr⁻¹ in 2020. Indirect emissions also continuously increased from 0.02 Tg N yr⁻¹ in 1980 to 0.04 Tg N yr⁻¹ in 2020. Perturbed fluxes from changes in climate, CO₂, and land cover had no significant trend (Fig. 14).

BU and TD approaches gave consistent estimates of total N₂O emissions from the Middle East in the 2010s, with values of 0.29 (0.12–0.49) and 0.32 (0.26–0.39) Tg N yr⁻¹ for the BU and TD approaches (Fig. 15), respectively. Natural emissions were 0.04 (0.02–0.08 Tg N yr⁻¹), accounting for 15% of total emissions during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.12 (0.05–0.21), 0.09 (0.07–0.10), and 0.03 (0.02–0.04) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.01 Tg N yr⁻¹ and a range of -0.04 to 0.05 Tg N yr⁻¹.

### 4.5.14 China (CHN)

BU and TD approaches agreed very well regarding the magnitudes and trends of N₂O emissions from China. Both approaches suggested that China's total N₂O emissions significantly increased during 1997–2020, and the increase rates estimated by the BU and TD approaches were 12.6 × 10⁻³ and 16.5 × 10⁻³ Tg N yr⁻¹, respectively (Fig. 13p). According to the BU results, China's total N₂O emissions increased from 0.76 Tg N yr⁻¹ in 1980 to 1.38 Tg N yr⁻¹ in 2020. Direct

emissions from N additions in agriculture made the largest contribution to the increase in China's N₂O emissions, which increased from 0.29 Tg N yr⁻¹ in 1980 to 0.71 Tg N yr⁻¹ in 2016 and then decreased to 0.64 Tg N yr⁻¹ in 2020 due to decreased N fertilizer application (Fig. 14). Both indirect emissions and other direct emissions continuously increased, from 0.09 and 0.11 Tg N yr⁻¹ in 1980 to 0.24 and 0.27 Tg N yr⁻¹ in 2020, respectively. The total anthropogenic N₂O emissions from China increased at an average rate of 18.9 × 10⁻³ Tg N yr⁻² during 1980–2020, which was the largest among the 18 regions and contributed 40% of the increase in global anthropogenic N₂O emissions.

The BU and TD approaches gave consistent estimates of China's total N₂O emissions in the 2010s, with values of 1.41 (0.82–2.23) and 1.33 (1.06–1.60) Tg N yr⁻¹ for the BU and TD approaches (Fig. 15), respectively. According to the BU results, natural sources only contributed 21% of total emissions (0.29, 0.20–0.51 Tg N yr⁻¹) during this period. Nitrogen additions in agriculture were the dominant source of N₂O emissions, contributing 48% of the total emissions (0.68, 0.48–1.03 Tg N yr⁻¹). Emissions from other direct anthropogenic sources and indirect emissions from anthropogenic nitrogen additions were 0.23 (0.23–0.23) and 0.24 (0.17–0.28) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of -0.03 Tg N yr⁻¹ and a range of -0.25 to 0.18 Tg N yr⁻¹.

### 4.5.15 Korea and Japan (KAJ)

TD approaches suggested a smaller magnitude of total N₂O emissions from Korea and Japan than BU approaches over the 1997–2020 period (Fig. 13q), with values of 0.06 (0.03–0.11) and 0.11 (0.06–0.16) Tg N yr⁻¹, respectively. Both approaches suggested that total N₂O emissions from Korea and Japan decreased during 1997–2020, and the decrease rates estimated by the BU and TD approaches were -1.4 × 10⁻³ and -0.5 × 10⁻³ Tg N yr⁻², respectively. Other direct emissions (fossil fuel and industry, particularly) dominated the temporal variations in N₂O emissions from Korea and Japan, which increased from 0.04 Tg N yr⁻¹ in 1980 to 0.08 Tg N yr⁻¹ in 1997 and then decreased to 0.04 Tg N yr⁻¹ in 2020. Emissions from agriculture, indirect sources, and perturbed fluxes remained relatively stable during 1997–2020 (Fig. 14).

In the 2010s, BU estimates (0.10, 0.05–0.15 Tg N yr⁻¹) of total N₂O emissions were on average 0.04 Tg N yr⁻¹ higher than the TD estimate (0.06, 0.04–0.11 Tg N yr⁻¹) (Fig. 15). Natural sources accounted for 26% of total emissions (0.03, 0.00–0.05 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.03 (0.02–0.04), 0.04 (0.04–0.04), and 0.01 (0.01–0.02) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on
showed an overall increasing trend with large interannual variability (Fig. 14).

In the 2010s, the TD estimates (0.20, 0.10–0.32 Tg N yr⁻¹) were on average 0.05 Tg N yr⁻¹ higher than the BU estimates (0.15, 0.01–0.30 Tg N yr⁻¹) (Fig. 15). Natural sources accounted for 30% of total emissions (0.04, 0.01–0.11 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.06 (0.02–0.08), 0.02 (0.01–0.02), and 0.02 (0.01–0.03) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.02 Tg N yr⁻¹ and a range of −0.04 to 0.07 Tg N yr⁻¹.

### 4.5.13 The Middle East (MIDE)

BU and TD estimates are comparable with respect to the magnitude of the total N₂O emissions from the Middle East during 1997–2020, with values of 0.27 (0.11–0.45) and 0.30 (0.25–0.36) Tg N yr⁻¹ (Fig. 13o), respectively. BU and TD estimates were consistent regarding the trend in total N₂O emissions during 1997–2020, with increase rates of 4.4 × 10⁻³ and 3.9 × 10⁻³ Tg N yr⁻², respectively. According to the BU results, direct agricultural emissions increased from 0.07 Tg N yr⁻¹ in 1980 to 0.13 Tg N yr⁻¹ in 2020. Emissions from other anthropogenic sources (fossil fuel and industry, particularly) had the largest increase, from 0.03 Tg N yr⁻¹ in 1980 to 0.10 Tg N yr⁻¹ in 2020. Indirect emissions also continuously increased from 0.02 Tg N yr⁻¹ in 1980 to 0.04 Tg N yr⁻¹ in 2020. Perturbed fluxes from changes in climate, CO₂, and land cover had no significant trend (Fig. 14).

BU and TD approaches gave consistent estimates of total N₂O emissions from the Middle East in the 2010s, with values of 0.29 (0.12–0.49) and 0.32 (0.26–0.39) Tg N yr⁻¹ for the BU and TD approaches (Fig. 15), respectively. Natural emissions were 0.04 (0.02–0.08 Tg N yr⁻¹), accounting for 15% of total emissions during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.12 (0.05–0.21), 0.09 (0.07–0.10), and 0.03 (0.02–0.04) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.01 Tg N yr⁻¹ and a range of −0.04 to 0.05 Tg N yr⁻¹.

### 4.5.14 China (CHN)

BU and TD approaches agreed very well regarding the magnitudes and trends of N₂O emissions from China. Both approaches suggested that China's total N₂O emissions significantly increased during 1997–2020, and the increase rates estimated by the BU and TD approaches were 12.6 × 10⁻³ and 16.5 × 10⁻³ Tg N yr⁻¹, respectively (Fig. 13p). According to the BU results, China's total N₂O emissions increased from 0.76 Tg N yr⁻¹ in 1980 to 1.38 Tg N yr⁻¹ in 2020. Direct

emissions from N additions in agriculture made the largest contribution to the increase in China's N₂O emissions, which increased from 0.29 Tg N yr⁻¹ in 1980 to 0.71 Tg N yr⁻¹ in 2016 and then decreased to 0.64 Tg N yr⁻¹ in 2020 due to decreased N fertilizer application (Fig. 14). Both indirect emissions and other direct emissions continuously increased, from 0.09 and 0.11 Tg N yr⁻¹ in 1980 to 0.24 and 0.27 Tg N yr⁻¹ in 2020, respectively. The total anthropogenic N₂O emissions from China increased at an average rate of 18.9 × 10⁻³ Tg N yr⁻² during 1980–2020, which was the largest among the 18 regions and contributed 40% of the increase in global anthropogenic N₂O emissions.

The BU and TD approaches gave consistent estimates of China's total N₂O emissions in the 2010s, with values of 1.41 (0.82–2.23) and 1.33 (1.06–1.60) Tg N yr⁻¹ for the BU and TD approaches (Fig. 15), respectively. According to the BU results, natural sources only contributed 21% of total emissions (0.29, 0.20–0.51 Tg N yr⁻¹) during this period. Nitrogen additions in agriculture were the dominant source of N₂O emissions, contributing 48% of the total emissions (0.68, 0.48–1.03 Tg N yr⁻¹). Emissions from other direct anthropogenic sources and indirect emissions from anthropogenic nitrogen additions were 0.23 (0.23–0.23) and 0.24 (0.17–0.28) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of −0.03 Tg N yr⁻¹ and a range of −0.25 to 0.18 Tg N yr⁻¹.

### 4.5.15 Korea and Japan (KAJ)

TD approaches suggested a smaller magnitude of total N₂O emissions from Korea and Japan than BU approaches over the 1997–2020 period (Fig. 13q), with values of 0.06 (0.03–0.11) and 0.11 (0.06–0.16) Tg N yr⁻¹, respectively. Both approaches suggested that total N₂O emissions from Korea and Japan decreased during 1997–2020, and the decrease rates estimated by the BU and TD approaches were −1.4 × 10⁻³ and −0.5 × 10⁻³ Tg N yr⁻², respectively. Other direct emissions (fossil fuel and industry, particularly) dominated the temporal variations in N₂O emissions from Korea and Japan, which increased from 0.04 Tg N yr⁻¹ in 1980 to 0.08 Tg N yr⁻¹ in 1997 and then decreased to 0.04 Tg N yr⁻¹ in 2020. Emissions from agriculture, indirect sources, and perturbed fluxes remained relatively stable during 1997–2020 (Fig. 14).

In the 2010s, BU estimates (0.10, 0.05–0.15 Tg N yr⁻¹) of total N₂O emissions were on average 0.04 Tg N yr⁻¹ higher than the TD estimate (0.06, 0.04–0.11 Tg N yr⁻¹) (Fig. 15). Natural sources accounted for 26% of total emissions (0.03, 0.00–0.05 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.03 (0.02–0.04), 0.04 (0.04–0.04), and 0.01 (0.01–0.02) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on
N₂O emissions, with a mean value of -0.01 Tg N yr⁻¹ and a range of -0.02 to 0.01 Tg N yr⁻¹.

### 4.5.16 South Asia (SAS)

BU and TD estimates are comparable in terms of both the magnitude and trend of the total N₂O emissions from South Asia (Fig. 13r). During 1997–2020, the magnitudes of total N₂O emissions estimated by the BU and TD approaches were 1.04 (0.35–1.80) and 1.21 (0.96–1.56) Tg N yr⁻¹, respectively. Both approaches suggested that the total N₂O emissions from South Asia significantly increased during 1997–2020, and the increase rates estimated by the BU and TD approaches were 17.7 × 10⁻³ and 20.2 × 10⁻³ Tg N yr⁻², respectively. Direct emissions from nitrogen additions in agriculture made the largest contribution to the increase in N₂O emissions in South Asia, which increased from 0.19 Tg N yr⁻¹ in 1980 to 0.55 Tg N yr⁻¹ in 2020 due to increased N fertilizer application (Fig. 14). Other direct emissions and indirect emissions also significantly increased, from 0.06 and 0.06 Tg N yr⁻¹ in 1980 to 0.14 and 0.17 Tg N yr⁻¹ in 2020, respectively. Fluxes from changes in climate, CO₂, and land cover showed an overall increasing trend with large interannual variability.

BU estimates (1.15, 0.41–2.06 Tg N yr⁻¹) were on average 0.21 Tg N yr⁻¹ lower than the TD estimate in the 2010s (1.36, 1.05–1.84 Tg N yr⁻¹) (Fig. 15). Natural sources accounted for 28% of total emissions (0.32, 0.12–0.56 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.49 (0.25–0.75), 0.13 (0.13–0.13), and 0.15 (0.10–0.19) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.06 Tg N yr⁻¹ and a range of -0.19 to 0.43 Tg N yr⁻¹.

### 4.5.17 Southeast Asia (SEAS)

TD approaches suggested a smaller magnitude of the total N₂O emissions from Southeast Asia than BU approaches over the 1997–2020 period (Fig. 13s), with values of 0.69 (0.50–1.02) and 0.92 (0.24–2.04) Tg N yr⁻¹, respectively. Both approaches suggested that total N₂O emissions from Southeast Asia increased during 1997–2020, and the rates of increase estimated by the BU and TD approaches were 5.1 × 10⁻³ and 2.3 × 10⁻³ Tg N yr⁻², respectively. Direct agricultural emissions, other direct emissions, and indirect emissions significantly increased during the study period, from 0.09, 0.08, and 0.04 Tg N yr⁻¹ in 1980 to 0.30, 0.11, and 0.12 Tg N yr⁻¹ in 2020, respectively. Meanwhile, perturbed fluxes from changes in climate, CO₂, and land cover significantly decreased from -0.07 Tg N yr⁻¹ in 1980 to -0.12 Tg N yr⁻¹ in 2020 (Fig. 14).

The BU and TD approaches gave comparable estimates of the total N₂O emissions from Southeast Asia in the

2010s, with values of 0.95 (0.24–2.09) and 0.72 (0.51–1.12) Tg N yr⁻¹ for the BU and TD approaches (Fig. 15), respectively. Natural sources accounted for 62% of total emissions (0.59, 0.24–1.30 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.26 (0.20–0.35), 0.11 (0.09–0.14), and 0.10 (0.06–0.14) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of -0.12 Tg N yr⁻¹ and a range of -0.35 to 0.16 Tg N yr⁻¹.

### 4.5.18 Australasia (AUS)

BU and TD estimates are comparable in terms of the magnitude of the total N₂O emissions from Australasia during 1997–2020 (Fig. 13t). The magnitudes of the total N₂O emissions estimated by the BU and TD approaches were 0.43 (0.01–0.92) and 0.52 (0.21–0.72) Tg N yr⁻¹, respectively. TD estimates increased at a rate of 4.4 × 10⁻³ Tg N yr⁻² over 1997–2020, whereas BU estimates did not show a notable trend during this period (Fig. 13t). According to the BU results, direct agricultural emissions increased from 0.08 Tg m yr⁻¹ in 1980 to 0.09 Tg N yr⁻¹ in 2020, while emissions from all the other three anthropogenic sectors remained stable (Fig. 14).

In the 2010s, the magnitudes of total N₂O emissions estimated by the BU and TD approaches were 0.42 (0.01–0.91) and 0.53 (0.20–0.71) Tg N yr⁻¹, respectively. Natural sources accounted for 59% of total emissions (0.25, 0.05–0.50 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.09 (0.06–0.11), 0.08 (0.06–0.11), and 0.01 (0.01–0.02) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of -0.01 Tg N yr⁻¹ and a range of -0.17 to -0.17 Tg N yr⁻¹ (Fig. 15).

## 5 Data availability

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

The global N₂O budget 1980–2020 – global emission data spreadsheet includes the following items:

1. a summary;

2. bottom-up estimates – global BU N₂O budget from 1980 to 2020, including 20 individual sources and sinks;

3. top-down estimates – N₂O emissions from land, ocean, and global during 1997–2020 estimated by the four atmospheric inversion models;
N₂O emissions, with a mean value of -0.01 Tg N yr⁻¹ and a range of -0.02 to 0.01 Tg N yr⁻¹.

### 4.5.16 South Asia (SAS)

BU and TD estimates are comparable in terms of both the magnitude and trend of the total N₂O emissions from South Asia (Fig. 13r). During 1997–2020, the magnitudes of total N₂O emissions estimated by the BU and TD approaches were 1.04 (0.35–1.80) and 1.21 (0.96–1.56) Tg N yr⁻¹, respectively. Both approaches suggested that the total N₂O emissions from South Asia significantly increased during 1997–2020, and the increase rates estimated by the BU and TD approaches were 17.7 × 10⁻³ and 20.2 × 10⁻³ Tg N yr⁻², respectively. Direct emissions from nitrogen additions in agriculture made the largest contribution to the increase in N₂O emissions in South Asia, which increased from 0.19 Tg N yr⁻¹ in 1980 to 0.55 Tg N yr⁻¹ in 2020 due to increased N fertilizer application (Fig. 14). Other direct emissions and indirect emissions also significantly increased, from 0.06 and 0.06 Tg N yr⁻¹ in 1980 to 0.14 and 0.17 Tg N yr⁻¹ in 2020, respectively. Fluxes from changes in climate, CO₂, and land cover showed an overall increasing trend with large interannual variability.

BU estimates (1.15, 0.41–2.06 Tg N yr⁻¹) were on average 0.21 Tg N yr⁻¹ lower than the TD estimate in the 2010s (1.36, 1.05–1.84 Tg N yr⁻¹) (Fig. 15). Natural sources accounted for 28% of total emissions (0.32, 0.12–0.56 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.49 (0.25–0.75), 0.13 (0.13–0.13), and 0.15 (0.10–0.19) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall positive effect on N₂O emissions, with a mean value of 0.06 Tg N yr⁻¹ and a range of -0.19 to 0.43 Tg N yr⁻¹.

### 4.5.17 Southeast Asia (SEAS)

TD approaches suggested a smaller magnitude of the total N₂O emissions from Southeast Asia than BU approaches over the 1997–2020 period (Fig. 13s), with values of 0.69 (0.50–1.02) and 0.92 (0.24–2.04) Tg N yr⁻¹, respectively. Both approaches suggested that total N₂O emissions from Southeast Asia increased during 1997–2020, and the rates of increase estimated by the BU and TD approaches were 5.1 × 10⁻³ and 2.3 × 10⁻³ Tg N yr⁻², respectively. Direct agricultural emissions, other direct emissions, and indirect emissions significantly increased during the study period, from 0.09, 0.08, and 0.04 Tg N yr⁻¹ in 1980 to 0.30, 0.11, and 0.12 Tg N yr⁻¹ in 2020, respectively. Meanwhile, perturbed fluxes from changes in climate, CO₂, and land cover significantly decreased from -0.07 Tg N yr⁻¹ in 1980 to -0.12 Tg N yr⁻¹ in 2020 (Fig. 14).

The BU and TD approaches gave comparable estimates of the total N₂O emissions from Southeast Asia in the

2010s, with values of 0.95 (0.24–2.09) and 0.72 (0.51–1.12) Tg N yr⁻¹ for the BU and TD approaches (Fig. 15), respectively. Natural sources accounted for 62% of total emissions (0.59, 0.24–1.30 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.26 (0.20–0.35), 0.11 (0.09–0.14), and 0.10 (0.06–0.14) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of -0.12 Tg N yr⁻¹ and a range of -0.35 to 0.16 Tg N yr⁻¹.

### 4.5.18 Australasia (AUS)

BU and TD estimates are comparable in terms of the magnitude of the total N₂O emissions from Australasia during 1997–2020 (Fig. 13t). The magnitudes of the total N₂O emissions estimated by the BU and TD approaches were 0.43 (0.01–0.92) and 0.52 (0.21–0.72) Tg N yr⁻¹, respectively. TD estimates increased at a rate of 4.4 × 10⁻³ Tg N yr⁻² over 1997–2020, whereas BU estimates did not show a notable trend during this period (Fig. 13t). According to the BU results, direct agricultural emissions increased from 0.08 Tg m yr⁻¹ in 1980 to 0.09 Tg N yr⁻¹ in 2020, while emissions from all the other three anthropogenic sectors remained stable (Fig. 14).

In the 2010s, the magnitudes of total N₂O emissions estimated by the BU and TD approaches were 0.42 (0.01–0.91) and 0.53 (0.20–0.71) Tg N yr⁻¹, respectively. Natural sources accounted for 59% of total emissions (0.25, 0.05–0.50 Tg N yr⁻¹) during this period. Direct agricultural emissions, other direct emissions, and indirect emissions were 0.09 (0.06–0.11), 0.08 (0.06–0.11), and 0.01 (0.01–0.02) Tg N yr⁻¹, respectively. Changes in climate, CO₂, and land cover had an overall negative effect on N₂O emissions, with a mean value of -0.01 Tg N yr⁻¹ and a range of -0.17 to -0.17 Tg N yr⁻¹ (Fig. 15).

## 5 Data availability

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

The global N₂O budget 1980–2020 – global emission data spreadsheet includes the following items:

1. a summary;

2. bottom-up estimates – global BU N₂O budget from 1980 to 2020, including 20 individual sources and sinks;

3. top-down estimates – N₂O emissions from land, ocean, and global during 1997–2020 estimated by the four atmospheric inversion models;
4. Atmospheric_chemical_sink – global atmospheric chemical sink estimated by the four atmospheric inversion models (1997–2020) and one satellite and photolysis model (2005–2020);

5. N₂O_dry_moleFraction – monthly N₂O dry mole fraction and its growth rate during 2000–2020 estimated by the three observation networks;

6. Future_N₂O_dry_mole_fraction – the projected N₂O dry mole fractions from the four illustrative Representative Concentration Pathways (RCPs) in the IPCC Fifth Assessment Report (2000–2050) as well as the seven illustrative Socioeconomic Pathways (SSPs) used in CMIP6 (2005–2050).

The global N₂O budget 1980–2020 – regional emission data spreadsheet includes the following items:

1. a summary;

2. Anthropogenic_sectors_1980_2020 – N₂O emissions from the four anthropogenic sources for the 18 regions during 1980–2020;

3. Bottom-up_estimates – total N₂O emissions from the 18 regions during 1980–2020 estimated by the BU approaches;

4. Top-down_estimates – N₂O emissions from the 18 regions during 1997–2020 estimated by the four atmospheric inversion models;

5. Decadal_mean_2010s – regional N₂O emissions estimated by the TD and BU approaches in the 2010s.

The global N₂O budget 1980–2020 – modeled gridded emission data spreadsheet includes the spatial patterns of N₂O emissions from different sources (unit: g N m⁻² yr⁻¹) estimated by different models as follows:

1. NMIP2 – a total of 16 maps showing the spatial distribution of soil N₂O emissions, including estimates of eight process-based models that participated in NMIP2 (CLASSIC, DLEM, ELM, ISAM, LPX-Bern, O-CN, ORCHIDEE, and VISIT) and two periods (the 1850s and 2010s);

2. open-ocean emissions – a total of four maps showing the spatial distribution of open-ocean N₂O emissions, including estimates of four ocean models, namely, Bern-3D, NEMO-PlankTOM10.2, NEMOv3.6-PISCESv2-gas, and UVic2.9;

3. shelf emissions – a total of three maps showing the spatial distribution of continental shelves' N₂O emissions, including estimates of three products, namely, CNRM, ECCO, and MEM-RF;

4. top-down estimates – a total of four maps showing the global distribution of N₂O emissions, including estimates of four atmospheric inversion models, namely, GEOS-Chem, INVICAT, MIROC4-ACTM, and PyVAR-CAMS.

The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al., 2023).

# 6 Discussion

## 6.1 Emission sources and comparison with previous estimates of the global N₂O budget

In comparing the global N₂O budget estimates with previous studies, the definitions and terminology used in this study for N₂O sources and sinks are consistent with those in Tian et al. (2020). In this new synthesis, we have also included a new emission source, namely “Continental shelves”, corresponding to the shallow portion of the ocean overlying continental shelves (Laruelle et al., 2013), which was not explicitly reported in the previous global N₂O budget (Tian et al., 2020). Thus, a total of 18 sources and 3 sinks are quantified in the global N₂O budget reported here. We utilized a similar methodology to synthesize multiple TD and BU estimates. The TD estimates of global total emissions in this study are consistent with Tian et al. (2020). However, the TD estimates of emissions from the ocean are about 2.3 Tg N yr⁻¹ lower than the previous estimate in the 2000s, whereas the TD estimates of land emissions are about 2.4 Tg N yr⁻¹ higher than the previous estimate for the decade 2007–2016 (Tian et al., 2020). Global BU estimates in this study are about 1.2 Tg N yr⁻¹ higher than the previous estimate, primarily due to the inclusion of emissions from continental shelves (1.2 Tg N yr⁻¹), and 0.8 Tg N yr⁻¹ higher than the previous estimate for the natural soil baseline.

According to our analysis, natural soils contributed more than half of terrestrial N₂O emissions (Table 3), consistent with previous studies (Denman et al., 2007; Tian et al., 2020). The global natural soil emissions derived from this study are estimated to be 6.4 Tg N yr⁻¹, with a large uncertainty ranging from 3.9 to 8.6 Tg N yr⁻¹. Using the emission factor from the 2006 IPCC guidelines, Syakila and Kroeze (2011) estimated that global preindustrial N₂O emission from natural soils was 7 Tg N yr⁻¹. Xu et al. (2017) suggested that global natural soil N₂O emissions were about 6.2 Tg N yr⁻¹, with an uncertainty range of 4.8 to 8.1 Tg N yr⁻¹. Tian et al. (2019) estimated global soil N₂O emissions derived from NMIP using seven process-based terrestrial biosphere models and suggested a global soil N₂O emission of 6.3 ± 1.1 Tg N yr⁻¹ in the 1860s.

The total of direct agricultural emissions, other direct anthropogenic emissions, and indirect anthropogenic emissions in this study is the same as the previous estimates (Tian et al., 2020). However, the total anthropogenic emission in
4. Atmospheric_chemical_sink – global atmospheric chemical sink estimated by the four atmospheric inversion models (1997–2020) and one satellite and photolysis model (2005–2020);

5. N₂O_dry_moleFraction – monthly N₂O dry mole fraction and its growth rate during 2000–2020 estimated by the three observation networks;

6. Future_N₂O_dry_mole_fraction – the projected N₂O dry mole fractions from the four illustrative Representative Concentration Pathways (RCPs) in the IPCC Fifth Assessment Report (2000–2050) as well as the seven illustrative Socioeconomic Pathways (SSPs) used in CMIP6 (2005–2050).

The global N₂O budget 1980–2020 – regional emission data spreadsheet includes the following items:

1. a summary;

2. Anthropogenic_sectors_1980_2020 – N₂O emissions from the four anthropogenic sources for the 18 regions during 1980–2020;

3. Bottom-up_estimates – total N₂O emissions from the 18 regions during 1980–2020 estimated by the BU approaches;

4. Top-down_estimates – N₂O emissions from the 18 regions during 1997–2020 estimated by the four atmospheric inversion models;

5. Decadal_mean_2010s – regional N₂O emissions estimated by the TD and BU approaches in the 2010s.

The global N₂O budget 1980–2020 – modeled gridded emission data spreadsheet includes the spatial patterns of N₂O emissions from different sources (unit: g N m⁻² yr⁻¹) estimated by different models as follows:

1. NMIP2 – a total of 16 maps showing the spatial distribution of soil N₂O emissions, including estimates of eight process-based models that participated in NMIP2 (CLASSIC, DLEM, ELM, ISAM, LPX-Bern, O-CN, ORCHIDEE, and VISIT) and two periods (the 1850s and 2010s);

2. open-ocean emissions – a total of four maps showing the spatial distribution of open-ocean N₂O emissions, including estimates of four ocean models, namely, Bern-3D, NEMO-PlankTOM10.2, NEMOv3.6-PISCESv2-gas, and UVic2.9;

3. shelf emissions – a total of three maps showing the spatial distribution of continental shelves' N₂O emissions, including estimates of three products, namely, CNRM, ECCO, and MEM-RF;

4. top-down estimates – a total of four maps showing the global distribution of N₂O emissions, including estimates of four atmospheric inversion models, namely, GEOS-Chem, INVICAT, MIROC4-ACTM, and PyVAR-CAMS.

The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al., 2023).

# 6 Discussion

## 6.1 Emission sources and comparison with previous estimates of the global N₂O budget

In comparing the global N₂O budget estimates with previous studies, the definitions and terminology used in this study for N₂O sources and sinks are consistent with those in Tian et al. (2020). In this new synthesis, we have also included a new emission source, namely “Continental shelves”, corresponding to the shallow portion of the ocean overlying continental shelves (Laruelle et al., 2013), which was not explicitly reported in the previous global N₂O budget (Tian et al., 2020). Thus, a total of 18 sources and 3 sinks are quantified in the global N₂O budget reported here. We utilized a similar methodology to synthesize multiple TD and BU estimates. The TD estimates of global total emissions in this study are consistent with Tian et al. (2020). However, the TD estimates of emissions from the ocean are about 2.3 Tg N yr⁻¹ lower than the previous estimate in the 2000s, whereas the TD estimates of land emissions are about 2.4 Tg N yr⁻¹ higher than the previous estimate for the decade 2007–2016 (Tian et al., 2020). Global BU estimates in this study are about 1.2 Tg N yr⁻¹ higher than the previous estimate, primarily due to the inclusion of emissions from continental shelves (1.2 Tg N yr⁻¹), and 0.8 Tg N yr⁻¹ higher than the previous estimate for the natural soil baseline.

According to our analysis, natural soils contributed more than half of terrestrial N₂O emissions (Table 3), consistent with previous studies (Denman et al., 2007; Tian et al., 2020). The global natural soil emissions derived from this study are estimated to be 6.4 Tg N yr⁻¹, with a large uncertainty ranging from 3.9 to 8.6 Tg N yr⁻¹. Using the emission factor from the 2006 IPCC guidelines, Syakila and Kroeze (2011) estimated that global preindustrial N₂O emission from natural soils was 7 Tg N yr⁻¹. Xu et al. (2017) suggested that global natural soil N₂O emissions were about 6.2 Tg N yr⁻¹, with an uncertainty range of 4.8 to 8.1 Tg N yr⁻¹. Tian et al. (2019) estimated global soil N₂O emissions derived from NMIP using seven process-based terrestrial biosphere models and suggested a global soil N₂O emission of 6.3 ± 1.1 Tg N yr⁻¹ in the 1860s.

The total of direct agricultural emissions, other direct anthropogenic emissions, and indirect anthropogenic emissions in this study is the same as the previous estimates (Tian et al., 2020). However, the total anthropogenic emission in
this study is lower than our previous estimate (Tian et al., 2020), mainly because of the differences in perturbed fluxes from climate, CO₂, and land-cover change. According to our new estimate derived from NMIP2, the average perturbed flux from climate, CO₂, and land-cover change was -0.6 (-2.1 to 1.2) Tg N yr⁻¹ during 2010–2019 (Table 3). By contrast, the average perturbed flux during 2007–2016 reported by Tian et al. (2020) was 0.2 (-0.6 to 1.1) Tg N yr⁻¹, which was based on the first phase of NMIP (Tian et al., 2018). This study suggests a larger negative effect of increased CO₂ concentration and reduced mature forest area on N₂O emissions than Tian et al. (2020). Much uncertainty exists in estimating the perturbed fluxes of atmospheric CO₂ and mature forest conversion, as discussed in the following section on uncertainties.

Our estimate indicates that agricultural emissions were the major drivers of the increase in anthropogenic emissions during the past 4 decades, increasing from 3.0 Tg N yr⁻¹ in 1980 to 5.0 Tg N yr⁻¹ in 2020 (Fig. 16). Direct agricultural emissions had a larger increase than indirect agricultural emissions (2.2 Tg N yr⁻¹ in 1980 to 3.9 Tg N yr⁻¹ in 2020 versus 0.8 Tg N yr⁻¹ in 1980 to 1.2 Tg N yr⁻¹ in 2020). Agricultural emissions contributed 74 % of total anthropogenic emissions in the 2010s, with 56 % from direct agricultural emissions and 18 % from indirect emissions. Non-agricultural anthropogenic emissions had a slight decreasing trend during 1980–2020 because of a higher estimate of changes in climate, CO₂, and land cover than previous estimate.

This study divides the global land into 18 regions and provides a more detailed regional budget than a previous study, which had only 10 regions (Tian et al., 2020), thereby enhancing our understanding of the N₂O budget in subregions of North America, South America, Africa, and East Asia. In the 1980s, Europe made the largest contribution to global anthropogenic N₂O emissions (23.6 %), followed by China (11.6 %), South Asia (8.0 %), the USA (7.8 %), and Russia (7.3 %). During the study period, Europe and Russia had the largest decline in the share of anthropogenic N₂O emissions, from 23.6 % and 7.3 % in the 1980s to 11.8 % and 4.3 % in the 2010s, respectively. In contrast, China and South Asia had the largest increase, from 11.6 % and 8.0 % in the 1980s to 17.8 % and 13.2 % in the 2010s, respectively. In the 2010s, China (17.8 %), South Asia (13.2 %), Europe (11.8 %), the USA (6.7 %), and equatorial Africa (6.5 %) were the top five contributors to global anthropogenic N₂O emissions (Fig. 17).

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

emissions showed overall decreasing trends from 0.46 and 0.16 Tg N yr⁻¹ in 1980 to 0.38 and 0.12 Tg N yr⁻¹ in 2020, respectively. However, the decreasing trend in agricultural emissions has leveled off since the 2000s.

China and South Asia had the largest increase in N₂O emissions during the study period. The rates of increase in the anthropogenic emissions from China and South Asia were 18.9 × 10⁻³ and 14.3 × 10⁻³ Tg N yr⁻², respectively. The rates of increase in the anthropogenic emissions from China and South Asia contributed 40 % and 30 % to the global anthropogenic increase rate (0.05 Tg N yr⁻²), respectively. In these two regions, direct nitrogen additions in agriculture made the largest contribution, while other direct emissions and indirect emissions also steadily increased. Our results show a significant increase in anthropogenic N₂O emissions from South America, which is consistent with the previous budget (Tian et al., 2020). Moreover, we reveal that Brazil had a higher increase rate in anthropogenic N₂O emissions (4.2 × 10⁻³ Tg N yr⁻²) than northern South America (0.8 × 10⁻³ Tg N yr⁻²) and southwestern South America (0.4 × 10⁻³ Tg N yr⁻²) during 1980–2020, and direct emissions from agriculture made the largest contribution. Our results suggest that northern Africa made the largest contribution (58 %) to the increase in anthropogenic N₂O emissions from Africa, followed by equatorial Africa (38 %) and southern Africa (4 %). Anthropogenic N₂O emissions from the USA and Canada show similar weak increasing rates of 0.6 × 10⁻³ and 0.9 × 10⁻³ Tg N yr⁻² during the 1980–2020 period, respectively. Central America shows a higher anthropogenic N₂O emission increase rate (5.9 × 10⁻³ Tg N yr⁻²), which is attributed to an increase in emissions from fossil fuels and industry, from 0.01 Tg N yr⁻¹ in the 1980s to 0.16 Tg N yr⁻¹ in the 2010s. The data for Mexico from EDGAR have a known problem with their estimates of N₂O emissions from industry, and this issue requires further exploration. To support countries’ N₂O mitigation, it is essential to accurately estimate sources and sinks of N₂O at the national level.

## 6.2 Sources of uncertainties and suggestions for improvements

### 6.2.1 Uncertainties in N₂O emission factors

Four inventories of N₂O emissions (EDGAR, FAOSTAT, GFED, and UNFCCC) are integrated into the current synthesis of anthropogenic N₂O emissions. These EF-based inventory datasets (where EF refers to emission factors) used the IPCC default EFs at regional and global scales. Uncertainty in FAOSTAT N₂O emissions is ~60 % across typologies. In fact, it is asymmetrical, following the 2006 guideline values and IPCC uncertainty formulae, with $u_{min}$ ~ -30 % and $u_{max}$ ~ 90 % (Tubiello et al., 2013); for N₂O in EDGAR, the global uncertainty ranges defining the 95 % confidence intervals of a lognormal distribution are ±113 % for energy, be-
this study is lower than our previous estimate (Tian et al., 2020), mainly because of the differences in perturbed fluxes from climate, CO₂, and land-cover change. According to our new estimate derived from NMIP2, the average perturbed flux from climate, CO₂, and land-cover change was -0.6 (-2.1 to 1.2) Tg N yr⁻¹ during 2010–2019 (Table 3). By contrast, the average perturbed flux during 2007–2016 reported by Tian et al. (2020) was 0.2 (-0.6 to 1.1) Tg N yr⁻¹, which was based on the first phase of NMIP (Tian et al., 2018). This study suggests a larger negative effect of increased CO₂ concentration and reduced mature forest area on N₂O emissions than Tian et al. (2020). Much uncertainty exists in estimating the perturbed fluxes of atmospheric CO₂ and mature forest conversion, as discussed in the following section on uncertainties.

Our estimate indicates that agricultural emissions were the major drivers of the increase in anthropogenic emissions during the past 4 decades, increasing from 3.0 Tg N yr⁻¹ in 1980 to 5.0 Tg N yr⁻¹ in 2020 (Fig. 16). Direct agricultural emissions had a larger increase than indirect agricultural emissions (2.2 Tg N yr⁻¹ in 1980 to 3.9 Tg N yr⁻¹ in 2020 versus 0.8 Tg N yr⁻¹ in 1980 to 1.2 Tg N yr⁻¹ in 2020). Agricultural emissions contributed 74 % of total anthropogenic emissions in the 2010s, with 56 % from direct agricultural emissions and 18 % from indirect emissions. Non-agricultural anthropogenic emissions had a slight decreasing trend during 1980–2020 because of a higher estimate of changes in climate, CO₂, and land cover than previous estimate.

This study divides the global land into 18 regions and provides a more detailed regional budget than a previous study, which had only 10 regions (Tian et al., 2020), thereby enhancing our understanding of the N₂O budget in subregions of North America, South America, Africa, and East Asia. In the 1980s, Europe made the largest contribution to global anthropogenic N₂O emissions (23.6 %), followed by China (11.6 %), South Asia (8.0 %), the USA (7.8 %), and Russia (7.3 %). During the study period, Europe and Russia had the largest decline in the share of anthropogenic N₂O emissions, from 23.6 % and 7.3 % in the 1980s to 11.8 % and 4.3 % in the 2010s, respectively. In contrast, China and South Asia had the largest increase, from 11.6 % and 8.0 % in the 1980s to 17.8 % and 13.2 % in the 2010s, respectively. In the 2010s, China (17.8 %), South Asia (13.2 %), Europe (11.8 %), the USA (6.7 %), and equatorial Africa (6.5 %) were the top five contributors to global anthropogenic N₂O emissions (Fig. 17).

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

emissions showed overall decreasing trends from 0.46 and 0.16 Tg N yr⁻¹ in 1980 to 0.38 and 0.12 Tg N yr⁻¹ in 2020, respectively. However, the decreasing trend in agricultural emissions has leveled off since the 2000s.

China and South Asia had the largest increase in N₂O emissions during the study period. The rates of increase in the anthropogenic emissions from China and South Asia were 18.9 × 10⁻³ and 14.3 × 10⁻³ Tg N yr⁻², respectively. The rates of increase in the anthropogenic emissions from China and South Asia contributed 40 % and 30 % to the global anthropogenic increase rate (0.05 Tg N yr⁻²), respectively. In these two regions, direct nitrogen additions in agriculture made the largest contribution, while other direct emissions and indirect emissions also steadily increased. Our results show a significant increase in anthropogenic N₂O emissions from South America, which is consistent with the previous budget (Tian et al., 2020). Moreover, we reveal that Brazil had a higher increase rate in anthropogenic N₂O emissions (4.2 × 10⁻³ Tg N yr⁻²) than northern South America (0.8 × 10⁻³ Tg N yr⁻²) and southwestern South America (0.4 × 10⁻³ Tg N yr⁻²) during 1980–2020, and direct emissions from agriculture made the largest contribution. Our results suggest that northern Africa made the largest contribution (58 %) to the increase in anthropogenic N₂O emissions from Africa, followed by equatorial Africa (38 %) and southern Africa (4 %). Anthropogenic N₂O emissions from the USA and Canada show similar weak increasing rates of 0.6 × 10⁻³ and 0.9 × 10⁻³ Tg N yr⁻² during the 1980–2020 period, respectively. Central America shows a higher anthropogenic N₂O emission increase rate (5.9 × 10⁻³ Tg N yr⁻²), which is attributed to an increase in emissions from fossil fuels and industry, from 0.01 Tg N yr⁻¹ in the 1980s to 0.16 Tg N yr⁻¹ in the 2010s. The data for Mexico from EDGAR have a known problem with their estimates of N₂O emissions from industry, and this issue requires further exploration. To support countries’ N₂O mitigation, it is essential to accurately estimate sources and sinks of N₂O at the national level.

## 6.2 Sources of uncertainties and suggestions for improvements

### 6.2.1 Uncertainties in N₂O emission factors

Four inventories of N₂O emissions (EDGAR, FAOSTAT, GFED, and UNFCCC) are integrated into the current synthesis of anthropogenic N₂O emissions. These EF-based inventory datasets (where EF refers to emission factors) used the IPCC default EFs at regional and global scales. Uncertainty in FAOSTAT N₂O emissions is ~60 % across typologies. In fact, it is asymmetrical, following the 2006 guideline values and IPCC uncertainty formulae, with $u_{min}$ ~ -30 % and $u_{max}$ ~ 90 % (Tubiello et al., 2013); for N₂O in EDGAR, the global uncertainty ranges defining the 95 % confidence intervals of a lognormal distribution are ±113 % for energy, be-
<figure><img src="image_17.png" /><figcaption>**Figure 16.** Changes in N₂O emissions from anthropogenic emissions from agricultural and non-agricultural sources during 1980–2020 (a, c). Panels (b) and (d) show average anthropogenic emissions from different sources during 2010–2019; error bars indicate the spread between the minimum and the maximum values. Here, direct agricultural emissions include emissions from fertilizer and manure applied on agricultural soils, manure left on pasture, manure management, and aquaculture. Indirect agricultural emissions include emissions from anthropogenic nitrogen additions to inland waters, estuaries, and coastal vegetation as well as N deposition on land. Other anthropogenic emissions are classified as non-agricultural anthropogenic emissions. The letters A–E in panel (d) represent perturbed N₂O fluxes from climate, CO₂, and land-cover change; emissions from nitrogen deposition on the ocean; emissions from fossil fuels and industry; emissions from waste and wastewater; and emissions from biomass burning, respectively.</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>**Figure 16.** Changes in N₂O emissions from anthropogenic emissions from agricultural and non-agricultural sources during 1980–2020 (a, c). Panels (b) and (d) show average anthropogenic emissions from different sources during 2010–2019; error bars indicate the spread between the minimum and the maximum values. Here, direct agricultural emissions include emissions from fertilizer and manure applied on agricultural soils, manure left on pasture, manure management, and aquaculture. Indirect agricultural emissions include emissions from anthropogenic nitrogen additions to inland waters, estuaries, and coastal vegetation as well as N deposition on land. Other anthropogenic emissions are classified as non-agricultural anthropogenic emissions. The letters A–E in panel (d) represent perturbed N₂O fluxes from climate, CO₂, and land-cover change; emissions from nitrogen deposition on the ocean; emissions from fossil fuels and industry; emissions from waste and wastewater; and emissions from biomass burning, respectively.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>Figure 17. Contributions of the 18 regions to global anthropogenic N₂O emissions in the 1980s (a) and 2010s (b).</figcaption></figure>

tween -12% and +16% for industrial processes and product use, between -225% and +302% for agriculture, between -159% and +203% for waste, and ±112% for other sectors (Solazzo et al., 2021). However, the poorly captured dependence of EFs on regional climate, management practices such as tillage, legume effect, and soil physical and biochemical conditions are key causes of the large uncertainty in the estimates of agricultural N₂O emissions (Shcherbak et al., 2014; Tian et al., 2019; Lu et al., 2022), particularly for croplands where EFs have high spatial heterogeneity (Shang et al., 2019; Wang et al., 2020). There is evidence of a greater-than-linear dependence of emissions on N input where there is an excess of N; however, this is not represented in inventories that assume a linear dependence on N input (Cui et al., 2021). Higher IPCC-tier GHG inventories using the alternative EFs that are disaggregated by environmental factors and management-related factors (Buendia et al., 2019) could provide more accurate estimates, especially for regions where N input surplus is high, such as eastern China, India, and the USA. For example, the US national inventory uses a Tier-3 modeling approach (Del Grosso et al., 2022). Establishing national and regional N₂O flux measurement networks could improve the accuracy of EF estimates for regions with different vegetation types and management measures. Furthermore, inventory datasets based on EF methods also suffer from large uncertainties induced by the underlying agriculture and rural data and statistics used as input, including statistics on fertilizer applications, livestock manure availability, storage and applications, and nutrient, crop, and soil management.

According to the ensemble of process-based land model emissions derived from NMIP2, we estimate that the EF of fertilizer and manure applied on global croplands was 1.9% (1.2%-3.3%) in the 2010s, which is significantly larger than the IPCC Tier-1 default for direct emission of 1%. This

higher EF derived from process-based models suggests a strong interactive effect between N additions and other global environmental changes (Perturbed fluxes from climate, atmospheric CO₂, and land-cover change in Table 3). Figure 18 shows the spatial pattern of the cropland N₂O EF during the 2010s and highlights that the EF was high in eastern China, Southeast Asia, western Europe, and the central USA where anthropogenic N inputs were high (Fig. B3). Previous field experiments reported a better fit to local observations of soil N₂O emissions when assuming a nonlinear response to fertilizer N inputs under varied climate and soil conditions (Shcherbak et al., 2014; Wang et al., 2020). The nonlinear response is also likely associated with long-term N accumulation in agricultural soils from N fertilizer use and in aquatic systems from N loads (the legacy effect) (Van Meter et al., 2016), which provides more substrate for microbial processes (Firestone and Davidson, 1989). The increasing N₂O emissions estimated by process-based models (Tian et al., 2019) also suggest that recent climate change (particularly warming) may have boosted soil nitrification and denitrification processes, contributing to the growing trend in N₂O emissions with rising N additions to agricultural soils (Griffis et al., 2017; Pärn et al., 2018; Smith, 1997).

### 6.2.2 Uncertainties in estimates of soil N₂O emissions

Both process-based land biosphere modeling and measurement-based upscaling approaches have been used to estimate global soil N₂O emissions (Table 3), with large uncertainties in their estimates. As shown in Fig. 19, NMIP2 models exhibit the highest uncertainties in the estimates of soil N₂O emissions from tropical forests, such as the Amazon Basin, the Congo Basin, and Southeast Asia, as well as in regions with high fertilizer application rates, including eastern China, northern India, and the US Corn Belt. For NMIP2 estimates of direct agricultural
<figure><img src="image_18.png" /><figcaption>Figure 17. Contributions of the 18 regions to global anthropogenic N₂O emissions in the 1980s (a) and 2010s (b).</figcaption></figure>

tween -12% and +16% for industrial processes and product use, between -225% and +302% for agriculture, between -159% and +203% for waste, and ±112% for other sectors (Solazzo et al., 2021). However, the poorly captured dependence of EFs on regional climate, management practices such as tillage, legume effect, and soil physical and biochemical conditions are key causes of the large uncertainty in the estimates of agricultural N₂O emissions (Shcherbak et al., 2014; Tian et al., 2019; Lu et al., 2022), particularly for croplands where EFs have high spatial heterogeneity (Shang et al., 2019; Wang et al., 2020). There is evidence of a greater-than-linear dependence of emissions on N input where there is an excess of N; however, this is not represented in inventories that assume a linear dependence on N input (Cui et al., 2021). Higher IPCC-tier GHG inventories using the alternative EFs that are disaggregated by environmental factors and management-related factors (Buendia et al., 2019) could provide more accurate estimates, especially for regions where N input surplus is high, such as eastern China, India, and the USA. For example, the US national inventory uses a Tier-3 modeling approach (Del Grosso et al., 2022). Establishing national and regional N₂O flux measurement networks could improve the accuracy of EF estimates for regions with different vegetation types and management measures. Furthermore, inventory datasets based on EF methods also suffer from large uncertainties induced by the underlying agriculture and rural data and statistics used as input, including statistics on fertilizer applications, livestock manure availability, storage and applications, and nutrient, crop, and soil management.

According to the ensemble of process-based land model emissions derived from NMIP2, we estimate that the EF of fertilizer and manure applied on global croplands was 1.9% (1.2%-3.3%) in the 2010s, which is significantly larger than the IPCC Tier-1 default for direct emission of 1%. This

higher EF derived from process-based models suggests a strong interactive effect between N additions and other global environmental changes (Perturbed fluxes from climate, atmospheric CO₂, and land-cover change in Table 3). Figure 18 shows the spatial pattern of the cropland N₂O EF during the 2010s and highlights that the EF was high in eastern China, Southeast Asia, western Europe, and the central USA where anthropogenic N inputs were high (Fig. B3). Previous field experiments reported a better fit to local observations of soil N₂O emissions when assuming a nonlinear response to fertilizer N inputs under varied climate and soil conditions (Shcherbak et al., 2014; Wang et al., 2020). The nonlinear response is also likely associated with long-term N accumulation in agricultural soils from N fertilizer use and in aquatic systems from N loads (the legacy effect) (Van Meter et al., 2016), which provides more substrate for microbial processes (Firestone and Davidson, 1989). The increasing N₂O emissions estimated by process-based models (Tian et al., 2019) also suggest that recent climate change (particularly warming) may have boosted soil nitrification and denitrification processes, contributing to the growing trend in N₂O emissions with rising N additions to agricultural soils (Griffis et al., 2017; Pärn et al., 2018; Smith, 1997).

### 6.2.2 Uncertainties in estimates of soil N₂O emissions

Both process-based land biosphere modeling and measurement-based upscaling approaches have been used to estimate global soil N₂O emissions (Table 3), with large uncertainties in their estimates. As shown in Fig. 19, NMIP2 models exhibit the highest uncertainties in the estimates of soil N₂O emissions from tropical forests, such as the Amazon Basin, the Congo Basin, and Southeast Asia, as well as in regions with high fertilizer application rates, including eastern China, northern India, and the US Corn Belt. For NMIP2 estimates of direct agricultural
<figure><img src="image_19.png" /><figcaption>**Figure 18.** Spatial pattern of the emission factor (EF) of fertilizer and manure applied on global croplands in the 2010s derived from NMIP2.</figcaption></figure>

emissions, the maximum estimate is about 60% higher than the ensemble mean, and the minimum estimate is about 40% lower than the ensemble mean. A large discrepancy in natural soil emissions among NMIP2 models exists, ranging from 3.9 to 8.6 Tg N yr⁻¹, which needs to be reconciled in future research.

## Uncertainties associated with NMIP2 models

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

First, the NMIP2 models use divergent schemes to represent the flows of reactive N through ecosystems (biological N fixation, N deposition, N leaching, N volatilization, nitrification, and denitrification), which could result in large discrepancies in soil mineral N that serves as substrates for N₂O production. Explicit representation of these processes is a critical need with respect to enhancing model simulation accuracy.

Second, several important processes are missing in most process-based land models. Human management measures like tillage and legume cultivation can alter the physical and chemical characteristics of soil in croplands (Raji and Dörsch, 2020; Z. Yu et al., 2020), but they are not adequately represented in most NMIP2 models. Parameterizing these processes in the models is necessary to reduce uncertainty. Additionally, N addition in pasture and rangeland (e.g., live-

stock excreta deposition, manure, and mineral fertilizer application) constitutes an important source of global soil N₂O emissions (Davidson, 2009), accounting for more than half of the global agricultural N₂O emissions (Dangal et al., 2019). However, only DLEM considered these processes. The consideration of N addition in managed grasslands is an essential task for process-based models to estimate grassland soil N₂O emissions accurately. Moreover, most process-based models did not explicitly consider seasonal freeze–thaw processes or the thawing of permafrost, which can emit substantial amounts of N₂O (Marushchak et al., 2011, 2021; Repo et al., 2009; Voigt et al., 2017; Del Grosso et al., 2022). It is recommended to include explicit representation of permafrost physics and seasonal freeze–thaw processes in process-based models, as this would help better catch the “hotspot” and “hot moment” of soil N₂O emissions in northern regions (Wagner-Riddle et al., 2017). Current process-based models also face challenges in adequately representing the fine-grained landscape structure of Arctic ecosystems (e.g., landscape elements that act as ultra-emitters of N₂O, like organic soil non-vegetated fractions); thus, integrating sub-grid information and processes into models may provide a solution for fine-grained physical–hydrological modeling.

Third, microbial nitrification and denitrification processes are regulated by multiple environmental factors, including substrate availability, precipitation, temperature, oxygen status, pH, vegetation type, and atmospheric CO₂ concentration (Butterbach-Bahl et al., 2013; Dijkstra et al., 2012; Li et al., 2020; Tian et al., 2019; Yin et al., 2022; Yu et
<figure><img src="image_19.png" /><figcaption>**Figure 18.** Spatial pattern of the emission factor (EF) of fertilizer and manure applied on global croplands in the 2010s derived from NMIP2.</figcaption></figure>

emissions, the maximum estimate is about 60% higher than the ensemble mean, and the minimum estimate is about 40% lower than the ensemble mean. A large discrepancy in natural soil emissions among NMIP2 models exists, ranging from 3.9 to 8.6 Tg N yr⁻¹, which needs to be reconciled in future research.

## Uncertainties associated with NMIP2 models

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

First, the NMIP2 models use divergent schemes to represent the flows of reactive N through ecosystems (biological N fixation, N deposition, N leaching, N volatilization, nitrification, and denitrification), which could result in large discrepancies in soil mineral N that serves as substrates for N₂O production. Explicit representation of these processes is a critical need with respect to enhancing model simulation accuracy.

Second, several important processes are missing in most process-based land models. Human management measures like tillage and legume cultivation can alter the physical and chemical characteristics of soil in croplands (Raji and Dörsch, 2020; Z. Yu et al., 2020), but they are not adequately represented in most NMIP2 models. Parameterizing these processes in the models is necessary to reduce uncertainty. Additionally, N addition in pasture and rangeland (e.g., live-

stock excreta deposition, manure, and mineral fertilizer application) constitutes an important source of global soil N₂O emissions (Davidson, 2009), accounting for more than half of the global agricultural N₂O emissions (Dangal et al., 2019). However, only DLEM considered these processes. The consideration of N addition in managed grasslands is an essential task for process-based models to estimate grassland soil N₂O emissions accurately. Moreover, most process-based models did not explicitly consider seasonal freeze–thaw processes or the thawing of permafrost, which can emit substantial amounts of N₂O (Marushchak et al., 2011, 2021; Repo et al., 2009; Voigt et al., 2017; Del Grosso et al., 2022). It is recommended to include explicit representation of permafrost physics and seasonal freeze–thaw processes in process-based models, as this would help better catch the “hotspot” and “hot moment” of soil N₂O emissions in northern regions (Wagner-Riddle et al., 2017). Current process-based models also face challenges in adequately representing the fine-grained landscape structure of Arctic ecosystems (e.g., landscape elements that act as ultra-emitters of N₂O, like organic soil non-vegetated fractions); thus, integrating sub-grid information and processes into models may provide a solution for fine-grained physical–hydrological modeling.

Third, microbial nitrification and denitrification processes are regulated by multiple environmental factors, including substrate availability, precipitation, temperature, oxygen status, pH, vegetation type, and atmospheric CO₂ concentration (Butterbach-Bahl et al., 2013; Dijkstra et al., 2012; Li et al., 2020; Tian et al., 2019; Yin et al., 2022; Yu et
<figure><img src="image_20.png" /><figcaption>Figure 19. Spatial distribution of uncertainty (1 standard deviation) in NMIP2 estimations of soil N₂O emissions in the 2010s.</figcaption></figure>

al., 2022). However, there is significant divergence among NMIP2 models in their response to these factors. For example, simulated soil N₂O emissions in response to N addition (i.e., fertilizer and manure N applications and N deposition) exhibit large divergence among the participating NMIP2 models, primarily due to differences in model representation of N processes and parameterization schemes. Moreover, in contrast to our findings indicating N fertilizer application and manure additions as dominant drivers, Harris et al. (2022) identified N deposition as the primary contributor to anthropogenic N₂O emissions, accounting for 41 ± 14% of all anthropogenic emissions. These different findings highlight the complex nature of N₂O emissions and the need for further research to better understand the relative contributions of different N sources. For the climatic effects on soil N₂O emissions, our NMIP2 models indicate enhanced N₂O emissions due to warming, consistent with findings from experiment-based studies (Smith, 1997; Cui et al., 2018; Voigt et al., 2017; Wang et al., 2017), as the denitrifying bacteria community may adapt to higher temperature (Pärn et al., 2018). Additionally, considering that microbial nitrification and denitrification are also largely controlled by soil moisture (Butterbach-Bahl et al., 2013), it is important to address the discrepancies in NMIP2 models concerning soil moisture representation, such as soil depth, root distribution, root water uptake, and water movement processes (Ostle et al., 2009; Raats, 2007; Raoult et al., 2018).

At the global scale, although NMIP2 models show large discrepancies in the CO₂ effect on soil N₂O emissions, most NMIP2 models show a negative effect, suggesting that enhanced plant N uptake caused by a rising CO₂ concentration played a dominant role (Usyskin-Tonne et al., 2020; Tian

et al., 2019). Nevertheless, observation-based results of the CO₂ effect diverge among different ecosystem types, with some studies reporting reduced N₂O emissions in forests under elevated CO₂ (Phillips et al., 2001), while others found increased emissions in grasslands (Moser et al., 2018; Regan et al., 2011). It should be noted that the interactions among environmental factors influencing soil N₂O emissions are still poorly represented in the NMIP2 models. Further targeted continuous measurements and manipulation experiments are needed to better represent the interactive effects of multiple environmental factors on N₂O emissions in the models to improve the simulation of complex N₂O dynamics. Finally, simulations targeted to explain the reconstructed increase in terrestrial N₂O emissions over the deglaciation and during past abrupt climate events will further help to constrain process-based models (Fischer et al., 2019; Joos et al., 2020).

## Land-cover change/deforestation

The two methods for estimating deforestation-induced N₂O changes have their limitations. The accuracy of the empirical estimates of post-deforestation pulse N₂O emissions in tropical forests strongly depends on the availability of paired N₂O observations in deforested and nearby intact forest sites (Melillo et al., 2001; Verchot et al., 1999), which are extremely scarce. Moreover, a fixed value was adopted as the default reference N₂O emission rate for tropical forests to simplify computation, but it inevitably ignored the spatiotemporal heterogeneity in tropical forest N₂O emissions (Barthel et al., 2022). It is also noted that there were no empirical post-deforestation N₂O emission estimates in extra-
<figure><img src="image_20.png" /><figcaption>Figure 19. Spatial distribution of uncertainty (1 standard deviation) in NMIP2 estimations of soil N₂O emissions in the 2010s.</figcaption></figure>

al., 2022). However, there is significant divergence among NMIP2 models in their response to these factors. For example, simulated soil N₂O emissions in response to N addition (i.e., fertilizer and manure N applications and N deposition) exhibit large divergence among the participating NMIP2 models, primarily due to differences in model representation of N processes and parameterization schemes. Moreover, in contrast to our findings indicating N fertilizer application and manure additions as dominant drivers, Harris et al. (2022) identified N deposition as the primary contributor to anthropogenic N₂O emissions, accounting for 41 ± 14% of all anthropogenic emissions. These different findings highlight the complex nature of N₂O emissions and the need for further research to better understand the relative contributions of different N sources. For the climatic effects on soil N₂O emissions, our NMIP2 models indicate enhanced N₂O emissions due to warming, consistent with findings from experiment-based studies (Smith, 1997; Cui et al., 2018; Voigt et al., 2017; Wang et al., 2017), as the denitrifying bacteria community may adapt to higher temperature (Pärn et al., 2018). Additionally, considering that microbial nitrification and denitrification are also largely controlled by soil moisture (Butterbach-Bahl et al., 2013), it is important to address the discrepancies in NMIP2 models concerning soil moisture representation, such as soil depth, root distribution, root water uptake, and water movement processes (Ostle et al., 2009; Raats, 2007; Raoult et al., 2018).

At the global scale, although NMIP2 models show large discrepancies in the CO₂ effect on soil N₂O emissions, most NMIP2 models show a negative effect, suggesting that enhanced plant N uptake caused by a rising CO₂ concentration played a dominant role (Usyskin-Tonne et al., 2020; Tian

et al., 2019). Nevertheless, observation-based results of the CO₂ effect diverge among different ecosystem types, with some studies reporting reduced N₂O emissions in forests under elevated CO₂ (Phillips et al., 2001), while others found increased emissions in grasslands (Moser et al., 2018; Regan et al., 2011). It should be noted that the interactions among environmental factors influencing soil N₂O emissions are still poorly represented in the NMIP2 models. Further targeted continuous measurements and manipulation experiments are needed to better represent the interactive effects of multiple environmental factors on N₂O emissions in the models to improve the simulation of complex N₂O dynamics. Finally, simulations targeted to explain the reconstructed increase in terrestrial N₂O emissions over the deglaciation and during past abrupt climate events will further help to constrain process-based models (Fischer et al., 2019; Joos et al., 2020).

## Land-cover change/deforestation

The two methods for estimating deforestation-induced N₂O changes have their limitations. The accuracy of the empirical estimates of post-deforestation pulse N₂O emissions in tropical forests strongly depends on the availability of paired N₂O observations in deforested and nearby intact forest sites (Melillo et al., 2001; Verchot et al., 1999), which are extremely scarce. Moreover, a fixed value was adopted as the default reference N₂O emission rate for tropical forests to simplify computation, but it inevitably ignored the spatiotemporal heterogeneity in tropical forest N₂O emissions (Barthel et al., 2022). It is also noted that there were no empirical post-deforestation N₂O emission estimates in extra-
tropical areas, as no feasible empirical relationships between N₂O emissions and years after deforestation were available. The accuracy of process-based estimates (specifically by DLEM here) could be regulated by model-specific configurations for land-use change pathways. For example, in modeling tropical shift cultivation, DLEM assumed that agricultural lands newly converted from forests can only be reforested after at least 15 years to be consistent with the LUHv2 data (Hurtt et al., 2020). Meanwhile, treatments of different nitrogen pools (such as leaf, stem, root, and litter pools) during land conversion would directly influence the nitrogen substrate for nitrification and denitrification. The DLEM model follows the biomass allocation scheme proposed by previous studies (Houghton et al., 1983; McGuire et al., 2001), which may introduce uncertainty in varied land management practices. A bias in the LUHv2 land-use change data in regions experiencing drastic land conversions could also contribute to uncertainty in deforestation-induced GHG emissions, for example, in areas with large-scale plantations (Yu et al., 2022).

In addition, developing forcing datasets with high quality and high spatiotemporal resolution is also important for reducing uncertainties in simulated N₂O fluxes. Among various input variables, precise information regarding fertilizer and manure application (including crop-specific application rate, type, timing, and frequency) is pivotal for improving the accuracy of model simulations. However, this crucial information was not unified in NMIP2 simulations, leading to increased modeling uncertainty. To mitigate this issue, it is strongly recommended to use improved fertilizer and manure datasets that provide detailed information on crop-specific application rate, timing, and frequency to drive models in future intercomparison projects. Moreover, with the availability of additional high-precision datasets from manipulation field experiments (e.g., microbial data), we could use these datasets to constrain our models and delve deeper into the underlying mechanisms that regulate N₂O fluxes (e.g., the role of soil microbes) and further incorporate these mechanisms into models to reduce uncertainties.

## Uncertainties associated with the measurement-based upscaling approach

Measurement-based upscaling estimates are subject to uncertainties due to various factors. One major reason is the limited recording of microscale variables and the incomplete quantification of local EFs related to microbial N₂O production. Sampling limitations also contribute to uncertainties, as the frequency and repeatability of measurements may not fully capture the high spatiotemporal variability in the N₂O flux. The lack of the history of control sites further complicates the exclusion of observation data with significant legacy fluxes, thereby biasing our estimates. Additionally, gaps in global agricultural management datasets, particularly regarding fertilization details, enlarge the prediction

<figure><img src="image_21.png" /><figcaption>**Figure 20.** Relative standard deviation in the global cropland N₂O EF. The figure breaks down the uncertainty in the EF per source of uncertainty (i.e., random coefficients, fixed coefficients, input data, or all combined). The uncertainty due to each source can be quantified by holding the coefficients for the other sources fixed.</figcaption></figure>

interval of EFs and introduce uncertainties. We then used a Monte Carlo simulation to estimate three sources of uncertainty for predicting EFs based on a flux upscaling approach: (i) the fixed coefficients, (ii) the random coefficients, and (iii) input data. The uncertainty from sampling frequency and replication is reflected in the first source, while the uncertainty from unquantified sources related to field measurements is reflected in the second source. Each of the crop-specific SRNM models was run by randomly generating the fixed and random coefficients from their fitted multivariate normal distribution as well as climate, soil, and other relevant factors following independent normal distributions with the mean of the value in our dataset and standard deviation of the absolute difference between the dataset used in this study and other global datasets. Fertilizer frequency was randomly selected using a Bernoulli distribution. Predicted values were calculated through 1000 iterations to construct a 95% prediction interval. The breakdown of uncertainty revealed that the random coefficients contributed the most to the estimation uncertainty, with observations showing that they explained more variance in EFs compared with fixed effects (47%–74% versus 19%–35%) and contributed to most of estimation uncertainty (Fig. 20).

To address these limitations and reduce uncertainties, concerted efforts should be made to enhance the availability of N₂O observations representing diverse agroecological conditions. Meanwhile, improving the availability of high-precision datasets (e.g., microbial data) and integrating these datasets and the derived underlying mechanisms to our models could also reduce uncertainties. Currently, most available field N₂O observations (see the Supplement) are made in Europe, the USA, and China and are scarce in most developing countries (such as sub-Saharan Africa). Therefore, extending the global coverage of direct and indirect N₂O flux measurements to encompass all major agricultural land-use types and climates, land-use changes, and management practices as well as conducting long-term high-frequency monitoring
tropical areas, as no feasible empirical relationships between N₂O emissions and years after deforestation were available. The accuracy of process-based estimates (specifically by DLEM here) could be regulated by model-specific configurations for land-use change pathways. For example, in modeling tropical shift cultivation, DLEM assumed that agricultural lands newly converted from forests can only be reforested after at least 15 years to be consistent with the LUHv2 data (Hurtt et al., 2020). Meanwhile, treatments of different nitrogen pools (such as leaf, stem, root, and litter pools) during land conversion would directly influence the nitrogen substrate for nitrification and denitrification. The DLEM model follows the biomass allocation scheme proposed by previous studies (Houghton et al., 1983; McGuire et al., 2001), which may introduce uncertainty in varied land management practices. A bias in the LUHv2 land-use change data in regions experiencing drastic land conversions could also contribute to uncertainty in deforestation-induced GHG emissions, for example, in areas with large-scale plantations (Yu et al., 2022).

In addition, developing forcing datasets with high quality and high spatiotemporal resolution is also important for reducing uncertainties in simulated N₂O fluxes. Among various input variables, precise information regarding fertilizer and manure application (including crop-specific application rate, type, timing, and frequency) is pivotal for improving the accuracy of model simulations. However, this crucial information was not unified in NMIP2 simulations, leading to increased modeling uncertainty. To mitigate this issue, it is strongly recommended to use improved fertilizer and manure datasets that provide detailed information on crop-specific application rate, timing, and frequency to drive models in future intercomparison projects. Moreover, with the availability of additional high-precision datasets from manipulation field experiments (e.g., microbial data), we could use these datasets to constrain our models and delve deeper into the underlying mechanisms that regulate N₂O fluxes (e.g., the role of soil microbes) and further incorporate these mechanisms into models to reduce uncertainties.

## Uncertainties associated with the measurement-based upscaling approach

Measurement-based upscaling estimates are subject to uncertainties due to various factors. One major reason is the limited recording of microscale variables and the incomplete quantification of local EFs related to microbial N₂O production. Sampling limitations also contribute to uncertainties, as the frequency and repeatability of measurements may not fully capture the high spatiotemporal variability in the N₂O flux. The lack of the history of control sites further complicates the exclusion of observation data with significant legacy fluxes, thereby biasing our estimates. Additionally, gaps in global agricultural management datasets, particularly regarding fertilization details, enlarge the prediction

interval of EFs and introduce uncertainties. We then used a Monte Carlo simulation to estimate three sources of uncertainty for predicting EFs based on a flux upscaling approach: (i) the fixed coefficients, (ii) the random coefficients, and (iii) input data. The uncertainty from sampling frequency and replication is reflected in the first source, while the uncertainty from unquantified sources related to field measurements is reflected in the second source. Each of the crop-specific SRNM models was run by randomly generating the fixed and random coefficients from their fitted multivariate normal distribution as well as climate, soil, and other relevant factors following independent normal distributions with the mean of the value in our dataset and standard deviation of the absolute difference between the dataset used in this study and other global datasets. Fertilizer frequency was randomly selected using a Bernoulli distribution. Predicted values were calculated through 1000 iterations to construct a 95% prediction interval. The breakdown of uncertainty revealed that the random coefficients contributed the most to the estimation uncertainty, with observations showing that they explained more variance in EFs compared with fixed effects (47%–74% versus 19%–35%) and contributed to most of estimation uncertainty (Fig. 20).

To address these limitations and reduce uncertainties, concerted efforts should be made to enhance the availability of N₂O observations representing diverse agroecological conditions. Meanwhile, improving the availability of high-precision datasets (e.g., microbial data) and integrating these datasets and the derived underlying mechanisms to our models could also reduce uncertainties. Currently, most available field N₂O observations (see the Supplement) are made in Europe, the USA, and China and are scarce in most developing countries (such as sub-Saharan Africa). Therefore, extending the global coverage of direct and indirect N₂O flux measurements to encompass all major agricultural land-use types and climates, land-use changes, and management practices as well as conducting long-term high-frequency monitoring

<figure><img src="image_21.png" /><figcaption>**Figure 20.** Relative standard deviation in the global cropland N₂O EF. The figure breaks down the uncertainty in the EF per source of uncertainty (i.e., random coefficients, fixed coefficients, input data, or all combined). The uncertainty due to each source can be quantified by holding the coefficients for the other sources fixed.</figcaption></figure>
are particularly important to increase the reliability of EFs as well as upscale results from site to regional scales.

### 6.2.3 Uncertainties in estimates of ocean N₂O emissions

Global open-ocean N₂O emissions derived from the ocean biogeochemistry models (Table 1) for the 2010–2019 period are estimated to be 3.5 (2.5–4.7) Tg N yr⁻¹. All models show the highest emissions associated with equatorial and coastal upwelling zones as well as with the major oxygen minimum zones (OMZs; e.g., the eastern equatorial Pacific and the Arabian Sea region of the northern Indian Ocean; see Fig. 21). These are regions characterized by high levels of biological productivity and higher subsurface organic matter remineralization, resulting in higher N₂O yields in suboxic waters. The four participating models capture these characteristics but also show varying degrees of intensity in regional N₂O emissions. The models also show good agreement with respect to representing the ocean regions of relatively low N₂O ocean–atmosphere fluxes (i.e., open-ocean gyres where biological productivity is low).

The spatial distribution of uncertainty in ocean N₂O emissions among the models (Fig. 21) is similar to that of the net N₂O ocean–atmosphere flux, with the highest uncertainties observed in the equatorial upwelling and low-oxygen waters associated with high subsurface N₂O production (Babin et al., 2020; Ganesan et al., 2020). The largest uncertainties are found in the equatorial Pacific, the Benguela upwelling region of the Atlantic, and the eastern equatorial Indian Ocean. Uncertainties in the ocean models’ representation of N₂O fluxes result from a range of model characteristics (Zamora and Oschlies, 2014; Martinez-Rey et al., 2015; Buitenhuis et al., 2018; Battaglia and Joos, 2018; Landolfi et al., 2017; Berthet et al., 2023), including the following: (i) uncertainties in ocean circulation – particularly the representation of upwelling zones and the ocean circulation features (often sub-grid scale) that control the extent and intensity of OMZs; (ii) simulation of ocean organic matter productivity, export production, and mesopelagic remineralization (a driver of the subsurface source function for N₂O production in models); (iii) the model biogeochemical parameterizations representing N₂O production and consumption from marine nitrification and denitrification processes, including their dependence on local dissolved oxygen concentrations and thresholds; and (iv) parameterization of ocean–atmosphere gas-exchange fluxes.

Model simulations of oceanic N₂O are closely linked to the underlying modeled oxygen distributions, as the embedded biogeochemical parameterizations for N₂O include the sensitivity of N₂O cycling processes (e.g., nitrification and denitrification) to the local oxygen level (Ji et al., 2018). Significant uncertainties in modeled N₂O fluxes result from model biases in the representation of dissolved oxygen, especially in low-oxygen zones such as the eastern equato-

rial Pacific (Zamora and Oschlies, 2014; Martinez-Rey et al., 2015). Many ocean model simulations of dissolved oxygen display biases, especially in OMZs critical for N₂O cycling (Martinez-Rey et al., 2015). To reduce potential sources of error from model-simulated oxygen, one N₂O model in this analysis employs observation-based oxygen distributions when simulating ocean N₂O (Buitenhuis et al., 2018). However, this approach also restricts a model’s response to climate-related feedback on ocean oxygen. In addition, the models in this analysis include optimization and calibration of N₂O cycle parameters by incorporating constraints from ocean observations (e.g., surface and interior N₂O and microbially mediated process rates) (Battaglia and Joos, 2018; Buitenhuis et al., 2018; Berthet et al., 2023). A more detailed error analysis of N₂O model parameters (including uncertainty in gas-exchange fluxes) in one of the component models (Buitenhuis et al., 2018) suggests estimated uncertainties in global fluxes from biogeochemical parameter specifications of ~33%. Further, a 1000-member ensemble with 11 parameters varied with one of the models and constrained with both surface and subsurface N₂O observations yields an observation-constrained standard deviation of ±36% around the median of 4.3 Tg N yr⁻¹ (Battaglia and Joos, 2018), consistent with a recent surface pN₂O-based estimate of 4.2 ± 1 Tg N yr⁻¹ (Yang et al., 2020).

Landolfi et al. (2017) also note that uncertainties arise in current model predictions of marine N₂O fluxes due to the neglect of feedback from impacts of external nutrient sources and ocean acidification on marine productivity and the ocean nitrogen and oxygen cycles. Reducing uncertainties in model estimates of the evolution of ocean N₂O fluxes will require accounting for these impacts in the underlying biogeochemical parameterizations. In addition, due to the high sensitivity of modeled N₂O production and consumption rates to oxygen level in the key ocean OMZ zones, an important priority in reducing modeled ocean N₂O flux uncertainties is to achieve a more accurate simulation of the ocean circulation and oxygen distribution of these regions.

### 6.2.4 Uncertainties in emissions estimates from the continental shelves

Estimates of N₂O emissions vary by a factor of 2–3 in the continental shelf (one observation-based product and two models). The MEM-RF observational estimate (1.63 Tg N yr⁻¹; Yang et al., 2020) falls at the high end of the two high-resolution model estimates (1.39 and 0.61 Tg N yr⁻¹ for CNRM-0.25° and ECCO-Darwin, respectively). Shelf N₂O flux emissions from MEM-RF, CNRM-0.25°, and ECO-Darwin broadly agree with respect to the main patterns and magnitude. Emission hotspots in productive, low-O₂ upwelling systems (e.g., eastern boundary upwellings and upwellings of the northwestern Indian Ocean) appear to be underestimated by models. Lower emissions in models likely reflect the inability of models to resolve com-
are particularly important to increase the reliability of EFs as well as upscale results from site to regional scales.

### 6.2.3 Uncertainties in estimates of ocean N₂O emissions

Global open-ocean N₂O emissions derived from the ocean biogeochemistry models (Table 1) for the 2010–2019 period are estimated to be 3.5 (2.5–4.7) Tg N yr⁻¹. All models show the highest emissions associated with equatorial and coastal upwelling zones as well as with the major oxygen minimum zones (OMZs; e.g., the eastern equatorial Pacific and the Arabian Sea region of the northern Indian Ocean; see Fig. 21). These are regions characterized by high levels of biological productivity and higher subsurface organic matter remineralization, resulting in higher N₂O yields in suboxic waters. The four participating models capture these characteristics but also show varying degrees of intensity in regional N₂O emissions. The models also show good agreement with respect to representing the ocean regions of relatively low N₂O ocean–atmosphere fluxes (i.e., open-ocean gyres where biological productivity is low).

The spatial distribution of uncertainty in ocean N₂O emissions among the models (Fig. 21) is similar to that of the net N₂O ocean–atmosphere flux, with the highest uncertainties observed in the equatorial upwelling and low-oxygen waters associated with high subsurface N₂O production (Babin et al., 2020; Ganesan et al., 2020). The largest uncertainties are found in the equatorial Pacific, the Benguela upwelling region of the Atlantic, and the eastern equatorial Indian Ocean. Uncertainties in the ocean models’ representation of N₂O fluxes result from a range of model characteristics (Zamora and Oschlies, 2014; Martinez-Rey et al., 2015; Buitenhuis et al., 2018; Battaglia and Joos, 2018; Landolfi et al., 2017; Berthet et al., 2023), including the following: (i) uncertainties in ocean circulation – particularly the representation of upwelling zones and the ocean circulation features (often sub-grid scale) that control the extent and intensity of OMZs; (ii) simulation of ocean organic matter productivity, export production, and mesopelagic remineralization (a driver of the subsurface source function for N₂O production in models); (iii) the model biogeochemical parameterizations representing N₂O production and consumption from marine nitrification and denitrification processes, including their dependence on local dissolved oxygen concentrations and thresholds; and (iv) parameterization of ocean–atmosphere gas-exchange fluxes.

Model simulations of oceanic N₂O are closely linked to the underlying modeled oxygen distributions, as the embedded biogeochemical parameterizations for N₂O include the sensitivity of N₂O cycling processes (e.g., nitrification and denitrification) to the local oxygen level (Ji et al., 2018). Significant uncertainties in modeled N₂O fluxes result from model biases in the representation of dissolved oxygen, especially in low-oxygen zones such as the eastern equato-

rial Pacific (Zamora and Oschlies, 2014; Martinez-Rey et al., 2015). Many ocean model simulations of dissolved oxygen display biases, especially in OMZs critical for N₂O cycling (Martinez-Rey et al., 2015). To reduce potential sources of error from model-simulated oxygen, one N₂O model in this analysis employs observation-based oxygen distributions when simulating ocean N₂O (Buitenhuis et al., 2018). However, this approach also restricts a model’s response to climate-related feedback on ocean oxygen. In addition, the models in this analysis include optimization and calibration of N₂O cycle parameters by incorporating constraints from ocean observations (e.g., surface and interior N₂O and microbially mediated process rates) (Battaglia and Joos, 2018; Buitenhuis et al., 2018; Berthet et al., 2023). A more detailed error analysis of N₂O model parameters (including uncertainty in gas-exchange fluxes) in one of the component models (Buitenhuis et al., 2018) suggests estimated uncertainties in global fluxes from biogeochemical parameter specifications of ~33%. Further, a 1000-member ensemble with 11 parameters varied with one of the models and constrained with both surface and subsurface N₂O observations yields an observation-constrained standard deviation of ±36% around the median of 4.3 Tg N yr⁻¹ (Battaglia and Joos, 2018), consistent with a recent surface pN₂O-based estimate of 4.2 ± 1 Tg N yr⁻¹ (Yang et al., 2020).

Landolfi et al. (2017) also note that uncertainties arise in current model predictions of marine N₂O fluxes due to the neglect of feedback from impacts of external nutrient sources and ocean acidification on marine productivity and the ocean nitrogen and oxygen cycles. Reducing uncertainties in model estimates of the evolution of ocean N₂O fluxes will require accounting for these impacts in the underlying biogeochemical parameterizations. In addition, due to the high sensitivity of modeled N₂O production and consumption rates to oxygen level in the key ocean OMZ zones, an important priority in reducing modeled ocean N₂O flux uncertainties is to achieve a more accurate simulation of the ocean circulation and oxygen distribution of these regions.

### 6.2.4 Uncertainties in emissions estimates from the continental shelves

Estimates of N₂O emissions vary by a factor of 2–3 in the continental shelf (one observation-based product and two models). The MEM-RF observational estimate (1.63 Tg N yr⁻¹; Yang et al., 2020) falls at the high end of the two high-resolution model estimates (1.39 and 0.61 Tg N yr⁻¹ for CNRM-0.25° and ECCO-Darwin, respectively). Shelf N₂O flux emissions from MEM-RF, CNRM-0.25°, and ECO-Darwin broadly agree with respect to the main patterns and magnitude. Emission hotspots in productive, low-O₂ upwelling systems (e.g., eastern boundary upwellings and upwellings of the northwestern Indian Ocean) appear to be underestimated by models. Lower emissions in models likely reflect the inability of models to resolve com-
<figure><img src="image_22.png" /><figcaption>**Figure 21.** Spatial uncertainty distribution (1 standard deviation) in open-ocean N₂O emissions in the 2010s. Note that the color scale in this figure is different from that in Fig. 19.</figcaption></figure>

plex near-shore dynamical circulation and biogeochemical processes key to the production, transport, and evasion of N₂O. This includes under-resolved dynamics in upwelling systems and shallow OMZs with high N₂O emissions (Resplandy et al., 2024), strong spatial gradients introduced by patterns of high production/high remineralization and enhanced land–sea inputs of N in shallow shelves (e.g., Baltic Sea as well as Southeast and East Asia), sedimentary processes, and production in estuarine and coastal vegetated ecosystems, which is subsequently transported offshore. Conversely, our ability to reconstruct spatial patterns in N₂O air–sea fluxes from observations (MEM-RF; Yang et al., 2020), in particular along continental margins, is severely limited by the number of N₂O observations, which is 2 orders of magnitude smaller than for CO₂. Observations tend to be localized in regions of strong air–sea disequilibrium and, thus, might be biased high (e.g., Babbin et al., 2020; Ganesan et al., 2020). In addition, many coastal regions remain undersampled, further limiting the performance of MEM-RF. For instance, models point to coastal N₂O flux hotspots along midlatitude western boundaries (e.g., the US East Coast, the North Pacific east of Japan, the southeast coast of Australia, and the southeastern tip of Africa) that are not diagnosed in the observational product (Resplandy et al., 2024). Furthermore, N₂O fluxes are highly spatially heterogeneous (scales of 1 to 100 km) due to land–ocean gradients and mesoscale and submesoscale features such as eddies (Arévalo-Martínez et al., 2017, 2019; Yang et al., 2020; Grundle et al., 2017). Eddies are instrumental in setting suboxic conditions favorable for N₂O production, and it has been suggested that N₂O production weakens within eddies during their transit across the

shelf and further offshore (Arévalo-Martínez et al., 2016). These small-scale circulation features are important controls on N₂O dynamics but are poorly accounted for in data-based reconstructions and models.

This assessment provides the most up-to-date estimate of N₂O climatological emissions from the global shelves, but the variability in these emissions remains uncertain. Each product covers a different time period and only provides limited or missing information on seasonal fluctuations, interannual variability, and long-term trends. For instance, only a handful of observations per year are available in most regions, providing a limited picture of seasonality and even more limited information on interannual variability (e.g., El Niño–Southern Oscillation and Pacific Decadal Oscillation) and global longer-term trends. Disentangling such influences from limited observations alone remains a major challenge. The effects of extreme events, such as storms and marine heat waves, on N₂O fluxes are also currently not captured, and the intra-annual variability in hotspot regions, such as coastal upwelling systems, remains poorly constrained. Despite these limitations, data-based reconstructions and models suggest a vigorous seasonal cycle and, potentially, important variability on interannual timescales (Yang et al., 2020; Ganesan et al., 2020). The development of a global N₂O ocean observation network (N₂O-ON; Bange et al., 2019; Bange, 2022) is critically needed to better resolve spatiotemporal patterns and reduce uncertainties in N₂O emissions. Increasing the density of observations in regions of high N₂O disequilibrium and collecting long time series of N₂O measurements will allow for better characterization of interannual changes and their dynamics. Meanwhile, algo-
<figure><img src="image_22.png" /><figcaption>**Figure 21.** Spatial uncertainty distribution (1 standard deviation) in open-ocean N₂O emissions in the 2010s. Note that the color scale in this figure is different from that in Fig. 19.</figcaption></figure>

plex near-shore dynamical circulation and biogeochemical processes key to the production, transport, and evasion of N₂O. This includes under-resolved dynamics in upwelling systems and shallow OMZs with high N₂O emissions (Resplandy et al., 2024), strong spatial gradients introduced by patterns of high production/high remineralization and enhanced land–sea inputs of N in shallow shelves (e.g., Baltic Sea as well as Southeast and East Asia), sedimentary processes, and production in estuarine and coastal vegetated ecosystems, which is subsequently transported offshore. Conversely, our ability to reconstruct spatial patterns in N₂O air–sea fluxes from observations (MEM-RF; Yang et al., 2020), in particular along continental margins, is severely limited by the number of N₂O observations, which is 2 orders of magnitude smaller than for CO₂. Observations tend to be localized in regions of strong air–sea disequilibrium and, thus, might be biased high (e.g., Babbin et al., 2020; Ganesan et al., 2020). In addition, many coastal regions remain undersampled, further limiting the performance of MEM-RF. For instance, models point to coastal N₂O flux hotspots along midlatitude western boundaries (e.g., the US East Coast, the North Pacific east of Japan, the southeast coast of Australia, and the southeastern tip of Africa) that are not diagnosed in the observational product (Resplandy et al., 2024). Furthermore, N₂O fluxes are highly spatially heterogeneous (scales of 1 to 100 km) due to land–ocean gradients and mesoscale and submesoscale features such as eddies (Arévalo-Martínez et al., 2017, 2019; Yang et al., 2020; Grundle et al., 2017). Eddies are instrumental in setting suboxic conditions favorable for N₂O production, and it has been suggested that N₂O production weakens within eddies during their transit across the

shelf and further offshore (Arévalo-Martínez et al., 2016). These small-scale circulation features are important controls on N₂O dynamics but are poorly accounted for in data-based reconstructions and models.

This assessment provides the most up-to-date estimate of N₂O climatological emissions from the global shelves, but the variability in these emissions remains uncertain. Each product covers a different time period and only provides limited or missing information on seasonal fluctuations, interannual variability, and long-term trends. For instance, only a handful of observations per year are available in most regions, providing a limited picture of seasonality and even more limited information on interannual variability (e.g., El Niño–Southern Oscillation and Pacific Decadal Oscillation) and global longer-term trends. Disentangling such influences from limited observations alone remains a major challenge. The effects of extreme events, such as storms and marine heat waves, on N₂O fluxes are also currently not captured, and the intra-annual variability in hotspot regions, such as coastal upwelling systems, remains poorly constrained. Despite these limitations, data-based reconstructions and models suggest a vigorous seasonal cycle and, potentially, important variability on interannual timescales (Yang et al., 2020; Ganesan et al., 2020). The development of a global N₂O ocean observation network (N₂O-ON; Bange et al., 2019; Bange, 2022) is critically needed to better resolve spatiotemporal patterns and reduce uncertainties in N₂O emissions. Increasing the density of observations in regions of high N₂O disequilibrium and collecting long time series of N₂O measurements will allow for better characterization of interannual changes and their dynamics. Meanwhile, algo-
<figure><img src="image_23.png" /><figcaption>**Figure 22.** Spatial distribution of posterior uncertainty (1 standard deviation) in TD model estimates of N₂O emissions in the 2010s.</figcaption></figure>

rithmic approaches that address the observational limitations should be developed and refined to extrapolate N₂O measurements to global and interannual timescales, leveraging advancements made for CO₂ disequilibrium and flux reconstructions.

Parallel efforts based on the development of mechanistic models are also needed to strengthen our understanding of the dynamics underlying interannual N₂O flux variability and to detect and attribute long-term anthropogenic effects. However, the representation of N₂O processes in biogeochemical models remains limited, and very few climate models include marine emissions of N₂O fluxes (only 4 out of 26 CMIP6 models considered in Séfélian et al., 2020). Uncertainty persists regarding the various (micro-)biological processes that drive N₂O cycling in coastal waters and sediments (Bange, 2022). Current global ocean biogeochemical models typically adopt an indirect representation of N₂O production, which is diagnosed from environmental conditions (e.g., temperature) and O₂ consumption during mineralization of organic matter, without explicitly representing the bacterial pools and chemical reactions responsible for N₂O production in suboxic waters (e.g., Aumont et al., 2015; Battaglia and Joos, 2018). In addition, key aspects of air-sea N₂O exchange, such as the effects of surfactants in the sea surface microlayer (Kock et al., 2012), remain poorly understood. Finally, the interannual variability in N₂O fluxes and its attribution to climatic and anthropogenic drivers is largely unknown. Disentangling these influences will benefit from (1) interannually varying observational N₂O flux

reconstructions at scales fine enough to capture high emissions along continental margins, (2) statistical methods that address the limited number of observations in space and time, and (3) N₂O cycle simulations with forward mechanistic models. A blueprint for this work already exists with the approaches developed by the oceanic CO₂ community (Gruber, 2022). Similar approaches would enable attribution of N₂O flux changes to specific drivers, leading to better predictability.

### 6.2.5 Uncertainties in emissions estimates from atmospheric inversions

The four atmospheric inversion frameworks show uncertainties in the estimates of N₂O emissions, especially in hotspot regions such as eastern China, India, Europe, the US Corn Belt, and northern South America (Fig. 22). The uncertainties in inversion estimates are mainly from errors in the modeled atmospheric transport, the dependence on the prior information, and the availability of atmospheric observations. Every inversion framework in this study used a different atmospheric transport model with different horizontal and vertical resolutions (Table 1). By including estimates from multiple inversion frameworks with different modeled atmospheric transport, the systematic error can be assessed to some extent. The inversion estimates are dependent on the spatial pattern and magnitude of the prior flux estimates to an extent that is determined by the density of the observations. Using the same prior information might reduce the range in
<figure><img src="image_23.png" /><figcaption>**Figure 22.** Spatial distribution of posterior uncertainty (1 standard deviation) in TD model estimates of N₂O emissions in the 2010s.</figcaption></figure>

rithmic approaches that address the observational limitations should be developed and refined to extrapolate N₂O measurements to global and interannual timescales, leveraging advancements made for CO₂ disequilibrium and flux reconstructions.

Parallel efforts based on the development of mechanistic models are also needed to strengthen our understanding of the dynamics underlying interannual N₂O flux variability and to detect and attribute long-term anthropogenic effects. However, the representation of N₂O processes in biogeochemical models remains limited, and very few climate models include marine emissions of N₂O fluxes (only 4 out of 26 CMIP6 models considered in Séfélian et al., 2020). Uncertainty persists regarding the various (micro-)biological processes that drive N₂O cycling in coastal waters and sediments (Bange, 2022). Current global ocean biogeochemical models typically adopt an indirect representation of N₂O production, which is diagnosed from environmental conditions (e.g., temperature) and O₂ consumption during remineralization of organic matter, without explicitly representing the bacterial pools and chemical reactions responsible for N₂O production in suboxic waters (e.g., Aumont et al., 2015; Battaglia and Joos, 2018). In addition, key aspects of air-sea N₂O exchange, such as the effects of surfactants in the sea surface microlayer (Kock et al., 2012), remain poorly understood. Finally, the interannual variability in N₂O fluxes and its attribution to climatic and anthropogenic drivers is largely unknown. Disentangling these influences will benefit from (1) interannually varying observational N₂O flux

reconstructions at scales fine enough to capture high emissions along continental margins, (2) statistical methods that address the limited number of observations in space and time, and (3) N₂O cycle simulations with forward mechanistic models. A blueprint for this work already exists with the approaches developed by the oceanic CO₂ community (Gruber, 2022). Similar approaches would enable attribution of N₂O flux changes to specific drivers, leading to better predictability.

### 6.2.5 Uncertainties in emissions estimates from atmospheric inversions

The four atmospheric inversion frameworks show uncertainties in the estimates of N₂O emissions, especially in hotspot regions such as eastern China, India, Europe, the US Corn Belt, and northern South America (Fig. 22). The uncertainties in inversion estimates are mainly from errors in the modeled atmospheric transport, the dependence on the prior information, and the availability of atmospheric observations. Every inversion framework in this study used a different atmospheric transport model with different horizontal and vertical resolutions (Table 1). By including estimates from multiple inversion frameworks with different modeled atmospheric transport, the systematic error can be assessed to some extent. The inversion estimates are dependent on the spatial pattern and magnitude of the prior flux estimates to an extent that is determined by the density of the observations. Using the same prior information might reduce the range in
<figure><img src="image_24.png" /><figcaption>**Figure 23.** Uncertainty reduction (1 – sigma_posterior/sigma_prior) from the PyVAR-CAMS inversion framework. Asterisks (*) represent atmospheric observational stations used in the inversion framework.</figcaption></figure>

the atmospheric inversion estimates but not the uncertainty, as this depends on the spatiotemporal density of the atmospheric observations and the accuracy of the modeled transport. The uncertainty reduction (calculated as 1 minus the ratio of the posterior to prior uncertainty) indicates the degree of constraint on the inversion estimates (Fig. 23). It shows that the areas of South America, Africa, central and southern Asia, and Australasia are poorly constrained by observations. The relatively sparse distribution of current N₂O observation sites underscores the necessity of establishing more sites and regular aircraft profiles, especially in tropical and subtropical regions, to better constrain inversion models and to further reduce the posterior uncertainty.

to be the key factors determining net N₂O emission of termites (Brauman et al., 2015; Miambi et al., 2022). Termites that consume N-rich material, such as soil organic matter and fungi, exhibit high N₂O production rates and emit N₂O into the atmosphere, while those feeding on N-deficient wood can consume atmospheric N₂O (Brauman et al., 2015). It is difficult to scale up calculations of net N₂O emission by termites due to the lack of data on their abundance and biomass across global ecosystems; therefore, our understanding of the precise contribution of termites to the atmospheric N₂O budget on a global scale remains limited, and it is not considered in our analysis.

### 6.2.6 Other missing fluxes

We recognize that N₂O emissions contributed by termites could be a significant natural source in tropical and subtropical ecosystems (Brümmer et al., 2009; Miambi et al., 2022). The metabolic activity of microbial symbionts in the termite gut can maintain steep oxygen gradients, which facilitates nitrification and denitrification processes and the production of N₂O (Brauman et al., 2015; Brune et al., 1995). Nevertheless, termites have a wide trophic diversity, and their N₂O emission rates vary significantly, with some species creating emission hotspots (Brümmer et al., 2009), while others function as net sinks (Majeed et al., 2012). Feeding habits and the abundance of nitrifiers and denitrifiers in the gut are reported
<figure><img src="image_24.png" /><figcaption>**Figure 23.** Uncertainty reduction (1 – sigma_posterior/sigma_prior) from the PyVAR-CAMS inversion framework. Asterisks (*) represent atmospheric observational stations used in the inversion framework.</figcaption></figure>

the atmospheric inversion estimates but not the uncertainty, as this depends on the spatiotemporal density of the atmospheric observations and the accuracy of the modeled transport. The uncertainty reduction (calculated as 1 minus the ratio of the posterior to prior uncertainty) indicates the degree of constraint on the inversion estimates (Fig. 23). It shows that the areas of South America, Africa, central and southern Asia, and Australasia are poorly constrained by observations. The relatively sparse distribution of current N₂O observation sites underscores the necessity of establishing more sites and regular aircraft profiles, especially in tropical and subtropical regions, to better constrain inversion models and to further reduce the posterior uncertainty.

to be the key factors determining net N₂O emission of termites (Brauman et al., 2015; Miambi et al., 2022). Termites that consume N-rich material, such as soil organic matter and fungi, exhibit high N₂O production rates and emit N₂O into the atmosphere, while those feeding on N-deficient wood can consume atmospheric N₂O (Brauman et al., 2015). It is difficult to scale up calculations of net N₂O emission by termites due to the lack of data on their abundance and biomass across global ecosystems; therefore, our understanding of the precise contribution of termites to the atmospheric N₂O budget on a global scale remains limited, and it is not considered in our analysis.

### 6.2.6 Other missing fluxes

We recognize that N₂O emissions contributed by termites could be a significant natural source in tropical and subtropical ecosystems (Brümmer et al., 2009; Miambi et al., 2022). The metabolic activity of microbial symbionts in the termite gut can maintain steep oxygen gradients, which facilitates nitrification and denitrification processes and the production of N₂O (Brauman et al., 2015; Brune et al., 1995). Nevertheless, termites have a wide trophic diversity, and their N₂O emission rates vary significantly, with some species creating emission hotspots (Brümmer et al., 2009), while others function as net sinks (Majeed et al., 2012). Feeding habits and the abundance of nitrifiers and denitrifiers in the gut are reported
Appendix A: Additional tables

<table><caption>Table A1. Comparison of terminologies used in this study and previous reports.</caption><thead><tr><th></th><th>GCP terminology (this study)</th><th>IPCC AR6 (IPCC, 2021)</th><th>National GHG inventories (used by UNFCCC according to IPCC, 2006, 2019)</th><th>UNFCCC/IPCC 2006 source sector</th></tr></thead><tbody><tr><td colspan="5"><strong>Anthropogenic sources</strong></td></tr><tr><td rowspan="4">Direct emissions of N additions in the agricultural sector (agriculture)</td><td>Direct soil emissions (mineral N and manure fertilization, cultivation of organic soils, and crop residue returns)</td><td rowspan="3">Agriculture</td><td>Direct N<sub>2</sub>O emissions from managed soils (except due to grazing animals)</td><td>Part of 3C4</td></tr><tr><td>Manure left on pasture</td><td>Urine and dung deposited by grazing animals</td><td>Part of 3C4</td></tr><tr><td>Manure management</td><td>Manure management</td><td>2A2</td></tr><tr><td>Aquaculture</td><td>–</td><td>–</td><td>–</td></tr><tr><td rowspan="3">Other direct anthropogenic sources</td><td>Fossil fuel and industry</td><td>Fossil fuel combustion and industrial processes</td><td>Energy and industrial processes</td><td>1, 2</td></tr><tr><td>Waste and wastewater</td><td>Human excreta</td><td>Waste</td><td>4C1, 4C2<br>4D1, 4D2</td></tr><tr><td>Biomass burning (from crop residue, grassland, shrubland and savannas, peat fires, tropical forests, boreal forests, and temperate forests)</td><td>Biomass and biofuel burning</td><td>Prescribed burning of savannas, field burning of agricultural residues</td><td>3E, 3F</td></tr><tr><td rowspan="3">Indirect emissions from anthropogenic N additions</td><td>Inland and coastal waters (rivers, lakes, reservoirs, estuaries, and coastal vegetation)</td><td>Rivers, estuaries, coastal vegetation</td><td>Indirect emissions due to leaching and runoff</td><td>Part of 3C5, 3C6</td></tr><tr><td>Atmospheric N deposition on land</td><td>Atmospheric deposition on land</td><td>Indirect emissions due to atmospheric deposition (of agricultural as well as other anthropogenic compounds emitted)</td><td>Part of 3C5, 5A</td></tr><tr><td>Atmospheric N deposition on ocean</td><td>Atmospheric deposition on ocean</td><td></td><td>Part of 3C5, 5A</td></tr><tr><td rowspan="3">Perturbed fluxes from climate, CO<sub>2</sub>, and land cover change</td><td>CO<sub>2</sub> effect</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Climate effect</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Post-deforestation pulse effect</td><td>–</td><td>–</td><td>–</td></tr><tr><td></td><td>Long-term effect of reduced mature forest area</td><td>–</td><td>–</td><td>–</td></tr><tr><td colspan="5"><strong>Natural sources and sinks</strong></td></tr><tr><td></td><td>Natural soil baseline</td><td>Soils under natural vegetation</td><td>–</td><td>–</td></tr><tr><td></td><td>Coastal and open-ocean baseline</td><td>Oceans</td><td>–</td><td>–</td></tr><tr><td></td><td>Natural (rivers, lakes, reservoirs, estuaries, and coastal vegetation)</td><td>–</td><td>–</td><td>–</td></tr><tr><td rowspan="2">Lightning and atmospheric production</td><td>Lightning</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Atmospheric chemistry</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Soil and wetland surface sink</td><td>Surface sink</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Atmospheric sink</td><td>Atmospheric sink</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>
Appendix A: Additional tables

<table><caption>Table A1. Comparison of terminologies used in this study and previous reports.</caption><thead><tr><th></th><th>GCP terminology (this study)</th><th>IPCC AR6 (IPCC, 2021)</th><th>National GHG inventories (used by UNFCCC according to IPCC, 2006, 2019)</th><th>UNFCCC/IPCC 2006 source sector</th></tr></thead><tbody><tr><td colspan="5"><strong>Anthropogenic sources</strong></td></tr><tr><td rowspan="4">Direct emissions of N additions in the agricultural sector (agriculture)</td><td>Direct soil emissions (mineral N and manure fertilization, cultivation of organic soils, and crop residue returns)</td><td rowspan="3">Agriculture</td><td>Direct N<sub>2</sub>O emissions from managed soils (except due to grazing animals)</td><td>Part of 3C4</td></tr><tr><td>Manure left on pasture</td><td>Urine and dung deposited by grazing animals</td><td>Part of 3C4</td></tr><tr><td>Manure management</td><td>Manure management</td><td>2A2</td></tr><tr><td>Aquaculture</td><td>–</td><td>–</td><td>–</td></tr><tr><td rowspan="3">Other direct anthropogenic sources</td><td>Fossil fuel and industry</td><td>Fossil fuel combustion and industrial processes</td><td>Energy and industrial processes</td><td>1, 2</td></tr><tr><td>Waste and wastewater</td><td>Human excreta</td><td>Waste</td><td>4C1, 4C2<br>4D1, 4D2</td></tr><tr><td>Biomass burning (from crop residue, grassland, shrubland and savannas, peat fires, tropical forests, boreal forests, and temperate forests)</td><td>Biomass and biofuel burning</td><td>Prescribed burning of savannas, field burning of agricultural residues</td><td>3E, 3F</td></tr><tr><td rowspan="3">Indirect emissions from anthropogenic N additions</td><td>Inland and coastal waters (rivers, lakes, reservoirs, estuaries, and coastal vegetation)</td><td>Rivers, estuaries, coastal vegetation</td><td>Indirect emissions due to leaching and runoff</td><td>Part of 3C5, 3C6</td></tr><tr><td>Atmospheric N deposition on land</td><td>Atmospheric deposition on land</td><td>Indirect emissions due to atmospheric deposition (of agricultural as well as other anthropogenic compounds emitted)</td><td>Part of 3C5, 5A</td></tr><tr><td>Atmospheric N deposition on ocean</td><td>Atmospheric deposition on ocean</td><td></td><td>Part of 3C5, 5A</td></tr><tr><td rowspan="3">Perturbed fluxes from climate, CO<sub>2</sub>, and land cover change</td><td>CO<sub>2</sub> effect</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Climate effect</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Post-deforestation pulse effect</td><td>–</td><td>–</td><td>–</td></tr><tr><td></td><td>Long-term effect of reduced mature forest area</td><td>–</td><td>–</td><td>–</td></tr><tr><td colspan="5"><strong>Natural sources and sinks</strong></td></tr><tr><td></td><td>Natural soil baseline</td><td>Soils under natural vegetation</td><td>–</td><td>–</td></tr><tr><td></td><td>Coastal and open-ocean baseline</td><td>Oceans</td><td>–</td><td>–</td></tr><tr><td></td><td>Natural (rivers, lakes, reservoirs, estuaries, and coastal vegetation)</td><td>–</td><td>–</td><td>–</td></tr><tr><td rowspan="2">Lightning and atmospheric production</td><td>Lightning</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Atmospheric chemistry</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Soil and wetland surface sink</td><td>Surface sink</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Atmospheric sink</td><td>Atmospheric sink</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>
<table><caption>**Table A2.** List of the countries used to define the 18 regions.</caption><thead><tr><th>Region no.</th><th>Region name</th><th>Countries or territories</th></tr></thead><tbody><tr><td>1</td><td>USA</td><td>USA with Alaska and Bermuda Islands</td></tr><tr><td>2</td><td>Canada</td><td>Canada</td></tr><tr><td>3</td><td>Central America</td><td>Anguilla, Antigua and Barbuda, the Bahamas, Barbados, Belize, British Virgin Islands, Cayman Islands, Costa Rica, Cuba, Dominica, Dominican Republic, El Salvador, Guadeloupe, Guatemala, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Puerto Rico, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Turks and Caicos Islands, and the United States Virgin Islands</td></tr><tr><td>4</td><td>Brazil</td><td>Brazil</td></tr><tr><td>5</td><td>Northern South America</td><td>Aruba, Colombia, French Guiana, Grenada, Guyana, Suriname, Trinidad and Tobago, and Venezuela</td></tr><tr><td>6</td><td>Southwest South America</td><td>Argentina, Bolivia, Chile, Ecuador, Falkland Islands (Malvinas), Paraguay, Peru, and Uruguay</td></tr><tr><td>7</td><td>Europe</td><td>Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Channel Islands, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Hungary, Iceland, Ireland, Isle of Man, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Montenegro, the Netherlands, North Macedonia, Norway, Poland, Portugal, Republic of Moldova, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, and the United Kingdom</td></tr><tr><td>8</td><td>Northern Africa</td><td>Algeria, Cabo Verde, Chad, Cote d'Ivoire, Djibouti, Egypt, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Libya, Mali, Mauritania, Morocco, Niger, Saint Helena, Sao Tome and Principe, Senegal, Somalia, Republic of the Sudan, South Sudan, Tunisia, and Western Sahara</td></tr><tr><td>9</td><td>Equatorial Africa</td><td>Benin, Burkina Faso, Burundi, Cameroon, Central African Republic, Congo, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Ghana, Kenya, Liberia, Nigeria, Rwanda, Sierra Leone, Tanzania, Togo, and Uganda</td></tr><tr><td>10</td><td>Southern Africa</td><td>Angola, Botswana, Comoros, Eswatini, Lesotho, Madagascar, Malawi, Mauritius, Mayotte, Mozambique, Namibia, Reunión, Seychelles, South Africa, Zambia, and Zimbabwe</td></tr><tr><td>11</td><td>Russia</td><td>Russia</td></tr><tr><td>12</td><td>Central Asia</td><td>Kazakhstan, Kyrgyzstan, Mongolia, Tajikistan, Turkmenistan, and Uzbekistan</td></tr><tr><td>13</td><td>The Middle East</td><td>Armenia, Azerbaijan, Bahrain, Georgia, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Palestine, Qatar, Saudi Arabia, Syria, Türkiye, United Arab Emirates, and Yemen</td></tr><tr><td>14</td><td>China</td><td>China mainland, Hong Kong SAR, Macao SAR, and Taiwan</td></tr><tr><td>15</td><td>Korea and Japan</td><td>Japan, North Korea, and South Korea</td></tr><tr><td>16</td><td>South Asia</td><td>Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka</td></tr><tr><td>17</td><td>Southeast Asia</td><td>Brunei Darussalam, Cambodia, Cook Islands, Fiji, French Polynesia, Guam, Indonesia, Kiribati, Laos, Malaysia, Maldives, Marshall Islands, Myanmar, Nauru, New Caledonia, Niue, Norfolk Islands, Northern Mariana Islands, Pacific Islands Trust Territory, Palau, Papua New Guinea, Philippines, Pitcairn, Samoa, Singapore, Solomon Islands, Thailand, Timor-Leste, Tokelau, Tonga, Tuvalu, Vanuatu, Viet Nam, and Wallis and Futuna Islands</td></tr><tr><td>18</td><td>Australasia</td><td>Australia and New Zealand</td></tr></tbody></table>
<table><caption>**Table A2.** List of the countries used to define the 18 regions.</caption><thead><tr><th>Region no.</th><th>Region name</th><th>Countries or territories</th></tr></thead><tbody><tr><td>1</td><td>USA</td><td>USA with Alaska and Bermuda Islands</td></tr><tr><td>2</td><td>Canada</td><td>Canada</td></tr><tr><td>3</td><td>Central America</td><td>Anguilla, Antigua and Barbuda, the Bahamas, Barbados, Belize, British Virgin Islands, Cayman Islands, Costa Rica, Cuba, Dominica, Dominican Republic, El Salvador, Guadeloupe, Guatemala, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Puerto Rico, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Turks and Caicos Islands, and the United States Virgin Islands</td></tr><tr><td>4</td><td>Brazil</td><td>Brazil</td></tr><tr><td>5</td><td>Northern South America</td><td>Aruba, Colombia, French Guiana, Grenada, Guyana, Suriname, Trinidad and Tobago, and Venezuela</td></tr><tr><td>6</td><td>Southwest South America</td><td>Argentina, Bolivia, Chile, Ecuador, Falkland Islands (Malvinas), Paraguay, Peru, and Uruguay</td></tr><tr><td>7</td><td>Europe</td><td>Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Channel Islands, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Hungary, Iceland, Ireland, Isle of Man, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Montenegro, the Netherlands, North Macedonia, Norway, Poland, Portugal, Republic of Moldova, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, and the United Kingdom</td></tr><tr><td>8</td><td>Northern Africa</td><td>Algeria, Cabo Verde, Chad, Cote d'Ivoire, Djibouti, Egypt, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Libya, Mali, Mauritania, Morocco, Niger, Saint Helena, Sao Tome and Principe, Senegal, Somalia, Republic of the Sudan, South Sudan, Tunisia, and Western Sahara</td></tr><tr><td>9</td><td>Equatorial Africa</td><td>Benin, Burkina Faso, Burundi, Cameroon, Central African Republic, Congo, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Ghana, Kenya, Liberia, Nigeria, Rwanda, Sierra Leone, Tanzania, Togo, and Uganda</td></tr><tr><td>10</td><td>Southern Africa</td><td>Angola, Botswana, Comoros, Eswatini, Lesotho, Madagascar, Malawi, Mauritius, Mayotte, Mozambique, Namibia, Reunión, Seychelles, South Africa, Zambia, and Zimbabwe</td></tr><tr><td>11</td><td>Russia</td><td>Russia</td></tr><tr><td>12</td><td>Central Asia</td><td>Kazakhstan, Kyrgyzstan, Mongolia, Tajikistan, Turkmenistan, and Uzbekistan</td></tr><tr><td>13</td><td>The Middle East</td><td>Armenia, Azerbaijan, Bahrain, Georgia, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Palestine, Qatar, Saudi Arabia, Syria, Türkiye, United Arab Emirates, and Yemen</td></tr><tr><td>14</td><td>China</td><td>China mainland, Hong Kong SAR, Macao SAR, and Taiwan</td></tr><tr><td>15</td><td>Korea and Japan</td><td>Japan, North Korea, and South Korea</td></tr><tr><td>16</td><td>South Asia</td><td>Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka</td></tr><tr><td>17</td><td>Southeast Asia</td><td>Brunei Darussalam, Cambodia, Cook Islands, Fiji, French Polynesia, Guam, Indonesia, Kiribati, Laos, Malaysia, Maldives, Marshall Islands, Myanmar, Nauru, New Caledonia, Niue, Norfolk Islands, Northern Mariana Islands, Pacific Islands Trust Territory, Palau, Papua New Guinea, Philippines, Pitcairn, Samoa, Singapore, Solomon Islands, Thailand, Timor-Leste, Tokelau, Tonga, Tuvalu, Vanuatu, Viet Nam, and Wallis and Futuna Islands</td></tr><tr><td>18</td><td>Australasia</td><td>Australia and New Zealand</td></tr></tbody></table>
<table><caption>**Table A3.** The sectors in the N₂O budget and its sources. (An asterisk denotes that a sector only includes the maximum, mean, and minimum.)</caption><thead><tr><td>ID</td><td>N₂O budget sectors (global scale)</td><td>Sources</td></tr></thead><tbody><tr><td>1</td><td>Aquaculture</td><td>EF0.5, EF5, and EF1.8</td></tr><tr><td>2</td><td>Manure left on pasture</td><td>DLEM, EDGAR, and FAO</td></tr><tr><td>3</td><td>Manure management</td><td>EDGAR</td></tr><tr><td>4</td><td>Direct soil emissions global</td><td>EDGAR, FAO, NMIP2/DLEM, and SRNM/DLEM</td></tr><tr><td>5</td><td>Inland water, estuaries, and coastal vegetation; anthropogenic</td><td>Meta-analysis and process-based models, EDGAR, and FAO</td></tr><tr><td>6</td><td>N deposition on land</td><td>NMIP2/EDGAR v7.0 and NMIP2</td></tr><tr><td>7</td><td>CO₂</td><td>CLASSIC, DLEM, ELM, ISAM, LPX-Bern, O-CN, ORCHIDEE, and VISIT</td></tr><tr><td>8</td><td>Climate</td><td>CLASSIC, DLEM, ELM, ISAM, LPX-Bern, O-CN, ORCHIDEE, and VISIT</td></tr><tr><td>9</td><td>Post-deforestation pulse effect</td><td>DLEM and bookkeeping model</td></tr><tr><td>10</td><td>Natural soil baseline</td><td>CLASSIC, DLEM, ELM, ISAM, LPX-Bern, O-CN, ORCHIDEE, and VISIT</td></tr><tr><td>11</td><td>Open ocean</td><td>BERN, CNRM, UViC, and UEA-NEMO-PlankTOM</td></tr><tr><td>12</td><td>N deposition on ocean*</td><td>Suntharalingam et al. (2012)</td></tr><tr><td>13</td><td>Biomass burning</td><td>FAO, DLEM, and GFED</td></tr><tr><td>14</td><td>Fossil fuel industry</td><td>EDGAR and EDGAR/UNFCCC</td></tr><tr><td>15</td><td>Waste and wastewater</td><td>EDGAR/UNFCCC</td></tr><tr><td>16</td><td>Inland water, estuaries, and coastal vegetation; natural*</td><td>DLEM, stochastic mechanistic model, RF model, and meta-analyses-based estimates</td></tr><tr><td>17</td><td>Lightning and atmospheric production*</td><td>Schlesinger (2013) and Syakila et al. (2010)</td></tr><tr><td>18</td><td>Long-term reduction effect</td><td>DLEM and bookkeeping model</td></tr><tr><td>19</td><td>Continental shelves*</td><td>ECCO, CNRM, and MEM-RF</td></tr></tbody></table>
<table><caption>**Table A3.** The sectors in the N₂O budget and its sources. (An asterisk denotes that a sector only includes the maximum, mean, and minimum.)</caption><thead><tr><th>ID</th><th>N₂O budget sectors (global scale)</th><th>Sources</th></tr></thead><tbody><tr><td>1</td><td>Aquaculture</td><td>EF0.5, EF5, and EF1.8</td></tr><tr><td>2</td><td>Manure left on pasture</td><td>DLEM, EDGAR, and FAO</td></tr><tr><td>3</td><td>Manure management</td><td>EDGAR</td></tr><tr><td>4</td><td>Direct soil emissions global</td><td>EDGAR, FAO, NMIP2/DLEM, and SRNM/DLEM</td></tr><tr><td>5</td><td>Inland water, estuaries, and coastal vegetation; anthropogenic</td><td>Meta-analysis and process-based models, EDGAR, and FAO</td></tr><tr><td>6</td><td>N deposition on land</td><td>NMIP2/EDGAR v7.0 and NMIP2</td></tr><tr><td>7</td><td>CO₂</td><td>CLASSIC, DLEM, ELM, ISAM, LPX-Bern, O-CN, ORCHIDEE, and VISIT</td></tr><tr><td>8</td><td>Climate</td><td>CLASSIC, DLEM, ELM, ISAM, LPX-Bern, O-CN, ORCHIDEE, and VISIT</td></tr><tr><td>9</td><td>Post-deforestation pulse effect</td><td>DLEM and bookkeeping model</td></tr><tr><td>10</td><td>Natural soil baseline</td><td>CLASSIC, DLEM, ELM, ISAM, LPX-Bern, O-CN, ORCHIDEE, and VISIT</td></tr><tr><td>11</td><td>Open ocean</td><td>BERN, CNRM, UViC, and UEA-NEMO-PlankTOM</td></tr><tr><td>12</td><td>N deposition on ocean*</td><td>Suntharalingam et al. (2012)</td></tr><tr><td>13</td><td>Biomass burning</td><td>FAO, DLEM, and GFED</td></tr><tr><td>14</td><td>Fossil fuel industry</td><td>EDGAR and EDGAR/UNFCCC</td></tr><tr><td>15</td><td>Waste and wastewater</td><td>EDGAR/UNFCCC</td></tr><tr><td>16</td><td>Inland water, estuaries, and coastal vegetation; natural*</td><td>DLEM, stochastic mechanistic model, RF model, and meta-analyses-based estimates</td></tr><tr><td>17</td><td>Lightning and atmospheric production*</td><td>Schlesinger (2013) and Syakila et al. (2010)</td></tr><tr><td>18</td><td>Long-term reduction effect</td><td>DLEM and bookkeeping model</td></tr><tr><td>19</td><td>Continental shelves*</td><td>ECCO, CNRM, and MEM-RF</td></tr></tbody></table>
<table><caption>**Table A4.** Funding supporting the production of the various components of the global nitrous oxide budget in addition to the authors' supporting institutions (see also Acknowledgements).</caption><thead><tr><td>Funder and grant number (where relevant)</td><td>Authors/simulations/ observations</td></tr></thead><tbody><tr><td>Australian National Environmental Science Program – Climate Systems Hub</td><td>Josep G. Canadell</td></tr><tr><td>Copernicus Atmosphere Monitoring Service, implemented by ECMWF on behalf of the European Commission.</td><td>Rona Thompson</td></tr><tr><td>Deutsche Forschungsgemeinschaft (DFG) (grant no. SFB754/3 B1 D1807)</td><td>Angela Landolfi</td></tr><tr><td>Dutch Ministry of Education, Culture and Science through the Netherlands Earth System Science Center (NESSC)</td><td>Junjie Wang</td></tr><tr><td>European Space Agency (ESA) RECCAP2 project (grant no. ESRIN/4000123002/18/I-NB)</td><td>Philippe Ciais</td></tr><tr><td>European Union's Horizon 2020 Research and Innovation program under grant agreement no. 101003536 (ESM2025 – Earth System Models for the Future)</td><td>Pierre Regnier, Sönke Zaehle, Nicolas Vuichard, and Sarah Berthet</td></tr><tr><td>European Union's Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie grant agreement no. 101030750</td><td>Luke M. Western</td></tr><tr><td>European Union's Horizon Europe Research and Innovation program under grant agreement no. 101081395 (EYE-CLIMA)</td><td>Glen Peters, Wilfried Winiwarter, and Rona Thompson</td></tr><tr><td>French state aid, managed by ANR under the “Investissements d'avenir” program (grant no. ANR-16-CONV-0003)</td><td>Ronny Lauerwald</td></tr><tr><td>Hatch Act (accession no. IDA01722) through the USDA National Institute of Food and Agriculture</td><td>Daniele Tonina</td></tr><tr><td>Hutchinson Postdoctoral Fellowship from the Yale Institute for Biospheric Studies at Yale University</td><td>Judith A. Rosentreter</td></tr><tr><td>Member countries to FAOSTAT through the FAO's regular budget</td><td>Francesco N. Tubiello</td></tr><tr><td>MIROC4-ACTM from the Environment Research and Technology Development Fund (SII-8; grant no. JP-MEERF21S20800) and the Arctic Challenge for Sustainability phase II (ArCS-II; grant no. JP-MXD1420318865) project</td><td>Prabir K. Patra</td></tr><tr><td>National Natural Science Foundation of China (grant no. 42107393)</td><td>Minpeng Hu</td></tr><tr><td>National Natural Science Foundation of China (grant nos. 42225102 and 41977082)</td><td>Feng Zhou</td></tr><tr><td>Natural Environment Research Council through its grants to the UK National Centre for Earth Observation (NCEO; NERC grant nos. NE/R016518/1 and NE/N018079/1)</td><td>Chris Wilson</td></tr><tr><td>Swiss National Science Foundation (grant no. 200020_200511)</td><td>Fortunat Joos, Aurich Jeltsch-Thoemmes, and Qing Sun</td></tr><tr><td>US Department of Energy through the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Scientific Focus Area (RUBISCO SFA) project</td><td>Qing Zhu</td></tr><tr><td>US National Science foundation (grant no. 1903722)</td><td>Hanqin Tian, Shufen Pan, and Chaoqun Lu</td></tr><tr><td>US National Science Foundation (grant no. OCE-1847687)</td><td>Daniele Bianchi</td></tr><tr><td>US Department of Agriculture Capacity Building Grants (CBG; grant no. TENX12899)</td><td>Hanqin Tian</td></tr><tr><td>US National Science Foundation (grant no. 1922687)</td><td>Shufen Pan</td></tr></tbody></table>
<table><caption>**Table A4.** Funding supporting the production of the various components of the global nitrous oxide budget in addition to the authors' supporting institutions (see also Acknowledgements).</caption><thead><tr><td>Funder and grant number (where relevant)</td><td>Authors/simulations/ observations</td></tr></thead><tbody><tr><td>Australian National Environmental Science Program – Climate Systems Hub</td><td>Josep G. Canadell</td></tr><tr><td>Copernicus Atmosphere Monitoring Service, implemented by ECMWF on behalf of the European Commission.</td><td>Rona Thompson</td></tr><tr><td>Deutsche Forschungsgemeinschaft (DFG) (grant no. SFB754/3 B1 D1807)</td><td>Angela Landolfi</td></tr><tr><td>Dutch Ministry of Education, Culture and Science through the Netherlands Earth System Science Center (NESSC)</td><td>Junjie Wang</td></tr><tr><td>European Space Agency (ESA) RECCAP2 project (grant no. ESRIN/4000123002/18/I-NB)</td><td>Philippe Ciais</td></tr><tr><td>European Union's Horizon 2020 Research and Innovation program under grant agreement no. 101003536 (ESM2025 – Earth System Models for the Future)</td><td>Pierre Regnier, Sönke Zaehle, Nicolas Vuichard, and Sarah Berthet</td></tr><tr><td>European Union's Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie grant agreement no. 101030750</td><td>Luke M. Western</td></tr><tr><td>European Union's Horizon Europe Research and Innovation program under grant agreement no. 101081395 (EYE-CLIMA)</td><td>Glen Peters, Wilfried Winiwarter, and Rona Thompson</td></tr><tr><td>French state aid, managed by ANR under the “Investissements d'avenir” program (grant no. ANR-16-CONV-0003)</td><td>Ronny Lauerwald</td></tr><tr><td>Hatch Act (accession no. IDA01722) through the USDA National Institute of Food and Agriculture</td><td>Daniele Tonina</td></tr><tr><td>Hutchinson Postdoctoral Fellowship from the Yale Institute for Biospheric Studies at Yale University</td><td>Judith A. Rosentreter</td></tr><tr><td>Member countries to FAOSTAT through the FAO's regular budget</td><td>Francesco N. Tubiello</td></tr><tr><td>MIROC4-ACTM from the Environment Research and Technology Development Fund (SII-8; grant no. JP-MEERF21S20800) and the Arctic Challenge for Sustainability phase II (ArCS-II; grant no. JP-MXD1420318865) project</td><td>Prabir K. Patra</td></tr><tr><td>National Natural Science Foundation of China (grant no. 42107393)</td><td>Minpeng Hu</td></tr><tr><td>National Natural Science Foundation of China (grant nos. 42225102 and 41977082)</td><td>Feng Zhou</td></tr><tr><td>Natural Environment Research Council through its grants to the UK National Centre for Earth Observation (NCEO; NERC grant nos. NE/R016518/1 and NE/N018079/1)</td><td>Chris Wilson</td></tr><tr><td>Swiss National Science Foundation (grant no. 200020_200511)</td><td>Fortunat Joos, Aurich Jeltsch-Thoemmes, and Qing Sun</td></tr><tr><td>US Department of Energy through the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Scientific Focus Area (RUBISCO SFA) project</td><td>Qing Zhu</td></tr><tr><td>US National Science foundation (grant no. 1903722)</td><td>Hanqin Tian, Shufen Pan, and Chaoqun Lu</td></tr><tr><td>US National Science Foundation (grant no. OCE-1847687)</td><td>Daniele Bianchi</td></tr><tr><td>US Department of Agriculture Capacity Building Grants (CBG; grant no. TENX12899)</td><td>Hanqin Tian</td></tr><tr><td>US National Science Foundation (grant no. 1922687)</td><td>Shufen Pan</td></tr></tbody></table>
<table><caption>**Table A4. Continued.**</caption><thead><tr><th>Funder and grant number (where relevant)</th><th>Authors/simulations/ observations</th></tr></thead><tbody><tr><td><em>Computing Resources</em></td><td></td></tr><tr><td>Computational resources from the Expanse system at the San Diego Supercomputer Center through allocation TG-OCE170017 from the Advanced Cyber infrastructure Coordination Ecosystem: Services and Support (ACCESS) program, which is supported by National Science Foundation grant nos. 2138259, 2138286, 2138307, 2137603, and 2138296.</td><td>Daniele Bianchi</td></tr><tr><td>Computing resources from LSCE</td><td>Rona Thompson</td></tr><tr><td>Computing resources from Auburn University and Boston College</td><td>Hanqin Tian and Shufen Pan</td></tr><tr><td><em>Support for atmospheric observations</em></td><td></td></tr><tr><td>CSIRO for long-term support of the operation and maintenance of the CSIRO GASLAB and flask network, the Australian Bureau of Meteorology, Australian Institute of Marine Science, Australian Antarctic Division, NOAA USA, and Environment & Climate Change Canada</td><td>CSIRO flask network and Paul B. Krummel</td></tr><tr><td>NOAA's Climate Program Office under the Atmospheric Chemistry Carbon Cycle and Climate (AC4) theme</td><td>NOAA observational network, Xin Lan, and Geoffrey Dutton</td></tr><tr><td>Funding from the US NASA Upper Atmospheric Research Program in the United States (under grant nos. NNX07AE89G, NNX16AC98G, and 80NSSC21K1369 to MIT and grant nos. NNX07AF09G, NNX07AE87G, NNX16AC96G, NNX16AC97G, 80NSSC21K1210, and 80NSSC21K1201 to SIO); NASA award to MIT with sub-award to University of Bristol for Mace Head and Barbados (grant no. 80NSSC21K1369); NASA award to MIT with sub-award to CSIRO for Cape Grim (grant no. 80NSSC21K1369); UK Department for Business, Energy, and Industrial Strategy (BEIS; contract no. 1028/06/2015); US NOAA (contract no. 1305M319CNRMJ0028).</td><td>AGAGE flask network and Jens Mühle</td></tr></tbody></table>
<table><caption>**Table A4. Continued.**</caption><thead><tr><th>Funder and grant number (where relevant)</th><th>Authors/simulations/ observations</th></tr></thead><tbody><tr><td><em>Computing Resources</em></td><td></td></tr><tr><td>Computational resources from the Expanse system at the San Diego Supercomputer Center through allocation TG-OCE170017 from the Advanced Cyber infrastructure Coordination Ecosystem: Services and Support (ACCESS) program, which is supported by National Science Foundation grant nos. 2138259, 2138286, 2138307, 2137603, and 2138296.</td><td>Daniele Bianchi</td></tr><tr><td>Computing resources from LSCE</td><td>Rona Thompson</td></tr><tr><td>Computing resources from Auburn University and Boston College</td><td>Hanqin Tian and Shufen Pan</td></tr><tr><td><em>Support for atmospheric observations</em></td><td></td></tr><tr><td>CSIRO for long-term support of the operation and maintenance of the CSIRO GASLAB and flask network, the Australian Bureau of Meteorology, Australian Institute of Marine Science, Australian Antarctic Division, NOAA USA, and Environment & Climate Change Canada</td><td>CSIRO flask network and Paul B. Krummel</td></tr><tr><td>NOAA's Climate Program Office under the Atmospheric Chemistry Carbon Cycle and Climate (AC4) theme</td><td>NOAA observational network, Xin Lan, and Geoffrey Dutton</td></tr><tr><td>Funding from the US NASA Upper Atmospheric Research Program in the United States (under grant nos. NNX07AE89G, NNX16AC98G, and 80NSSC21K1369 to MIT and grant nos. NNX07AF09G, NNX07AE87G, NNX16AC96G, NNX16AC97G, 80NSSC21K1210, and 80NSSC21K1201 to SIO); NASA award to MIT with sub-award to University of Bristol for Mace Head and Barbados (grant no. 80NSSC21K1369); NASA award to MIT with sub-award to CSIRO for Cape Grim (grant no. 80NSSC21K1369); UK Department for Business, Energy, and Industrial Strategy (BEIS; contract no. 1028/06/2015); US NOAA (contract no. 1305M319CNRMJ0028).</td><td>AGAGE flask network and Jens Mühle</td></tr></tbody></table>
## Appendix B: Additional figures

<figure><img src="image_25.png" /><figcaption>**Figure B1.** Spatial distribution of global N₂O emissions in the 2010s estimated by different atmospheric inversion frameworks (TD approach).</figcaption></figure>
Appendix B: Additional figures

<figure><img src="image_25.png" /><figcaption>**Figure B1.** Spatial distribution of global N₂O emissions in the 2010s estimated by different atmospheric inversion frameworks (TD approach).</figcaption></figure>
<figure><img src="image_26.png" /><figcaption>Figure B2. Spatial distribution of preindustrial (1850s) soil N₂O emissions estimated by different NMIP2 terrestrial biosphere models.</figcaption></figure>
<figure><img src="image_26.png" /><figcaption>Figure B2. Spatial distribution of preindustrial (1850s) soil N₂O emissions estimated by different NMIP2 terrestrial biosphere models.</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>**Figure B3.** Spatial-temporal changes in fertilizer N and manure N applications as well as atmospheric N deposition to global terrestrial ecosystems derived from the HaNi dataset (Tian et al., 2022), which were used to drive NMIP2 terrestrial biosphere models.</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>**Figure B3.** Spatial–temporal changes in fertilizer N and manure N applications as well as atmospheric N deposition to global terrestrial ecosystems derived from the HaNi dataset (Tian et al., 2022), which were used to drive NMIP2 terrestrial biosphere models.</figcaption></figure>
<figure><img src="image_28.png" /><figcaption>**Figure B4.** Spatial distribution of soil N₂O emissions during 2010–2019 estimated by NMIP2 terrestrial biosphere models.</figcaption></figure>
<figure><img src="image_28.png" /><figcaption>**Figure B4.** Spatial distribution of soil N₂O emissions during 2010–2019 estimated by NMIP2 terrestrial biosphere models.</figcaption></figure>
<figure><img src="image_29.png" /></figure>

**Figure B5.** Spatial distribution of N₂O emissions from open oceans during 2010–2019 estimated by different ocean biogeochemistry models and Earth system models.

<figure><img src="image_30.png" /></figure>

**Figure B6.** N₂O emission from continental shelves as estimated by three methods.
<figure><img src="image_29.png" /></figure>

**Figure B5.** Spatial distribution of N₂O emissions from open oceans during 2010–2019 estimated by different ocean biogeochemistry models and Earth system models.

<figure><img src="image_30.png" /></figure>

**Figure B6.** N₂O emission from continental shelves as estimated by three methods.
**Supplement.** The supplement related to this article is available online at: https://doi.org/10.5194/essd-16-2543-2024-supplement.

**Author contributions.** HT, RLT, and JGC designed and coordinated the study. HT, NP, SP, YL, RLT, PS, and PR gathered the bottom-up and top-down datasets and performed the post-processing, analysis, and synthesis. HT, NP, RLT, JGC, PS, PR, EAD, MJP, PC, MM, SP, WW, SZ, FZ, and RBJ wrote the paper. RLT led atmospheric inversions, teaming with PKP, KCW, DBM, and CW; HT led land biosphere modeling, teaming with NP, SP, SZ, AI, AKJ, FJ, SKG, CL, HS, QS, and QZ; PS led ocean geochemical modeling, teaming with EB, AL, SB, AJT, and FJ; PR led the synthesis of LOAC (Land–Ocean Aquatic Continuum), teaming with RL, TM, YY, MH, PR, JR, LR, MM, SB, HB, DB, and HT; JW and LB provided the N₂O flux from aquaculture data. GRW and JY provided the N₂O emissions from biomass burning data. FZ provided cropland N₂O flux data from a statistical model and field observations. MM, FNT, and WW provided N₂O inventory data. MJP provided stratospheric and tropospheric sink data. GP provided RCP and SSP scenario data and analysis. XL and GD provided a global N₂O monitoring dataset of NOAA/ESRL-GMD. JM and LMW provided a global N₂O monitoring dataset of AGAGE stations. PK provided a global N₂O monitoring dataset of CSIRO stations. All coauthors reviewed and commented on the manuscript.

**Competing interests.** At least one of the (co-)authors is a member of the editorial board of *Earth System Science Data*. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

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

**Acknowledgements.** This paper is the result of a collaborative international effort under the umbrella of the Global Carbon Project (a project of Future Earth and a research partner of the World Climate Research Programme) in partnership with the International Nitrogen Initiative (INI). We acknowledge all of the people and institutions who provided the data used in the global nitrous oxide budget as well as the institutions funding parts of this effort (see Table A4). We acknowledge the modeling groups for making their simulations available for this analysis. The PyVAR-CAMS modeling results were funded through the Copernicus Atmosphere Monitoring Service, implemented by ECMWF on behalf of the European Commission, and were generated using computing resources from LSCE. Hanqin Tian and Shufen Pan acknowledge computational and administrative support from the Center for Earth System Science and Global Sustainability, Schiller Institute for Integrated Science and Society at Boston College. Josep G. Canadell thanks the Australian National Environmental Science Program – Climate

Systems Hub for supporting the GHG budget activities of the Global Carbon Project (GCP), including the global and regional N₂O budget work. We are grateful to the EDGAR team (Monica Crippa, Diego Guizzardi, Edwin Schaaf, Marilena Muntean, Efisio Solazzo, Federico Pagani, and Manjola Banja) for the work needed to publish the EDGARv7.0 Global Greenhouse Gas Emissions dataset (https://edgar.jrc.ec.europa.eu/dataset_ghg70, last access: 10 February 2022). Atul K. Jain thanks Shijie Shu and Tzu-Shun Lin for their involvement in developing and analyzing the ISAM model products used here. Giulia Conchedda, Griffiths Obli-Layrea, and Nathan Wanner contributed significant efforts to the generation of fertilizer, livestock, and soil nutrient data that underlie FAO's estimates of N₂O data.

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

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

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