Introduction
Nitrous oxide (N2O) acts as the third most important greenhouse gas (GHG)
after carbon dioxide (CO2) and methane (CH4), contributing greatly to the current radiative forcing (Myhre et al., 2013). Nitrous
oxide is also the most long-lived reactant, resulting in the destruction of
stratospheric ozone (Prather et al., 2015; Ravishankara et al., 2009). The
atmospheric concentration of N2O has increased from 275 to 329 parts per
billion (ppb) since the preindustrial era until 2015 at a rate of
approximately 0.26 % per year, as a result of human activities (Davidson,
2009; Forster et al., 2007; NOAA2006A, 2016). The human-induced N2O emissions
from the terrestrial biosphere have offset about 50 % of terrestrial
CO2 sink and contributed a net warming effect on the climate system
(Tian et al., 2016). In the contemporary period, anthropogenic N2O
emissions are mainly caused by the expansion of agricultural land area and an increase in nitrogen (N) fertilizer application, as well as industrial
activities, biomass burning, and indirect emissions from reactive N (Galloway
et al., 2004; Reay et al., 2012). Human-induced biogenic N2O emissions
were calculated by subtracting the preindustrial emissions (Tian et al.,
2016), even though a small amount of anthropogenic N2O emissions was
present before 1860, which was estimated as being 1.1 Tg N yr-1 for 1850 by
Syakila and Kroeze (2011) and 0.7 (0.6–0.8) Tg N yr-1 (including
anthropogenic biogenic emissions from soils and biomass burning) for 1860 by
Davidson (2009). Therefore, it is necessary to provide a robust reference of
preindustrial N2O emission for assessing the climate forcing of
anthropogenic N2O emission from the land biosphere.
Numerous studies have reported the sources and estimates of N2O
emission since the preindustrial era (Davidson and Kanter, 2014; Galloway
et al., 2004; Kroeze et al., 1999; Prather et al., 2012, 2015; Syakila and
Kroeze, 2011). According to the Intergovernmental Panel on Climate Change
Guidelines (IPCC, 1997), the global N2O emission evaluated by Kroeze et
al. (1999) is 11 (8–13) Tg N yr-1 (natural soils:
5.6–6.6 Tg N yr-1; anthropogenic: 1.4 Tg N yr-1), which is consistent with the
estimation from global preagricultural N2O emissions in soils (6–7 Tg N yr-1) (Bouwman et al., 1993). While taking into account the new
emission factor from the IPCC 2006 Guidelines (Denman et al., 2007), Syakila
and Kroeze (2011) conducted an updated estimate based on the study of Kroeze
et al. (1999) and reported that the global preindustrial N2O emission
is 11.6 Tg N yr-1 (anthropogenic: 1.1 Tg N yr-1; natural soils:
7 Tg N yr-1). Based on the IPCC AR5, Davidson and Kanter (2014)
indicated that the central estimates of both top–down and bottom–up
approaches for preindustrial natural emissions were in agreement at 11
(10–12) Tg N yr-1, including natural emission from soils at 6.6
(3.3–9.0) Tg N yr-1 (Syakila and Kroeze, 2011). Prather et al. (2015) provided an estimate of the preindustrial emissions (total natural
emission: 10.5 Tg N yr-1) based on the most recent study with a
corrected lifetime of 116 ± 9 years. Although these previous estimates
intend to provide a baseline of preindustrial N2O emission at a global level, information on preindustrial N2O emissions at fine
resolutions such as biome, sector or country, and regional levels remains
unknown but is needed for climate change mitigation.
Large uncertainties in the estimates of preindustrial N2O emission
could derive from different approaches (i.e., top–down and bottom–up), as
mentioned above. Nitrous oxide, as an important component of the N cycle, is
produced by biological processes such as denitrification and nitrification
in terrestrial and aquatic systems (Schmidt et al., 2004; Smith and Arah,
1990; Wrage et al., 2001). In order to accurately estimate preindustrial
N2O emissions using the process-based Dynamic Land Ecosystem Model
(DLEM; Tian et al., 2010), uncertainties associated with key parameters,
such as maximum nitrification and denitrification rates, biological N
fixation (BNF) rates, and the adsorption coefficient for soil ammonium
(NH4+) and nitrate (NO3-), were required to be
considered in model simulation. Upper and lower limits of these parameters
were used to derive a range of preindustrial N2O emissions from
terrestrial ecosystems.
In this study, the DLEM was used to simulate global N2O emission in the
preindustrial era at a resolution of 0.5∘ × 0.5∘ latitude × longitude. Since there are no observational data of
N2O emission in the preindustrial period, the estimates of natural
emission from Prather et al. (2012, 2015) were used to validate the
simulation results. In addition, site-level N2O emissions from
different natural vegetation were used to test model performance in the
contemporary period. The objectives in this study include (1) providing a
global estimation of N2O emission from terrestrial soils in 1860, (2) offering
the continental-, biome-, and country-scale N2O emission
amounts and flux rates, and (3) discussing uncertainties in estimating
N2O budget in the preindustrial era. Finally, our estimates on global and biome scales were compared with previous estimates.
Methodology
Model description
The DLEM is a highly integrated process-based ecosystem model, which
combines biophysical characteristics, plant physiological processes,
biogeochemical cycles, vegetation dynamics, and land use to make daily,
spatially explicit estimates of carbon, nitrogen, and water fluxes and pool
sizes in terrestrial ecosystems from site and regional to global scales
(Lu and Tian, 2013; Tian et al., 2012, 2015). The DLEM is
characterized of cohort structure, multiple soil layer processes, coupled
carbon, water, and nitrogen cycles, multiple GHG emissions simulation,
enhanced land surface processes, and dynamic linkages between terrestrial
and riverine ecosystems (Liu et al., 2013; Tian et al., 2010, 2015). The
previous results of GHG emissions from DLEM simulations have been validated
against field observations and measurements at various sites (Lu and Tian,
2013; Ren et al., 2011; Tian et al., 2010, 2011; Zhang et al., 2016). The
estimates of water, carbon, and nutrient fluxes and storage were also
compared with the estimates from different approaches on regional,
continental, and global scales (Pan et al., 2014; Tian et al., 2015; Yang
et al., 2015). Different soil organic pools and calculations of
decomposition rates have been described in Tian et al. (2015). The decomposition
and nitrogen mineralization processes in the DLEM have been described in
previous publications (Lu and Tian, 2013; Yang et al., 2015).
The N2O module
Previous work provided a detailed description of trace gas modules in the
DLEM (Tian et al., 2010). However, both denitrification and nitrification
processes have been modified based on first-order kinetics (Chatskikh et
al., 2005; Heinen, 2006).
In the DLEM, the N2O production and fluxes are determined by soil
inorganic N content (NH4+ and NO3-) and
environmental factors, such as soil texture, temperature, and moisture:
FN2O=(Rnit+Rden)F(Tsoil)(1-F(Qwfp)),
where FN2O is the N2O flux from soils to the atmosphere
(g N m2 d-1), Rnit is the daily nitrification rate
(g N m2 d-1), Rden is the daily denitrification rate
(g N m2 d-1), F(Tsoil) is the function of daily soil
temperature on nitrification process (unitless), and F(Qwfp) is
the function of water-filled porosity (unitless).
Global potential natural vegetation map used by DLEM in the
preindustrial era. BNEF: boreal needleleaf evergreen forest; BNDF: boreal
needleleaf deciduous forest; TBDF: temperate broadleaf deciduous forest;
TBEF: temperate broadleaf evergreen forest; TNEF: temperate needleleaf
evergreen forest; TNDF: temperate needleleaf deciduous forest; TrBDF:
tropical broadleaf deciduous forest; TrBEF: tropical broadleaf evergreen
forest; Dshrub: deciduous shrubland; Eshrub: evergreen shrubland.
The spatial distribution of cropland area in 1860.
The comparison of the DLEM-simulated N2O emissions with field
observations. All sites are described in the Supplement (Table S1).
Nitrification, a process converting NH4+ into NO3-, is
simulated as a function of soil temperature, moisture, and soil
NH4+ concentration:
Rnit=knitF(Tsoil)F(ψ)CNH4,
where knit is the daily maximum fraction of NH4+ that is
converted into NO3- or gases (d-1), F(ψ) is the soil
moisture effect (unitless), and CNH4 is the soil NH4+
content (g N m-2). Unlike in Chatskikh et al. (2005), who set
knit to 0.10 d-1, knit varies with different
plant functional types (PFTs) in the DLEM with a range of 0.04 to
0.15 d-1. The detailed calculations of F(Tsoil) and F(ψ) have been described in Pan et al. (2015) and Yang et al. (2015).
Denitrification is the process that converts NO3- into three types of
gases, namely, nitric oxide, N2O, and dinitrogen. The denitrification
rate is simulated as a function of soil temperature, water-filled porosity,
and NO3- concentration CNO3 (g N g-1 soil):
Rden=αF(Tsoil)F(Qwfp)FN(CNO3),
where FN(CNO3) is the dependency of the
denitrification rate on NO3- concentration (unitless) and α is
the maximum denitrification rate (g N m-2 d-1). The detailed
calculations of F(Qwfp), FN(CNO3), and
α have been described in Yang et al. (2015).
In each grid cell, there are four natural vegetation types and one crop
type. The sum of N2O emission in each grid d-1 is calculated by
the following formula:
E=∑i=162481∑j=15Nij×fij×Ai×106/1012,
where E is the daily sum of N2O emission from all PFTs in total grids (Tg N yr-1 d-1); Nij (g N m-2) is
the N2O emission in the grid cell ifor PFTj; fij is the fraction of
cell used for PFT j in grid cell i; and Ai (km2) is the area of the
ith grid cell. The factor 106 converts square kilometers to square meters, and 1012 converts grams to teragrams.
Input datasets
Input data to drive the DLEM simulation include static and transient data
(Tian et al., 2010). Several additional datasets were generated to better
represent the terrestrial environment in the preindustrial period as
described below. The natural vegetation map was developed based on LUH (Land
Use Harmonization; Hurtt et al., 2011) and a new joint 1 km global land
cover product (SYNMAP) (Jung et al., 2006), which rendered the fractions of
47 vegetation types in each 0.5∘ grid. These 47 vegetation types were
converted to 15 PFTs used in the DLEM through a cross-walk table (Fig. 1).
Cropland distribution in 1860 was developed by aggregating the 5 arcmin
resolution HYDE (History Database of the Global Environment) v3.1 global
cropland distribution data (Fig. 2). Half-degree daily climate data
(including average, maximum, minimum air temperature, precipitation, relative
humidity, and shortwave radiation) were derived from CRU-NCEP (Climate
Research Unit – National Centers for Environmental Prediction) climate
forcing data (Wei et al., 2014). As a global climate dataset was not
available prior to the year 1900, long-term average climate datasets from
1901 to 1930 were used to represent the initial climate state in 1860. The
nitrogen deposition dataset was developed based on the atmospheric chemistry
transport model (Dentener, 2006) constrained by the EDGAR(Emission Database
for Global Atmospheric Research)-HYDE nitrogen emission data (Aardenne et
al., 2001). The nitrogen deposition dataset provided interannual variations
in NHx–N and NOy–N deposition rates. The manure nitrogen
production during 1961–2014 was derived by integrating the national level
livestock population from FAO (http://faostat.fao.org) and the default
N excretion rate for different livestock from IPCC 2006 Tier 1 (Zhang et al.,
2017). Estimates of manure production from 1860 to 1960 were
retrieved from the global estimates in Holland et al. (2005).
Model simulation
The implementation of the DLEM simulation includes three steps:
(1) equilibrium run, (2) spin-up run, and (3) transient run. In this study,
we first used a land use and land cover (LULC) map for 1860, long-term mean
climate during 1901–1930, N input datasets for 1860 (the concentration
levels of N deposition and manure application rate), and atmospheric CO2
in 1860 to run the model to an equilibrium state. In each grid, the
equilibrium state was assumed to be reached when the interannual variations
in carbon, nitrogen, and water storage were less than 0.1 g C m-2,
0.1 g N m-2 and 0.1 mm, respectively, during two consecutive periods
of 50 years. After the model reached equilibrium state, the model was spun up
by the de-trended climate data from 1901 to 1930 to eliminate system
fluctuation caused by the model mode shift from the equilibrium to transient
run (i.e., three spins with 10-year climate data each time). Finally, the
model was run in the transient mode with daily climate data, annual CO2
concentration, manure application, and N deposition inputs for 1860 to
simulate preindustrial N2O emissions. An additional description of model
initialization and the simulation procedure can be found in previous
publications (Tian et al., 2010, 2011).
Model validation
Observations of annual N2O emission accumulations (g N m-2 yr-1) were selected to compare with the simulated emissions
at different sites. As there were no field measurements in the preindustrial
era, observations during 1970–2009 were collected to test the model
performance in the contemporary period. All environmental factors (climate,
CO2 concentration, soil property, N deposition, LULC) in the exact
year were used as input datasets for N2O simulations. The selected
sites include temperate forest, tropical forest, boreal forest, savanna, and
grassland globally. As shown in Fig. 3, the simulated N2O emissions
have a good correlation with field observations (R2= 0.79, p<0.001). This indicates that the DLEM has the capacity to simulate N2O emissions in
the preindustrial era driven by environmental factors back then. Detailed information for each site can be found in Table S1 in the Supplement.
Estimate of uncertainty
In this study, uncertainties in the simulated N2O emission were
evaluated through a global sensitivity and uncertainty analysis as described
in Tian et al. (2011). Based on sensitivity analyses of key parameters that
affect terrestrial N2O fluxes, the most sensitive parameters were
identified to conduct uncertainty simulations with the DLEM. These parameters
include potential denitrification and nitrification rates, BNF rates, and the
adsorption coefficient for soil NH4+ and NO3- (Gerber et al.,
2010; Tian et al., 2015; Yang et al., 2015). The ranges of five parameters
were obtained from previous studies. Chatskikh et al. (2005) set
knit to 0.10 d-1; however, it was set to a range of 0.04 to
0.15 d-1 and varied with different PFTs in the DLEM simulations. The
uncertainty ranges of potential nitrification rates were based on previous
studies (Hansen, 2002; Heinen, 2006); the global preindustrial N fixation was
estimated to be 58 Tg N yr-1, ranging from 50 to
100 Tg N yr-1 (Vitousek et al., 2013). The spatial distribution of
BNF was based on the estimates by Cleveland et al. (1999). The potential
denitrification rate was set to an uncertainty range of 0.025–0.74 d-1
and varied with different PFTs in the DLEM. The uncertainty ranges of the
adsorption coefficient were based on the sensitivity analysis conducted in
Yang et al. (2015). Parameters used in the DLEM simulations for uncertainty
analysis were assumed to follow a normal distribution. The improved Latin
hypercube sampling (LHS) approach was used to randomly select an ensemble of
100 sets of parameters (R version 3.2.1) (Tian et al., 2011, 2015).
The spatial distribution of N2O emission in the preindustrial era.
In the DLEM, after the model reached equilibrium state, a spin-up run was
implemented using de-trended climate data from 1901 to 1930 for each set of
parameter values. Then, each set of the model was run in transient mode for 1860 to produce the result of the preindustrial N2O emissions. All
results from 100 groups of simulations are shown in Table S2. The
Shapiro–Wilk test was used on 100 sets of results to check the normality
of DLEM simulations. It turned out that the distribution is not normal (P value < 0.05, R version 3.2.1), as shown in Fig. S1 in the Supplement. Thus, the
uncertainty range was represented as the minimum and maximum value of 100
sets of DLEM simulations.
Results and discussion
Magnitude and spatial distribution of N2O emission
The global mean preindustrial soil N2O emission was 6.20 Tg N yr-1. We define
the parameter-induced uncertainty of our global estimates as a range between
the minimum (4.76 Tg N yr-1) and the maximum (8.13 Tg N yr-1) of
100 sets of DLEM simulations. The terrestrial ecosystem in the
preindustrial period acted as a source of N2O, and its spatial pattern
mostly depends on the biome distribution across the global land surface. The
spatial distribution of annual N2O emission in a 0.5∘ × 0.5∘ grid (Fig. 4) shows that strong sources were
found near the Equator, such as Southeast Asia, central Africa, and Central
America, where N2O emission reached as high as 0.45 g N m-2 yr-1. Weak N2O sources were observed in the northern areas of
North America and Asia, where the estimated N2O emission was less than
0.001 g N m-2 yr-1. Microbial activity in soils determined the
rate of nitrification and denitrification processes, which accounts for
approximately 70 % of global N2O emissions (Smith and Arah, 1990;
Syakila and Kroeze, 2011). The tropical regions near the Equator could
provide optimum temperatures and soil moistures for microbes to decompose soil
organic matter and release more NOx and CO2 into the atmosphere
(Butterbach-Bahl et al., 2013). Referring to the observational data from
field experiments and model simulations in the tropics, it has been
argued that the tropics are the main sources within the total N2O
emissions from natural vegetation (Bouwman et al., 1995; Werner et al.,
2007; Zhuang et al., 2012).
Estimated N2O emission rates (a) and emissions (b) with
uncertainty ranges at continental level in 1860. Solid line within each box
refers to the median value of N2O emission rate or amount.
(a) Estimated N2O emission rate at the biome level in 1860 with the
median value (solid line), the mean (solid dot), and the uncertainty range
of emission rates from different biomes. The emission rate in the tundra was
removed because of the extremely small value (less than 0.003 g N m-2 yr-1). (b) Estimated N2O emission (Tg N yr-1) with
uncertainty ranges and its percentage (%) at the biome level in 1860.
Preindustrial N2O emissions from natural vegetation and
croplands in different countries. 1 Mha = 104 km2.
Vegetation
Natural soils
Cropland
Total
Country
area (Mha)
(Gg N yr-1)
(Gg N yr-1)
(Gg N yr-1)
China
756.3
188
62
250
India
306.8
121
64
185
United States
913.9
296
81
377
Pakistan
65.1
5
6
11
Indonesia
174.1
181
2
183
France
52.3
7
9
16
Brazil
835.1
1017
11
1028
Canada
914.6
94
2
96
Germany
36.0
9
4
13
Turkey
74.3
17
11
28
Mexico
191.0
118
3
121
Vietnam
31.7
41
2
43
Spain
48.2
14
6
20
Russian Federation
1575.3
234
19
253
Bangladesh
12.4
2
5
7
Thailand
49.3
56
3
59
In this study, Asia is divided into two parts: Southern Asia and Northern
Asia, where the PFTs and climate conditions are significantly contrasting.
As shown in Fig. 1, tropical forest and cropland were dominant PFTs in
Southern Asia. In contrast, temperate and boreal forests were the main PFTs in
Northern Asia. The estimates of N2O emissions from seven land regions
are shown in Fig. 5. On continental scales, the N2O emission was 2.09
(1.63–2.73) Tg N yr-1 in South America, 1.46 (1.13–1.91) Tg N yr-1 in Africa, and 1.16 (0.90–1.52) Tg N yr-1 in Southern Asia.
South America, Africa, and Southern Asia accounted for 33.77, 23.60, and 18.73 %, respectively, together, which was 76.10 % of global total
emission. Europe and Northern Asia contributed 0.45 (0.32–0.66) Tg N yr-1, which was less than 10 % of the total emission.
Nitrous oxide emissions varied remarkably among different ecosystems.
Forest, grassland, shrub, tundra, and cropland contributed 76.90,
3.11, 13.14, 0.18, and 6.67 %, respectively, to the total
emission globally (Fig. 6). In different biomes, the tropics accounted for
more than half of the total N2O emission, which is comparable to the
conclusion drawn by Bouwman et al. (1993). In the preindustrial era, the
major inputs of reactive N to terrestrial ecosystems were from BNF, which
relies on the activity of a phylogenetically diverse list of bacteria,
archaea, and symbioses (Cleveland et al., 1999; Vitousek et al., 2013).
Tropical savannas have been considered “hot spots” of BNF by legume
nodules that provide the major input of available N (Bate and Gunton, 1982).
The substantial inputs of N into tropical forests could contribute to higher
amount of gaseous N losses as N2O or nitrogen gas (Cleveland et
al., 2010; Hall and Matson, 1999). In contrast, as the largest terrestrial
biome, boreal forests lack available N because the rate of BNF is
restricted by cold temperatures and low precipitation during the growing season
(Alexander and Billington, 1986). Morse et al. (2015) conducted field
experiments in northeastern North American forests. They found that
denitrification does vary coherently with patterns of N availability in
forests, and there are no significant correlations between atmospheric N deposition,
potential net N mineralization, and nitrification rates. Thus, it is
reasonable that boreal forests contributed the least amount of N2O
emission among different forests.
As shown in Fig. 2, cropland areas varied spatially. The regions with large areas of cropland were all of Europe, India, eastern China, and
the central–eastern United States. The global N2O emission from croplands
was estimated as being 0.41 (0.32–0.55) Tg N yr-1, which is about 10 times
less than the estimate reported in the IPCC AR5 (Ciais et al., 2014). As no
synthetic N fertilizer was applied to the cropland in 1860, leguminous crops
were the major source of N2O emission from croplands, most of which
were planted in the central–eastern United States (Fig. 4). Rochette et al. (2004) conducted the experiments on the N2O emission from soybean
without the application of N fertilizer. Their work was in agreement with the
suggestion that legumes may increase N2O emissions compared with
non-BNF crops (Duxbury et al., 1982) The background emission from
ground-based experiments was as high as 0.31–0.42 kg N ha-1 in Canada
(Duxbury et al., 1982; Rochette et al., 2004).
Preindustrial N2O emission at country level could serve as a reference
for calculating human-induced N2O emission in today's nations. We
estimated preindustrial N2O emissions from 17 countries that
are hot spots of N2O sources in the contemporary period (Table 1).
The order of countries was based on Gerber et al. (2016) that indicated
the top 17 countries in terms of total N application in 2000.
Preindustrial N2O emissions from natural soils and croplands varied
significantly on country scales. The United States, China, and India were
the top countries accounting for emissions from preindustrial croplands.
Countries close to or located in the tropics, such as Mexico, Indonesia, and
Brazil, accounted for negligible emissions from croplands but a substantial
amount from natural vegetation in the preindustrial era. Previous studies
indicated that agriculture produces the majority of anthropogenic N2O
emissions (Ciais et al., 2014; Davidson and Kanter, 2014). Our estimate on country scales could be used as a reference to quantify the net increase
in N2O emissions from agriculture activities in countries of hot
spots.
There is a debate about whether the natural wetlands and peatlands act as sinks or
sources of N2O. Previous studies showed that N2O emissions from
natural peatlands are usually negligible; however, the drained peatlands
with lower water tables might act as sources of N2O (Augustin et al.,
1998; Martikainen et al., 1993). High water tables in wetlands might block
the activity of nitrifiers and limit the denitrification (Bouwman et al.,
1993). The fluxes of N2O were negligible in the pelagic regions of
boreal ponds and lakes due to the limitation of nitrification and/or nitrate
inputs (Huttunen et al., 2003). Couwenberg et al. (2011) mentioned that
N2O emissions always decreased after rewetting when conducting field
experiments, which had been excluded from their future analysis of GHG
emissions in peatlands. Hadi et al. (2005) pointed out that tropical
peatlands ranged from sources to sinks of N2O, highly affected by
land use and hydrological zone. We were unable to examine N2O fluxes
from wetlands and peatlands in 1860 as human-induced land use in those
ecosystems was unknown. Thus, we excluded the N2O emissions from
wetlands and peatlands in this study.
Revisiting preindustrial global N2O emission by incorporating top–down
estimates
The “top–down” methodology used to estimate N2O emissions is based on
atmospheric measurements and inversion modeling (Thompson et al., 2014).
Prather et al. (2012) provided an estimate of
9.1 ± 1.0 Tg N yr-1 of natural emissions in the preindustrial
era using observed preindustrial abundances of 270 ppb and model estimates
of lifetime decreases from 142 years in the preindustrial era to
131 ± 10 years in the present day. Later, Prather et al. (2015)
reevaluated N2O lifetime based on Microwave Limb Sounder satellite
measurements of the stratosphere, which were consistent with modeled values
in the present day. The lifetime in the preindustrial era and the present day
was estimated to be 123 and 116 ± 9 years, respectively. The current
lifetime increases the preindustrial natural emission from 9.1 ± 1.0 to
10.5 Tg N yr-1.
Natural sources for N2O include soil under natural vegetation, oceans,
and atmospheric chemistry (Ciais et al., 2014). The emission from atmospheric
chemistry was estimated as being 0.6 with an uncertainty range of
0.3–1.2 Tg N yr-1. Syakila and Kroeze (2011) estimated global
natural emissions from oceans as 3.5 Tg N yr-1. Oceanic emission was
estimated as being 3.8 with an uncertainty range of
1.8–5.8 Tg N yr-1 in the IPCC AR4. However, the uncertainty range
became larger (1.8–9.4 Tg N yr-1) in the IPCC AR5. In our study, the
simulated N2O emission came from agricultural and natural soils. The
natural emission was estimated as being 5.78 (4.4–7.72) Tg N yr-1.
Combining the atmospheric chemistry and the ocean emissions in the IPCC AR5
with the natural emissions from our study, the global total natural N2O
emissions were 10.18 (6.5–18.32) Tg N yr-1. The large uncertainty
range was attributed to the uncertainty from oceanic emission, atmospheric
chemistry emission, and our estimation. The estimated global total amount
(10.18 Tg N yr-1) in this study was comparable to the estimate
(10.5 Tg N yr-1) by Prather et al. (2015) using the top–down
approach.
Comparison with estimates using the bottom–up methodology
The “bottom–up” approach includes the estimations based on inventory,
statistical extrapolation of local flux measurements, and process-based
modeling (Tian et al., 2016). The global preagricultural N2O emission
was estimated as being 6.8 Tg N yr-1 based on the regression
relationship between measured N2O fluxes and modeled N2O production
indices (Bouwman et al., 1993). This estimate was adopted to retrieve the
trends of atmospheric N2O concentration in Syakila and Kroeze (2011). In
our study, the preindustrial N2O emission from natural vegetation was
estimated as being 5.78 (4.4–7.72) Tg N yr-1, which is about
1 Tg N yr-1 lower than the estimate from Bouwman et al. (1993). An
estimate from the tropics (±30∘ of the Equator) was about
4.57 Tg N yr-1, which is 0.83 Tg N yr-1 lower than the
estimate from Bouwman et al. (1993). For the rest of natural vegetation, our
estimate was 1.21 Tg N yr-1, which is close to 1.4 Tg N yr-1
estimated in Bouwman et al. (1993).
Although Bouwman et al. (1993) has studied the potential N2O emission
from natural soils, our study provided a first estimate of spatially
distributed N2O emission in 1860 using the biogeochemical process-based
model. Bouwman et al. (1993) provided 1∘ × 1∘
monthly N2O emission using the monthly controlling factors without
considering the impact of N deposition. In their study, the soil fertility
and carbon content were constant for every month, which cannot reflect the
monthly dynamic changes of carbon and N pools in natural soils. Moreover,
although their study represented a spatial distribution of potential N2O
emission from natural soils, they did not provide that estimate on biome,
continent, and country scales. Thus, their result was hardly to be used as a
regional reference for the net human-induced N2O emissions from some hot
spots, such as Southern Asia. In contrast, in our study, using a daily
climate and N deposition dataset better reflects the real variation in
N2O emission through the growing season in natural ecosystems. The
comparison with field observations during 1997–2001 indicated that the DLEM
can catch the daily peak N2O emissions in Hubbard Brook Forest (Tian et
al., 2010) and Inner Mongolia (Tian et al., 2011).
Regarding the N2O emission from croplands, our estimate is comparable
to the estimate of 0.3 (0.29–0.35) Tg N yr-1 extracted from Syakila
and Kroeze (2011) by digitizing graphs using the Getdata Graph Digitizer. In
their study, the estimation was based on the relationship between the crop
production and human population during 1500–1970. In contrast, the result
in our study was estimated based on the cropland area of a specific crop type (mainly soybean, rice, corn, and wheat) in 1860.
Thus, the DLEM is capable of providing an estimate of N2O emission from
natural ecosystems on regional and biome scales with a higher spatial
resolution. This could be a useful reference for quantifying the effects of
human activities – such as LULC change, N fertilizer and manure
application, and, increasingly, atmospheric N deposition – on N2O emissions
in different terrestrial ecosystems or sectors in the contemporary period.
The N2O budget in the preindustrial era
The observed N2O concentration is the result of dynamic production and
consumption processes in soils as soils act as sources or sinks of N2O
through denitrification and nitrification (Chapuis-Lardy et al., 2007).
There was a slight increase in atmospheric N2O concentration during
1750–1860 according to the ice core records, but these showed a rapid increase
from 1860 to present (Ciais et al., 2014). Natural sources of N2O
emissions have been discussed in Sect. 3.2 and 3.3. Previous studies
found that there were some anthropogenic N2O emissions along with the
natural sources in the preindustrial era (Davidson, 2009; Syakila and
Kroeze, 2011). Syakila and Kroeze (2011) found that anthropogenic N2O
emission began in 1500 because of biomass burning and agriculture.
The total anthropogenic N2O emission in their study was estimated as being
1.1 Tg N in 1850. In addition, Davidson (2009) derived a time-course
analysis of sources and sinks of atmospheric N2O since 1860. The
preindustrial anthropogenic N2O sources in his study included biomass
burning, agriculture (e.g., manure and fertilizer application and the
cultivation of legumes) and human sewage, the sum of which was 0.7
(0.6–0.8) Tg N yr-1 (Davidson, 2009). Thus, anthropogenic N2O
emission already existed in 1860, but it was smaller than the contemporary amount.
Davidson (2009) mentioned that there was possibly a certain amount of
N2O loss in the preindustrial period through atmospheric sink and the
reduced emission from tropical deforestation. He estimated the anthropogenic
sink as being 0.26 Tg N in 1860. In addition, the deforestation of tropical forest
may have caused a loss of N2O emissions in 1860, which was estimated as being 0.03 Tg N (Davidson, 2009). However, studies have shown that the
conversion of forest to pasture and cropland could increase or have no
effect on N2O emissions because the effects depended on the disturbance
intensity of human activities on soil conditions (van Lent et al., 2015).
For instance, N2O emissions tended to increase during the first 5–10
years after conversion and thereafter might decrease to average upland
forest or low canopy forest levels in the non-fertilized croplands and
pastures. In contrast, emissions were at a high level during and after
fertilization in fertilized croplands (van Lent et al., 2015). Thus, more
work is needed to study how forest degradation affects N2O fluxes
(Mertz et al., 2012).
Future research needs
Large uncertainty still exists in the DLEM simulation associated with the
quality of input datasets and parameters applied in simulations. Although
input datasets could play a significant role in the variety of the model
output, it is difficult to obtain accurate datasets going back to the year 1860.
Average climate data from 1901 to 1930 were used to run the model simulation,
which could raise the uncertainty in estimating N2O emission in 1860.
The datasets of LULC, N deposition, and manure application in 1860 could
introduce uncertainties into this estimate. The average oceanic and
atmospheric chemistry emissions cited from the IPCC AR5 could introduce uncertainty into the calculation of the total natural emissions in 1860 when
compared with the estimate done by Prather et al. (2015). Thus, a more
accurate estimate of oceanic N2O emissions is significant for narrowing the
confidence estimate of the preindustrial terrestrial sources. The N2O
fluxes from wetlands and peat need to be included in any future study.