Global ammonia (NH3) emission is expected to
continue to rise due to intensified fertilization for growing food to
satisfy the increasing demand worldwide. Previous studies have focused mainly on
estimating the land-to-atmosphere NH3 injection but seldom addressed
the other side of the bidirectional nitrogen exchange – deposition.
Ignoring this significant input source of soil mineral nitrogen may lead to
an underestimation of NH3 emissions from natural sources. Here, we used
an Earth system model to quantify NH3-induced changes in atmospheric
composition and the consequent impacts on the Earth's radiative budget and
biosphere as well as the impacts of deposition on NH3 emissions from
the land surface. We implemented a new scheme into the Community Land Model
version 5 (CLM5) of the Community Earth System Model version 2 (CESM2) to
estimate the volatilization of ammonium salt (NH4+) associated
with synthetic and manure fertilizers into gaseous NH3. We further
parameterized the amount of emitted NH3 captured in the plant canopy to
derive a more accurate quantity of NH3 that escapes to the atmosphere.
Our modified CLM5 estimated that 14 Tg N yr-1 of global NH3
emission is attributable to fertilizers. Interactively coupling terrestrial
NH3 emissions to atmospheric chemistry simulations by the Community
Atmospheric Model version 4 with chemistry (CAM4-chem), we found that such
emissions favor the formation and deposition of NH4+ aerosol,
which in turn influences the aerosol radiative effect and enhances soil
NH3 volatilization in regions downwind of fertilized croplands. Our
fully coupled simulations showed that global-total NH3 emission is
enhanced by 3.3 Tg N yr-1 when 30 % more synthetic fertilizer is
used compared to the 2000-level fertilization. In synergy with observations
and emission inventories, our work provides a useful tool for stakeholders
to evaluate the intertwined relations between agricultural trends, fertilizer
use, NH3 emission, atmospheric aerosols, and climate so as to derive
optimal strategies for securing both food production and environmental
sustainability.
Introduction
Global NH3 emission has risen from 59 to 65 Tg N yr-1 during
2000–2008, driven mainly by the increasing fertilizer use and manure
handling in farms and animal operations
(Sutton et al., 2013). After
entering the atmosphere, NH3 gas readily neutralizes sulfuric acid
(H2SO4) and nitric acid (HNO3), which are derived from the
oxidation of sulfur dioxide (SO2) and nitrogen oxides (NOx),
forming inorganic sulfate–nitrate–ammonium (SNA) aerosols
(Behera
and Sharma, 2012). These secondary ammonium (NH4+) aerosols can
constitute 25 %–75 % of inorganic fine particulate matter (PM2.5,
particles with an aerodynamic diameter <2.5µm)
(Ianniello
et al., 2011; Snider et al., 2016), which causes not only haze and smog that
lower visibility, but also respiratory and cardiovascular diseases that harm
human health
(Tie
and Cao, 2009; Xing et al., 2016; Yang et al., 2019). In 2010 alone, an
estimated 2.6 million premature deaths were associated with PM2.5
pollution (Wang
et al., 2017). Without proper controls, unbridled use of fertilizer to boost
food production for the fast-growing population can further enhance global
agricultural NH3 emissions by ∼ 12 % in 2050 compared
to the 2010 level, posing an even greater health risk via PM2.5
formation (Bodirsky et al., 2014). The global
public health system may have to spend USD 20–290 billion more each year to
compensate for the NH3-derived detrimental effects on air quality and
health
(Gu
et al., 2012; Paulot and Jacob, 2014; Guthrie et al., 2018).
Excessive atmospheric NH3 also threatens ecosystems. The highly soluble
NH3 gas and aerosol NH4+ (together known as NHy)
eventually return to the Earth's surface via dry and wet deposition, thus
modifying the terrestrial nitrogen cycle. NHy deposited on canopy
foliage can be taken up and become readily available to promote
photosynthesis (Wortman et al., 2012),
but if highly concentrated it can also injure plant tissues and suppress
biomass growth
(Fangmeier
et al., 1994; Krupa, 2003). Though NHy deposition can enrich soil
nutrients, it also brings several adverse effects, including soil
acidification and forest biodiversity loss
(Tian and Niu,
2015; Lu et al., 2008). Nitrifying bacteria often oxidize soil
NH4+ in excess, and the resulting NO3-, which is prone
to leaching, can lower soil nutrient content as well as contaminate
groundwater, streams, rivers, and coastal waters, causing eutrophication
(Lin et al.,
2001; Beeckman et al., 2018). NHy directly falling onto natural waters
is potentially toxic to aquatic life, even in low concentrations, and can
deteriorate marine biodiversity
(Zhang
and Liu, 1994; Shou et al., 2018).
The severity of the aforementioned consequences of excessive reactive
nitrogen in the environment has called for better management of these
compounds, including better monitoring and mitigation of agricultural
NH3. In the recent decade, the space-based Infrared Atmospheric
Sounding Interferometer (IASI) has been deployed to gauge atmospheric
NH3 concentration within air columns
(Clarisse et al., 2009). This new ensemble of
satellite observations offers significant progress to address previous
observational deficiencies and allows daily monitoring of global NH3
distribution
(Clarisse
et al., 2010; Van Damme et al., 2014). Continued refinement in retrieval
schemes and incorporation of machine-learning techniques have further
improved the sensitivity and reliability of measured NH3 concentrations
(Van Damme et al., 2017). It enables the
creation of high-resolution maps of atmospheric NH3 and the possibility
of pinpointing industrial and agricultural emission hotspots with diameters
smaller than 50 km (Van Damme et al., 2018a). The works of Van Damme et al. (2018a) have provided valuable datasets not only for monitoring agricultural
emissions but also for benchmarking and improving emission inventories and
numerical models.
NH3 emission inventories are generally compiled by surveyed activity
data and empirical emission factors associated with primary sources
including animal populations, synthetic nitrogen fertilizers, biomass
burning, and natural sources. A 1∘×1∘
inventory, which was among the first back then, estimated a global emission
of 54 Tg N yr-1 for 1990, of which 34 Tg N yr-1 is agricultural,
excluding field burning, and 2.4 Tg N yr-1 from natural soil
(Bouwman
et al., 1997). Since then, much effort has been put into refining the
estimation of anthropogenic emissions. Recent inventories adjusted the
estimated agricultural emissions (including manure management and both
synthetic and manure fertilizers) in 2000–2010 to 33–37 Tg N yr-1
(Sutton
et al., 2013; Janssens-Maenhout et al., 2015; Hoesly et al., 2018). One of
the state-of-the-art inventories, the Emissions Database for Global
Atmospheric Research (EDGAR) version 4.3.2, provides global anthropogenic
emission estimates in 1∘×1∘
resolution for the period 1970–2012
(Crippa et al.,
2018). The accuracy of these inventories is not only affected by the
integrity of the activity data surveyed but also constrained by the
suitability of emission factors. Simply adopting emission factors from other
countries may result in biases because of regional differences in
technologies, farming practices, climate, and soil conditions
(Huang et al.,
2012). This pitfall has motivated the development of other national and
regional inventories in the US (e.g., US Environmental
Protection Agency, 2014), China (e.g., Zhang et al., 2018), and Europe (e.g.,
European Environment Agency, 2013). These emission inventories
are useful tools for source apportionment and input data for forward models,
but as the NH3 emissions are prescribed they do not respond to changes
in, for example, nitrogen deposition and meteorology, making them insufficient for
models to represent the full dynamics of the NHy cycle.
The global NHy cycle has proven to be challenging to study because of
the various feedback mechanisms within the Earth system. The reactive nature
of NH3 and the contribution of deposited NH4+ to the
re-emission of NH3 from natural and agricultural soils have created a
convoluted relationship between emissions and deposition. NH4+
particles can be transported along with airflows and dispersed across a more
extensive geographical range than the highly reactive gaseous NH3. Such
transport can introduce large heterogeneity in the spatial distribution of
reactive nitrogen, rendering it not only a local but also a pan-regional problem (Asman et al., 1998). Moreover, NH3
volatilization is a temperature-dependent process, while the presence of
atmospheric NH3 affects the composition of aerosols and their radiative
forcing, thus in turn modifying the Earth's surface energy budget
(Ansari and Pandis, 1998).
In this study, we hence aim to enable modeling of the land–atmosphere
bidirectional exchange of NHy so that we can quantify the dynamically
evolving NHy cycle and feedback mechanisms associated with it under a
changing environment. We employed the Community Earth System Model version 2
(CESM2), which has state-of-the-art model components representing the land,
atmosphere, sea ice, and oceans. These sub-models can run independently or
in various coupled configurations
(Hurrell et al.,
2013). Many studies have employed CESM for studying processes in both the
atmospheric and terrestrial nitrogen cycles, e.g., NOx and N2O
emission
(Saikawa
et al., 2013, 2014; Zhao et al., 2017), deposition
(Lamarque
et al., 2013), denitrification and nitrate leaching
(Nevison et al., 2016), crop nitrogen uptake
(Levis et al., 2018), and
reactive nitrogen input to the ecosystem associated with synthetic and manure
fertilizers
(Riddick
et al., 2016; Vira et al., 2020, 2021). Yet, these studies did not consider
the dynamic bidirectional transfer of NH3 and NH4+ between
the land and atmosphere. To add the dynamic cycle of NHy back to CESM2,
we adopted a process-based approach to parameterize NH3 emission from
cropland soils, which is different from the bidirectional
“voltage resistance” models
(Zhu
et al., 2015; Riddick et al., 2016; Pleim et al., 2019; Vira et al., 2020).
Our approach determines the multistage processes of soil NH4+ to
NH3, including adsorption, dissociation, and volatilization. The
process-based nature of this scheme allows us to evaluate the response of
NH3 emission to soil climate, soil nitrogen content, fertilization,
deposition, competition against other soil biogeochemical processes
(nitrification, microbial uptake, etc.), and vegetation growth. Comparing to
other approaches, our scheme, which borrowed from a biogeochemical model,
DeNitrification–DeComposition (DNDC), requires variables that are mostly
already modeled in CLM5, allowing us to largely capture the dynamic nature
of NH3 emission. We also developed a prognostic parameterization for
canopy capture of NH3 instead of using a fixed generic value, e.g.,
one constant canopy reduction factor for all plants as used in some other
studies
(e.g.,
Riddick et al., 2016; Bouwman et al., 1997). Implementing these new schemes
in the Community Land Model version (CLM5)
(Lawrence et al., 2019), we could
then estimate the emission associated with fertilizer use and perform fully
coupled simulations with the Community Atmosphere Model version 4 with
Chemistry (CAM4-chem)
(Lamarque et al.,
2012) that allow two-way exchange of NHy bridged by online emission and
deposition to understand the subsequent effects on aerosol formation,
climate, terrestrial ecosystems, and crop growth. We also compared our
results with available emission inventories to evaluate model accuracy and
uncertainty.
This paper demonstrates a framework that could help unfold the complicated
interactions between fertilizer use, NH3 emission, aerosol formation,
climate, terrestrial ecosystems, and crop production. For instance, a recent
study based on our model framework showed that enhanced nitrogen deposition
induced by future fertilize use could modify the meteorological environment
via changes in vegetation and soil biogeochemistry and modulate future
ozone pollution (Liu et al.,
2021).
MethodsCommunity Earth System Model
We introduced new functionalities into CESM2 to enable the simulation of a
coupled land–atmosphere nitrogen cycle and to further investigate the
impacts of fertilizer-induced NH3 emission on atmospheric composition,
terrestrial biogeochemistry, and climate change. In particular, we
implemented into CLM5 new parameterization schemes to quantify NH3
volatilized from soil due to fertilizer application and captured by plant
canopies. We further bridged CLM5 and CAM4-chem to enable two-way exchange
of soil NH3 emission and deposition of NH4+ to model a fully
coupled, prognostic land–atmospheric NHy cycle.
Our model development was based on CLM5 with active biogeochemical cycles
and a crop sub-model (CLM5-BGC-Crop, or CLM5 for short), which represents
terrestrial carbon and nitrogen cycling with prognostic vegetation and crop
growth. The model uses a sub-grid hierarchy (from grid cells, land units,
columns, to patches) to capture the biogeophysical and biogeochemical
differences between various land types within a model grid cell. In
particular, CLM5 handles natural soil and croplands differently: multiple
natural vegetation patches are configured to occupy a single unmanaged soil
column sharing a single pool of nutrients, while each crop patch has a
dedicated column. Such a setting allows no resource competition between
natural vegetation and crops, nor among crops
(Drewniak et al., 2013). There are 16
types of natural vegetation (including bare ground) and 8 active crops
(temperate soybean, tropical soybean, temperate corn, tropical corn, spring
wheat, cotton, rice, and sugarcane) in this model
(Lombardozzi et al., 2020).
Vegetation and crops are represented by plant functional types (PFTs), each
having specific ecophysiological, phenological, and biogeochemical parameters
(Levis et al., 2018).
Default PFT distributions of natural vegetation and crops are derived from
satellite observations (e.g., MODIS) and agricultural census data
(Lawrence
and Chase, 2007; Portmann et al., 2010). The beginning of plant growth
stages (seedling, leaf emerging, and grain filling), as well as crop sowing
dates and planting durations, is controlled by cumulative warm-enough hours
at the beginning of spring. Crops obtain nutrients from the soil mineral
nitrogen pool, which is supplied by nitrogen deposition and fertilization.
Fertilizer is applied to each patch for 20 consecutive days evenly when the
crops enter the leaf emergence phase. Synthetic fertilizer input was
prescribed by crop type and country at the 2000 level based on Land-Use
Harmonization (LUH2) fertilization rates
(Hurtt
et al., 2011). The manure fertilizer application rate is assumed constant for
all crops at 2 g N m-2 yr-1, the same as the model default
(Lombardozzi et al., 2020).
All added depositional and fertilizer N is added to the soil
NH4+ pool of each layer from ground surface to 0.4 m underground
according to a model-defined soil profile (Table S1)
(Lawrence et al., 2018). Crops are harvested once they
reach maturity or after a predefined maximum number of growing days (typically 150–165 d)
(Lawrence et al., 2018).
In addition to the NH3 schemes, we also modified CLM5 to better
simulate the terrestrial nitrogen cycle, specifically on the emission of
NOx from denitrification and nitrification and microbial
mineralization of soil nitrogen. These modifications are documented in the
Supplement.
Soil ammonia emission and canopy capture
Figure 1 summarizes the primary pathways of
the terrestrial nitrogen cycle in CLM5. The model tracks nitrogen content in
soil, plant, and organic matter as an array of separate nitrogen pools and
biogeochemical processes as exchange fluxes of nitrogen between these pools.
Soil mineral nitrogen, NH4+, and NO3- are competed for
among plant uptake, microbial immobilization, nitrification, and
denitrification based on the relative demand from each process. Release of
nitrous oxide (N2O) and NOx as byproducts of nitrification and
denitrification and leaching of soil nitrate also deplete soil
NH4+ and NO3-, which can be replenished by fertilization
and deposition of atmospheric NHy and NOx. The deposition rates
were prescribed in the default configuration and dynamically computed by
CAM4-chem in our version. Other sources of soil mineral nitrogen include
biological fixation by microbes or soybean and decomposition of plant litter
and soil organic matter. Our proposed NH3 emission scheme was borrowed
from a standalone biogeochemical model, DNDC version 9.5
(Li
et al., 2012; Gilhespy et al., 2014; source code of DNDC v9.5 provided by
Changsheng Li via personal communication on 18 June 2015), which has
been extensively used for studying agricultural NH3 emission
(e.g.,
Li et al., 2012; Balasubramanian et al., 2015, 2017; Zhang and Niu, 2016).
Major pathways modeled by CLM5 nitrogen (N) cycle. Blue
arrows indicate N entering the soil N nitrogen pool, while orange arrows are
for leaving. The default model tracks only N pools in boxes enclosed by
solid lines but not those with dashed lines. N contents in crop tissues are
modeled as pools inside the green regions. The red arrow indicates the
missing pathway of NH3 volatilization in the default model. Nitrogen
gas (N2) emitted by denitrification is not shown. SOM denotes soil
organic matter.
Following the treatment in DNDC, our scheme considered NH3
volatilization a multistage process and estimated the potential soil
NH3 prone to emission (Fsoil,pot; g N m-3) based on soil
NH4+ content. In each soil layer of a column or patch
Fsoil,pot=NH4soil+1-fadsfdisfvol,
where [NH4,(soil)+] (g N m-3) is the amount of soil
NH4+, fads accounts for the portion of NH4+ adsorbed
onto the surface of the soil matrix, fdis is the fraction of the
non-adsorbed NH4+ that dissociated into aqueous NH3, and
fvol is the fraction of aqueous NH3 volatilized as gaseous
NH3. The adsorbed fraction fads, which is also bounded between 0 and
1, is given by Li et al. (1992) and Dutta et
al. (2016):
fads=0.99(7.2733fclay3-11.22fclay2+5.7198fclay+0.0263),
where fclay is the soil clay fraction as prescribed by the CLM5 surface data
(Bonan et al., 2002); we adopted a factor of 0.99
instead of the one reported in Dutta et al. (2016),
as per the source code of DNDC.
The non-adsorbed NH4+ dissociates reversibly into aqueous
NH3 and hydrogen ions (NH4(aq)+ NH3(aq)+H+), and hence, fdis is determined by the following equations
(Li et al., 2012; Sutton,
1990; Sutton et al., 1993):
3fdis=KwKaH+4Kw=100.08946+0.03605[∘C-1]Tsoil×10-155Ka=(1.416+0.01357[∘C-1]Tsoil)×10-56H+=10-pH,
where Ka (mol L-1) and Kw (mol L-2) are dissociation
constants for NH4+andNH3 as well as hydrogen- and hydroxide-ion
equilibria, respectively; Tsoil (∘C) is soil temperature;
and [H+] (mol) is the concentration of aqueous hydrogen ion in the soil
calculated from soil pH. CLM5 is currently not capable of calculating soil
pH implicitly, so we performed our simulations using a constant pH of 6.5 for
a more focused analysis. This pH value is consistent with the value used in
the nitrification and denitrification schemes in CLM5. We further evaluated
the uncertainty induced by our choice of pH and presented the sensitivity
test results in the Supplement. Briefly, a higher pH
would promote the model NH3 emission rate exponentially as the emission
rate is of the order of 10pH. This high sensitivity warrants the need
to include crucial chemical processes in the model for accurately
determining soil pH online.
Lastly, we used this equation to calculate fvol
(Li et al., 1992; Gardner, 1965; source
code of DNDC v9.5):
fvol=1.5s1[ms-1]+sTsoil50∘C+Tsoillmax-llmax,
where s (m s-1) is surface wind speed; Tsoil (∘C) is soil
temperature; and l and lmax (both in meters) are the depth of each soil layer and
the maximum depth of a soil column, respectively. Our scheme assumes that
vaporized soil NH3 in a deeper layer diffuses upward to the surface but
does not explicitly simulate the process. Instead, it is represented in the
last term in Eq. (7) as a ratio of (lmax-l)/l for the
NH3(g) contained in each soil layer. Hence, soil NH4+ in
deeper layers is also subject to loss to NH3 volatilization but at much
slower rates than that in the upper layers. Details of the soil profile are
provided in Table S1.
The actual soil NH3 to be emitted (Fsoil,act; g N m-3) from
each soil layer is then determined as
Fsoil,act=minFsoil,pot,NH4+available,
where Fsoil,pot is from Eq. (1), and
[NH4+]available (g N m-3) is the concentration of
available soil NH4+ in the soil layer. The model distributes
available soil NH4+ to all competing processes, namely NH3
emission, plant uptake, microbial immobilization, and nitrification,
according to their relative demands (individual potential flux to sum of all
four potential fluxes) without bias toward any process
(Lawrence et al., 2019). The
column-level actual soil NH3 prone to emission (Fsoil; g N m-2) is then computed as the sum of the product of Fsoil,act and
the soil layer thickness (in meters) across all vertical layers.
Finally, assuming such NH3 is released to the atmosphere at a constant
rate over a model time step size (Δt=1800 s in this study), our
model estimates the NH3 emission flux (F˙soil; g N m-2 s-1) as
F˙soil=FsoilΔt.
If vegetation is present above the soil, some of the emitted NH3 can be
retained by the plant canopy, which is known to be related to the adsorption
of hydrophilic NH3 onto the leaf surface and molecular diffusion via
the leaf stomata (Van Hove et al.,
1987). Some studies represented the amount of captured NH3 using
constant scaling factors (e.g., 0.6 for all vegetation in
Riddick et al., 2016; 0.8, 0.5, and 0.2 for
tropical rainforests, other forests, and all other vegetation types,
respectively, in
Bouwman et
al., 1997). Here, we calculated the flux of NH3 captured by the canopy
following the equation used in DNDC that accounts for the change in
in-canopy NH3 concentration, deposition velocity of ammonia, leaf area
index (LAI), and air moisture (Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, 2017). To include the
dynamic growth of crop canopy, we further adopted the canopy height
adjustment factor employed by the Community Multiscale Air Quality (CMAQ)
regional chemical transport model (Pleim et al.,
2013). The portion of NH3 flux from the soil that is not captured by
plant canopies (F˙atm; g N m-2 s-1) is thus
10F˙atm=F˙soil1-fcan11fcan=Ls10vcφcb(htop-hbot),
where L is one-sided snow-free LAI; s10 is the wind speed (m s-1) at
10 m height; vc is the deposition velocity of NH3 (0.05 m s-1
as in DNDC); φc is in-canopy relative humidity; b is a
correction factor for the effect of canopy thickness (14 m-1 is used
here as suggested by Pleim et al., 2013); and
htop and hbot are heights of canopy top and bottom (both in meters),
respectively. Except for vc and b, all variables are calculated within
CLM5 (see Lawrence et al., 2019,
for detailed calculation methods). These two equations estimate the
concentration of NH3 exposed to plant canopy under a given soil
emission rate at each time step: dividing soil NH3 emission rate by
s10 gives an approximate in-canopy NH3 concentration, and
multiplying the latter with vc and L produces an estimated quantity of
NH3 retained by the canopy. The last three terms account for the
influence of in-canopy moisture and canopy thickness on the effectiveness of
canopy capturing. We used F˙atm as an input of the
ammonia emissions to drive chemistry calculations in CAM4-chem. The captured
NH3 can re-enter the soil surface along with water throughfall or be
metabolized by the plants if it diffuses into the leaf tissues
(Hutchinson et al., 1972). Since the detailed
mechanisms are still uncertain and beyond the focus of this study, we
decided to assume that all captured NH3 returns to the soil directly as
NH4+ and discuss how it will affect our analysis in
the “Conclusions” section.
Simulations of the land–atmosphere NHy cycle
For the atmospheric component, we employed CAM4-chem
(Lamarque et al.,
2012), with chemistry based on the tropospheric chemistry mechanism of
MOZART-4 (Emmons et al.,
2010). CAM4-chem employs a bulk aerosol approach and predicts the formation
of PM2.5 components including SO42-, NO3-, and
NH4+, where the injection rates of precursors – sulfur dioxide
(SO2), NOx, and NH3 – are prescribed by the Coupled Model
Intercomparison Project phase 6 (CMIP6), also known as the Community Emissions Data System
(CEDS) emission inventory (CMIP6 hereinafter) for anthropogenic activities
(Hoesly et al., 2018). The biomass
burning emissions used for our simulations are described by von Marle et al. (2016, 2017)
and are all assumed as surface emissions without plume rise or predefined
vertical distribution. Biogenic emissions, e.g., of isoprene, are updated
online from CLM5 using the Model of Emissions of Gases and Aerosols from
Nature (MEGAN) version 2.1
(Guenther
et al., 2012). In our coupled simulations, we substituted the portion of
NH3 emission associated with fertilizers from the CAM4-chem inventory
input (CESD) for our online simulated emission rates from CLM5. This study
did not consider manure spreading on pastures and grazing animals.
Atmospheric NH3 and NH4+ formed sequentially return to CLM5
through deposition. We note that the NOx emission inputs for CAM4-chem
were solely from the emission inventories and did not include those from our
modified denitrification and nitrification schemes.
Dry deposition in CAM4-chem is handled using the resistance approach
(Wesely,
1989; Emmons et al., 2010). For NH3 vapor, the model calculates the
aerodynamic and the boundary-layer (laminar sublayer) resistance based on
the online atmospheric dynamics, while the surface resistance over land is
determined according to the online CLM5 surface variables, e.g., canopy
height and LAI, as well as a species-specific reactivity factor for oxidation
and effective Henry's law coefficients. For particle-phase NH4+,
the aerodynamic resistance is the same as that of NH3, but the
boundary-layer and surface resistances are replaced by a single resistance
term that depends on the surface friction velocity. The deposition
velocities of NH3 and NH4+ are the reciprocal of the sum of
their corresponding resistance terms, and their deposition rates are the
product of their deposition velocities and concentrations. Wet deposition in
CAM4-chem follows the Neu and Prather (2012)
scheme, which assumes a first-order loss of chemicals due to in-cloud and
below-cloud scavenging processes. The wet deposition rates of NH3 and
NH4+ are the products of their concentration, their loss
frequencies (based on their Henry's law coefficients), and the fraction of
the grid box subject to scavenging (e.g., cloudy or raining). These NHy
deposition fluxes (together with NOx) then become the input to CLM5 for
the soil NH4+ pool (Lawrence et al., 2018).
In the default configuration, atmospheric chemistry interacts with the
climate solely through radiation in CAM4-chem
(Lamarque et al.,
2012). Furthermore, atmospheric reactive nitrogen (NH4+ or
NO3-) does not directly interact with radiative transfer in the
model. Instead, its radiative implications are manifested via altering the
gas–aqueous partitioning of sulfate
(Emmons et al.,
2010; Metzger, 2002) and the subsequent changes in direct radiative effect
due to any changes in sulfate aerosols. The subsequent sulfate-induced
changes in cloud optical properties (indirect radiative effect) were not
considered in this work. A detailed description of the radiative transfer
processes in CAM4-chem is provided in Neale et al. (2010).
Recent studies on NH3 emission using CESM2 (e.g.,
Riddick et al., 2016, and
Vira et al., 2020) focused only on the
one-way land-to-atmosphere flux of NH3 while neglecting the enhancing
effect of nitrogen deposition on NH3 emission. By coupling CLM5 and
CAM4-chem, we allowed the model land–atmosphere NHy cycle to evolve in
response to any changes in the bidirectional exchange of NH3 and
NH4+ via online emission and deposition. It also makes our method
more suitable than a one-way model for studying the feedback effects of
future changes in climate and agricultural activities on the biogeochemical
cycles.
Table 1 provides configuration details of our
experiments. [CAM4_CLM5_2000] and
[CAM4_CLM5_2050] encapsulated the full
functionality of our implementation, i.e., CAM4-chem receives the online
CLM5 NH3 emission rates as input to predict atmospheric NH3
concentration, the subsequent formation of secondary ammonium aerosols
(modeled as changes in sulfate aerosols in the model), and the corresponding
instantaneous sulfate aerosol radiative effect, whilst CLM5 obtains the
online CAM4-chem dry and wet deposition rates of NHy and NOx to
calculate the addition of soil NH4+ via deposition. The deposited
nitrogen will eventually enrich soil fertility and fuel the re-emission of
soil NH3, while the aforementioned aerosol radiative effect can cool the
Earth's surface and suppress NH3 volatilization. The
[CAM4_CLM5_NDEP] cases were set to isolate the
impact of NHy deposition on NH3 emission and crop growth. In this
setup, CAM4-chem used prescribed gases (except for water vapor) and aerosols
in the radiation transfer calculation (i.e., aerosol–radiation interaction
is disabled). Hence, any changes in the atmospheric sulfate aerosol loading
induced by the addition of or reduction in NH3 would not affect radiative
transfer. We note that the differences in radiative budget between the
[CAM4_CLM5_NDEP] and other configurations with
aerosol–radiation interaction enabled would include the effects attributable
to both NH3-induced sulfate changes and the differences in
spatial distribution between the prescribed and prognostic aerosols. For
instance, the differences between [CAM4_CLM5_NDEP] and [CAM4_CLM5] are substantial for sulfate (up to
4 % in zonal mean mass ratio; mostly inland) and dust (up to 30 %
zonally; mostly in sub-Saharan Africa and other desert regions) and are
unlikely related to NH3 changes; meanwhile, the differences are rather
negligible for organic carbon, black carbon, and sea salt. This
configuration was intended to isolate the enhanced fertilization effect of N
deposition. Similarly, [CAM4_CLM5_CLIM] cases
were prescribed with constant nitrogen deposition fluxes so that we could
quantify the impacts of the changes in instantaneous aerosol radiative
effects. We hypothesized that an increased NH3 emission would promote
the formation of sulfate aerosols, and the subsequent aerosol cooling effect
would be observed in this setup. Finally, we further evaluated the impacts
of intensive fertilizer use to promote agricultural production in the future
as projected by FAO (2007) by repeating the first three
simulations with fertilization at present-day (2000; model default) and
future (2050; assuming 30 % more synthetic fertilizers, while manure
fertilizer is kept at 2000 level) rates. We note that future increases in
agricultural production might also involve cropland expansion, but such
a practice was not included in this study.
Details of simulation designs.
AliasSynthetic fertilizer usageFertilizer-induced NH3 emissionN depositionAerosol–radiation interactionCAM4_CLM5_2000 Same as 2000 levelModified CLM5 in this studyDynamicEnabledCAM4_CLM5_205030 % more than 2000 levelModified CLM5 in this studyDynamicEnabledCAM4_CLM5_NDEP_2000Same as 2000 levelModified CLM5 in this studyDynamicDisabledCAM4_CLM5_NDEP_205030 % more than 2000 levelModified CLM5 in this studyDynamicDisabledCAM4_CLM5_CLIM_2000Same as 2000 levelModified CLM5 in this studyFrom [CAM4_CLM5] assuming 2000-level fertilizationEnabledCAM4_CLM5_CLIM_205030 % more than 2000 levelModified CLM5 in this studyFrom [CAM4_CLM5] assuming 2000-level fertilizationEnabledCAM4_CMIP6_2000Same as 2000 levelCMIP6 inventoryDynamicEnabled
All simulations were run for 30 years using the spun-up year 2000 initial
conditions with the corresponding land cover data provided out of the box by
CLM5. The first 10 years of outputs were used to further stabilize the model
(such that the interannual variability in the emission fluxes could be
<±10 %) after our ammonia scheme was implemented. Our
analysis in the next section focuses on the averages of the last 20 years of
simulated results to minimize influence from any long-term meteorological
variability. Only the atmosphere (CAM4-chem) and the land (CLM5) components
were active. CAM4-chem was run with free dynamics in the standard spatial
resolution of 1.9∘×2.5∘ horizontally with
27 vertical layers (from surface to ∼40 km). CLM5 was run in
the same horizontal resolution with 25 soil layers down to ∼50 m below ground. Sea surface temperature (SST) and sea ice conditions
(Hurrell et al., 2008) as well as the
mixing ratios of greenhouse gases
(Meinshausen
et al., 2017) were all fixed at the 2000 levels. Our analysis focuses on the
changes in fluxes of soil biogeochemical processes, the evolution of
atmospheric NH3, the sequential changes in sulfate aerosols, and the
influence of the bidirectional NHy exchange on crop production.
Datasets for model validation
We also compared our simulation results with various available global
observations and emission inventories. CLM5-modeled NH3 emission was
compared with multiple emission inventories including CMIP6, EDGAR, and the
Magnitude And Seasonality of Agricultural Emissions for NH3 (MASAGE).
CAM4-chem-simulated atmospheric NH3 using CLM5 NH3 and CMIP6 was compared against the satellite-derived IASI NH3 concentration field
(gridded and reported in Van Damme et al., 2018a).
Details of these datasets are tabulated in
Table 2. The datasets were regridded to
match our model resolution of 1.9∘×2.5∘
using bilinear interpolation. We note also that model–inventory comparisons
are not meant to be exact given that our simulations were performed using
free-running dynamics and thus did not necessarily match the meteorological
years of the inventories, and the synthetic fertilizer use was not identical
to the ones assumed when inventories were compiled. Thus, these results are
presented as qualitative comparisons to indicate where our estimation is
consistent with the inventories and where it is not.
Details of observations and emission inventories used in this study for model comparison and validation.
NameCoverageResolutionPeriod of dataData type: sources extracted for model comparisonMASAGE (Paulot et al., 2014)Global0.5∘×0.5∘ monthly mean2006Emission inventory: NH3 emission from agricultural soil associated with synthetic fertilizer onlyEDGAR (Crippa et al., 2018)Global0.1∘×0.1∘ monthly mean2010Emission inventory: NH3 emission from agricultural soil with both synthetic and manure fertilizersCMIP6 (Hoesly et al., 2018)Global0.01∘×0.01∘ monthly mean2000–2015Emission inventory: NH3 emission from agricultural soil with both synthetic and manure fertilizersIASI (Van Damme et al., 2018a)Global0.01∘×0.01∘ annual mean2008–2016Satellite-based measurement: column NH3 densityResultsFertilizer-induced NH3 emission
We first evaluated the fertilizer-induced NH3 emission simulated by the
fully coupled land–atmosphere simulation,
[CAM4_CLM5_2000]. Figure 2 shows
the annual-total global NH3
emission at above-canopy level from
different land types averaged over the remaining 20 years of simulation. We
also compared our NH3 emission with inventory estimates reported by
CMIP6 (Hoesly et al., 2018), EDGAR v4.3.2 (Crippa et al., 2018), and MASAGE
(Paulot et al., 2014). We extracted the monthly fertilizer-induced NH3 emission estimates from MASAGE and assumed that one-third of the total agricultural NH3 emission reported by CMIP6 and EDGAR is associated with synthetic
fertilizer, which is consistent with the apportionment reported in previous
studies and environmental reports (Paulot et al., 2014; Riddick et al., 2016; National Oceanic and Atmospheric
Administration, 2000; European Environment Agency, 2010; Gu et al., 2012;
Paulot et al., 2015; Zheng et al., 2017).
Regional emission totals are summarized in
Table 3. A grid-cell-by-grid-cell
model–inventory spatial comparison of the annual-total NH3 emission
rates was conducted by computing Pearson's correlation coefficients (R) and
slopes (β) of linear regression using the reduced major axis method
as well as normalized mean biases (NMBs; Σ(Mi-Oi)/Σ(Oi),
where Mi and Oi are simulated and inventory NH3 emission in each grid cell) and mean fractional biases
(MFBs; 2Σ[(Mi-Oi)/(Mi+Oi)]/N, where N is the
number of grid cells). A summary of these statistics is shown in
Fig. 2a. We also computed the
R values between the monthly emission rates computed by CLM5 (as in the fully
coupled case, [CAM4_CLM5_2000]) and each
inventory for each grid cell (see Fig. 2d, f, and h). We highlighted with
overlaying black dots the grid cells with high coefficients of
determination, which are statistically significant (i.e., R2>0.5 and p<0.05), indicating where our simulation can
reproduce more than half of the variability in the inventory estimates.
Regional fertilizer-induced NH3 emission totals (Tg N yr-1) estimated by our model and reported by other inventories.
a SDs over 20 years shown in the brackets.
b SDs over 16 years are shown in the brackets (2000–2015).
c Variation in 2012 in both cultural statistics and emission factors ranged from 186 % to 294 % (Crippa et al., 2018).
Globally, CLM5 estimates that the annual-total fertilizer-induced NH3
emission reaches 14 Tg N yr-1, while the global fertilizer input is 96 Tg N yr-1 (of which 71 % is synthetic fertilizers). Our estimation is
close to the 12 Tg N yr-1 (from synthetic fertilizer only) and 18 Tg N yr-1 (11 Tg N yr-1 from synthetic fertilizer and 6.5 Tg N yr-1 from manure application) reported by two similar studies,
Riddick et al. (2016) and
Vira et al. (2020), respectively. Our
estimate is higher than all three inventories of NH3 emissions
associated with synthetic fertilizers, which are 10 Tg N yr-1 for CMIP6
and EDGAR and 9.1 Tg N yr-1 for MASAGE. The global R values are
positive and lie within 0.4–0.6 across all inventories, indicating a fairly
good correlation between CLM5 and all three inventories, especially CMIP6
and MASAGE. Systematic model high biases are implied by the
greater-than-unity β values, with small NMB and MFB values ranging
between +32 %–+56 % and -22 %–+32 %,
respectively.
Top food-producing countries are responsible for a major portion of the
fertilizer-induced NH3 emission: 20 % of CLM5 global total was from
India (2.8 Tg N yr-1), 15 % from the US (2.1 Tg N yr-1), and
8.6 % from China (1.2 Tg N yr-1). Emission hotspots are found close
to their cropping regions in the model and the inventories, but their
spatial gradients are different. In India, CLM5 shows more concentrated
emission sources over the northern regions, resulting in higher local
emission rates than the inventories. This distribution pattern resembles India's gradient of higher fertilization in the north and lower fertilization in the south adopted by the
model. In contrast, CMIP6 estimates a more evenly distributed emission
spatial pattern over India and higher emission rates over the southern
regions. EDGAR and MASAGE show a spatial gradient of NH3 emission
decreasing from north to south. Such gradients may explain their low R and
high β values against our revised CLM5. Despite the spatial mismatch,
the NMB (+68 %–+74 %) and MFB (+14 %–+15 %) for the model estimation over India are relatively small.
CLM5 estimates more intense emission hotspots in the US, which are located
near the “Corn Belt” of the central US and southern California. US
emission rates by CLM5 are much higher than the other three inventories, as
seen in the difference maps in Fig. 2, as
indicated by the large β (>3.2) and large regional NMB.
Differences in the spatial distribution of NH3 emission are also
observed over China. CLM5 estimates that more NH3 is emitted from
central and northeastern China, while the emission hotspots in CMIP6 and
EDGAR are found in northeastern China, and those of MASAGE are in eastern
China. Such deviation may be attributable to different fertilizer usage
schedules used by CLM5 and other inventories. For example, MASAGE considers
multiple types of fertilizers that can be more or less prone to NH3 loss
than urea (Bouwman et al., 2002) and assumes a
three-stage fertilization at sowing, growth, and harvesting
(Paulot et al.,
2014). EDGAR also reported a high uncertainty (∼97 %) of
present-day NH3 emissions in China due to incomplete information about
the agricultural sector
(Crippa et al.,
2018).
Fertilizer-induced NH3 emission estimated by CLM5
(synthetic and manure) and other emission inventories (synthetic only).
Correlation analysis between CLM5-simulated annual-total emission and other
inventories with regional breakdowns is summarized in (a).
Spatial distribution of annual-total fertilizer-induced NH3 emission
simulated by [CAM4_CLM5_2000] and estimated
by CMIP6, EDGAR, and MASAGE is illustrated in (b),
(c), (e), and (g), respectively. Panels (d), (f), and (h) show the spatial distribution
of differences in annual-total NH3 between CLM5 and CMIP6, EDGAR, and
MASAGE, correspondingly. Overlaying black dots in the difference maps
indicate grid cells with a high statistically significant spatiotemporal
correlation (i.e., R2>50 %, p<0.05) between CLM5
and the corresponding inventories. Color scales are saturated at respective
values, and ranges of values are shown in the legend titles.
Figure 3 shows the seasonality of NH3
emission associated with artificial fertilizer in the Northern Hemisphere and Southern
Hemisphere. CLM5 assumes that each crop receives a specific amount of fertilizer
(as soil NH4+) applied evenly for 20 consecutive days since leaf
emergence. This soil NH4+ input speeds up plant uptake, microbial
immobilization, nitrification, and NH3 volatilization,
explaining the Northern Hemisphere peaking in emission in April and May and
Southern Hemisphere peaking in October, overlapping with the regional
cropping seasons. All inventories show springtime peaks in each hemisphere,
but the peak of EDGAR always leads the others by a month. CMIP6 has multiple
peaks (two in the Northern Hemisphere and three in the Southern Hemisphere).
These deviations exist mainly because of the differences in planting
schedule and duration of fertilization used by the inventories. The higher
CLM5 peaks are consistent with the systematic overestimation discussed
above. NH3 emission returns to “background” levels when it is not in
the planting seasons. EDGAR and CMIP6 have higher background levels than
MASAGE because the original estimates used in this study accounted for not
only synthetic fertilizer but also manure application (for both) and
management (for CMIP6 only), which are not necessarily in phase with the
cropping seasons (Huijsmans et
al., 2018).
We concluded our model–inventory comparison by computing the correlation of
monthly NH3 emission rates in each model grid cell (see
Fig. 2d, f, h). CLM5 can capture a large portion of emission hotspots of
CMIP6 over the US, Europe, India, China, and South America. With MASAGE, our
estimate shows good agreement over mid-range emission regions, in North
America, South America, Europe, and southern Africa. CLM5 differs the most
from EDGAR among the three inventories. The resemblance with CMIP6 and
MASAGE indicates that our NH3 scheme has allowed CLM5 to produce
reasonable NH3 emission inputs for CAM4-chem simulations over most high-
to medium-emission hotspots. It is also noteworthy that the magnitude and
spatial distribution of NH3 emission among inventories are also not
consistent. Since environmental conditions control the rate of biological
and chemical processes that release NH3, processes such as urea
hydrolysis and NH4+/NH3 equilibrium can induce further
inventory uncertainties (Hoesly et
al., 2018). Inter-inventory uncertainties are also attributable to the
choice of global and/or regional emission factors, which is crucial to
reflect different agricultural procedures across the world, such as
fertilization methods and fertilizer types, but not always well represented
in global inventories
(Paulot
and Jacob, 2014; Riddick et al., 2016; Zhang et al., 2018).
Monthly NH3 emission associated with synthetic
fertilizer use in the Northern Hemisphere and Southern Hemisphere estimated by CLM5,
CMIP6, EDGAR, and MASAGE.
Atmospheric NH3 concentration
Previous studies have evaluated the reactive nitrogen processes in CESM
against satellite and ground observations, e.g., depositional fluxes of
NH3 and NH4+
(Lamarque
et al., 2013), and ground concentration of gaseous NH3
(He
et al., 2015). Here, we estimated the CAM4-chem-simulated annual-mean
atmospheric NH3 using two different sets of fertilizer-induced
emissions: (1) simulated by our revised CLM5 ([CAM4_CLM5_2000]) and (2) one prescribed from CMIP6
([CAM4_CMIP6_2000]). The sources of non-fertilizer-related NH3 and other reactive gases were identical in these two cases.
Figure 4 shows these results, aggregated to
column total NH3, alongside the 8-year annual-average IASI satellite
retrievals.
Both simulations can capture the high NH3 zones observed by IASI over
the US, South America, and Europe, with global and regional R values
>0.5, indicating a good correlation between the modeled results
and observations. However, both [CAM4_CLM5_2000] and [CAM4_CMIP6_2000] NH3 are
generally lower than IASI. One except is over India, in which
[CAM4_CLM5_2000] estimates higher than IASI
with a regional β of 3.3. [CAM4_CLM5_2000] matches IASI better over the US (regional β=0.9), where
CLM5 estimates high emission rates. Even more significant underestimation is
seen in [CAM4_CMIP6_2000] (regional β=0.5) over the US. The magnitudes of NMB and MFB of [CAM4_CLM5_2000] are less negative than [CAM4_CMIP6_2000] in most regions, reflecting the fact that using CLM5 as
NH3 emission input reduces the model NH3 underestimation of
CAM4-chem with the default CMIP6 inventory.
Mild differences are seen in North America and northeastern China, which are
both intense agricultural regions; the discrepancies are likely attributable
to the mismatch in crop growth map between CLM5 and the real world. Larger
differences are shown over India and western Europe, indicating the
low biases in the model of emission from tropical biomass burning regions
(Whitburn et al.,
2017; Van Damme et al., 2018a).
We further compared the NH3 burden of a run with online NH3
emission but prescribed N deposition, i.e., [CAM4_CLM5_CLIM_2000], against IASI to examine
whether enabling the online bidirectional exchange can improve the
estimation of NH3 in CLM5. The global-total NH3 emission increases
by 0.45 % by enabling dynamic nitrogen deposition ([CAM4_CLM5_2000]–[CAM4_CLM5_CLIM_2000]), while the regional changes
are not uniform (Fig. S3). The most prominent increase is found
in Asia (+0.06 Tg N yr-1 or +0.9 % regionally), while the largest
decrease is seen in Europe (-0.03 Tg N yr-1 or -2.9 %). When
compared with IASI, [CAM4_CLM5_2000] and
[CAM4_CLM5_CLIM_2000] both have
closer-to-one β and closer-to-zero NMB and MFB than
[CAM4_CMIP6_2000] (Fig. S4),
indicating that coupling the land–atmosphere N cycle could reduce model
low bias.
Annual-mean atmospheric NH3 estimated by CAM4-chem
with online CLM5 simulation and CMIP6 emission inventory as inputs of
fertilizer-induced NH3 emission, which are aliased as
[CAM4_CLM5_2000] and [CAM4_CMIP6_2000], respectively. Panel (a) summarizes the
correlation analysis between the two simulations and the IASI satellite
retrievals. Panels (b), (c), and (e) show the
column NH3 concentration of IASI and the two cases correspondingly.
Panels (d) and (f) show concentration differences between
each case and the IASI observations. Overlaying black dots in the difference
maps indicate grid cells with a high statistically significant
spatiotemporal correlation (i.e., R2>50 %, p<0.05) between CLM5 and IASI. Color scales are saturated at respective
values, and ranges of values are shown in the legend titles.
When 30 % more synthetic fertilizer is applied globally – a case
study to reveal the importance of nitrogen deposition and aerosol–climate
effect on NH3 emission and grain production in a future scenario
Fertilizer use is predicted to increase by >30 % of the 2000 level
to boost grain production to meet the fast-growing food demand by 2050
(FAO, 2007). Such injection of soil nitrogen will not only enhance
soil NH3 emission but also alter atmospheric NH4+ formation
and its subsequent climate effects and deposition, which will induce
secondary impacts on crop growth and NH3 re-emission. Here, we used the
modified CLM5 and CAM4-chem to attribute such secondary impacts to nitrogen
deposition and the aerosol–climate effect. We performed this case study by
scaling up the amounts of synthetic fertilizer application by 30 %
globally as input to the simulations detailed in
Table 1. The application rate of manure
fertilizer was assumed to be at the same level in both scenarios since
increased usage would likely raise nitrogen leakage during production and
transportation, which is difficult to quantify and beyond the scope of this
study.
Table 4 summarizes the changes in
annual-total fertilizer-induced NH3 emission estimated by these
simulations when the global synthetic fertilizer use rises to 130 % of the
2000 level. The total fertilization rate at the 2050 level was 117 Tg N yr-1 (or +21 % from the present-day total fertilization rate at
96.5 Tg N yr-1, which is comparable to ∼100 Tg N yr-1 suggested by FAO, 2008). We also computed the nitrogen
leakage ratio (NLR) and nitrogen use efficiency (NUE) for each case. NLR
remains at ∼15 % for [CAM4_CLM5_2000] and [CAM4_CLM5_2050],
while NUE decreases from 23 % to 22 %, respectively, indicating that the
crops are under nitrogen surplus under this future fertilization scenario.
This is also confirmed by the reduced ratio of crop uptake to fertilization
from ∼130 % to ∼115 % (Table S3).
Summary of N fluxes in the simulations averaged over 20 years.
FertilizationNH3 EmissionNLRaGrain N harvestedNUEb(Tg N yr-1)(Tg N yr-1)(%)(Tg N yr-1)(%)CAM4_CLM5_200096.514.214.721.922.7CAM4_CLM5_2050117.017.514.925.521.8CAM4_CLM5_CLIM_2050117.017.014.524.320.8CAM4_CLM5_NDEP_2050117.017.915.325.621.9
a Nitrogen leakage ratio (NLR) = NH3 emission / fertilization. b Nitrogen use efficiency (NUE) = grain N harvested/fertilization.
Figure 5b shows the changes in
the fully coupled case, [CAM4_CLM5_2050],
which estimated that global emission will rise by 3.3 Tg N yr-1 of
fertilizer-associated NH3 emission than the baseline case, i.e.,
[CAM4_CLM5_2000]. The super-linear increase in
NH3 emission (+24 %) relative to total fertilizer (+21 %) is
associated with a sub-linear rise in nitrification (+17 %), crop uptake
(+5.8 %), and other loss processes of soil NH4+. It is a result
of a larger tendency of NH3 volatilization compared to other loss
processes during the 20 d of intensive fertilizer application,
highlighting that our model modifications enabled CLM5 to simulate the
dynamic competing processes of soil nitrogen. Regionally, changes in Indian
and Chinese emissions are the highest, generally overlapping with the high
fertilization zones (Fig. S5), totaling +1.6 Tg N yr-1
or +24 % for Asia relative to the baseline.
Changes in annual-total fertilizer-induced NH3
emission at the present-day synthetic fertilizer usage in the future
scenario. Panel (a) summarizes regional changes relative to
[CAM4_CLM5_2000]. Spatial distribution of the
changes from [CAM4_CLM5_2000] with
[CAM4_CLM5_2050] is shown in (b), with [CAM4_CLM5_CLIM_2050] in (c), and with [CAM4_CLM5_NDEP_2050] in (d). Overlaying
black dots indicate grid cells with a statistically significant difference
under two-sample t tests (i.e., p<0.05) between corresponding
simulations. Color scales are saturated at respective values.
CLM5 assumes a harvest efficiency of 85 % (Lawrence et
al., 2018). [CAM4_CLM5_2050] estimated that,
with nitrogen deposition and the aerosol climate effect, 243 Tg more grain (in
dry matter hereinafter) is produced per year than the baseline (see
Fig. 6b). Such enhancement is
found dominantly over the food-producing regions over Europe (+76 Tg yr-1 or +17 %) and North America (+78 Tg yr-1 or +17 %).
The grain production in Asia has divergent responses to induced fertilizers
(increases in northern China and decreases in southern India). This results
in a smaller Asian grain production increase of +55 Tg yr-1
(+4 %).
Same as Fig. 5 but for annual-total grain production (in teragrams of dry matter per year).
Contrasting the difference in NH3 emission (Tg N yr-1). Overlaying black dots indicate grid cells with a statistically
significant difference under two-sample t tests (i.e., p<0.05)
between corresponding simulations. Only results of grid cells with croplands
are shown.
We evaluated the impacts of dynamic processes in our simulations by
comparing the fully coupled simulation with other cases, i.e., the effects
of including dynamic nitrogen deposition (Δndep= [CAM4_CLM5_2050]–[CAM4_CLM5_CLIM_2050]) and aerosol–climate
interaction (Δclim= [CAM4_CLM5_2050]–[CAM4_CLM5_NDEP_2050])
in the model.
As agricultural NH3 is injected to the atmosphere, it rapidly
neutralizes other acidic chemicals and facilitates the formation of
particulate NH4+. Some of the nitrogen particles return to the
surface via deposition, sequentially fuel soil NH3 emission, and boost
crop growth. Our simulation shows that nitrogen deposition is enhanced by
1.6 Tg N yr-1, mostly in the US and India (Fig. S8a). It
translates to an increased NH3 emission by 0.47 Tg N yr-1 globally
(Fig. 7), but the enhancement is seen
mostly in India. In contrast, there is a decrease in NH3 emission
over the US. The increase (decrease) is associated with a higher (lower)
annual-mean surface temperature (Fig. S9a). In addition to
directly enhancing the tendency of NH3 volatilization, a warmer surface
temperature also shortens the crop growth and grain filling period, resulting in
lower crop nitrogen uptake (Fig. S10a) and less grain
production (Fig. 8a), e.g., in
India. Though the impacts of the larger nitrogen deposition in Δndep are not evenly distributed spatially, it boosts grain production
by 138 Tg N globally.
On the other hand, we expected that the sulfate aerosols induced by agricultural
NH3, which directly increases aerosol albedo, would reduce the amount
of insolation reaching the Earth's surface. Comparing the 2000 and
2050 fertilization levels, our fully coupled simulation estimated -0.005 W m-2 in global downward radiative flux (i.e., cooling), which is
virtually negligible compared to the 16-model mean total anthropogenic
aerosol radiative forcing of -0.27 W m-2 reported in
(Myhre
et al., 2013). Though the global impact is also negligible (+0.004 W m-2), Δclim reveals a substantial regional cooling at the
surface level (Fig. S11b) largely because there are more
prognostic dusts in [CAM4_CLM5_2050] over
sub-Saharan Africa than prescribed in [CAM4_CLM5_NDEP_2050]. Such a cooling effect results
in lower surface temperature (Fig. S9b) and also suppression of the formation of particulate sulfate (Fig. S12b). These changes
in surface temperature reduce NH3 emission substantially over Africa while promoting NH3 volatilization in India and North and South
America, resulting in a net decrease by 0.41 Tg N yr-1 globally. Grain
production decreases by 36 Tg N yr-1 in Δclim, indicating
that the warming induced by aerosol–radiation interaction partly offsets the
benefit of nitrogen deposition as an extra input of soil NH4+ for
crop growth and yield, though the deposition rate is increased slightly over
the US (Fig. S8b).
Contrasting the difference in grain production
(teragrams of dry matter per year). Overlaying black dots indicate grid cells with a
statistically significant difference under two-sample t tests (i.e., p<0.05) between corresponding simulations. Only results of
grid cells with croplands are shown.
Conclusions
In this study, we implemented into the land and biogeochemical model, CLM5,
new mechanistic schemes to better represent fertilizer-induced NH3
emission from agricultural soil. Our modifications allowed CLM5 and
CAM4-chem to dynamically exchange fluxes associated with reactive nitrogen
deposition and NH3 emission. These new features enabled CESM2 to
perform a more reliable estimation of soil NH3 emission and atmospheric
NH3 concentration than using constant emission inventory values under
dynamic climate and environmental conditions. We verified that a fully
coupled simulation case, [CAM4_CLM5_2000],
produced an estimation of NH3 emission that agrees fairly well
spatially and temporally with the emission inventories, MASAGE
(Paulot et al.,
2014) and CMIP6 (Hoesly et al.,
2018), especially over high-emission regions (see
Fig. 2). When compared to the IASI
satellite observations (Van Damme et al., 2018a),
online NH3 emission input in CLM5 reduces the low biases exhibited in
CAM4-chem estimation of atmospheric NH3 using the CMIP6 NH3
emission inventory.
Our modifications also enabled us to understand how NH3 emission
influences aerosol formation and aerosol radiative effect and their
secondary impacts on the re-emission of NH3 and grain production. Our
fully coupled simulation, [CAM4_CLM5_2000],
recreates the spatial distribution of the emission hotspots observed by
satellite over intensive agricultural regions including China, India,
Europe, and the US (see Fig. 4). We also
estimate that if the synthetic fertilizer use were to increase by 30 % from
the 2000 level, NH3 emission would rise by 3.3 Tg N yr-1 globally
(see Fig. 5). We further performed two
simulations to highlight the importance of including a fully coupled
nitrogen cycle in the model. Our results showed that lacking dynamic N
deposition would underestimate NH3 emission and grain production under
intensified fertilization, while ignoring the aerosol–radiation interaction
would lead to overestimation.
This study demonstrates a modeling approach to estimate the climatic and
environmental sensitivity of NH3 emission, with a focus on sources
associated with manure and synthetic fertilizer only. Other primary sources
of atmospheric NH3 include manure management and application (47 %),
ocean (16 %), and biomass burning (11 %)
(e.g.,
Bouwman et al., 1997; Sutton et al., 2013; Paulot et al., 2014, 2015).
Unlike soil emission, whereby the volatilization of NH3 depends on a
series of biogeochemical processes, emissions associated with manure
management are typically estimated differently, e.g., collecting activity
data and emission factors from factory managers and installing monitoring
instruments at outlets of confined facilities such as animal factories
(Bouwman
et al., 1997; Paulot et al., 2014). Manure production, storage, and usage
can be collected by surveying practices adopted by farmers, while its
associated NH3 emission can be estimated using source-specific emission
factors and weather data, especially dominant factors such as air
temperature, wind speed, and humidity. Fire emission directly injects the
reactive nitrogen into the atmosphere, and satellite measurement is capable
of capturing such short-term ammonia blooms
(Van Damme et al., 2017). We did not
include manure management in our study due to the high uncertainty and data
insufficiency for validation. It is noteworthy that manure is attributable
to up to ∼60 % of total soil NH3 emission
(Vira et al., 2020) and hence shall
warrant further research efforts in terms of its downstream impact on
ecosystems via nitrogen deposition and aerosol radiative effect.
We also incorporated a prognostic parameterization for canopy capture of the
emitted NH3, which is an improvement when compared to previous studies
that assigned blanket reduction factors to all vegetated land types
(Bouwman
et al., 1997; Riddick et al., 2016; Vira et al., 2020). Despite such
addition, our model still shows systematic high biases, implying room for
improvement, including further calibration of the canopy capture effects
against field measurements. Another source of uncertainty stems from the
model's initial soil NH4+ content, which determines the potential
emission rate of NH3. The overestimation by CLM5 in this study may
point to the more-fertile-than-reality soil conditions in the model,
highlighting the need for a more realistic soil nitrogen map compiled by
field surveys to better constrain the initial conditions for the model. We
also note that such field surveys, especially in underrepresented regions
with low data coverage, would also be useful to infer a soil pH map that
constrains the uncertainty in simulations using a constant pH, like those
reported in this study.
Our schemes simplified the fate of NH3 captured by the canopy and
assumed that such NH3 is returned to the soil and becomes immediately
accessible to plants, soil microbes, and bacteria, due to limited knowledge
of the consequences of the canopy capturing process. A chamber study
suggested that soybean can absorb up to 20 kg N ha-1 of NH3 via
leaf capturing (Hutchinson et al., 1972), which is a
significant amount compared to average fertilizer use for soybean of 13–45 kg N ha-1 in CLM5. On the other hand, concentrated NH3 could
damage leaf tissues if the contacting plant fails to metabolize or detoxify
such a reactive gas in time (Nemitz et
al., 2001). The remaining captured NH3 on the leaf surface can return
to the soil via throughfall, but its magnitude is difficult to measure.
These unspecified processes may induce uncertainties in our simulations,
especially for plant growth and soil NH4+ content. This knowledge
gap points to a demand for more field experiments to investigate the impacts
of these processes.
FAO projects that fertilizer use will be increased by >30 %
(FAO, 2007) to boost food production to meet the fast-growing food
demand by 2050. Such additional fertilizer injects mineral nitrogen into the
soil that further fuels the volatilization of NH3 spontaneously and
hence promotes the subsequent formation of aerosol particles. This study
shows the nonlinear impacts of nitrogen deposition and aerosol radiative
effect on the environment. Thus, our work makes it possible to evaluate the
intertwined consequences of such soaring use of fertilizer on NH3
emission, atmospheric aerosol composition, and the corresponding
aerosol–climate effect. Our results can provide scientific information to
aid stakeholders in evaluating various global and regional plans for
mitigating climate change and safeguarding a sustainable environment.
Code availability
The modified codes of CESM2 developed in this study will be available will
be available upon request.
Data availability
The NH3 satellite data mentioned in this study were obtained from the PANGAEA repository (10.1594/PANGAEA.894736) (Van Damme at al., 2018b).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-1635-2022-supplement.
Author contributions
All co-authors participated in designing the experiments. KMF and MVM
developed the model code. KMF performed the simulations. KMF prepared the
manuscript with contributions from all co-authors.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Maria Val Martin acknowledges funding from the Leverhulme Trust through a Leverhulme Research
Centre award (grant no. RC-2015-029) and the UKRI Future Leaders Fellowship program
(grant no. MR/T019867/1). We would also like to acknowledge the high-performance
computing support from Cheyenne (10.5065/D6RX99HX) provided by the NCAR's
Computational and Information Systems Laboratory, sponsored by the National
Science Foundation. We also thank the reviewers of this paper for their
constructive feedback.
Financial support
This work was supported by the General Research Fund (project no. 14323116) and the National Natural Science Foundation of China (NSFC)/Research Grants Council (RGC) (grant no. N_CUHK440/20) awarded by the RGC of the University Grants Committee of Hong Kong to Amos P. K. Tai.
Review statement
This paper was edited by Sönke Zaehle and reviewed by two anonymous referees.
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