ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-12823-2015Global evaluation of ammonia bidirectional exchange and livestock diurnal variation schemesZhuL.HenzeD.daven.henze@colorado.eduBashJ.https://orcid.org/0000-0001-8736-0102JeongG.-R.https://orcid.org/0000-0003-2935-6441Cady-PereiraK.ShephardM.https://orcid.org/0000-0002-2867-9612LuoM.PaulotF.https://orcid.org/0000-0001-7534-4922CappsS.Department of Mechanical Engineering, University of Colorado, Boulder, Colorado, USAUS Environmental Protection Agency, Research Triangle Park, North Carolina, USAAtmospheric and Environmental Research, Inc., Lexington, Massachusetts, USAEnvironment Canada, Toronto, Ontario, CanadaJet Propulsion Laboratory, California Institute of Technology Pasadena, CA, USAProgram in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey, USAGeophysical Fluid Dynamics Laboratory/National Oceanic and Atmospheric Administration, Princeton, New Jersey, USAD. Henze (daven.henze@colorado.edu)19November2015152212823128437December201420February201510October20159November2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/12823/2015/acp-15-12823-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/12823/2015/acp-15-12823-2015.pdf
Bidirectional air–surface exchange of ammonia (NH3) has been neglected in
many air quality models. In this study, we implement the bidirectional
exchange of NH3 in the GEOS-Chem global chemical transport model. We also
introduce an updated diurnal variability scheme for NH3 livestock
emissions and evaluate the recently developed MASAGE_NH3 bottom-up
inventory. While updated diurnal variability improves comparison of
modeled-to-hourly in situ measurements in the southeastern USA, NH3
concentrations decrease throughout the globe, up to 17 ppb in India and
southeastern China, with corresponding decreases in aerosol nitrate by up to
7 µg m-3. The ammonium (NH4+) soil pool in the
bidirectional exchange model largely extends the NH3 lifetime in the
atmosphere. Including bidirectional exchange generally increases NH3
gross emissions (7.1 %) and surface concentrations (up to 3.9 ppb)
throughout the globe in July, except in India and southeastern China. In
April and October, it decreases NH3 gross emissions in the Northern
Hemisphere (e.g., 43.6 % in April in China) and increases NH3 gross
emissions in the Southern Hemisphere. Bidirectional exchange does not
largely impact NH4+ wet deposition overall. While bidirectional exchange
is fundamentally a better representation of NH3 emissions from
fertilizers, emissions from primary sources are still underestimated and thus
significant model biases remain when compared to in situ measurements in the
USA. The adjoint of bidirectional exchange has also been developed for the
GEOS-Chem model and is used to investigate the sensitivity of NH3
concentrations with respect to soil pH and fertilizer application rate. This
study thus lays the groundwork for future inverse modeling studies to more
directly constrain these physical processes rather than tuning bulk
unidirectional NH3 emissions.
Introduction
Ammonia (NH3) is an important precursor of particulate matter (PM2.5)
that harms human health and impacts climate
through aerosol and short-lived greenhouse gas concentrations
. Global emissions of NH3 have increased by a factor of
2 to 5 since preindustrial times, and they are projected to continue to rise
over the next 100 years . NH3 is an important
component of the nitrogen cycle and accounts for a significant fraction of
long-range transport (100s of km) of reactive nitrogen .
Excessive deposition of NH3 already threatens many sensitive ecosystems
.
Uncertainties in estimates of NH3 emissions are significant. Surface-level
NH3 measurements have been limited in spatial and temporal coverage,
leading to large discrepancies in emissions estimates .
Additional information from remote sensing observations has been used to gain
a better understanding of NH3 distributions
. These observations have
also been used as inverse modeling constraints on NH3 emissions
. While this approach leads to improved results regarding the
comparison of air quality model estimates to independent surface observations
in the USA , several limitations of this approach were
identified. First, model biases in NHx wet deposition were not reduced.
Emission constraints from remote sensing measurements available only once per
day were very sensitive to the model's diurnal variation of NH3 sources.
Also, the remote sensing observations used in are sparsely
distributed, leading to a quantifiable sampling bias. Other inverse modeling
studies of NH3 emissions have been performed using in situ observations,
such as aerosol SO42+ and NO3-, aircraft
observations of NH3, or wet deposition of NH4+. However, these approaches still have disadvantages as they
are limited to the small spatiotemporal coverage of available aircraft
measurements or are sensitive to large model biases in HNO3 or precipitation .
The modest success of previous inverse modeling studies suggests that updates
to the dynamic and physical processes governing NH3 are needed in addition
to improvements in emissions estimates. Nighttime NH3 concentrations are
consistently overestimated in many air quality models (e.g., GEOS-Chem global
chemical transport model and the Community Multi-scale Air Quality (CMAQ) model).
This may contribute to an overestimate of monthly averaged NH3
concentration following the assimilation of Tropospheric Emission
Spectrometer (TES) observations .
Another area in which many air quality models are currently deficient is in
treatment of the air–surface exchange of NH3. Rigorous treatment of the
bidirectional flux of NH3 can substantially impact NH3 deposition,
emission, re-emission, and atmospheric lifetime . Re-emission
of NH3 from soils can be a significant part of NH3 sources in some
regions. However, this bidirectional exchange mechanism is neglected by many
air quality models (e.g., GEOS-Chem). Several recent studies have begun to
include resistance-based bidirectional exchange wherein the NH3 flux
direction is determined by comparing the ambient NH3 concentration to the
NH3 in-canopy compensation point. and
began with the air–canopy exchange model and extended the model by including
air–soil exchange but with no soil resistance. and
developed and extended the model to include a soil capacitance
which assumes that NH3 and NH4+ exist in equilibrium in the soil. This
NH3 bidirectional exchange scheme has been evaluated in a regional
air quality model (CMAQ) by and .
Based on these previous studies, investigating the diurnal patterns of NH3
emissions and bidirectional air–surface exchange is critical for reducing
uncertainties in the GEOS-Chem model, which may in turn afford better
top-down constraints on NH3 source distributions and seasonal variations.
In this paper, we apply a new diurnal distribution pattern to NH3
livestock emissions in GEOS-Chem, which is developed based on observations of
emissions in the Concentrated Animal Feeding Operation (CAFO) dominated areas
in North Carolina . We then implement bidirectional exchange of
NH3 in a global chemical transport model – GEOS-Chem – following
and compare the model to in situ observations. As a first
step towards including bidirectional exchange in NH3 inverse modeling, we
also develop the adjoint of bidirectional exchange in GEOS-Chem; this also
provides a useful method for quantifying the sensitivities of GEOS-Chem
simulations with respect to important parameters in the bidirectional model,
such as soil pH and fertilizer (only mineral fertilizer is considered in
NH3 bidirectional exchange) application rate, which are themselves
uncertain.
Section describes the model we use in this study.
Section introduces the in situ observation networks we use
for evaluation. The impacts of implementing the new diurnal variation pattern
of NH3 emissions are presented in Sect. . The
details of developing bidirectional exchange and its adjoint in GEOS-Chem
are described in Sect. , followed by the evaluations
and adjoint sensitivity analysis in Sect. . We present
our conclusions in Sect. .
MethodsGEOS-Chem
GEOS-Chem is a chemical transport model driven with assimilated meteorology
from the Goddard Earth Observing System (GEOS) of the NASA Global Modeling
and Assimilation Office . We use the nested grid of the model
(horizontal resolution 1/2∘× 2/3∘
(∼ 50 km × 67 km) over the USA and
2∘× 2.5∘ (∼ 200 km × 250 km)
horizontal resolution for the rest of the world). The year 2008 is simulated
with a spin-up period of 3 months. The tropospheric oxidant chemistry
simulation in GEOS-Chem includes a detailed ozone–NOx–hydrocarbon–aerosol
chemical mechanism coupled with a sulfate–nitrate–ammonia
aerosol thermodynamics module described in . The wet deposition
scheme of soluble aerosols and gases is described in . The dry
deposition of aerosols and gases scheme is based on a resistance-in-series
model , updated here to include bidirectional exchange (see
Sect. ).
Global anthropogenic and natural sources of NH3 are from the GEIA
inventory 1990 . The anthropogenic emissions are updated by
the following regional inventories: the 2005 US EPA National Emissions
Inventory (NEI) for the USA, the Criteria Air Contaminants (CAC) inventory for
Canada , the inventory of for Asia,
and the co-operative program for monitoring and evaluation of the long-range
transmission of air pollutants in Europe (European Monitoring and Evaluation Program, EMEP) inventory for Europe
. Monthly biomass burning emissions are from
, and biofuel emissions are from . The
anthropogenic emissions inventories described here are only used for base
case nested grid model runs over the USA. Variants will be explained in the
following sections. Table 1 is a summary of various emissions inventories
used in different sections.
A summary of various emissions inventories used in different
sections.
Gross emissions SectionRegionHorizontalModelAnthropogenic emissionsin region (Tg) resolutionversioninventoryAprilJulyOctober4.2USAa1/2∘× 2/3∘Static and dynamicNEI 2005b0.2000.4070.2234.3Global2∘× 2.5∘Static and dynamicMASAGE_NH3c6.796.595.016.1.1, 6.1.2USA1/2∘× 2/3∘BASEdNEI 20050.2000.4070.223BIDIdNEI 2005 livestock + upward BIDI fluxe0.1530.4280.1926.1.3USA2∘× 2.5∘BASEOptimized emissions inventoriesf1.041.111.27BIDI1.121.211.406.2, 6.3, 6.4Global2∘× 2.5∘BASEMASAGE_NH36.796.595.01BIDI5.626.304.73
a Continental USA. b NEI
2005 does not distinguish the livestock emissions sector. Thus, the livestock
fractions calculated from NEI 2008 are used in the dynamic case.
c MASAGE_NH3 contains livestock and fertilizer sectors.
d All BASE and BIDI cases include the new dynamic scheme.
e In all BIDI cases, fertilizer emissions in BASE case will be
replaced by the upward BIDI flux. f Optimized emissions inventories
from .
GEOS-Chem adjoint model
An adjoint model is an efficient tool for investigating the sensitivity of
model estimates with respect to all model parameters simultaneously. This
approach has been applied in recent decades in chemical transport models for
source analysis of atmospheric tracers and for
constraining emissions of tropospheric chemical species .
Adjoint models have also been used in air quality model sensitivity studies
e.g.,. The adjoint of GEOS-Chem is fully described and
validated in . It has been used for data assimilation using in
situ observations e.g., and remote sensing
observations e.g.,. In this paper, we develop
the adjoint of bidirectional exchange and we use this adjoint model to
investigate the sensitivity of modeled NH3 with respect to soil pH and
fertilizer application rate.
ObservationsSurface measurements
We use surface observations of NH3 and wet deposited NH4+ from several
networks to evaluate model estimates.
The SouthEastern Aerosol Research and Characterization (SEARCH) network
contains monitoring stations throughout the southeastern USA. The SEARCH network
provides different sampling frequencies, such as daily, 3-day, 6-day, 1 min,
5 min, and hourly, at different sites. Three of the monitoring stations (Oak
Grove, MS, Jefferson Street, GA, and Yorkville, GA) provide 5 min long
surface NH3 observations. In order to see the diurnal variations, we
convert the 5 min long observations to be hourly average NH3 concentration
for each of these three sites in July 2008. We then average the hourly
observations of these three sites to compare with the modeled results of
corresponding sites.
The Ammonia Monitoring Network (AMoN) of the National Atmospheric Deposition
Program (NADP) contains 21 sites across the USA with 2-week-long sample
accumulation . We average the 2-week-long observations
from November 2007 through June 2010 to monthly NH3 concentrations. The
Interagency Monitoring of Protected Visual Environments (IMPROVE) network
consists of more than 200 sites in the continental USA which
collect PM2.5 particles over 24 h every third day. We use monthly
average sulfate and nitrate aerosols concentrations.
We use wet NH4+ deposition observations from several monitoring networks
around the world. The NADP National Trends Network (NTN)
(http://nadp.sws.uiuc.edu/NTN) contains more than 200 sites in the USA
which are predominately located in rural areas. It provides wet deposition
observations of ammonium with week-long sample accumulation. The Canadian Air
and Precipitation Monitoring Network (CAPMoN)
(http://www.on.ec.gc.ca/natchem) contains about 26 sites which are
predominately located in central and eastern Canada with 24 h integrated
sample times. The EMEP network
(http://www.nilu.no/projects/ccc/emepdata.html) contains about 70 sites
which are predominately located away from local emission sources. It has
daily, weekly, and biweekly observations of ammonium available in different
sites. The Acid Deposition Monitoring Network in eastern Asia (EANET)
(http://www.eanet.asia/product) contains 54 sites (21 urban, 13 rural,
and 20 remote sites) with monthly observations of wet deposition of ammonium.
We only use nonurban sites (∼ 30) of EANET to avoid large local
emission sources influences. We convert the daily/weekly/biweekly
observations to monthly average NH4+ concentration in 2008.
Diurnal variability of ammonia livestock emissionDevelopment of new diurnal distribution scheme
Simulated NH3 surface concentrations in GEOS-Chem are significantly
overestimated at nighttime compared to hourly observations from the SEARCH
network . The standard NH3 emissions in GEOS-Chem are evenly
distributed throughout the 24 h of each day of the month, as indicated by
the blue line in Fig. . That the simulated NH3 emissions
do not have any diurnal variation is a likely explanation for this
discrepancy with hourly observation. Thus, a new diurnal distribution scheme
for NH3 livestock emissions has been developed in CMAQ . Here
we implement this algorithm in GEOS-Chem. The hourly NH3 livestock
emission, Eh(t), is calculated from the monthly total emission,
Em, as
Eh(t)=EmNmet(t),
where Nmet(t) is the hourly fraction of the NH3 livestock
emission during the month. This depends on the aerodynamic resistance,
Ra [s-1 m], and surface temperature, T [K]:
Nmet(t)=H(t)/Ra(t)∑t=1n(H(t)/Ra(t)),
where n is the number of hours in a month, t is the time during the
month, from 1 to n, and H(t) is the Henry's equilibrium, calculated
following :
H(t)=161 500Te-10 380/T.
More details of the development of this diurnal variability scheme can be found in .
Monthly averaged diurnal variation fractions of livestock emissions
of year 2008 over the USA. The blue line is the standard GEOS-Chem. Dark green,
red, and black lines are the newly developed diurnal pattern of NH3
livestock emissions in April, July, and October, respectively.
Evaluation with in situ NH3 observations
We replace the standard GEOS-Chem livestock emissions, which are evenly
distributed for each hour of the day (static), with this new diurnal
variability of livestock emissions that peaks in the middle of the day
(dynamic) (Fig. ). This also introduces daily variability of
livestock emissions into the simulation, which is not considered in the
standard GEOS-Chem model. As the standard GEOS-Chem anthropogenic emissions
do not distinguish the livestock emissions sector (described in
Sect. ), we calculate the absolute NH3 livestock
emissions based on the fraction of livestock emissions in anthropogenic
emissions in the 2008 NEI.
Significant improvements are found when we compare surface NH3
concentrations to SEARCH observations after implementing the dynamic diurnal
emissions (see Fig. ). The dynamic case (black) decreases
the surface NH3 concentration relative to the static case (red) by several
ppb at night and increases concentrations slightly (up to 1 ppb) in the day.
This reduces the model mean bias by up to 2.9 ppb at night.
Diurnal variation of NH3 surface concentrations from SEARCH
observations (blue) and GEOS-Chem model with (black) and without (red) dynamic
emissions scheme in July 2008.
Spatial distribution of GEOS-Chem simulated NH3 concentration at
surface level in static cases, dynamic cases, and their differences. Monthly
averages are shown for April, July, and October of 2008.
Global distribution
To apply the dynamic emissions scheme globally, we implement a new global
NH3 anthropogenic emissions inventory Magnitude And Seasonality of
AGricultural Emissions model (MASAGE_NH3; ), which
contains sector-specific emissions for different agriculture sources, such as
livestock emissions (the standard GEOS-Chem NH3 emissions do not clearly
distinguish this sector). Comparisons between the emissions of
MASAGE_NH3 and GEOS-Chem standard inventories are in .
Figure shows the global distribution of surface NH3
concentrations from the GEOS-Chem static and dynamic cases in April, July,
and October of 2008. The third column shows the difference between the
dynamic and the static cases. In general, the dynamic case decreases the
monthly NH3 surface concentration throughout the world with significant
changes in southeast China and India in all 3 months, which can be up to
17.1 ppb in China in October and 12.1 ppb in India in April. There are also
large decreases in the eastern USA (up to 3.3 ppb) and southeastern South
America.
The modeled representative volume mixing ratio (RVMR)
underestimates the observed RVMR from TES in the USA and most places of the
globe . In this study, we also compare the modeled
RVMR from static and dynamic cases to the TES RVMR. We calculate modeled RVMR
at the same time and locations of TES retrievals during 2006 through 2009. We
average the RVMRs at the 2∘× 2.5∘ grid resolution
for each month (April, July, and October). The static RVMR underestimates the
TES RVMR throughout the globe in all 3 months except in India and
southeastern China in April. With the new diurnal variability scheme (dynamic
case), the modeled RVMR increases in many places (e.g., eastern China,
northern India, South America) and decreases in the mid-USA and northern
Europe. The differences between the dynamic and static RVMR are from -1.5
to 1.6 ppb. These changes generally reduce differences between modeled and
observed RVMR, while the differences are enhanced in a few locations, such as
northern India in April. However, the magnitude of these changes is small
compared to the differences (from -11.4 to 3 ppb) between the static RVMR
and TES RVMR. We are able to detect more obvious changes between the static
and dynamic cases when focusing on a livestock source region (California) and
a hotter day, during which the dynamic RVMR increases 3.4 ppb .
Stronger constraints on diurnal variability would be evident from potential
future geostationary measurements .
High biases of surface nitrate aerosol concentrations in GEOS-Chem are found
in the USA e.g.,. Here we consider the impact of
dynamic NH3 livestock emissions on surface nitrate concentration in the
USA as well as globally. Figure presents the global
distribution of surface nitrate concentration from the GEOS-Chem static and
dynamic cases in April, July, and October of 2008. The dynamic case decreases
the nitrate concentration significantly in eastern China in all 3 months,
which can be as large as 7 µg m-3 in October. There are also
large decreases in the eastern USA which can be up to
2.7 µg m-3 in July. In October, there are large decreases in
the dynamic case in comparison to static case in northern India (up to
3.9 µg m-3) and Europe (up to 2.4 µg m-3 in
Poland).
Spatial distribution of GEOS-Chem simulated nitrate concentration at
surface level in static cases, dynamic cases, and their differences. Monthly
averages are shown for April, July, and October of 2008.
Investigating the impacts of dynamic NH3 livestock emissions on nitrogen
deposition is also of interest. In Fig. , we show the
global distribution of total nitrogen deposition (wet deposition of NH3,
ammonium, HNO3 and nitrate and dry deposition of NH3, ammonium, NO2,
PAN, N2O5, HNO3, and nitrate) from GEOS-Chem static and dynamic
cases in April, July, and October of 2008. The dynamic case decreases
nitrogen deposition in most places in the world, yet increases it in several
locations. The largest decrease of nitrogen deposition occurs in northern
India in April by up to 3.6 kg N ha-1 month-1. The total amount
of nitrogen deposition in India decreases by 8.6 % in April. Decreases in
nitrogen deposition in the dynamic case occur in southeastern China in all 3
months, with the total amount of nitrogen deposition in China decreasing by
4.7 % in April, 2.8 % in July, and 3.1 % in October. The new diurnal
variability scheme has more NH3 from livestock emissions emitted in the
daytime, when the boundary layer is thicker than nighttime. Typically, this
lowers deposition largely at night. However, it may also be conducive to more
export of NH3 in the atmosphere during the day. Thus, slight increases of
nitrogen in the dynamic cases occur downwind of regions with large NH3
sources in the base cases, such as increases in northeastern China owing to
enhanced NH3 export from eastern China.
Spatial distribution of GEOS-Chem simulated total N deposition in
static cases, dynamic cases, and their differences. Monthly averages are shown for
April, July, and October of 2008.
Bidirectional exchange of NH3Bidirectional flux calculation
The dry deposition scheme in the standard GEOS-Chem model is based on the
resistance in series formulation of , which only considers
the unidirectional flux of NH3 from the air to the surface. However, the
air–surface exchange is known to actually be bidirectional. In this paper,
we update the dry deposition of NH3 to combine NH3 dry deposition from
the atmosphere and emission from vegetation. A simplified schematic of the
updated air–surface exchange process of NH3 is shown in
Fig. . More details of this bidirectional scheme can be
found in and . The total air–surface exchange
flux, Ft, is calculated as a function of the gradient between the
ambient NH3 concentration in the first (surface) layer of the model and
the canopy compensation point :
Simplified schematic of NH3 bidirectional exchange model.
Ca, Cg, and Cst are the NH3 concentrations in
the atmosphere, soil, and stomata, respectively. Cc is the NH3
concentration at the canopy compensation point.
Ft=Cc-CaRa+0.5Rinc,
where Ca is the ambient NH3 concentration of the first
atmospheric layer of the model, Cc is the canopy compensation point
(which is set at one half of the in-canopy resistance, since NH3 can come
from either air or soil to the canopy, thus, splitting Rinc
symmetrically is appropriate), Ra is the aerodynamic resistance,
and Rinc is the in-canopy aerodynamic resistance. Ca>Cc will result in deposition from air to surface, and Ca<Cc will result in emission from surface to air. Cc is
calculated as Cc=CaRa+0.5Rinc+CstRb+Rst+Cg0.5Rinc+Rbg+Rsoil(Ra+0.5Rinc)-1+(Rb+Rst)-1+(Rb+Rw)-1+(0.5Rinc+Rbg+Rsoil)-1,
where Rb, Rbg, Rst, Rsoil, and
Rw are the resistances at the quasi-laminar boundary layer of leaf
surface, the quasi-laminar boundary layer of ground surface, the leaf
stomatal, soil, and cuticle, respectively. Ra, Rb,
Rbg, Rst, and Rw are already defined and used in
the standard GEOS-Chem deposition scheme. Here we define and calculate
Rsoil and Rinc following . Cst
and Cg are the NH3 concentrations in the leaf stomata and soil
pores, respectively. They are calculated as functions of temperature and
NH3 emission potential (Γst,g, dimensionless) in the leaf
stomata and soil .
Γ=[NH4+][H+]Γst is calculated as a function of land cover type, and the
values of different land cover types are based on .
Γg is calculated as a function of soil pH and NH4+
concentration in the soil, [NH4+]soil. Soil pH data are taken
from ISRIC – World Soil Information with a
0.5∘× 0.5∘ global resolution
(http://www.isric.org/data/data-download). We model the
[NH4+]soil as an ammonium pool in the soil, which is a function
of fertilizer application rate, deposition, nitrification, soil moisture, and
emission in bidirectional exchange. The calculation of
[NH4+]soil is described in the next section.
To compare the deposition (downward) flux and emission (upward) flux of the
bidirectional case to the base case, we define diagnostic variables for
gross deposition flux Fdep and emission flux Femis as
follows ,
Fdep=Cc-CaRa+0.5Rinc|Cst=0,Cg=0,
Femis=CcRa+0.5Rinc|Ca=0,
where Fdep is calculated under the assumption that there is no
NH3 emission potential from the soil and canopy, and Femis is
calculated under the assumption that there is no NH3 in the atmosphere.
Thus, Fdep+Femis=Ft.
Soil ammonium pool
Here we introduce a NH4+ pool to track the NH3 and NH4+ in the
atmosphere and in the soil. The inputs to the ammonium pool in the soil are
NHx (NH3 and NH4+) deposition from the atmosphere, NH3 emission
from the soil, and N fertilizer application rate. The annual N fertilizer
application rates are from , which
have chemical fertilizer (global total 70 Tg N yr-1) with a
0.5∘× 0.5∘ resolution for the year 2000. We assume
that all forms of N fertilizers will convert to NH4+ rapidly after
fertilizer application. This data set is also used to develop the global soil
nitric oxide emissions in GEOS-Chem in . We use the same
treatment of annual total fertilization as to derive daily
fertilizer application rates by applying 75 % of the annual total
fertilization amount around the first day of the growing season (green-up
day), distributed with a Gaussian distribution 1 month after. The other
25 % is evenly distributed over the remaining time before the end of the
growing season (brown-down day). The determination of green-up and brown-down
days is based on the growing season dates derived from the MODIS Land Cover
Dynamics product (MCD 12Q2) using the MODIS enhanced vegetation index
.
Using the fertilizer inputs described above, in addition to inputs from
deposition and outputs from emission, the time-dependent soil NH4+ pool
[mol L-1] is calculated as
[NH4+]soil=[NHx]depdsθNA+[N]fertdsθMN-[NH3]bidi emitdsθNA,
where [NHx]dep [molec cm-2] is deposition from wet and dry
deposition of NH3 and NH4+, [N]fert [N g m-2] is the
NH4+ from fertilizer, [NH3]bidi emit [molec cm-2] is
the gross NH4+ emitting from the soil due to bidirectional exchange,
MN is the molar mass of nitrogen, ds is the depth of the
soil layer, taken to be 0.02 m, θ is the soil wetness
[m3 m-3], and NA is Avogadro's number. We then solve the
mass balance equation for [NHx]dep and [N]fert:
d[NHx]depdt=Sdep-[NHx]depτ-Ldep,d[N]fertdt=Sfert-[N]fertτ,
where τ is the decay time owing to nitrification rate of NH4+ in
soil. We assume τ is 15 days, since almost all NH4+ will convert to
NO3- within that timespan . Sdep is the
deposition rate, Sfert is the fertilizer application rate, and
Ldep is the deposition loss rate. We use the same assumption as
that only 60 % of this deposited NHx will enter the
soil, while the rest of the NHx deposition will runoff into waterways.
Here we do not consider the production of NH4+ from NO3- in the
nitrogen cycle from mineralization nor immobilization. The timescale of
these processes can be years, which is much larger than the timescale of the
NH4+ simulations considered here; also found these
processes were not needed to accurately simulate NH3 over managed lands on
similar timescales.
Adjoint of bidirectional exchange
To investigate the sensitivity of modeled NH3 concentrations to the
parameters in the bidirectional exchange model, and to facilitate future
inverse modeling, we develop the adjoint of our updated NH3 flux scheme.
Here we consider two key parameters, soil pH and fertilizer application rate,
since their values are highly approximate.
The adjoint sensitivity is defined as
λσ=∂J(NH3)∂σ,
where J(NH3) is the total mass of ammonia at surface level in each grid
box during 1 week. The unit of J(NH3) is kg box-1. σ in
this study is defined as the soil pH scaling factor (σpH) or
fertilizer application rate scaling factor
(σfert_rate). σpH is defined as
pHpH0 and σfert_rate is
defined as fert_ratefert_rate0.
pH0 and fert_rate0 are the initial estimate of
soil pH from ISRIC and fertilizer application rates from .
λσ is the sensitivity of J(NH3) with respect to the
bidirectional exchange model parameters σ.
Validating the adjoint of bidirectional exchange
We validate the accuracy of the adjoint model by comparing the sensitivity of
NH3 surface concentrations with respect to soil pH and fertilizer
application rate calculated using the adjoint model with sensitivities
calculated using the finite differences method. In order to make such
comparisons efficiently throughout the model domain, horizontal transport is
turned off for these tests e.g.,. Figure
shows the comparison of sensitivities calculated by adjoint and finite
difference. The cost function is evaluated once at the end of a 1-week
simulation. The slope of a linear regression and square of correlation
coefficient, R2, are both close to unity, demonstrating the accuracy of
adjoint of the bidirectional model.
The adjoint sensitivity of NH3 surface level concentration with
respect to soil pH (left) and fertilizer application rate (right) compared to
finite difference gradients. The cost function is evaluated once at the end
of a 1-week simulation which excludes horizontal transport.
Spatial distribution of ammonia total emissions from GEOS-Chem with
(BIDI) and without (BASE) bidirectional exchange and their differences in
April, July, and October of 2008. The total emissions in the BIDI case are the
sum of upward fluxes from soil and vegetation from the bidirectional
exchange and emissions from all other sources except fertilizers.
Results and discussion
For the US region, we use nested horizontal resolution
(1/2∘× 2/3∘) simulations with the standard set of
GEOS-Chem emission inventories. For the global simulation, we introduce a new
bottom-up emission inventory for NH3 agriculture sources, MASAGE_NH3
. The full description of the differences between the
GEOS-Chem standard NH3 emission inventories and MASAGE_NH3 is in
. We perform global simulation at a horizontal resolution of
2∘× 2.5∘. All simulations include the dynamic
treatment of the diurnal variability of livestock emissions described in
Sect. .
USA
We run the GEOS-Chem model for April, July, and October of 2008 with the
updated diurnal variation of NH3 livestock emissions and the bidirectional
exchange mechanism. Figure shows the NH3 total
gross emissions from GEOS-Chem with (BIDI) and without (BASE) the
bidirectional air–surface exchange. The total gross emissions of BIDI case
are the sum of primary emissions and upward fluxes from soil and vegetation.
Bidirectional exchange generally increases gross emissions in most parts of
the USA in July (up to 0.43 Gg month-1) and decreases gross emissions
throughout the USA in October (up to 0.29 Gg month-1). Significant
decreases occur in the Great Plains region in both April and October with a
magnitude of up to 0.23 Gg month-1 in April and 0.29 Gg month-1
in October. Bidirectional exchange does not much alter the total modeled
emissions in the USA in July (increase by 5.2 %) and October (decrease by
13.9 %) but does lead to a decrease of 23.5 % in April. With the ammonium
soil pool, the model can preserve ammonia/ammonium in the soil rather than
emitting it directly after fertilizer application. This is the main reason
that gross emissions decrease in the Great Plains in April and October. In
July, there is not as much fertilizer applied as in April. However, the
bidirectional exchange between the air and surface can induce NH3 to be
re-emitted from the ammonium soil pool which reserve ammonium from previous
deposition and fertilizer application.
The spatial distributions of surface NH3 concentrations in GEOS-Chem are
shown in Fig. . In general, bidirectional exchange
decreases monthly NH3 surface concentrations in April (up to 1.8 ppb) and
October (up to 2.1 ppb) and increases it in July (up to 2.8 ppb)
throughout the USA. There are peak decreases in NH3 surface concentrations
in the Great Plains in both April and October and increases in California in
July. These changes of surface NH3 concentration are consistent with the
pattern of changes to NH3 emissions in Fig. .
Spatial distribution of ammonia concentration at surface level of
GEOS-Chem with (BIDI) and without (BASE) bidirectional exchange and their
differences in April, July, and October of 2008.
Evaluation with NH3
We evaluate the GEOS-Chem simulation with bidirectional exchange by
comparing the model values to in situ observations from AMoN.
Figure shows the comparison of GEOS-Chem surface
NH3 concentrations in the BASE and BIDI cases with AMoN observations.
Bidirectional exchange decreases the normalized mean bias (NMB) from
-0.227 to -0.165 in July and increases the NMB from -0.701 and
-0.197 to -0.829 and 0.283 in April and October, respectively. The root
mean square error (RMSE) decreases by 18.3 % in July and increases by
16.7 % in April and 19.2 % in October. R2 values increase by 20.6 %
in July and decrease by 37.6 % in April and 49.1 % in October. The slope
slightly increases by 0.5 % in July and decreases by 53.5 and 37.5 % in
April and October, respectively. The changes in slopes can also be seen in
Fig. as bidirectional exchange decreases the
NH3 monthly average concentration at AMoN sites in April and October while
it increases the NH3 monthly average concentrations in July. Modeled
surface NH3 concentrations are significantly lower than the AMoN
observations in April and October by a factor of 2–5, which is not
unreasonable given likely underestimates in primary emissions
. Such large underestimation is not corrected
by applying the NH3 bidirectional exchange to the model. Other
improvements in the model besides bidirectional exchange, such as updating
primary NH3 emissions, are also required for better estimating NH3
surface concentrations.
Comparison of GEOS-Chem simulated NH3 concentration at surface
level in BASE and BIDI cases with AMoN observations in April, July, and
October of 2008. R2 is the square of the correlation coefficient. Solid
lines are regressions. Gray dashed lines are 1 : 1.
Evaluation with aerosol nitrate
We also compare the simulated nitrate aerosol concentrations to the aerosol
observations from IMPROVE. Figure shows the
simulated monthly average nitrate aerosol surface concentration from the
GEOS-Chem BASE and BIDI cases in comparison to IMPROVE observations in 2008.
GEOS-Chem overestimates nitrate in the BASE case in all 3 months. The
overestimates in BASE cases can be 5 times larger in October. Bidirectional
exchange generally decreases the nitrate concentrations in April, which makes
the slope of the regression line decrease by 45.4 %. However there are
still large overestimates (∼ a factor of 2 on average) in the
Northeastern USA and large underestimates (up to 1.7 µg m-3) in Southern
California in the BIDI case in April. Bidirectional exchange slightly
increases (less than 0.5 µg m-3) nitrate in July and
decreases (less than 0.4 µg m-3) nitrate in October, which
does not significantly impact the comparison of modeled nitrate with IMPROVE
observations.
Overestimation of nitrate in GEOS-Chem is a long recognized problem
.
recommend that reducing the nitric acid to 75 % would bring the magnitude
of nitrate aerosol concentration into agreement with the IMPROVE
observations. In our study, based on the comparison of BASE modeled nitrate
concentration and IMPROVE observation, we perform sensitivity studies by
reducing the nitric acid to 50 % in July and to 20 % in October at each
time step in the GEOS-Chem model for both BASE and BIDI cases. Modeled nitrate
concentrations reduce dramatically with this adjustment in July and October,
but overestimates still exist in many places in the eastern USA. We also
compare the modeled NH3 surface concentrations in the sensitivity
simulations with adjusted nitric acid concentrations to the AMoN
observations, since reducing the nitric acid in the model may cause NH3 to
partition more to the gas phase, which could bring modeled NH3
concentrations into better agreement with AMoN observations. However, no
significant impacts are found in NH3 concentrations at AMoN site locations
with these nitric acid adjustments, consistent with earlier assessments that
the model's nitrate formation is NH3 limited throughout much of the USA
. Overall, overestimation of model nitrate by a factor of 3 to
5 appears to be a model deficiency beyond the issue of NH3 bidirectional
exchange.
Comparison of GEOS-Chem simulated nitrate aerosol concentration at
surface level in BASE and BIDI cases with IMPROVE observations in April,
July, and October of 2008. R is the correlation coefficient.
Comparison to inverse modeling
Inverse modeling estimates of unidirectional NH3 emissions using TES
observations lead to overestimates of ammonia concentration in comparison to
surface observations from AMoN in July , and emissions estimates
in July are much higher than other top-down or bottom-up studies
. It is thus of interest to evaluate whether bidirectional
exchange of NH3 would reduce this high bias. Although repeating the
inverse modeling with TES NH3 observations and bidirectional exchange is
beyond the scope of this work, we can use the optimized emissions from
as the basis upon which bidirectional exchange is applied.
Figure shows the modeled NH3 monthly average
surface concentrations in comparison to the AMoN observations. The left
column of Fig. is from the optimized NH3
estimates from . In the right column, the modeled NH3 monthly
average concentrations are from GEOS-Chem with NH3 bidirectional exchange
using the optimized emissions from . The model with
bidirectional exchange decreases the high bias in July: the NMB decreases by
80.4 % and the RMSE decreases by 56.7 %. The R2 value increases by
43.3 %. However, the model with bidirectional exchange now underestimates
the NH3 monthly average concentrations in April and October. The RMSE
increases by 4.1 % in April and 28.8 % in October. The impacts of NH3
concentration with respect to emissions in the model with bidirectional
exchange are nonlinear. Using the optimized NH3 emissions inventories from
the TES NH3 assimilation with the BASE model does not guarantee a better
estimation of NH3 surface concentrations with the BIDI model. Therefore,
full coupling of inverse modeling with TES NH3 observations and
bidirectional exchange is necessary. Also, investigating the sensitivities
of bidirectional model results to the NH3 emissions, as well as other
critical parameters, is important for improving the NH3 concentration
estimation.
Left column: comparison of GEOS-Chem optimized NH3 concentration
at surface level from with AMoN observations. Right column:
comparison of GEOS-Chem simulated NH3 concentration at surface level in
BIDI case using optimized NH3 emissions from with AMoN
observations. R2 is the square of the correlation coefficient. Gray dashed
lines are 1 : 1.
Global distribution of ammonia gross emissions from GEOS-Chem with
(BIDI) and without (BASE) bidirectional exchange and their differences in
April, July, and October of 2008. The total emissions in the BIDI case are the
sum of upward fluxes from soil and vegetation from the bidirectional
exchange and emissions from all other sources except fertilizers.
Global modeling results
While bidirectional exchange of NH3 has previously been implemented in
regional models e.g.,, with the
GEOS-Chem model we have the chance to evaluate NH3 bidirectional exchange
on global scales for the first time. The global distribution of NH3 gross
emissions in both BASE and BIDI cases, as well as their differences, are
shown in Fig. . Generally, bidirectional exchange
decreases NH3 emissions in the Northern Hemisphere and increases NH3
gross emissions in the Southern Hemisphere in April and October. Total NH3
emissions in the Northern Hemisphere decrease by 22.6 % in April and
7.8 % in October. In July, bidirectional exchange increases NH3
emissions in most places (7.1 % globally), except China and India. As
evident from the figure, the differences in many places throughout the globe
are very slight. With positive and negative differences, the global mean and
median of the changes are quite small (for example, the mean and median
differences in July are -0.02 Gg month-1 and 0, respectively).
However, there are areas where the differences deviate significantly from
0 (for example the standard deviation of the difference in July is
3.76 Gg month-1 in China). We thus focus our discussion on the range
of differences in particular regions that are evident from
Fig. . Significant decreases in NH3 emissions in
the BIDI case occur in southeastern China and northern India in all 3
months. The magnitudes of the decreases can be up to 18.4 Gg month-1
in China and 16.5 Gg month-1 in India in July. Total NH3 emissions
in China decrease by 43.6 % in April, 31.4 % in July, and 24.7 % in
October. Total NH3 emissions in India decrease by 28.8 % in April,
22.8 % in July, and 7.2 % in October. There are also large decreases of
total NH3 emissions in the USA, Mexico, and Europe in April of up to
6.5 Gg month-1.
Global distribution of original ammonia fertilizer emissions in BASE
case (BASE fertilizer), upward flux from soil and vegetation in BIDI case
(BIDI fertilizer), and ammonia emissions from all other sources except
fertilizers (all others) in April, July, and October of 2008.
Percentage of gross emissions owing to fertilizer in the global BIDI
case in April, July, and October of 2008.
The changes of NH3 gross emissions between BASE and BIDI cases can be seen
more directly from the comparison of fertilizers emissions in the BASE case
with those in the BIDI case. In Fig. , we show the
global distribution of NH3 fertilizer emissions in the BASE and BIDI
cases. In the BIDI case, the fertilizer emissions are the upward fluxes from soil
and vegetation from bidirectional exchange. The third column is the NH3
emissions from all other sources except fertilizers in April, July, and
October of 2008. In the BASE case, fertilizers emissions have peak values in
eastern China and middle-east Asia and much smaller values elsewhere.
Fertilizers emissions in the BIDI case increase in many places where there
are no or near 0 values in the BASE case. In the BIDI case, the fertilizer
emissions distribution is much more homogeneous. As we described in
Sect. , fertilizer emissions are lower in the BIDI
case under cool spring and fall time conditions due to the temperature
effects on NH3 emissions and storage in the soil ammonium pool. The
deposition and re-emission processes in bidirectional exchange model thus
extend the effect of NH3 emissions from fertilizers. There are obvious
trends that fertilizer emissions in the Northern Hemisphere are larger than
those in the Southern Hemisphere in April and July, and fertilizer emissions
in the Southern Hemisphere are larger than those in the Northern Hemisphere
in October. The global amount of NH3 fertilizer emissions is 27.8 % of
total emissions from all sources in the BASE case and 12.8 % in the BIDI
case in April. Figure shows the percentage of
emissions from fertilizers in BIDI case in the global simulations. BIDI
fertilizers contribute more to gross emissions in July than in other months
in the Northern Hemisphere, which again demonstrates the delayed effect of
fertilizer NH3 (mostly applied in the springtime) in the BIDI model.
Global distribution of ammonia concentration at surface level of
GEOS-Chem with (BIDI) and without (BASE) bidirectional exchange and their
differences in April, July, and October of 2008.
Figure shows the global distribution of NH3
monthly surface concentrations in the BASE and BIDI cases and their
differences in April, July, and October. Although bidirectional exchange
changes NH3 concentrations slightly throughout the globe (mean and median
values of the changes are all nearly 0 in all 3 months), significant
changes still exist in many places. In general, bidirectional exchange
increases NH3 concentrations in July by up to 3.9 ppb. It decreases
NH3 concentrations in the Northern Hemisphere (up to 27.6 ppb) and
increases NH3 concentrations in the Southern Hemisphere (up to 4.2 ppb)
in April and October. Significant decreases of NH3 concentrations occur in
China in all 3 months with up to 20.6 ppb in April, 12.8 ppb in July,
and 15.7 ppb in October. indicated the MASAGE NH3
emissions, which we use in this study, were higher than the bottom-up NH3
emissions from in China in April and July and similar to the
emissions from in April, July, and October. Overestimation
of NH3 surface concentrations in GEOS-Chem in China is found in
when using NH3 emissions from , leading to
an overestimation of nitrate aerosol concentrations in China. Observations
from the Infrared Atmospheric Sounding Interferometer (IASI) remote sensing
instrument have discrepancies over China with NH3 concentrations in
GEOS-Chem that may in part be improved by the
impacts of bidirectional exchange. However, observations from TES show
NH3 concentrations in GEOS-Chem (with NH3 emissions from
) are underestimated in many places of the globe including
China . We must note that the lower NH3 concentrations
presented here are daily averages, while IASI and TES data are for a
particular hour of the day. The changes in the emissions profile may reduce
the model underestimate against the satellite observations while decreasing
the mean NH3 concentrations. However, the ability of remote sensing
instruments on satellites in low-earth orbits to observe the impact of
bidirectional exchange on NH3 concentrations is limited compared to
observations from potential future geostationary measurements .
Wet deposition evaluation (global and USA)
We compare the model NH4+ wet deposition to in situ observations in
several regions of the world using NTN for the continental USA, CAPMoN for
Canada, EMEP for Europe, and EANET for eastern Asia; see
Fig. . For the model NH4+ wet deposition, we
also include the model NH3 wet deposition since NH4+ wet deposition
from in situ observations includes precipitated NH3. Since there are
biases in the modeled precipitation, we scale the model wet deposition by
multiplying the modeled deposition by the ratio of the observed to modeled
precipitation, Fluxmodel⋅(PobsPmodel)0.6, following the correction
method in . We only include observations that have 0.25<PobsPmodel<4 to limit the effect of this
correction , and we also exclude observations which are
beyond 3 times the standard deviation of observed NH4+ wet deposition to
avoid outliers.
In general, the GEOS-Chem model underestimates NH4+ wet deposition
throughout the world in the BASE case. Large increases in NH4+ wet
deposition in the BIDI cases are found in the USA, Canada, and Europe in July
(up to 6.31 kg ha-1 yr-1). The slopes of the regression line
when compared to observations increase by 37.9 % in the USA, 54.9 % in Canada,
and 17.7 % in Europe in the BIDI cases in July, all becoming closer to
unity. However, the bidirectional exchange increases the RMSE by 64.3 % in
the USA, 37.2 % in Canada, and 36.0 % in Europe.
Comparisons of GEOS-Chem modeled NH4+ wet deposition in BASE
(blue) and BIDI (red) cases with in situ observations in the USA (first column),
Canada (second column), Europe (third column), and eastern Asia (fourth column) in April
(first row), July (second row), and October (third row) of 2008. The y axis
represents the model values, and the x axis represents observations from NTN
(for USA), CAPMoN (for Canada), EMEP (for Europe), and EANET (for eastern Asia).
R2 is the square of the correlation coefficient.
Bidirectional exchange does not impact the NH4+ wet deposition much in
April and October. It decreases NH4+ wet deposition slightly (up to
3.77 kg ha-1 yr-1 in Europe) at most of the observation
locations in the USA, Canada, and Europe in April. The slopes decrease by
14.3 % in the USA, 6.8 % in Canada, and 12.3 % in Europe. Bidirectional
exchange decreases the NMB by 46.4 % in the USA, 37.6 % in Europe in
April, but increases the NMB by 28.3 % in Canada, and 11.6 % in eastern
Asia. In October, bidirectional exchange increases NH4+ wet deposition
slightly at most of the observation locations (up to
3.85 kg ha-1 yr-1). The changes in RMSE between BASE and BIDI
cases are small, less than 10 %.
The overall differences of NH4+ wet deposition between the BASE and BIDI
cases are generally small (from -4.95 to 6.31 kg ha-1 yr-1),
even when the differences in NH3 emissions are substantial. For example,
NH3 emissions differences between the BASE and BIDI range from -61.2 to
1.16 kg ha-1 yr-1 in China in April with bidirectional
exchange, but changes in NH4+ wet deposition are not very large (from
-4.95 to 2.52 kg ha-1 yr-1). While implementing NH3
bidirectional exchange leads to improvements in some regions and seasons, it
does not uniformly reduce error in model estimation of NH4+ wet
deposition.
In Sect. , we demonstrated the accuracy of the sensitivities
calculated using the adjoint of the GEOS-Chem bidirectional model. In this
section, we present the adjoint sensitivities of NH3 surface
concentrations with respect to the important parameters in the bidirectional
model. Figure shows the adjoint sensitivities of NH3
surface concentration with respect to the scaling factors for the soil pH
(left) and for the fertilizer application rate (right) in April, July, and
October 2008. The sensitivities with respect to both parameters are always
positive throughout the globe. Sensitivities of NH3 to fertilizer
application rate are positive as excess fertilizer application will increase
the NH3 soil emission potential. Sensitivities of NH3 to soil pH are
also positive as low H+ concentrations in soil (high soil pH) increases
dissociation of NH4+ to NH3, thereby increasing the potential for
volatilization of NH3.
The relationship between NH3 concentration and soil pH is stronger during
the growing season since more ammonium is in the soil pool. Slight changes in
pH may have large impacts on the amount of NH3 emitted from soil and
further induce large differences in NH3 surface concentrations. As we can
see in the left column of Fig. , the sensitivities of
NH3 surface concentrations with respect to soil pH scaling factors are
larger in the Northern Hemisphere than those in the Southern Hemisphere in
April and July and less in the Northern Hemisphere than those in the
Southern Hemisphere in October, since the growing seasons are in April in the
Northern Hemisphere and in October in the Southern Hemisphere. Large
sensitivities in July in the Northern Hemisphere are due to ammonium in the
soil pool accumulated from CAFO emissions via deposition. However, some
caution is warranted in interpreting the seasonality of these sensitivities,
as our model does not include any seasonal variations in soil pH. Seasonal
variability of soil pH is driven by fertilizer rate, timing of fertilizer
application, root and bacterial activity, soil moisture, organic matter, and
salt levels . Soil pH is observed to be highest at or near
mid-winter and lowest at late summer . Variation of soil pH
can be more than one unit from spring to fall ; thus the
uncertainty in the constant annual soil pH used here could be about 20 %
owing to neglecting seasonality.
The relationship between NH3 concentration and fertilizer application rate
is also seasonally dependent. The seasonal trends of sensitivities of NH3
to fertilizer application rate are similar to sensitivities of NH3 to soil
pH. Larger sensitivities appear in places with lower fertilizer application
rates than those with plenty of fertilizer. For example, the largest
fertilizer application rates appear in southeast China, northwest Europe and
northern India in April, and sensitivities are nearly 0 in each of these
locations. That the magnitude of the fertilizer application rates itself is
an important factor in determining the sensitivities of NH3 concentration
to the fertilizer application rate is indicative of the nonlinear
relationship introduced by treatment of bidirectional exchange.
The adjoint sensitivities of NH3 surface level concentration with
respect to soil pH scaling factor (left) and fertilizer application rate
scaling factor (right) in April, July, and October of 2008. Note that
sensitivities in the left and right columns have different scales.
Through investigating the sensitivities of NH3 surface concentration to
the soil pH and the fertilizer application rate, we know that NH3 surface
concentrations are very sensitive to these parameters in many places of
globe. We also find that NH3 surface concentrations are more sensitive to
soil pH than fertilizer application rate in general. In addition to the
adjoint sensitivity analysis of NH3 concentrations to the soil pH and the
fertilizer application rate, it is also interesting to know the ranking of
sensitivities of NH3 concentrations with respect to other parameters, such
as NH3 concentrations at compensation points (Cc, Cst,
Cg), NH3 emission potentials (Γg,
Γst), and resistances (Ra, Rinc,
Rsoil, Rg, Rst, Rbg, Rw).
Knowledge of the sensitivity of NH3 concentrations with respect to these
parameters may help improve the model estimation of the spatial and temporal
distributions as well as the magnitudes of NH3 concentrations.
Comparison to in situ NH3 with adjusted BIDI parameters
Based on the adjoint sensitivity analysis we have shown above and forward
sensitivity analysis for all the parameters mentioned above (results not
shown), we know that soil pH is one of the most critical parameters in the
GEOS-Chem bidirectional exchange model. It is interesting to explore to what
extent biases in the modeled NH3 concentrations may be explained by
uncertainties in the parameters of the bidirectional model, rather than,
e.g., revising livestock NH3 emissions. To test this, we increase the soil
pH value by a factor of 1.1, since uncertainties of seasonal soil pH are
about 20 %. As expected, the NH3 surface concentrations generally
increase over the globe (e.g., up to 3.4 ppb in April). Large increases
occur in places with large sensitivities to soil pH
(Fig. , upper left). NH3 concentrations are
underestimated in the model in comparison to the AMoN observations in the USA.
They are also underestimated in many parts of globe in comparison to TES
observations . With this adjustment to soil pH, the
discrepancy between TES observations and the model in upper levels of the
boundary layer may potentially be reduced in regions where GEOS-Chem NH3
is underestimated before the growing seasons and overestimated after the
growing seasons. Slight increases in NH3 surface concentrations are found
throughout the USA as NH3 is not very sensitive to soil pH in the USA (see
Fig. ). Thus, this adjustment does not improve the
comparison to AMoN observations in the USA.
Comparison of NH3 surface concentrations from GEOS-Chem with
bidirectional exchange to AMoN observations. The livestock emissions in the
model are increased by a factor of 6 in April and 3 in October.
In this study, we did not consider the adjustment of soil pH in agricultural
areas by the farmers who limit the soil pH in a certain range to improve crop
yield . However, no significant changes in the modeled
surface NH3 concentrations occur with bidirectional exchange when we
limit the soil pH in the agricultural areas between 5.5 and 6.5 (generally
less than 1 ppb over the globe, up to 3.4 ppb in India), since
sensitivities are not very strong in the agricultural areas (see left column
of Fig. ).
The adjoint sensitivities of NH3 surface level concentration at
88∘ W, 40∘ N with respect to NH3 anthropogenic emission
scaling factor at all grid cells in both BASE (left) and BIDI (right) cases
in April 2008.
Small differences between bidirectional and unidirectional fluxes in the USA
are also indicated in , wherein sensitivity tests were
performed varying the soil emission potential (Γg, a parameter
which includes both soil pH and fertilizer application rate) in CMAQ. It was
found that the impact on total N deposition at continental scales was
generally small (< 5 %), with very few (< 10 %) grid cells having
differences up to 20 %.
From , we know that the underestimation of NH3 emissions in
the unidirectional model can be as much as a factor of 9 in the USA. We also
notice that NH3 may not change much when fertilizer emissions increase a
lot in regions such as midwest USA and northern Australia (see
Figs. and ). Thus,
low emissions from other sources, such as livestock, may be a big part of the
reason for underestimating NH3 concentrations in the bidirectional
exchange model. To better understand this, we also test increasing NH3
livestock emissions by a factor of 8 in April and 3 in October as NH3
concentrations are generally underestimated by around 8 and 3 times
(Fig. ) compared to AMoN observations in April and
October, respectively. These adjustments bring the NH3 concentrations into
a much better agreement with the magnitude of AMoN observations; see
Fig. . However, uniformly increasing the
livestock emissions does not well represent the NH3 spatial distribution
with the AMoN observations (correlations of model and observation are very
low). Overall, treatment of bidirectional exchange can improve our
understanding of NH3 emissions from fertilizers, but this alone may not
improve estimation of NH3 concentrations, NH4+ wet depositions, and
nitrate aerosol concentrations. Additional work including bidirectional
exchange in NH3 inverse modeling is needed, as large underestimates in
NH3 primary sources exist in the model and simply applying the scheme to
optimized emissions from inverse modeling can not capture well the spatial
variability of NH3 concentrations that are the responses of both
bidirectional exchange processes and emissions.
Spot sensitivity analysis
Here we investigate to what extent bidirectional exchange increases the
NH3 lifetime, which is a critical issue for controlling nitrogen
deposition and PM2.5 formation. Through the adjoint method, we are able
to assess source contributions to model estimates in particular response
regions e.g.,. In Fig. , we show the
adjoint sensitivity of NH3 surface concentration at a single location
(88∘ W, 40∘ N) with respect to the NH3 anthropogenic
emissions at all grid cells in April 2008. In the BASE case (left panel), the
NH3 surface concentration is most sensitive to the emissions from the same
grid cell and is less sensitive to the emissions from surrounding grid
cells. With the bidirectional exchange (right panel), the NH3
concentration is sensitive to the emissions from a much wider range, which
extends all the way to Canada. Some of the sensitivities are very strong even
though they are a long distance away from the location of the NH3
concentration under consideration. The deposition and re-emission processes
in the bidirectional exchange extends the spatial range of influence of
NH3 emissions and, in effect, the NH3 lifetime. Thus, modeled NH3
concentrations in Illinois can be impacted by the emissions from Kansas or
even from Canada.
Conclusions
In this study, we have considered a more detailed, process-level treatment of
NH3 sources in a global chemical transport model (GEOS-Chem) and evaluated
the model behavior in terms of biases in estimated NH3, nitrate, and
NH4+ wet deposition and the factors driving these processes in the
model. First, we update the diurnal variability of NH3 livestock
emissions. In general, by implementing this diurnal variability scheme, the
global NH3 concentrations, nitrate aerosol concentrations, and nitrogen
deposition all decrease. The largest decreases always occur in southeastern
China and northern India. More NH3 from livestock emitted in the daytime
largely decreases the NH3 surface concentrations in the night and
increases concentrations during the day, which is more conducive to export of
NH3.
We have also developed bidirectional exchange of NH3 and its adjoint in
the GEOS-Chem model. Bidirectional exchange generally increases NH3 gross
emissions in most parts of the USA and most places around the globe in July,
except China and India. These are mainly due to the NH3 re-emissions from
the ammonium soil pool that accumulates ammonium from previous months.
Bidirectional exchange generally decreases NH3 gross emissions in the USA
in April and October. On a global scale, bidirectional exchange decreases
NH3 gross emissions in the Northern Hemisphere in April and October and
increases NH3 gross emissions in the Southern Hemisphere. During the
growing seasons, the ammonium soil pool preserves ammonia/ammonium in the
soil rather than emitting it directly after fertilizer application.
Bidirectional exchange increases monthly NH3 surface concentrations
throughout the world in July, which improves comparison to the AMoN
observations in the USA. It decreases NH3 surface concentrations in the
Northern Hemisphere and increases NH3 concentrations in the Southern
Hemisphere in April and October. Bidirectional exchange does not have a
large impact on model biases in nitrate aerosol, which are likely owing to
overestimated nitric acid concentration . However, with the
deposition and re-emission of NH3 inherent in bidirectional exchange,
NH3 can be impacted by sources from a much greater distance, which is a
critical issue when considering strategies for controlling nitrogen
deposition and PM2.5 formation.
Bidirectional exchange largely increases NH4+ wet deposition in the USA,
Canada, and Europe in July but slightly decreases NH4+ wet deposition in
April and has little impact in October. The overall differences of NH4+
wet deposition between the BASE and BIDI cases are generally small, even when
the differences in NH3 fertilizer emissions are large. While observations
of wet deposition have been used to constrain NH3 sources in previous
works , this data set does not
appear sufficient to provide constraints on model treatment of bidirectional
exchange. Moreover, as the in situ measurements used here are limited in
space and time, the comparisons between model and measurements only
represents the ability of bidirectional parameterization at these specific
spatial (100s of km) and temporal (monthly) scales; more pronounced impacts
may occur at finer scales.
Using the adjoint of bidirectional exchange, we investigate the spatial and
seasonal dependency of NH3 surface concentrations in the GEOS-Chem model
on the soil pH and fertilizer application rate, which are themselves
uncertain. Soil pH is known to be seasonally variable. Updating the soil pH
with seasonal variability would impact the results of bidirectional exchange
across wide regions of globe. However, updating the soil pH with seasonal
variability does not seem sufficient to improve comparison with in situ
observations in the USA, as primary sources are likely underestimated by a
factor of 3 or more. Further, uniformly increasing the emissions from primary
sources degrades the spatial variability of simulated NH3.
Overall, bidirectional exchange largely extends the lifetime of NH3 in
the atmosphere via deposition and re-emission processes. This model provides
a better fundamental description of NH3 emissions from fertilizers.
However, implementing bidirectional exchange does not uniformly improve
estimation of NH3 concentrations, NH4+ wet deposition, and nitrate
aerosol concentrations. Domain-wide adjustments to soil pH or livestock
emissions do not improve the model comparison to the full suite of
measurements from different platforms, locations, and seasons considered here.
Thus, incorporating bidirectional exchange in an inverse model is required
in future work to correct the low biases in NH3 primary sources without
over adjusting these sources to account for model error from neglecting
bidirectional exchange processes. Measurements from recent
or future remote sensing platforms will be
of value for such endeavors.
Acknowledgements
This work is supported by NASA grants NNX09AN77G and NNX10AG63G and EPA STAR
award RD834559. While this manuscript has been reviewed by the Environmental
Protection Agency and approved for publication, it may not reflect official
agency views or policies. Edited by:
L. Ganzeveld
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