Introduction
Isoprene from vegetation comprises about one third of the global emission of
volatile organic compounds (VOCs) (Guenther et al., 2006). Emissions in the
southeastern US during summertime are some of the highest in the
world (Guenther et al., 2012). Isoprene oxidation fuels tropospheric ozone
formation in both rural and urban regions (Monks et al., 2015), and isoprene
oxidation products are a major source of organic aerosol (Carlton et al.,
2009). Regional air quality predictions are heavily dependent on isoprene
emission estimates (Pierce et al., 1998; Fiore et al., 2005; Hogrefe et al.,
2011; Mao et al., 2013). The uncertainty in isoprene emissions on a global
scale is estimated to be factor of 2 or more, with larger uncertainties on
local to regional scales (Guenther et al., 2012). Here, we use observations
of formaldehyde (HCHO) columns from the satellite-based Ozone Monitoring
Instrument (OMI) in the first high-resolution adjoint-based inverse analysis
of isoprene emissions at ecosystem-relevant scales, taking advantage of
detailed chemical measurements available over the southeast US to demonstrate
the capability of the satellite-based inversion.
Process-based “bottom-up” isoprene emission inventories are constructed by
estimating base leaf-level emission rates for individual plant functional
types (PFTs), mapping them onto gridded PFT distributions, and applying
factor dependences on environmental variables (temperature, insolation, leaf
area index and leaf age, soil moisture) (Guenther et al., 2006, 2012). The
largest uncertainty in the construction of bottom-up inventories stems from
the base emission rates, which are extrapolated from very limited
observations (Arneth et al., 2008). PFT distributions are an additional
source of uncertainty, with different land cover maps producing as much as a
factor of two difference in isoprene emissions (Millet et al., 2008).
Isoprene emissions can undergo large changes over decadal scales in response
to changing land cover (Purves et al., 2004; Zhu et al., 2017a). Factor
dependences on environmental variables are better understood, with the dominant
factor of variability being temperature (Palmer et al., 2006), although any
uncertainties in temperature will propagate into uncertainties in isoprene
emission estimates.
Satellite observations of formaldehyde atmospheric columns provide
“top-down” constraints on isoprene emissions to test inventories (Palmer et
al., 2003, 2006; Millet et al., 2008; Barkley et al., 2013; Marais et al.,
2014). HCHO is formed promptly and in high yield from isoprene oxidation, at
least when concentrations of nitrogen oxides (NOx ≡ NO +
NO2) originating from combustion or soils are relatively high (Wolfe et
al., 2016). A common approach has been to assume a local linear relationship
between HCHO columns and isoprene emissions (Palmer et al., 2003, 2006;
Millet et al., 2008), but this does not capture the spatial offset between
the point of isoprene emission and the resulting HCHO column. This spatial
offset can be hundreds of kilometers, depending in particular on NOx levels
(Marais et al., 2012). Tracing the observed HCHO back to the location of
isoprene emission requires accounting for this coupling between chemistry and
transport. Previous studies have applied adjoint-based global inversions to
account for transport in the isoprene-HCHO source–receptor relationship
(Stavrakou et al., 2009, 2015; Fortems-Cheiney et al., 2012; Bauwens et al., 2016), but they used horizontal resolutions of hundreds
of kilometers that do not capture the chemical timescales for isoprene conversion
to HCHO.
Here we apply the adjoint of the GEOS-Chem chemistry-transport model at
0.25∘ × 0.3125∘ horizontal resolution in an
inversion of OMI HCHO observations to infer isoprene emissions in the
southeast US during the summer of 2013. Our inversion takes advantage of
extensive aircraft observations of chemical composition from the NASA
SEAC4RS campaign (Toon et al., 2016). These observations were used to
validate the OMI HCHO retrievals (Zhu et al., 2016), and allowed a thorough
evaluation of isoprene and NOx chemistry in GEOS-Chem including
time-dependent HCHO yields from isoprene oxidation as a function of NOx
(Travis et al., 2016; Fisher et al., 2016; Chan Miller et al., 2017). They
further showed that the 0.25∘ × 0.3125∘ resolution
of GEOS-Chem captures the spatial segregation between isoprene and NOx
emissions which would be lost at a coarser model resolution and introduce error
in the HCHO yield (Yu et al., 2016). The SEAC4RS observations provide an
unprecedented test bed for determining the value of satellite HCHO
observations to quantifying isoprene emissions on ecosystem-relevant scales.
Methods
OMI observations
We use the OMI-SAO v003 Level 2 HCHO data as described by González Abad
et al. (2015). The OMI spectrometer flies aboard the NASA Aura research
satellite and provides daily global mapping with a local overpass time of
13:30 LT and a nadir resolution of 24 × 13 km2. Slant column
densities (SCDs, Ωs) of HCHO are calculated by direct fitting
of OMI radiances. The SCD over a remote Pacific reference sector is
subtracted and the difference is the enhancement over background (ΔΩs). The SCD is related to the vertical column density (VCD,
Ω) by an air mass factor (AMF), which accounts for the sensitivity of
the backscattered radiances to the HCHO vertical profile. The final VCD is
calculated by adding the background VCD (Ωo) from the GEOS-Chem
simulation over the Pacific reference sector:
Ω=ΔΩsAMF+Ωo.
The background contribution averages
3.8 × 1015 molecules cm-2, which is small relative to
the enhancements over the southeast US.
Zhu et al. (2016) validated the OMI-SAO v003 HCHO VCD satellite data during
SEAC4RS by comparison to two independent, in situ HCHO measurements
aboard the aircraft. They showed that the satellite data have accurate
spatial and temporal patterns in the satellite data but a 37 % low bias,
which they attributed to errors in spectral fitting and in assumed surface
reflectivity. Following the recommendation of Zhu et al. (2016), we correct
this bias by applying a uniform multiplicative factor of 1/(1-0.37)=1.59
to the satellite data. Independent evaluation with ground-based HCHO
observations provides support for this correction factor (Zhu et al., 2017b).
Simulation of the OMI data with the GEOS-Chem model requires that we use an
AMF consistent with the model vertical profile when converting observed SCDs
to VCDs (or equivalently when converting model VCDs to SCDs). Here we
calculate the AMF by applying the local OMI scattering weights from the
operational retrieval to the GEOS-Chem HCHO vertical profile (Qu et al.,
2017). The satellite data are filtered by the OMI-SAO quality flag, cloud
fractions less than 0.3, solar zenith angles less than 60∘, and values
within the range -0.5 to 10 × 1016 molecules cm-2 (Zhu
et al., 2016). We accumulate 192 889 individual scenes over the 8-week period
with an average of 35 single-scene observations per
0.25∘ × 0.3125∘ grid cell.
Single-scene measurement error includes (1) the spectral fitting error
reported as part of the operational product, and (2) the error in the AMF
calculation, which increases from 15 % under clear sky conditions to
20 % at a cloud fraction of 0.3 (Millet et al., 2006). We increase the
spectral fitting error by a factor of 1.59, the same factor used to correct
the mean bias in OMI VCDs. If the conversion of radiances to HCHO columns is
the cause of the bias, we would expect this bias to translate to the spectral
fitting error. This assumption is tested in Sect. 2.5. Spectral fitting
dominates the error budget, so that individual retrievals typically have an
80 % error over the southeast US. This error decreases when averaging
over a large number of retrievals (Boeke et al., 2011).
Figure 1 shows the error-weighted mean OMI HCHO VCD during the
August–September 2013 SEAC4RS period on the
0.25∘ × 0.3125∘ GEOS-Chem grid. The regional
enhancement over the southeast US is well known to be due to isoprene
emission (Abbott et al., 2003; Palmer et al., 2003, 2006; Millet et al.,
2006, 2008). The location of the maximum varies from year to year depending
on temperature (Palmer et al., 2006).
MEGAN emissions
We use the MEGAN v2.1 inventory as prior estimate of isoprene emission
(Guenther et al., 2012), as implemented in GEOS-Chem by Hu et al. (2015).
Base emission factors (top left panel of Fig. 2) are taken from the MEGAN
v2.2 land cover map and correspond to emissions under standard conditions
(temperature of 303 K, leaf area index = 5, canopy 80 % mature,
10 %, old and 10 % growing, and photosynthetic photon flux density of
∼ 1500 µmol m-2 s-1 at the canopy top). MEGAN v2.2
land cover was constructed for the year 2008 based on the National Landcover
Dataset (NLCD, Homer et al., 2004) and vegetation speciation from the Forest
Inventory and Analysis (FIA, http://www.fia.fs.fed.us). It uses the
16-PFT classification scheme of the Community Land Model 4 (CLM4) and further
specifies regionally variable base emission factors based on speciation. For
example, the PFT base emission factor for the “Broadleaf deciduous temperate
tree” category varies depending on the relative abundance of isoprene
emitters (e.g., oak) and non-emitters (e.g., maple). The highest base
emission factors are in the Ozarks of southeast Missouri where pine–oak
forests dominate the land cover (Wiedinmyer et al., 2005).
Error-weighted mean OMI HCHO vertical column densities for the
SEAC4RS time period (1 August 2013 – 25 September 2013). The Edwards
Plateau (EP), Ozarks (OZ), and South Central Plains (SCP) ecoregions are
denoted by black outlines
(https://www.epa.gov/eco-research/ecoregions, level 3 and 4 data).
Isoprene emissions in the southeast US. (a) MEGAN v2.1
base isoprene emission factors and emissions for the SEAC4RS time
period. (b) Scaling factors from the inversion and optimized
emissions. The color scale differs for MEGAN and optimized emissions. The
Edwards Plateau (EP), Ozarks (OZ), and South Central Plains (SCP) ecoregions
are denoted by black outlines.
Actual isoprene emissions are computed locally by multiplying the base
emission factors by environmental factors to account for local conditions of
leaf area index and leaf age, derived from MODIS observations (Myneni et al.,
2007), and temperature and direct and diffuse solar radiation, taken from the
GEOS-FP assimilated meteorological data used to drive GEOS-Chem. GEOS-FP
temperatures in the boundary layer averaged 1 K higher than the SEAC4RS
observations, and a downward correction is applied to the skin temperatures
used in the computation of isoprene emissions in GEOS-Chem. The resulting
emissions are shown in the top right panel of Fig. 2. The pattern differs
from the base emission factors, primarily because of temperature. The highest
emissions are in Louisiana and Arkansas, where temperatures are particularly
high. The general spatial patterns of OMI HCHO (Fig. 1) and MEGAN v2.1
emissions show broad similarities but also substantial differences. For
example, OMI shows no enhancement over the Edwards Plateau in Texas where
MEGAN v2.1 predicts high isoprene emissions. These differences will be
analyzed quantitatively in our inversion.
GEOS-Chem and its adjoint
We use the GEOS-Chem chemical transport model and its adjoint (Henze et al.,
2007), driven by assimilated NASA GEOS-FP meteorological data in a nested
configuration at 0.25∘ × 0.3125∘ horizontal
resolution (Zhang et al., 2015, 2016; Kim et al., 2015). Our model domain
covers the southeast US (102.812–77.188∘ W,
28.75–42.25∘ N; Fig. 1), taking initial and dynamic boundary
conditions from a global simulation with 4∘ × 5∘
horizontal resolution. We simulate an 8-week period
(1 August–25 September 2013) at the 0.25∘ × 0.3125∘
horizontal resolution.
The GEOS-Chem adjoint version is v35k, which is based on version v8 of
GEOS-Chem with updates through v9 (http://acmg.seas.harvard.edu/geos). Here
we update the chemical mechanism in v35k to GEOS-Chem v9.02 (Mao et al.,
2010, 2013) and further update isoprene chemistry to GEOS-Chem v11-02 as
described by Fisher et al. (2016) and Travis et al. (2016) in their
simulation of SEAC4RS observations. These updates specifically include
(1) explicit representation of isoprene peroxy radical (ISOPO2)
isomerization and subsequent hydroperoxy-aldehyde (HPALD) formation,
(2) formation of isoprene epoxides (IEPOX) and their oxidation, and (3) a
24 % increase in the HCHO yield from reaction of ISOPO2 with NO. The
updated oxidation mechanism better reproduces the time- and
NOx-dependence of HCHO production in the fully explicit Master Chemical
Mechanism v3.3.1 (Jenkin et al., 2015) and agrees with the HCHO yields
derived from SEAC4RS and SENEX aircraft measurements over the southeastern
US (Wolfe et al., 2016; Chan Miller et al., 2017; Marvin et al., 2017).
US anthropogenic emissions in GEOS-Chem are from the 2011 National Emissions
Inventory (NEI11) of the US Environmental Protection Agency, scaled to
2013 (EPA NEI, 2015). We decrease all anthropogenic sources of NOx other
than power plants by 60 %, resulting in a total reduction of 50 %.
Travis et al. (2016) showed this to be necessary in order to reproduce
SEAC4RS and other 2013 observations for NOx and its oxidation
products including OMI observations of NO2. Subsequent work has
supported this downward correction of US anthropogenic NOx emissions
(Chan Miller et al., 2017; Lin et al., 2017; McDonald et al., 2018). Soil
NOx emissions are reduced by 50 % across the midwestern US as in Travis et al. (2016), based on previous analysis of OMI NO2
observations (Vinken et al., 2014). We note that Wolfe et al. (2015) found
GEOS-Chem soil NO emissions to be too low over the Ozarks. Fire emissions,
lightning NOx emissions, soil NOx emissions, non-isoprene MEGAN
emissions, and updates to deposition are as in Travis et al. (2016). GEOS-FP
diagnosed mixing depths are reduced by 40 % to better match aerosol lidar
observations during SEAC4RS (Zhu et al., 2016).
Inversion approach
The state vector x to be optimized in the inversion consists of
temporally invariant scaling factors on the
0.25∘ × 0.3125∘ GEOS-Chem grid applied to the prior
MEGAN v2.1 isoprene emissions for the August – September 2013 SEAC4RS
period. It consists of 4138 elements covering the land grid cells of the
domain in Fig. 1. Zhu et al. (2016) previously found that decreasing MEGAN
v2.1 emissions by 15 % improved the simulation of SEAC4RS HCHO
observations and we include this correction in our prior estimate.
Non-methane VOCs other than isoprene contribute less than 20 % to the
HCHO column enhancements over the southeast US (Palmer et al., 2003; Millet
et al., 2006) and are not optimized as part of the inversion.
The observation vector y consists of daily OMI HCHO columns (VCDs)
calculated from OMI SCDs and GEOS-Chem AMFs mapped onto the
0.25∘ × 0.3125∘ GEOS-Chem grid. We relate y
to x using GEOS-Chem, denoted as F and representing the forward model for the inversion:
y=F(x)+εO.
GEOS-Chem HCHO columns are sampled at the OMI overpass time and filtered
according to the same requirements outlined in Sect. 2.1. The observational
error vector εO includes contributions from the forward model
error, the representation error, and the measurement error (Brasseur and
Jacob, 2017). The representation error can be neglected here because the
GEOS-Chem resolution is commensurate with the size of OMI pixels, and the
forward model error is expected to be small compared to the ∼ 80 %
measurement error for individual scenes. Thus we take the measurement error
as given in Sect. 2.1 to represent the observational error. The resulting
observational error standard deviation averages
1.5 × 1016 molecules cm-2 for the domain of the
inversion.
Assuming Gaussian error distributions and applying Bayes' theorem to weigh
the information from the observations and the prior estimate, the solution to
the optimization problem involves minimization of the cost function
J(x) (Brasseur and Jacob, 2017):
Jx=x-xATSA-1x-xA+Fx-yTSO-1Fx-y,
where xA=(0.85,…0.85)T is the prior estimate for x,
SA is the corresponding prior error covariance matrix, and SO=E[εOεOT] is the observational
error covariance matrix. We construct the prior error covariance matrix
SA by assuming 100 % uncertainty in bottom-up emissions
with no spatial error correlation. The sensitivity of the inversion to our
assumptions for SA and SO will be tested in what follows.
The adjoint-based inversion enables a computationally tractable solution to
the minimization of the cost function (3) when the forward model is highly
non-linear, as is the case here. Starting from xA as a first guess, the
GEOS-Chem adjoint model calculates the local gradient of the cost function
(∇JxA) and passes it through
the L-BFGS-B algorithm (Byrd et al., 1995; Zhu et al., 1997) to determine a
next guess x1. It then recomputes (∇Jx1) and so on until convergence to the optimal
value. Convergence is reached when the cost function decreases by less than
1 % over three consecutive iterations.
Error analysis
We examined the sensitivity of the inversion results to different assumptions
made regarding the specification of errors. In the first and all subsequent
sensitivity analyses, we use the reported spectral fitting error in the
operational retrieval without the factor of 1.59 increase. This gives an
average observational error standard deviation of
0.9 × 1016 molecules cm-2, 40 % smaller than in the base case.
Our assumed prior error estimate of 100 % on the MEGAN v2.1 isoprene
emissions in the base inversion is deliberately large to allow for the
possibility of emissions being misplaced on the 0.25∘ × 0.3125∘
grid. We conducted a sensitivity analysis with a 50 %
prior error estimate.
The prior errors in the base inversion have no spatial error correlation
(i.e., SA is diagonal), but some error correlation may in fact be
expected depending on the homogeneity of land cover types. To test this, we
conducted a sensitivity simulation where the state vector x of emission
scaling factors is not optimized on the 0.25∘ × 0.3125∘
grid but instead on a coarser irregular grid defined using a
hierarchical clustering algorithm (Johnson, 1967; Wecht et al., 2014) with
geographical proximity and commonality of MEGAN v2.1 emissions as clustering
parameters. The resulting state vector is composed of 500 clusters,
∼ 10 times fewer than the number of grid cells at
0.25∘ × 0.3125∘ resolution.
A general assumption in Bayesian optimization is that observational errors
are randomly distributed, as opposed to systematic bias. Previous analyses of
SEAC4RS observations provide some confidence as to this lack of bias.
The validation work of Zhu et al. (2016) led to removal of bias from the OMI
HCHO satellite data. The work of Travis et al. (2016) and Fisher et
al. (2016) removed bias in the GEOS-Chem simulation relating isoprene
emission to HCHO production. GEOS-FP biases in temperature and mixing depths
were corrected by Fisher et al. (2016) and Zhu et al. (2016), respectively.
All of these corrections have been implemented in our simulation.
Results
Optimal estimate of isoprene emissions
Figure 2 shows optimized scaling factors for our base inversion, and the
resulting isoprene emissions (optimized emissions = MEGAN
emissions × scaling factors). Isoprene emissions are lower than MEGAN v2.1 by
40 % on a regional average over the southeast US domain, with decreases
of more than a factor of 3 in some areas. Figure 3 summarizes the results
from the sensitivity analyses with different error assumptions. The base
inversion and the different sensitivity analyses show similar spatial
patterns for emissions, with correlation coefficients r=0.96–0.98 on the
0.25∘ × 0.3125∘ grid. The decrease in total regional
emissions relative to MEGAN v2.1 ranges between 40 and 54 %. The
cluster inversion shows the largest decrease, because the smaller-dimension
state vector allows for a stronger fit from observations. However, aggregation
errors in that inversion could cause overfit (Turner and Jacob, 2015).
Figure 4 shows the simulated HCHO columns from GEOS-Chem using the MEGAN v2.1
emissions and using the optimized emissions from the base inversion (Fig. 2).
The positive bias over high isoprene emitting regions using MEGAN v2.1
disappears when using the optimized emissions. The negative bias
(-3 × 1015 molecules cm-2) that persists over low-isoprene
emitting regions is not corrected due to the high error associated with the
OMI observations and the low isoprene emissions in those regions. We
attribute this to a bias in the background, unrelated to isoprene emission.
The best agreement between OMI and GEOS-Chem is provided by the base
inversion configuration, as shown in Figs. 2 and 4. The base inversion also
provides the best agreement with SEAC4RS data, as presented below.
Total isoprene emissions for the southeast US domain of Fig. 1 over
the period 1 August – 25 September 2013. The MEGAN v2.1 inventory value is
compared to results from the base inversion applied to the OMI formaldehyde
data (optimized emissions in Fig. 2) and to sensitivity inversions using
different error specifications (see text for details). Numbers on top of each
bar are the total isoprene emissions, and correlation coefficients (r)
describe the spatial consistency between the base inversion (r=1) and the
sensitivity inversions.
Simulated HCHO vertical column densities and model bias using prior
and optimized isoprene emissions. Values are averages for
1 August – 25 September 2013 at the OMI overpass time (13:30 LT), weighted by
the OMI measurement error as in Fig. 1. (b) and (d) show
the differences between the simulated columns and the OMI observations from
Fig. 1.
Mean boundary layer concentrations of isoprene and its oxidation
products measured in the SEAC4RS aircraft campaign
(1 August – 25 September 2013). The observations are for daytime
(09:00 – 18:00 LT) below 1.5 km altitude, and exclude urban and fire plumes
as described in the text.
Comparisons with SEAC4RS data
In situ measurements of isoprene and its oxidation products aboard the
SEAC4RS aircraft provide an independent test of the inversion results.
HCHO was measured in SEAC4RS via two different techniques: mid-IR
absorption spectroscopy using the CAMS (Richter et al., 2015), and
laser-induced fluorescence using the NASA GSFC ISAF (Cazorla et al., 2015).
The two measurements are well correlated (r=0.96 in the mixed layer),
with ISAF ∼ 10 % higher than CAMS measurements (Zhu et al., 2016).
Here we use the CAMS measurements, as these measurements were used in the
validation of the OMI SAO product (Zhu et al., 2016). The associated
measurement uncertainty is 4 %. Isoprene and the sum of methyl vinyl
ketone and methacrolein (MVK+MACR) were measured by PTR-MS (de Gouw and
Warneke, 2007), with reported uncertainties of 5 and 10 %, respectively.
Isoprene hydroperoxides (ISOPOOH) and isoprene nitrates (ISOPN) were measured
by the Caltech CIMS (Crounse et al., 2006; Paulot et al., 2009; St. Clair et
al., 2010), with respective uncertainties of 30 ppt + 40 % and
10 ppt + 30 %.
MVK+MACR measurements are corrected to account for the interference caused
by the degradation of ISOPOOH on instrument surfaces (Rivera-Rios et al.,
2014). The correction is calculated as MVK+MACRcorrected = MVK+MACRmeasured
- X × ISOPOOHmeasured, where
X = 0.44 with a relatively large uncertainty of +0.21/-0.12. Formaldehyde
measurements may suffer from a similar, but smaller interference. In the ISAF
instrument, conversion of ISOPOOH to HCHO contributes negligibly (< 4 %)
to the observed signal in ISOPOOH- and HCHO-rich environments, but a
delay in ISOPOOH conversion and a rapid transition in sampling environments
can manifest in more substantial (< 10 %) interferences
(St. Clair et al., 2016). This has not yet been examined for the CAMS instrument.
We exclude data influenced by urban plumes ([NO2] > 4 ppb),
open fire plumes ([CH3CN] > 200 ppt), and stratospheric air
([O3] / [CO] > 1.25 mol mol-1), and focus solely on
measurements within the daytime boundary layer (09:00–18:00 LT, < 1.5 km).
In all comparisons with model results, observations are averaged over
the GEOS-Chem grid at 10 min time steps.
Relationship of boundary layer isoprene and HCHO measured during
SEAC4RS, colored by observed NOx. Data are the same as in Fig. 5.
Comparison of SEAC4RS observations and modeled mixing ratios
using either MEGAN v2.1 (blue) or the optimized isoprene emissions (red) from
the base inversion of OMI HCHO data (Fig. 2). The dashed line indicates 1:1
agreement. The colored lines are the reduced major axis linear regressions
and the inset numbers are the corresponding slopes, with error standard
deviations inferred from bootstrap sampling.
Figure 5 shows the distribution of SEAC4RS observations. The aircraft
flew over the Ozarks on several hot days, leading to the particularly high
concentrations of isoprene and its oxidation products in the region. ISOPOOH
is produced by the low-NOx pathway for isoprene oxidation, while ISOPN
is produced by the high-NOx pathway. MVK and MACR are produced mostly by
the high-NOx pathway. The spatial patterns reflect the contributions of
both pathways across the southeast US (Travis et al., 2016). Formaldehyde is
more distributed because of the time lag in HCHO production from isoprene
emission (Chan Miller et al., 2017). The relatively low correlation between
isoprene and formaldehyde (r=0.49, Fig. 6) illustrates the importance
of accounting for transport in inversions of HCHO data to infer isoprene
emissions at fine resolution.
Figure 7 compares observed mixing ratios for isoprene and its oxidation
products to the values simulated by GEOS-Chem using either MEGAN v2.1
isoprene emissions or the optimal estimate from the inversion. MEGAN v2.1
emissions lead to a factor of 2.5 overestimate in SEAC4RS observations
of isoprene and ISOPOOH, a 50 % overestimate in HCHO, factor of 2
overestimate for MVK+MACR, and 20 % overestimate for ISOPN. The optimal
estimate decreases the simulated concentrations and produces agreement with
all observations within measurement uncertainty. The effect on isoprene and
ISOPOOH is particularly large because the correction of emissions is
strongest in high-emitting regions, which happen to also have low NOx
(Fig. 6; Yu et al., 2016). The reduction in HCHO, MVK+MACR, and ISOPN
is less pronounced. Zhu et al. (2016) previously found no GEOS-Chem model
bias relative to the SEAC4RS HCHO observations using MEGAN v2.1
emissions reduced by a uniform 15 %, but they used an older GEOS-Chem
version that did not include updates to anthropogenic emissions, deposition,
the isoprene oxidation mechanism (including a higher HCHO yield from the
ISOPO2 + NO reaction), and the inclusion of alpha-pinene and limonene
oxidation. Travis et al. (2016) previously reported a factor of 2
overestimate of ISOPOOH in their SEAC4RS simulation with MEGAN v2.1
reduced by 15 %, and the lower emissions in our optimal estimate
effectively correct that bias.
Comparison of simulated and observed HCHO concentrations along the
SEAC4RS flight tracks, using the model with optimized isoprene emissions
from the base inversion. The dashed line indicates 1:1 agreement. Data are
the same as in Fig. 7 (upper middle) but are colored by the local ratio of
simulated to observed NOx concentration.
Much of the residual scatter in the comparison of simulated vs. observed HCHO
using optimized isoprene emissions appears to be caused by local bias in
NOx (Fig. 8). There is no mean NOx bias in our inversion (Travis
et al., 2016) but there can be local bias. We find that local model biases in
simulating HCHO observations are strongly correlated with corresponding model
errors in NOx, reflecting the NOx-dependence of HCHO production
from isoprene (Fig. 6 and Chan Miller et al., 2017). When excluding points
with more than 50 % error in NOx, the correlation between measured
and simulated HCHO improves from r=0.62 to r=0.70 (n=1222 to n=708). This emphasizes the importance for inversions of HCHO data to use
unbiased NOx concentrations.
Implications for isoprene emission inventories
Our results indicate that MEGAN v2.1 isoprene emissions over the southeast US
should be decreased by an average of 40 %, consistent with previous
analyses of OMI HCHO data which inferred 25–50 % decreases (Millet et
al., 2008; Bauwens et al., 2016). MEGAN v2.1 isoprene emissions are typically
a factor of 2 higher than the emissions calculated from the BEIS3 inventory
often used in US air quality models (Warneke et al., 2010; Carlton and Baker,
2011). BEIS and MEGAN both
follow the emissions algorithms outlined in Guenther et al. (2006), but they
use different canopy models and base emission factors (Bash et al., 2016).
The geographic specificity of our high-resolution inversion allows us to
examine potential causes of the MEGAN v2.1 overestimate in various
environments. Below, we discuss three ecoregions in greater detail.
The high base isoprene emission factors in the Ozarks ecoregion (Fig. 2)
have to led this region being dubbed the “isoprene volcano” (Wiedinmyer et
al., 2005). We find a 46 % reduction in emissions in the region relative
to MEGAN v2.1, in good agreement with isoprene flux measurements from
SEAC4RS (Wolfe et al., 2015). Independent aircraft measurements over the
southeast US during the summer of 2013 found that MEGAN v2.1 was biased high
by a factor of two for mixed pine–oak forests typical of the Ozarks (Yu
et al., 2017). These authors suggest that non-emitting trees in the upper
canopy may shade emitting trees, leading to lower than anticipated isoprene
emissions.
The hot spot of isoprene emissions in the South Central Plains (Fig. 2) is
also reduced by 48 % in our inversion relative to MEGAN v2.1. This region
is dominated by needle leaf trees, with isoprene emissions stemming from the
sweetgum–tupelo understory. Again, vertical heterogeneity or an incorrect
fraction of emitters could lead to the MEGAN overestimate of emissions.
Alternatively, the base emission factor of sweetgum and tupelo could be
significantly less than the assigned MEGAN value.
The Edwards Plateau in central Texas is a major isoprene source region in
MEGAN v2.1, with base emission factors as high as in the Ozarks (Fig. 2), but
our inversion decreases emissions in that region by more than a factor of
3. A land cover map used for BEIS (BELD4) shows no isoprene emission
hotspot in the region (Wang et al., 2017), consistent with our result. Both
land cover maps are derived from the NLCD, but they follow different
methodologies for translating NLCD classifications to base emission factors.
NLCD-based maps show the Edwards Plateau dominated by broadleaf trees,
whereas the MODIS land cover product is dominated by grasses, leading to a
factor of 10 lower isoprene emissions (Huang et al., 2015). Uncertainty in
the dependence of isoprene emission on soil moisture could also affect
isoprene emission estimates for the Edwards Plateau (Sindelarova et al.,
2014).
Implications for surface air quality
Isoprene emissions can either increase or decrease surface ozone in air
quality models, depending on the local chemical environment and the chemical
mechanism used (Mao et al., 2013). We find in GEOS-Chem that our optimized
isoprene emissions lead to a decrease in mean surface afternoon O3
concentrations by ∼ 1–3 ppb over the southeast US relative to
the simulation using MEGAN v2.1 emissions. The GEOS-Chem simulation of Travis
et al. (2016) previously found an 8 ppb overestimate of surface ozone over
the southeast US during SEAC4RS; excessive isoprene emissions could
contribute.
Isoprene is also a precursor for organic aerosol (OA), which is a dominant
contributor to fine particulate matter (PM2.5) in surface air (Zhang et
al., 2007). Kim et al. (2015) found in their GEOS-Chem simulation of the
SEAC4RS period that isoprene contributes 40 % of total OA over the
southeast US in summer, assuming a 3 % mass yield from isoprene oxidation
and MEGAN v2.1 isoprene emissions reduced by 15 %. A more mechanistic
study of OA formation from isoprene oxidation under the SEAC4RS
conditions found a 3.3 % mass yield, most of which was produced in the
low-NOx pathway (Marais et al., 2016). Our work finds a factor of 2
decrease in ISOPOOH relative to the simulation using MEGAN v2.1 emissions
reduced by 15 %, and consistent with observations (Fig. 6). This suggests
that isoprene OA formation may be only half of the value found by Kim et
al. (2015), implying that other sources such as terpenes may make more
important contributions to OA (Pye et al., 2010, 2015; Xu et al., 2015; Zhang
et al., 2018).
Conclusions
We used newly validated HCHO observations from the OMI satellite instrument
to demonstrate the capability for applying these satellite observations to
fine-resolution inversion of isoprene emissions from vegetation. Our work
focused on the southeast US where aircraft observations from the NASA
SEAC4RS campaign provide detailed chemical information on isoprene and
its oxidation products (including HCHO) to independently evaluate the
inversion. The inversion used the adjoint of the GEOS-Chem chemical transport
model at 0.25∘ × 0.3125∘ horizontal resolution and
leveraged on previous studies that applied GEOS-Chem to simulation of the
SEAC4RS observations including in particular for NOx. HCHO yields
from isoprene oxidation are highly sensitive to NOx levels, and the high
resolution of the GEOS-Chem inversion allowed us to properly describe the
spatial segregation between isoprene and NOx emissions.
We found that the MEGAN v2.1 inventory of isoprene emissions commonly used in
atmospheric chemistry models is biased high on average by 40 % across the
southeast US. This is consistent with several previous top-down studies and
recent analyses using flight-based flux and eddy covariance measurements. Our
optimized emissions produce better agreement with SEAC4RS observations
of isoprene and its oxidation products including HCHO. Local model errors in
simulating HCHO observations along the aircraft flight tracks are highly
correlated with local model errors in NOx. This highlights the
importance of accurate NOx fields in inversions of HCHO observations to
infer isoprene emissions.
The high resolution of our inversion allows us to quantify isoprene
emissions and analyze MEGAN v2.1 biases on ecosystem-relevant scales. We
find that MEGAN v2.1 is biased high everywhere across the southeast US but
is correct in placing maximum 2013 emissions in
Arkansas, Louisiana, and Mississippi. The Ozarks Plateau in southeast Missouri has
particularly high base emission factors in MEGAN v2.1, reflecting the
abundance of oak trees, but isoprene emissions there are dampened by
relatively low temperatures and our results further suggest an overestimate
in the base emission factors. Another prominent overestimate is over the
Edwards Plateau in central Texas where MEGAN v2.1 emissions are biased high
by a factor of 3, possibly reflecting errors in land cover. Our results
suggest that the BEIS inventory may yield more accurate isoprene emissions
for these areas.
Our downward correction of isoprene emissions in GEOS-Chem as a result of the
inversion leads to a 1–3 ppb reduction in modeled surface O3,
correcting some of the overestimate previously found in the model. It also
decreases the contribution of isoprene to organic aerosol, possibly
suggesting a greater role for terpenes.
HCHO observations from space are expected to improve considerably in the near
future. TROPOMI, launched in October 2017 will provide global HCHO and
NO2 observations at 7 km × 7 km nadir resolution daily
(Veefkind et al., 2012), as compared to 24 km × 13 km for OMI.
Concurrent HCHO and NO2 observations can provide a check against model
bias in NOx affecting the yield of HCHO from isoprene (Marais et al.,
2012). The TEMPO geostationary instrument to be launched in the 2019 – 2022
window will provide HCHO and NO2 observations at
2 km × 4.5 km pixel resolution multiple times per day (Zoogman et
al., 2017). Coupled with the high-resolution inversion framework shown here,
these future observations may greatly improve our ability to quantify US
isoprene emissions from space.