ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-11861-2015How consistent are top-down hydrocarbon emissions based on formaldehyde observations from GOME-2 and OMI?StavrakouT.jenny@aeronomie.beMüllerJ.-F.BauwensM.De SmedtI.https://orcid.org/0000-0002-3541-7725Van RoozendaelM.De MazièreM.VigourouxC.HendrickF.GeorgeM.https://orcid.org/0000-0001-8897-7964ClerbauxC.CoheurP.-F.GuentherA.https://orcid.org/0000-0001-6283-8288Belgian Institute for Space Aeronomy, Avenue Circulaire 3, 1180, Brussels, BelgiumUPMC Univ. Paris 6; Université Versailles St.-Quentin; CNRS/INSU, LATMOS-IPSL, 75252, CEDEX 05, Paris, FranceSpectroscopie de l'Atmosphère, Service de Chimie Quantique et Photophysique, Université Libre de Bruxelles, 1050, Brussels, BelgiumAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington State, USAT. Stavrakou (jenny@aeronomie.be)26October20151520118611188419March201522April20159September201514October2015This 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/15/11861/2015/acp-15-11861-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/11861/2015/acp-15-11861-2015.pdf
The vertical columns of formaldehyde (HCHO) retrieved from two satellite
instruments, the Global Ozone Monitoring Instrument-2 (GOME-2) on Metop-A and
the Ozone Monitoring Instrument (OMI) on Aura, are used to constrain global
emissions of HCHO precursors from open fires, vegetation and human activities
in the year 2010. To this end, the emissions are varied and optimized using
the adjoint model technique in the IMAGESv2 global CTM (chemical transport
model) on a monthly basis and at the model resolution. Given the different
local overpass times of GOME-2 (09:30 LT) and OMI (13:30 LT), the simulated diurnal cycle of HCHO columns
is investigated and evaluated against ground-based optical measurements at
seven sites in Europe, China and Africa. The modeled diurnal cycle exhibits
large variability, reflecting competition between photochemistry and emission
variations, with noon or early afternoon maxima at remote locations (oceans)
and in regions dominated by anthropogenic emissions, late afternoon or
evening maxima over fire scenes, and midday minima in isoprene-rich regions.
The agreement between simulated and ground-based columns is generally better
in summer (with a clear afternoon maximum at mid-latitude sites) than in
winter, and the annually averaged ratio of afternoon to morning columns is
slightly higher in the model (1.126) than in the ground-based measurements
(1.043).
The anthropogenic VOC (volatile organic compound) sources are found to be
weakly constrained by the inversions on the global scale, mainly owing to
their generally minor contribution to the HCHO columns, except over strongly
polluted regions, like China. The OMI-based inversion yields total flux
estimates over China close to the bottom-up inventory (24.6 vs.
25.5 TgVOC yr-1 in the a priori) with, however, pronounced increases in the northeast of China
and reductions in the south. Lower fluxes are estimated based on GOME-2 HCHO
columns (20.6 TgVOC yr-1), in particular over the northeast, likely reflecting
mismatches between the observed and the modeled diurnal cycle in this
region.
The resulting biogenic and pyrogenic flux estimates from both optimizations
generally show a good degree of consistency. A reduction of the global annual
biogenic emissions of isoprene is derived, of 9 and 13 % according to
GOME-2 and OMI, respectively, compared to the a priori estimate of 363 Tg in
2010. The reduction is largest (up to 25–40 %) in the Southeastern US, in
accordance with earlier studies. The GOME-2 and OMI satellite columns suggest
a global pyrogenic flux decrease by 36 and 33 %, respectively, compared to
the GFEDv3 (Global Fire Emissions Database) inventory. This decrease is especially pronounced over tropical
forests, such as in Amazonia, Thailand and Myanmar, and is supported by
comparisons with CO observations from IASI (Infrared Atmospheric Sounding
Interferometer). In contrast to these flux reductions, the emissions due to
harvest waste burning are strongly enhanced over the northeastern China plain
in June (by ca. 70 % in June according to OMI) as well as over Indochina in
March. Sensitivity inversions showed robustness of the inferred estimates,
which were found to lie within 7 % of the standard inversion results at the
global scale.
Introduction
Besides a small direct source, the dominant source of formaldehyde (HCHO) is
its photochemical formation due to the oxidation of methane and non-methane
volatile organic compounds (NMVOCs) emitted by the biosphere, vegetation
fires and human activities. Methane oxidation is by far the largest
contributor to the HCHO formation (ca. 60 % on the global scale), while the
remainder is due to oxidation of a large variety of VOCs of anthropogenic,
pyrogenic and biogenic origin . The main removal
processes are the oxidation by OH,
HCHO+OH(+O2)→CO+HO2+H2O,
ultimately producing CO and converting OH to HO2 and
photolysis reactions,
HCHO+hν→CO+H2andHCHO+hν(+2O2)→CO+2HO2,
producing CO, H2 and HO2 radicals.
Because of its short photochemical lifetime (ca. 4–5 h), and of the short
lifetime of its main NMVOC precursors, most importantly isoprene, enhanced
levels of HCHO are directly associated with the presence of nearby
hydrocarbon emission sources. HCHO column densities retrieved from space by
solar backscatter radiation in the UV–visible spectral region
are used to
inform about the VOC precursor fluxes in a large body of literature studies.
The first studies focused on the derivation of isoprene fluxes in the US
constrained by HCHO columns from GOME (Global Ozone Monitoring Instrument) or OMI (Ozone Monitoring Instrument) instruments
. The estimation of isoprene
emissions was extended to cover other regions, e.g., South America
and Africa , with
special efforts to exclude satellite scenes affected by biomass burning, and
Europe . reported top-down isoprene and
anthropogenic reactive VOC fluxes over eastern and southern Asia and, more
recently, anthropogenic emissions of reactive VOCs in eastern Texas were
estimated using the oversampling technique applied to OMI HCHO observations
. Based on SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY) observations, space-based emissions of
isoprene and pyrogenic NMVOCs were derived on the global scale using the
adjoint model approach . Each of those
studies was constrained by one satellite data set and, in many cases,
conflicting answers were found regarding the magnitude and/or spatiotemporal
variability of the underlying VOC sources, mostly owing to differences in the
satellite column products, in the models used to infer top-down estimates
and in the emission inventories used as input in the models. The latter point
is very often a source of confusion, since a very large range of estimates
can be obtained using the same emission model depending on the choice of
input variables. Indeed, the isoprene fluxes estimated using MEGAN (Model of Emissions of Gases and Aerosols from Nature)
, the most commonly used bottom-up emissions model for
biospheric emissions, vary strongly depending on the driving variables used
(e.g., meteorology, and land cover), leading to an uncertainty of about a factor of
5 for the global isoprene emissions and underscoring the
need for clearly indicated a priori emission information in order to allow
for meaningful comparisons between different studies.
Despite significant progress in the field, the derivation of VOC emissions
using HCHO columns remains challenging, mainly owing to the large number and
diversity of HCHO precursors, to uncertainties regarding their sources and
speciation profiles, and to inadequate or incomplete knowledge of their
chemical mechanisms and pathways leading to HCHO formation. In addition, it
crucially depends on the quality of the satellite retrievals; therefore,
efforts to address aspects such as instrumental degradation, temporal
stability of the retrievals, noise reduction, and error characterization are
of primary importance .
The advent of new satellites measuring at different overpass times, like
GOME-2, SCIAMACHY and OMI, opens new avenues in the derivation of top-down
estimates. However, it also raises new questions regarding the consistency of
the estimated fluxes from different instruments. Indeed, a recent study
focusing on tropical South America reported a factor of 2 difference between
the SCIAMACHY- and OMI-based isoprene fluxes derived using the same model, a
difference which apparently could not be explained by differences in the
sampling features of the sensors or by uncertainties in the air mass factor
calculations, and which might be partly due to model deficiencies pertaining
to the diurnal cycle of the HCHO columns .
The main objective of this study is therefore to address the issue of
consistency between global VOC flux strengths inferred from one complete year
of GOME-2 and OMI HCHO column densities, taking into account their different
overpass times. Field campaign measurements show that the diurnal patterns of
surface HCHO concentrations are mostly influenced by the magnitude and
diurnal variability of precursor emissions and the development of the
boundary layer. A midday peak followed by gradual decrease in the evening
concentrations was observed at a tropical forest in Borneo
, whereas HCHO concentration peaked in the evening during
cool days and around midday in warm and sunny conditions at a forest site in
California and near a city location in the Po Valley
. Long-term diurnal measurements of HCHO columns are
limited but are less influenced by variations in boundary layer mixing and
are directly comparable with the satellite observations. Here, we investigate
first the diurnal variability of HCHO columns simulated by the IMAGESv2
global CTM (chemical transport model), and evaluate the model skill to reproduce the observed diurnal
cycle of HCHO columns at seven different locations in Europe, China, and
tropical regions.
Retrieved HCHO columns from GOME-2 and OMI, with local overpass times 09:30
and 13:30 LT, respectively, are used to constrain the VOC emissions. The
algorithms developed for the two sensors were designed to ensure the maximum
consistency between the two sets of observations, as described in detail in
. The top-down emission estimates are derived using an
inversion framework based on the adjoint of the IMAGESv2 CTM
and fluxes are optimized per month, model grid
and emission category (anthropogenic, biogenic and pyrogenic). The same
inversion setup is applied using either GOME-2 or OMI measurements as
top-down constraints for 2010, a particularly warm and dry year with intense
fires and enhanced biogenic emissions. Sensitivity studies are carried out to
assess the robustness of the findings to different assumptions, e.g., to
changes of the prescribed a priori errors on the emission fluxes in the
inversion.
In Sect. the IMAGESv2 model is briefly described and the
HCHO budget is discussed, whereas the formation of HCHO in the oxidation of
anthropogenic VOCs is presented in detail in the Supplement. The modeled and
observed diurnal cycle of HCHO columns is discussed in
Sect. . The satellite HCHO columns used to constrain the
inversions and the inversion methodology are presented in
Sects. and . An overview of the results
inferred from the inversions using GOME-2 and OMI data and global results
from sensitivity case studies are presented in Sect. . The
VOC emissions inferred at the mid-latitudes (North America, China) and in
tropical regions (Amazonia, Indonesia, Indochina, Africa) are thoroughly
described in Sects. and . Finally,
conclusions are drawn in Sect. .
HCHO simulated with IMAGESv2
The IMAGESv2 global CTM is run at 2∘× 2.5∘ horizontal
resolution and extends vertically from Earth's surface to the lower
stratosphere through 40 unevenly spaced sigma-pressure levels. It calculates
daily averaged concentrations of 131 transported and 41 short-lived trace
gases with a time step of 6 h. Meteorological fields are obtained from
ERA-Interim analyses of the European Centre of Medium-Range Weather Forecasts
(ECMWF). Advection is driven by monthly averaged winds, while the effect of
wind temporal variability at timescales shorter than 1 month is
represented as horizontal diffusion . Convection is
parameterized based on daily ERA-Interim updraft mass fluxes. Turbulent
mixing in the planetary boundary layer uses daily diffusivities also obtained
from ERA-Interim. Rain and cloud fields (and therefore also the photolysis
and wet scavenging rates) are also based on daily ERA-Interim fields. The
effect of diurnal variations are considered through correction factors on the
photolysis and kinetic rates obtained from model simulations accounting for
the diurnal cycle of photo rates, emissions, convection and boundary layer
mixing . A thorough model description is given in
and references therein. The target year of this study is
2010.
Anthropogenic emissions are obtained from the RETRO 2000 database
(http://eccad.sedoo.fr; ), except over Asia where the REASv2
(Regional Emission inventory in ASia) inventory for year 2008 is used . The diurnal profile of
anthropogenic emissions follows . Isoprene emissions
(including their diurnal, day-to-day and seasonal variations) are obtained
from the MEGAN–MOHYCAN-v2 inventory
(http://tropo.aeronomie.be/models/isoprene.htm; )
and are estimated at 363 Tg in 2010 (Fig. ). Monthly
averaged biogenic methanol emissions (∼ 100 Tg yr-1 globally) are taken
from a previous inverse modeling study using IMAGESv2
and methanol total columns from IASI (Infrared Atmospheric Sounding
Interferometer). Biogenic emissions of acetaldehyde (22 Tg yr-1) and ethanol (22 Tg yr-1) are calculated following .
The model also includes the biogenic emissions of ethene, propene,
formaldehyde, acetone and monoterpenes from MEGANv2 (http://eccad.sedoo.fr).
Note that the non-isoprene biogenic VOC emissions are not varied in the
source inversions.
A priori annually averaged pyrogenic NMVOC, biogenic isoprene and
anthropogenic NMVOC emissions used in the CTM. Units are
1010 molec cm-2 s-1.
Open vegetation fire emissions are taken from GFEDv3 Global Fire Emissions Database;, with
emission factors for tropical, extratropical, savanna and peat fire burning
provided from the 2011 update of the recommendations by .
The GFEDv3 emission is estimated at 105.4 TgVOC in 2010, equivalent to
2.26 Tmol (average molecular weight of 46.5 kg kmol-1) (Fig. ).
The diurnal profile of biomass burning emissions was derived based on a
complete year of geostationary active fires and fire radiative power
observations from the SEVIRI (Spinning Enhanced Visible and InfraRed Imager) imager over Africa . The
analysis of the fire cycle, performed over 20 different land cover types in
northern and southern hemispheric Africa, exhibits strong diurnal
variability and very similar patterns in both hemispheres. According to this
data set, fire activity is negligible during the night and low in the early
morning, it peaks around 13:30 LT and decreases rapidly in the
afternoon hours. This profile is in fairly good agreement with the averaged
diurnal cycle of active fire observations constructed from the GOES
geostationary satellite encompassing North, Central and South America
and therefore it is applied to all fires worldwide. Note,
however, that this specific temporal profile might not be appropriate for
some locations, e.g., peat fires over Russia.
The vertical profiles of pyrogenic emissions are taken from a new global
data set of vertical smoke profiles from open fires, based on
plume top heights computed by a semi-empirical model and
fire radiative power from the MODIS instrument. These profiles are highly
variable depending on the season and the year. Forest regions are
characterized by high-altitude plumes (up to 6–8 km), whereas grasslands
generally emit within 2–3 km. About half of the emitted flux is injected within
the boundary layer. The 5th, median, 80th and 99th
monthly percentiles of injection profile maps of this data set were obtained
from the GlobEmission website (http://www.globemission.eu) and implemented in
the CTM.
The chemical mechanism of isoprene oxidation accounts for OH recycling
according to the Leuven isoprene mechanism LIM0
and its upgraded version LIM1
. LIM1 is based on a theoretical re-evaluation of the
kinetics of isoprene peroxy radicals undergoing 1,5 and 1,6-shift
isomerization and is in satisfactory agreement (factor of ∼ 2) with
experimental yields of the hydroperoxy aldehydes (HPALDs) believed to be
major isomerization products . Based on box model
calculations using the kinetic preprocessor (KPP) chemical solver
, the isomerization of isoprene peroxy radicals is estimated
to decrease the molar HCHO yield by ∼ 8 % in high NOx conditions (2.39
vs. 2.60 mol mol-1 after 2 months of simulation at 1 ppbv NO2) and by
∼15 % in low NOx conditions (1.91 vs. 2.25 after 2 months at 0.1 ppbv
NO2). These estimated changes are however very uncertain, given their
dependence on the unimolecular reaction rates of isoprene peroxy radicals and
on the poorly constrained fate of the isomerization products.
Modeled diurnal variations of HCHO columns (normalized at noon) at
six locations, Central Alaska (65∘ N, 151∘ W), South
England (51∘ N, 2.5∘ W), Arkansas (35∘ N,
91∘ W), Borneo (4∘ N, 117∘ E), Manaus
(3∘ S, 61∘ W), and Mato Grosso (9∘ S,
51∘ W) in January, March, May, July, September and November 2010.
The simulations STD, NBB, and NDC of Table are shown in
red, orange and blue, respectively. The modeled ratios of 13:30 to 09:30 LT
columns are given inside each panel.
The speciation profile for anthropogenic NMVOC emissions is based on the UK
National Atmospheric Emissions Inventory (NAEI; ). According
to NAEI, 49 (out of the 650 considered) compounds account for ca. 81 % of the
total UK emissions, 17 of them are explicitly accounted for in IMAGESv2
while a lumped compound of OAHC (other anthropogenic hydrocarbons) accounts for
the remaining 32 species. The chemical mechanism of OAHC is adapted in order
to reproduce the yields of HCHO from the mix of 32 higher NMVOCs. This is
realized based on time-dependent box model calculations using the
semi-explicit Master Chemical Mechanism (MCMv3.2;
http://mcm.leeds.ac.uk/MCM/, ). Details are given
in the Supplement.
Based on IMAGESv2 model simulations, the global annual HCHO budget is
estimated at 1600 Tg HCHO and is dominated by photochemical production,
whereas less than 1 % is due to direct emissions. The most important source
of HCHO is methane oxidation (60 % globally), the remainder being due to the
oxidation of biogenic (30 %), anthropogenic (7 %) and pyrogenic (3 %)
hydrocarbons . The main removal process is photolysis,
which accounts for 70 % of the global sink, followed by OH oxidation (26 %)
and by dry and wet deposition. The aforementioned production and loss
processes result in a global lifetime of 4.6 h.
Modeled local time (in hours) of the maximum in HCHO column, for
July 2010. The locations of sites for which comparisons are shown in
Figs. , and and are shown as
crosses (x) and plus symbols (+), respectively. White color represents
regions with diurnal variability of less than 5 %. The distribution of
open fire NMVOC emissions (1010 molec cm-2 s-1) for the
same month is also shown inset.
Diurnal cycle of HCHO columnsModel processes and sensitivity
The top-down determination of VOC emissions based on GOME-2 and OMI data
assumes that the model reproduces reasonably well the diurnal cycle of HCHO
columns. To test this assumption would require a large number of well-distributed ground-based observations, which are however scarce and
intermittent. We present further below a comparison with a limited data set of
column observations at surface sites, most of which are located at or near
pollution centers at mid-latitudes. In order to better characterize the
diurnal cycle and to identify the factors influencing it in the model, we
present in Fig. the modeled diurnal variations of HCHO
columns at selected locations and, in Fig. , the distribution
of the local time of the maximum in the diurnal cycle of HCHO columns.
Figure also displays the results of sensitivity calculations
described in Table , which neglect either the diurnal cycle
of emissions (NDC) or the biomass burning emissions (NBB), in comparison to
the standard model results. The results of additional sensitivity simulations
related to vertical transport (Table ) are very similar to
the results of the base model simulation and not shown here for the sake of
simplicity.
Model simulations conducted to investigate the diurnal cycle of HCHO
columns (Sect. ).
NameDescriptionSTDstandard forward simulationNBBneglected biomass burning emissionsNDCneglected the diurnal cycle of emissionsNDCBLneglected diurnal cycle of boundary layer mixingNDCCneglected diurnal cycle of deep convection
A striking feature of Figs. and is the
large diversity of diurnal profiles across the seasons and locations. Very
little HCHO variations are seen at high latitudes during the winter, due to
the very low photochemical activity and absence of notable emissions. In
regions where anthropogenic emissions are the dominant source of HCHO
precursors, such as in northwestern Europe, eastern China, India and the Middle
East (Fig. ), the diurnal cycle displays a midday maximum
and a minimum at the end of the night (Fig. , S. England;
Fig. ). As can be seen in Fig. , the diurnal
cycle of anthropogenic emissions has a very small impact at these locations.
This is due to the fairly long photochemical lifetimes of most anthropogenic
NMVOCs. Their relatively low short-term HCHO yields in comparison with the
final yields (see Table S1 in the Supplement) implies that most HCHO
formation occurs days after the precursor has been emitted. The midday
maximum therefore reflects the diurnal cycle of OH concentrations, very low
at night and at maximum when radiation is highest .
Seasonally averaged observed (black) and modeled (red) diurnal
variations of HCHO columns normalized at noon at three European sites, Cabauw,
Observatoire de Haute Provence, and Uccle. The observed columns are obtained
using the MAX-DOAS technique (Sect. ). The error bars
correspond to the measurement standard deviation. Modeled columns calculated
assuming no diurnal emission variability are shown in blue. The observed and
modeled ratios (blue and red) of 13:30 to 09:30 LT columns are given
inset.
Over the eastern US, the wintertime (November–March) diurnal cycle
displays a similar pattern due to anthropogenic emissions. In the summer,
however, when biogenic isoprene is the dominant VOC, a completely different
behavior is predicted, with a noon minimum and a maximum in the evening or
even in the early morning (Fig. , Arkansas;
Fig. ). A relatively similar pattern is found in the Manaus
region in the Amazon in July–September (Fig. ), in agreement
with a previous modeling study using GEOS-Chem and focussing on Amazonia
. At all sites impacted by isoprene (Arkansas, Borneo,
Manaus and Mato Grosso), the simulation neglecting diurnal variations of
emissions (Fig. , NDC, blue curve) leads to a continuous HCHO
buildup during the night and to a pronounced morning maximum followed by a
gradual decrease during daytime until a minimum in the late afternoon or early
evening. The nighttime buildup in that simulation follows the slow isoprene
oxidation (mostly by ozone) and the near-absence of HCHO sinks, whereas the
gradual HCHO decrease during the day reflects the decline of the accumulated
isoprene and intermediate oxidation products due to OH oxidation. Although
the daytime chemical lifetime of isoprene is short (less than 1 h at an OH
concentration of 4×106 molec cm-3), a large fraction of the
formaldehyde production due to isoprene involves longer-lived intermediates
(such as methyl vinyl ketone, methacrolein, hydroxyacetone, and hydroperoxides) resulting in a delayed formaldehyde production.
When the diurnal cycle of isoprene emissions is taken into account
(Fig. , STD, red curve), the midday emission maximum leads to
a HCHO minimum and to an increase afterwards, due to the delayed production
from isoprene (Arkansas, Manaus and Mato Grosso). It has been pointed out
that the nighttime HCHO accumulation and morning maximum
near Manaus in September might be unrealistic, as models are often unable to
reproduce the observed rapid decline of isoprene concentrations during the
evening at different surface sites. Nighttime chemistry, deposition and
boundary layer processes might indeed be poorly represented in models,
causing significant deviations from the patterns described above. As is
obvious from Figs. and , different locations
or seasons often display very different diurnal patterns, for complex reasons
including radiation and NOx levels, the occurrence of biomass burning, mixing
processes, etc. Note, however, that sensitivity simulations neglecting the
diurnal cycle of boundary layer mixing and deep convection fluxes were found
to cause only minimal deviations from the columns of the standard model
calculations.
As in Fig. , comparison between modeled and
observed diurnal variations for four sites : Beijing, Xianghe, Bujumbura and
Reunion Island. The observations were obtained using the MAX-DOAS (Beijing,
Xianghe, Bujumbura) and FTIR (Reunion Island) techniques.
Vegetation fires are found to cause locally very strong variations with
maximum values in the evening, exceeding by up to 70 % the morning minimum
value (Central Alaska in May and July, Mato Grosso in September). As seen in
Fig. , strong emissions over eastern Siberia, European Russia,
central Canada, Angola, Brazil and northern Australia are most often
associated with HCHO column maxima in the late afternoon and evening.
Model evaluation
To evaluate the diurnal cycle of the modeled HCHO column, we use
ground-based, remotely sensed measurements at the seven following sites:
Cabauw, the Netherlands (52∘ N, 5∘ E), 8 June–21 July 2009
.
Observatoire de Haute Provence (OHP), France (43.94∘ N, 5.71∘ E), 26 June 2007–20 March 2013
.
Uccle, Belgium (50.78∘ N, 4.35∘ E), 1 May 2011–23 April 2012
.
Beijing, China (39.98∘ N, 116.38∘ E), 3 July 2008–17 April 2009 (,
see also ).
Xianghe, China (39.75∘ N, 116.96∘ E), 7 March 2010–26 December 2013
(, see also ).
Bujumbura, Burundi (3∘ S, 29∘ E), 25 November 2013–22 January 2014
.
Reunion Island, France (20.9∘ S, 55.5∘ E), 1 August 2004–25 October
2004, 21 May 2007–15 October 2007, 2 June 2009–28 December 2009, and 11 January 2010–16 December 2010 .
The MAX-DOAS (Multi-axis differential optical absorption spectroscopy)
technique was used in all cases, except at
Reunion Island where the FTIR (Fourier transform infrared spectroscopy)
technique is used . Total HCHO columns are
measured at all stations, and profiles are also measured at Beijing, Xianghe,
and Bujumbura.
Figures and illustrate the diurnal cycle
of observed and modeled HCHO columns seasonally averaged and normalized by
their noon values. The ratio of the observed columns at 13:30 and 09:30 LT ranges
mostly between 0.8 and 1.2, although values close to 1.4 are found at one
site (OHP). The modeled values of this ratio are most often higher than in
the measurements, except at OHP. The average ratio at all sites and seasons
is slightly higher in the model (1.126) than in the data (1.043), although
the average absolute deviation between model and data is large (20 %),
presumably mostly because of representativity issues. The coarse resolution
of the model makes it impossible to reproduce the very large differences
seen, for example, between the observed diurnal profiles at Beijing and
Xianghe, two sites very near to each other and within the same model grid cell. OHP
similarly lies in a region with strong gradients in the diurnal behavior of
the columns, as seen in Fig. .
Nevertheless, the diurnal cycle of HCHO columns at the four most polluted
sites (Uccle, Cabauw, Beijing and Xianghe) shows a consistent pattern during
summertime (also in spring and fall at Uccle) which is well reproduced by the
model. Additionally, at Reunion Island, the observed midday maximum is well
reproduced by the model. As pointed out above, the midday maximum at both
very remote and very polluted sites is primarily caused by the diurnal cycle
of OH levels, as the reaction with OH of the (mostly fairly long-lived)
anthropogenic VOCs as well as methane is the main source of HCHO in those
areas. In the Beijing area, the diurnal cycle of emissions is responsible for
a slight delay in the maximum towards the afternoon, in agreement with the
observations.
A broader network of measurements would be necessary to provide a more
detailed assessment of HCHO column diurnal variations, in particular over
forests and in biomass burning areas. Nevertheless, the comparison presented
above with the limited data set of available measurements revealed no large
systematic discrepancies, except for a slight overestimation (by 8 %) of the
average ratio of 13:30 to 09:30 LT columns.
Satellite observations
The current version (v14) of the HCHO retrievals applied to GOME-2/Metop-A
and OMI/Aura measurements is based on the algorithm developed for GOME-2
(version 12, ), with significant adaptations, as
detailed below.
A classical DOAS algorithm is used, including three main steps: (1) the fit
of absorption cross-section databases to the measured Earth reflectance in
order to retrieve HCHO slant columns, (2) a background normalization
procedure to eliminate remaining unphysical dependencies, and (3) the
calculation of tropospheric air mass factors using radiative transfer
calculations and modeled a priori profiles. In GOME-2 v12, two fitting
intervals were introduced to improve the treatment of BrO absorption
features, and to reduce the noise on the HCHO columns (328.5–359 nm for the
pre-fit of BrO, 328.5–346 nm for the fit of HCHO) .
In the current version, a third fitting interval (339–364 nm) is used to
pre-fit the O2–O2 slant columns in order to minimize the effect of
spectral interferences between the molecular absorptions. This results in a
global reduction of the HCHO slant columns over the continents compared to
the previous version, by 0–25 %, depending on the season and the altitude.
It is interesting to note that the effect is very similar when applied to
GOME-2 and OMI HCHO retrievals, i.e., it has little or no impact on the
diurnal variations . In order to improve the fit of the
slant columns, an iterative DOAS algorithm for removal of spike residuals has
been implemented . In addition, this version of the
algorithm makes use of radiance spectra, daily averaged in the equatorial
Pacific, which serve as reference spectra. The background normalization now
depends on the day, the latitude, and on the viewing zenith angle of the
observation. This also serves as a destriping procedure, needed for an imager
instrument such as OMI . The air mass factor calculation is
based on . Scattering weighting functions are calculated with
the LIDORT v3.3(linearized discrete ordinate radiative transfer) radiative transfer model .
The a priori profile shapes are provided by the IMAGES model, at 09:30 LT for
GOME-2 and 13:30 LT for OMI (cf. Sect. ). The OMI-based
surface reflection database from is used for both GOME-2
and OMI. Radiative cloud effects are corrected using the independent pixel
approximation and the respective cloud products of the
instruments provided by the TEMIS website (http://www.temis.nl), namely the
GOME-2 O2 A-band Frescov6 product and the OMI
O2–O2 cloud product . As for the previous
algorithm versions, v14 HCHO columns are openly available on the TEMIS
website (http://h2co.aeronomie.be/).
Monthly averaged HCHO columns from both instruments gridded onto the
resolution of the model are used as top-down constraints. The simulated
monthly averaged columns are calculated from daily values weighted by the
number of satellite (OMI or GOME-2) measurements for each day at each model
grid cell. Columns with a cloud fraction higher than 40 % are excluded from
the averages. HCHO data are also excluded over oceanic IMAGES grid cells (for
which the land fraction is lower than 0.2), since we aim to constrain only
continental sources, as well as in the region of the South Atlantic
geomagnetic anomaly, i. e. within less than 1500 km of its assumed epicenter
(47.0∘ W, 24.9∘ S). Finally, regridded columns for which the monthly and
spatially averaged retrieval error exceeds 100 % are also rejected. The error
of the satellite columns is defined as the square root of the squared sum of
the retrieval error and an absolute error of 2 ×1015 molec cm-2. In most VOC-emitting regions the error ranges between 40
and 60 %.
Observed (upper panels) HCHO columns by GOME-2 and OMI instruments
in July 2010. Simulated HCHO columns using IMAGESv2 CTM at the overpass times
of GOME-2 and OMI (middle panels), and optimized modeled columns derived
from the inversions using GOME-2 data (left) and OMI columns (right). The
columns are expressed in 1015 molec cm-2.
The monthly regridded HCHO columns from GOME-2 and OMI are shown in
Fig. for July 2010. As seen in this figure, and discussed in
, the early afternoon columns of OMI are higher than the
mid-morning values of GOME-2 at mid-latitudes, while the reverse is true at
most tropical locations, in qualitative agreement with the ground-based
measurements and modeling results (Figs. , ).
Inversion methodology
The flux inversion technique consists in minimizing the mismatch between the
model predictions and a set of chemical observations by adjusting the a
priori emission distributions Φi(x,t), where (x,t) denote the
spatial (latitude, longitude) and temporal (year, month) variables, and i
the different emission categories (biogenic, pyrogenic, and anthropogenic). We
express the optimized solution Φiopt(x,t) as
Φiopt(x,t)=∑j=1mefjΦi(x,t),
where f=(fj) is a vector of scaling factors (in log space) multiplying the a
priori emissions. This vector is determined so as to minimize the scalar
function J (also termed as cost function)
J(f)=12((H(f)-y)TE-1(H(f)-y)+fTB-1f),
which measures the discrepancy between the
modeled HCHO columns H(f) and the observations y. In this
expression T is the transpose of the matrix, E and B are the
matrices of errors on the observations y and on the variables f,
respectively. The gradient of the cost function J with respect to the input
variables (∂J/∂f) is calculated using the adjoint of
the model. A thorough description of the method and its implementation in the
IMAGESv2 CTM is given in and . The inversion is
performed at the model resolution (2∘× 2.5∘) using an
iterative algorithm suitable for large-scale problems .
Performed flux inversions.
NameDescriptionGOME-2use GOME-2 dataOMIuse OMI dataOMI-DEdoubled a priori errors on the emission fluxesOMI-HEhalved a priori errors on the emission fluxesOMI-CFuse only OMI data with cloud fraction < 0.2OMI-ISignore isomerization of isoprene peroxy radicals
The source inversions presented in Table infer the
emission rates of the three emission categories (anthropogenic, biogenic and
biomass burning) are adjusted per month and are constrained by either GOME-2
or OMI HCHO columns. On the global scale, ca. 63 000 flux parameters are
varied. The emission of a grid cell is not optimized when its maximum a
priori monthly value is lower than 1010 molec cm-2 s-1. The
assumed error on the a priori anthropogenic emission by country is set equal
to a factor of 1.5 and 2 for OECD (Organisation for Economic Co-operation and Development) and other countries, respectively, to a factor
of 2 for biogenic emissions and to a factor of 3 for fire burning emissions
.
A priori and top-down VOC emissions (Tg yr-1) by region. The
emission inversions are defined in Table . The regions
are defined as follows. North America: US and Canada, Southern America:
Mexico, Central and South America, Northern (Southern) Africa: north (south)
of the Equator, Tropics: 25∘ S–25∘ N, Southeastern US:
25–38∘ N, 60–100∘ W, Amazonia:
14∘ S–10∘ N, 45–80∘ W, Indonesia:
10∘ S–6∘ N, 95–142.5∘ E, Indochina:
6–22∘ N, 97.5–110∘ E, Europe extends to the Urals
(55∘ E), FSU = Former Soviet Union.
The sensitivity studies (Table ) aim at assessing the
impact of (i) the choice of a priori errors on the emission fluxes (OMI-DE,
OMI-HE), (ii) the cloud fraction filter applied to the satellite data
(OMI-CF), and (iii) the isomerization of isoprene peroxy radicals (OMI-IS).
The annual a priori and top-down fluxes of the two standard and the four
sensitivity inversions are summarized in Table . The a
priori model columns calculated at 09:30 and 13:30 LT are generally
higher than the GOME-2 or OMI HCHO column abundances (Fig. ),
e.g., over Europe, southern China, the United States, Amazonia and Northern
Africa. They are, however, found to agree generally well in terms of
seasonality (Fig. ).
Overview of the results
Globally, the cost function is reduced by a factor of 2 after optimization,
and its gradient is reduced by a factor of ca. 103. In general, the
consistency between the two inversions is highest in tropical regions. At
mid-latitudes, the emission updates (i.e., the ratios of optimized to prior
emissions) are almost systematically higher in the OMI-based inversion than in the
GOME-2-based inversion. This reflects ratios of 13:30 to 09:30 LT columns which
are lower in the model than suggested by the two satellite data sets.
Both GOME-2 and OMI inversions suggest a strong decrease in global biomass
burning VOC emissions with regard to the a priori GFEDv3 inventory, by 36
and 33 %, respectively. This decrease is most pronounced in tropical regions.
In contrast, both the OMI and GOME-2 optimizations lead to enhanced emissions
(by about 50 %) due to the extensive fires which plagued European Russia in
August 2010 (Sect. ) and to agricultural waste burning in
the North China Plain in June (Sect. ). The fire burning
estimates from the two base inversions are generally quite consistent, not
only globally but also over large emitting regions like Amazonia,
southeastern Asia, and Africa. The sensitivity studies provide global flux
estimates which are close (within 7 %) to the standard top-down results using
OMI.
The globally derived isoprene fluxes are reduced in both standard inversions,
by 9 % according to GOME-2 and by 13 % according to OMI, compared to the a
priori estimate of the MEGAN–ECMWF-v2 inventory (363.1 Tg yr-1,
Table ). The overall consistency between the global
estimates is high for this emission category, despite some significant
differences at a regional scale (cf. next sections). The biogenic top-down
fluxes derived from the sensitivity inversions of Table
lie within 5 % of the OMI-based estimates on the global scale, yet larger
differences are found in the regional scale.
Monthly averages of observed GOME-2 (blue asterisks) and OMI (red
asterisks) HCHO columns and modeled columns over nine selected regions.
Dashed and solid lines correspond to a priori and optimized model columns,
respectively, calculated at 09:30 LT (in blue) and at 13:30 LT (in red).
The units are 1015 molec cm-2. The mean absolute deviation
between the a priori (left) and optimized (right) modeled columns and the observed
columns is given inset in each panel (in blue for GOME-2, in red for OMI). Error
bars (blue for GOME-2, red for OMI) represent the retrieval error provided
for each data set.
Finally, the global anthropogenic source is decreased in the GOME-2
inversion, while it is slightly increased in the inversion using OMI. Despite
their limited capability to constrain this emission category on the global
scale due to its small contribution to the global HCHO budget
, the satellite observations are found to provide
constraints over highly polluted regions, notably in eastern China where
the discrepancy between the two sensors is most evident (see
Sect. ).
Annual emission updates for the different source categories and the monthly
variation of the a priori and optimized flux estimates are illustrated in
Figs. –.
Modifying the errors on the flux parameters infers global isoprene emission
decreases of 8.5 % (OMI-HE) and 16 % (OMI-DE) with regard to the initial
isoprene inventory and within 7 % of the standard OMI inversion; cf.
Table . As expected, due to the limited or stronger
confidence assigned to the a priori inventories in OMI-DE and OMI-HE
scenarios, respectively, most substantial departures from the a priori
inventory are obtained when doubling the errors on the emission parameters,
while the OMI-HE scenario lies closer to the a priori database. The impact of
the use of a stricter cloud criterion on the OMI scenes used as top-down
constraints (20 % for OMI-CF instead of 40 % in OMI base inversion) results
in weak increases of the globally inferred fluxes with respect to the OMI
inversion, but the enhancement is more important in extratropical regions
and amounts to 22 % for biomass burning emissions (Table
and left panel of Fig. ). Finally, suppressing the
isomerization channel in isoprene oxidation increases the HCHO yield from
isoprene and leads to slightly higher model columns over isoprene-rich
regions. As seen in the right panel of Fig. , the
resulting isoprene fluxes are only slightly lower compared to the reference
run (by 4 % lower on the global scale). Over Amazonia, this emission
reduction reaches 8 %.
Percentage difference of the total VOC emissions inferred by the
sensitivity inversions (OMI-CF, left panel, and OMI-IS, right panel) and the
standard OMI inversion for the month of July (see
Table ).
Emissions at the mid-latitudesNorth America
Biogenic isoprene emissions drive the HCHO column seasonality and explain the
summertime column peak in the eastern US (Fig. ). The a
priori model exhibits, however, a much stronger seasonal variability than the
observation with a summer to winter ratio of 4–5 compared to the observed
ratio of about 2. In the summertime, the a priori model overestimates the GOME-2
and OMI measurements by up to 50 and 35 %, respectively, in the eastern US.
This drives the significant decrease in the optimized isoprene fluxes, from
the a priori value of 17.8 to 11.6 Tg (GOME-2) and to 13.8 Tg (OMI) over
the US in 2010, in good agreement with our earlier flux estimates (13 Tg yr-1)
based on SCIAMACHY HCHO columns . Even larger reductions
are found in the Southeastern US, amounting to ca. 25 and 40 % in the OMI
and GOME-2 inversions, respectively (Fig. ). Anthropogenic
and pyrogenic emissions over the US are essentially unchanged by the
inversions.
Ratios of optimized to a priori pyrogenic VOC fluxes derived by
source inversion of HCHO columns from GOME-2 (upper panels) and OMI (lower
panels) in January, March, August and October 2010. Ratio values
between 0.9 and 1.1 are not shown for the sake of
clarity.
Same as Fig. , but for isoprene emissions in
January and July.
The estimated cumulative June–August US isoprene emissions from both
optimizations (7.8 Tg for GOME-2 and 9.5 Tg for OMI) agree well with reported
values based on earlier versions of OMI HCHO retrievals (9.3 Tg according to
the variable slope technique as described in ). The OMI-based
isoprene flux in July 2010, estimated at 3.23 Tg, is 30 % lower than the a
priori (4.62 Tg), corroborating the low values of the BEIS2 (Biogenic Emissions Inventory System, version 2) inventory
.
Same as Fig. , but for annual anthropogenic VOC
fluxes.
The model predictions are compared to HCHO measurements from the INTEX-A
aircraft campaign conducted in July–August 2004 over the eastern US
, shown in Fig. . It is worth noting that the
measurements by NCAR (National Center for Atmospheric Research) and URI
(Univ. Rhode Island) exhibit large differences between them, the NCAR values
being ca. 50 % higher than URI values below 2 km altitude (Fig. ).
The model simulations are performed for 2004, and the concentrations are
sampled at the locations and times of the airborne measurements. In the a
posteriori simulation shown in Fig. , the bottom-up isoprene
emissions for 2004 were multiplied by the isoprene emission update inferred
from either the OMI or the GOME-2 inversion for 2010. As seen in
Fig. , the average HCHO concentration below 2 km altitude
decreased by about 10 % in the OMI inversion (15 % in the case of GOME-2) and
remains within the range of the NCAR and URI measurements. Despite the marked
underestimation of the modeled HCHO (1.39 and 1.32 ppbv in the OMI and
GOME-2 inversions) in comparison to NCAR observations (1.83 ppbv), the
emission optimization results in an increased Pearson's spatial correlation
coefficient between the modeled and observed concentrations below 2 km, from
0.74 in the a priori to 0.79 and 0.80 in the OMI and GOME-2 inversions. A
similar improvement is found with respect to URI data.
Monthly variation of a priori and top-down biomass burning VOC
fluxes for Amazonia (14∘ S–10∘ N, 45–80∘ W),
Africa north and south of the Equator, Indochina (6–22∘ N,
97.5–110∘ E), Europe (including European Russia), N. America (US
and Canada), China, and worldwide, expressed in teragrams of VOCs per month. Solid lines are
used for the a priori emissions (black), and updated emissions inferred from
GOME-2 (blue) and OMI (red) observations. Dotted and dashed red lines are
used for the results of the sensitivity studies OMI-DE and OMI-HE
(Table ), respectively. For each inversion, annual fluxes
for 2010 (in TgVOC) are given inside the panels.
Monthly variation of a priori and satellite-derived isoprene fluxes
for Amazonia, Northern and Southern Africa, Europe, N. America (defined as in
Fig. ), Indonesia (10∘ S–6∘ N,
95–142.5∘ E), and the Southeastern US (25–38∘ N,
60–100∘ W). The color and line code is the same as in
Fig. . Units are teragrams of isoprene per month. Annual isoprene
fluxes per region are given in each panel in teragrams of
isoprene.
Comparison between HCHO measurements from the INTEX-A campaign and
model concentrations sampled at the measurement times and locations from the
a priori simulation and from the OMI-based inversion averaged between the
surface and 2 km. The HCHO data are reported from two different instruments,
from the National Center for Atmospheric Research (NCAR) and from the
University of Rhode Island (URI). The observed and modeled mean HCHO
concentrations over the flight domain and altitude range are given inside
each panel.
Russia
The a priori model underpredicts the observed OMI HCHO columns during the
Russian fires of July–August 2010 by up to a factor of 2, in particular over
a broad region extending to the north (61∘ N) and east (55∘ E) of Moscow
(Fig. , upper panel). Similar spatial patterns are also
observed in GOME-2 HCHO columns. However, the GOME-2 columns are lower than
OMI over this region, and the model underestimation is less severe, in this
case reaching 60 %. The lower GOME-2 values might be due to the lower
retrieval sensitivity of GOME-2 to lower tropospheric HCHO compared to OMI at
these latitudes, associated with larger solar zenith angles .
As a result, the increase of the pyrogenic emission fluxes is strongest in
the OMI inversion, from 440 GgVOC in the GFEDv3 inventory to 720 GgVOC
(630 GgVOC in GOME-2) in August 2010 over Europe. Accordingly, the isoprene
fluxes inferred from the OMI inversion in August are also larger, about 40 %
higher than the a priori estimate in the Moscow area, whereas the increase
derived by GOME-2 does not exceed 25 %. Overall, the OMI data suggest annual
isoprene fluxes in Europe are 11 % higher than the a priori inventory
(Table ). Note that, although the isoprene enhancement over
Russia peaks earlier (July) and at slightly higher latitudes (ca. 61∘ N) than the biomass burning emission enhancement
(55–57∘ N in August), the significant overlap of the two
distributions makes it impossible to rule out that pyrogenic emissions are
the only cause for the observed strong formaldehyde columns. The very
widespread extent of the observed formaldehyde plume cannot be easily
explained by the comparatively much more localized emissions of the GFEDv3
inventory, and an additional, more widespread formaldehyde source (such as
isoprene) could help to explain the observations. However, as discussed
below, the GFEDv3 total emissions over Russia are likely largely
underestimated, and their geographical distribution might also be in error.
It is therefore possible that these fires were more widespread than in GFEDv3
and that strong isoprene emission enhancements are not needed to explain the
observations.
Strongly enhanced fire emissions in the Moscow region between mid-July and
mid-August 2010 were reported based on satellite observations of CO from
MOPITT Measurements Of Pollution In The Troposphere; and IASI and on surface
measurements . The optimized fire emission inferred by
assimilation of IASI CO columns in lies within 22 and 27 Tg CO
during the fires, i.e., about 7–10 times higher than in the bottom-up
inventory (GFEDv3). These values are comparable with the ranges of 19–33 and
34–40 Tg CO suggested by and ,
respectively, but are much higher than reported values of ca. 10 Tg CO
derived using surface CO measurements in the Moscow area .
The latter study identifies the contribution of peat burning to the total CO
fire emission in this region to be as high as 30 %.
The IMAGESv2 a priori CO simulation (using GFEDv3 inventory) underestimates
substantially the IASI CO observations. Scaling the CO emissions in IMAGESv2
to the fire VOC increase suggested by the OMI HCHO optimization, i.e., ca.
60 % in July and August 2010, barely improves the model agreement with the
satellite, indicating that, in accordance with earlier studies, more drastic
fire flux enhancements (factor of 5–10) are required to reconcile CO
model-data mismatches. The reasons for the differences in the emission
increases inferred by CO and HCHO during the 2010 Russian fires are currently
unknown, but they could be related either to inadequate knowledge of emission
factors of CO and VOCs from peat fires and/or underestimated remotely sensed
HCHO columns over fire scenes due to possibly important aerosol effects not
accounted for in the retrievals.
China
The dominant emission source in China is anthropogenic and is estimated at
25.5 TgVOC in REASv2 for 2008. The biogenic source, mainly
located in southern China, amounts to 7 Tg in 2010 in the MEGAN–MOHYCAN-v2
inventory . In northern China, the
HCHO columns are underestimated by the a priori model in winter compared to
OMI, whereas a relatively good agreement is found in summer. In southern
China, a general model overestimation is found all year round
(Figs. , ).
Although the OMI-based inversion yields total Chinese anthropogenic emissions
very similar to the a priori (24.6 TgVOC), the emission patterns are modified
with increased emissions in northeastern China and especially around Beijing
(20–40 %) and emission reductions in the southeast and in particular around
Shanghai (15–47 %) and Guangzhou (15–30 %). The total GOME-2 emission,
estimated at 20.6 TgVOC, is lower than the OMI result but in good agreement
with the estimate (20.2 Tg in 2008) of the Multi-resolution Emission
Inventory for China (MEIC, http://www.meicmodel.org). The flux distributions
from both inversions have common features, e.g., decreased fluxes in Shanghai
and Guangzhou regions, but contradicting estimates in the northeast where
GOME-2 observations do not support the emission enhancements suggested by
OMI.
This discrepancy is primarily due to the lower modeled ratios of 13:30 to
09:30 LT columns (average ratio of 1.0 in the model in northern China between March
and November) compared to the satellite data sets (average ratio of 1.16).
Note that, however, the model was found to overestimate this ratio against
MAX-DOAS data at Beijing and Xianghe (Fig. ). Another
possible cause for the difference between the OMI and GOME-2 results is the
limited availability of GOME-2 data in wintertime
(Fig. ) due to the high solar zenith angles leading to
large retrieval errors frequently exceeding 100 %. For example, GOME-2
columns are unavailable from November to April over Beijing.
In the North China Plain, one of the largest agricultural plains on Earth,
the post-wheat-harvest-season fires in June of every year are a common
practice for farmers , and are responsible for poor air quality
conditions and environmental harm . Both OMI- and GOME-2-based
inversions suggest a considerable enhancement of the agricultural fire flux
in this region, by almost a factor of 2 in comparison with the a priori
inventory by ; cf. Fig. . The interannual
variability of these emissions will be addressed in a separate work currently in
preparation.
Finally, the Chinese isoprene emission are decreased from 7 to
6.5 Tg yr-1 (OMI) and 5.9 Tg yr-1 (GOME-2), with especially strong decreases in
southern China, as illustrated in Fig. .
Emissions in the TropicsSouth America
After the 2005 drought in Amazonia, characterized as a one-in-a-century event
, Amazonia suffered a second, even more severe drought in
2010 with major environmental impacts . Extensive wildfires
broke out in different regions from July to October, with central and
southern
Amazonia as the main epicenters. The massive fires are reflected in the
high HCHO columns (up to 15 ×1015 molec cm-2) detected by
GOME-2 and OMI during these months, about twice the observed columns in the
wet season (Fig. ). Both instruments agree very well on
the magnitudes and spatial patterns of the HCHO columns, as illustrated in
Fig. . The a priori model strongly overestimates the
observations during the dry season (by up to 70 % in August), indicating that
the GFEDv3 emissions for this region are most likely too high. The GOME-2 and
OMI inversions decrease the fire emission by factors of 2 and 2.5,
respectively (Fig. ). Even stronger decreases (factor of 3)
are found over northern Bolivia and central Amazonia
(Fig. ).
Observed, a priori and a posteriori model HCHO columns (in
1015 molec cm-2) derived from GOME-2 (upper) and OMI (middle)
inversions in Amazonia in August 2010. For the same month, observed CO
columns by IASI, a priori model CO columns and CO columns (in
1018 molec cm-2) from the OMI-based inversion are shown in the
bottom panels. CO results from the GOME-2 inversion are very similar to those
obtained from OMI and are therefore not shown here.
These emission reductions are supported by comparison with CO columns
observed by IASI . The use of fire emissions from GFEDv3
leads to strongly overestimated CO columns in comparison to IASI observations
in August 2010 (Fig. ), reaching almost a factor of 2 over
western Amazonia. Significant improvement in the model–data match is achieved
when the emission reduction inferred by the OMI-based inversion is
implemented and applied not only to NMVOCs but also to other compounds
including CO. The GFEDv3 emissions of CO in 2010 were also found to be
substantially overestimated, by a factor of ∼ 1.8 over South America
between 5 and 25∘ S, by inverse modeling of MOPITT CO
columns using the GEOS-Chem model . The most likely cause for
the lower emissions in 2010 compared to 2007 was proposed by these authors to
be a reduction of the combusted biomass density possibly due to dry
conditions and/or repeat fires. The good consistency found between results
using either CO or HCHO indicates that the emission factors used in the model
for NMVOC and CO (or at least their ratios) are appropriate, unless an error
compensation is responsible for the noted good agreement. Note also that,
besides the good consistency found between the emission estimates derived
from GOME-2 and OMI, the performed sensitivity inversions induce only very
weak departures from the standard inversion (Fig. ).
Isoprene fluxes over Amazonia derived by GOME-2 and OMI inversions are equal
to 92.5 and 73.7 Tg, respectively. These re 25 and 7 % lower than the
prior and in good agreement with previous studies using satellite HCHO
observations from the SCIAMACHY instrument . The seasonal
variation of the posterior fluxes is consistent with the a priori inventory,
except during the transitional wet-to-dry period (April–June) with both
GOME-2 and OMI satellite data sets pointing to a significant flux decrease of
ca. 25 % (Fig. ). This behavior confirms previous
comparisons using GOME HCHO observations suggesting that factors other than
the temperature influence the observed variability , such as
the growth of new leaves causing a temporary shutdown of the emissions
.
Indonesia
Fire activity was exceptionally low in 2010, with annual emissions of about
0.1 TgVOC, i.e., about 2 orders of magnitude less than for high years such as
2006 according to GFEDv3.
The GOME-2- and OMI-inferred isoprene estimates show good consistency over
Indonesia all year round, amounting to 10.3 and 11.1 Tg, respectively,
and close to the a priori estimate (11.6 Tg). The inferred isoprene emissions are,
however, half of the
reported fluxes of 25 Tg yr-1 based on SCIAMACHY HCHO
observations, which also decreased with respect to their a priori
isoprene flux of 35 Tg yr-1. In comparison to that study,
the isoprene a priori emissions used in the present work are strongly reduced
over this region, due to a drastic reduction by a factor of 4.1 of the
MEGANv2.1 basal isoprene rate for tropical rainforests over Asia, as
suggested by field measurements in Borneo . This reduction
implemented in the MEGAN–MOHYCAN-v2 model is found here
to be corroborated by GOME-2 and OMI HCHO measurements.
Indochina
The northern part of the Indochina Peninsula (primarily Myanmar, also Assam
in India and parts of Thailand) faces intense forest fires during the dry
season, as very well seen in the GOME-2 and OMI HCHO time series, with values
reaching 15 ×1015 molec cm-2 in March, about 3 times
higher than in the wet season (Fig. ).
Both the GOME-2 and OMI data point to substantial but very contrasting
updates in the pyrogenic fluxes during the fire season (March,
Fig. ): flux reductions by a factor of 2–5 over Myanmar
and surrounding forested areas, and flux increases by a factor of almost 2
(or more in the case of OMI) over the southeastern part of the peninsula,
which includes Cambodia, southern Laos and southern Vietnam. In the OMI-DE
inversion assuming doubled errors on the a priori fluxes, the updates are
even more pronounced and reach a factor of 4 over parts of Vietnam and Laos.
The annual emissions in the entire region are decreased by 15 and 26 %
according to the OMI and GOME-2 inversions, respectively. As illustrated in
Fig. , the optimization leads to a substantial improvement
of the model performance, although the HCHO columns remain significantly
underestimated (by up to 20 %) in the southern part of the peninsula
(e.g., Cambodia), most likely due to a too strong underestimation of the GFEDv3
emissions used as a priori in the model. The emission drop over Myanmar and
the need for higher emissions in the southeast are partly confirmed by
comparisons with IASI CO columns. Indeed, as seen in the lower panel of
Fig. , modeled CO simulations using biomass burning
fluxes optimized using OMI data (i.e., reduced by ca. 26 % in March relative
to GFEDv3) display a better agreement with the observed CO columns, despite an
underestimation by ∼ 10 % over most of the peninsula. This result is
consistent with the moderate reduction (ca. 20 % in March) of biomass burning
emissions of CO over tropical Asia inferred by in an inversion
based on IASI CO columns utilizing the TM5 (Transport Model 5) atmospheric model with GFEDv3 as a
priori inventory.
Same as Fig. , for the Indochinese Peninsula in
March 2010.
The strong enhancement of pyrogenic emissions required to comply to the
satellite data over the southeastern part of Indochina might be due to the
occurrence of agricultural fires in those regions (e.g., Cambodia), known to
be a common management practice . These fires are very
difficult to detect by satellite due to their limited spatial extent. It is
worth noting that the latest version of the Fire Inventory from NCAR
FINNv1.5; predicts much higher emissions from this
region: ca. 43 TgC in March in the region 100–108∘ E, 10–18∘ N, i.e., a
factor of 10 higher than in the GFEDv3 inventory (4.3 TgC). The differences
between GFEDv3 and FINN are most likely due to inherent differences in the
proxy variables used in the respective emission models, burned area in the
case of GFEDv3, and active fire counts in the FINN model, both retrieved from
MODIS satellite data. The two inventories provide, however, very similar
estimates for the more forested, northwestern part of Indochina (19–27∘ N,
97–100∘ E): 105 TgC in GFEDv3, 124 TgC in FINNv1.5.
Considerable cloudiness during the rainy season (May–October) causes gaps in
the OMI HCHO time series, due to the exclusion of scenes with ≥ 40 % cloud
cover. The GOME-2 data series are comparatively less affected by this issue,
due to the diurnal precipitation and cloudiness patterns during the monsoon
season. Indeed, long-term observations over Indochina
reveal an early afternoon rainfall peak (13:00–16:00 LT) and a heavy rainfall in
the early morning (04:00–07:00 LT) but lower precipitation rates between 07:00 and 10:00 LT.
GOME-2 observations are therefore less contaminated by clouds and offer a
better spatial coverage during the rainy season in this region.
Africa
Over Africa, the annual pyrogenic source, amounting to 40.7 TgVOC in the a
priori,
is reduced to 26.2 and 32.6 TgVOC in the GOME-2 and OMI inversions,
respectively (Table ). A smaller reduction is also inferred
for isoprene fluxes, estimated at 76.6 Tg (GOME-2) and 75.2 Tg (OMI),
within 10 % of the a priori value (81.6 Tg). These estimates are in line with
recently reported isoprene fluxes over Africa based on the NASA data set of
OMI HCHO columns (77 TgC or 87 Tg isoprene compared to 116 Tg isoprene in
the a priori reported fluxes, ). The spatial distribution of the emission
updates is displayed in Figs. ,
and .
African fire occurrence peaks in central Africa (e.g., the Democratic
Republic of the Congo or DRC) in early June (Fig. ) and
lasts until August with the end of the dry season. The GOME-2 and OMI
observations show an excellent accordance, with morning columns being about
10 % higher than in the afternoon, consistent with measurements in Bujumbura
(Fig. ), although the morning to afternoon ratio was found to
be higher in the ground-based observations (1.25). The model simulations
overpredict the observations of both sounders during the fire season by
10–25 %. The posterior bias reduction (cf. Fig. , DRC
region) is achieved by a significant biomass burning flux decrease, reaching
a factor of 2 in the southern part of DRC, and 20–30 % elsewhere between 2
and 12∘ S. In a similar manner, up to a factor of 2 in emissions decrease
is also needed in the region of the central African republic during the fire
season (November–February) to match the observed columns
(Fig. ), in good agreement with our previous study using
SCIAMACHY HCHO columns . Only small changes are inferred
for isoprene emissions in Northern Africa, with 11 % (GOME-2) and 16 % (OMI)
decrease compared to the a priori emissions, as illustrated in Fig. .
In Southern Africa, biogenic fluxes are highest in January and lowest in
July, while the fire season starts in May and peaks in September
(Figs. and ). Both GOME-2 and OMI
inversions infer only small isoprene flux updates (Table ).
Regarding fire emissions, in contrast with GOME-2 data suggesting a ca. 30 %
flux reduction (to 17.6 from 25.8 TgVOC), the OMI-based estimate lies within
10 % of the a priori, due to a compensation of flux decreases north of
approximately 15∘ S, and flux increases in the southernmost part of the continent,
south of ca. 15∘ S (Fig. ). Although the seasonal
patterns are essentially preserved by the optimization, both inversions
predict higher emissions at the end of the dry season, especially over Zambia
and surrounding regions (October;
Figs. , ). These updates are highest
(factor of ∼ 2) in the OMI optimization, but the patterns are very
similar in both inversions.
Conclusions
The emissions of NMVOCs in 2010 were optimized by inverse modeling using the
IMAGESv2 CTM and its adjoint with HCHO column abundances from either GOME-2
or OMI as observational constraint. Given their different overpass times, the
consistency of the inferred emissions depends on how the model can faithfully
reproduce the diurnal cycle of HCHO columns. The modeled diurnal cycle
displays a great variability mirroring the competing influences of
photochemical productions and losses as well as the diurnal profiles of
emissions and (to a lesser extent) meteorological parameters. Where
anthropogenic VOCs are dominant, daytime photochemical production and the
anthropogenic emission profile lead to an early afternoon maximum, in
agreement with MAX-DOAS observations in Belgium, Holland and (during
summertime) the Beijing area. Over oceanic areas, where methane oxidation is
the only significant source in the model, a similar behavior is also
simulated, in agreement with FTIR data at Reunion Island. The poor model
performance at several locations (Bujumbura, OHP, Beijing in winter/spring)
is likely at least partly due to the coarse model resolution, as shown by the
very different diurnal profiles observed at Beijing and Xianghe. This limited
representativity of local ground-based sites possibly explains (part of) the
large deviations (typically ±10–30 %) found between the calculated and
observed ratios of the HCHO columns at 13:30 and 09:30 LT. Despite this large
scatter, the average ratio of 13:30 LT columns to 09:30 LT columns is only slightly
higher (1.126) in the model compared to the MAX-DOAS and FTIR measurements
(1.043).
Unfortunately, no ground-based measurements are available in regions where the
simulated diurnal cycle amplitude is largest, namely over intense fire scenes
at both tropical and boreal latitudes. Over these regions, an evening maximum
is predicted, and the peak to trough ratio reaches up to 70 %. In
isoprene-rich areas, the diurnal HCHO cycle often, but not always, displays a
minimum around noon, when the photochemical sink is highest, and a maximum in
the late evening or early morning, in agreement with a previous modeling
study . Validation studies over forested areas will be
needed to determine how realistic these patterns are.
The ratio of 13:30 to 09:30 LT columns is most often between 0.8 and 1.2 according
to ground-based measurements. Similar but generally higher values of this
ratio are calculated by the model, 8 % higher on average. The satellite, on the
other hand, although in qualitative agreement with the above, suggests higher
ratios of 13:30 to 09:30 LT columns than the model at mid-latitudes, whereas no
clear pattern emerges in tropical regions (Fig. ).
Nevertheless, these discrepancies in terms of morning/afternoon ratios are
most often small in comparison with the model–data differences in the HCHO
columns themselves. As a consequence, the emission fluxes inferred from both
GOME-2 and OMI inversions are found to be generally very consistent. They
both suggest a strong decrease of the global biomass burning source, by about
35 %. The decrease is mostly concentrated in the Tropics, e.g., over Amazonia
(factor of > 2 reduction), over equatorial Africa and over Myanmar and
the surrounding regions (factor of 2–5 reduction in March). These updates are
confirmed by comparing CO columns predicted by the model using the biomass
burning emissions estimated by the OMI inversion with IASI CO columns. The
results are also consistent with a recent study using MOPITT CO columns
. The seasonal profile of the emissions is generally well
preserved by the inversions, except for a significant enhancement near the
end of the dry season, in particular over Southern Africa (in October) but
also Amazonia (in September) and Indochina (in April). Both satellite
data sets point to strong enhancements of agricultural fire fluxes in the
North China Plain in June (factor of almost 2) and in the southern part of
Indochina, compared to the a priori estimate.
Very good agreement between the inversion results is found for isoprene
fluxes, with global annual fluxes reduced by 9 % (GOME-2) and 13 % (OMI)
compared to the a priori 363 Tg. In the Southeastern US, both inversions
agree on a substantial decrease by ca. 25 % (OMI) and 40 % (GOME-2), in good
agreement with previous estimates based on SCIAMACHY and OMI HCHO data. This
reduction improves the correlation between calculated and observed HCHO
concentrations during the airborne INTEX-A campaign conducted over the
eastern US. Over Amazonia, the source of isoprene is consistently lower than
the prior, in particular during the wet-to-dry season transition
(April–June), in accordance with previously reported estimates. Over
Indonesia, the optimizations do not present significant deviations from the
prior, thereby validating the a priori isoprene inventory which incorporated
decreased basal emission rates for Asian tropical rainforests.
The results show that the global anthropogenic VOC fluxes are not well
constrained, as indicated by the negligible updates derived by the inversions
over most areas, except over highly polluted regions with a distinct
anthropogenic signal in the HCHO columns, like China. In this region, the
changes in the emission patterns found by the OMI-based optimization are not
well reproduced by the inversion of GOME-2 data, likely reflecting
discrepancies in the 13:30 to 09:30 LT columns ratio calculated by the model. In
spite of those discrepancies, our study demonstrates that a high degree of
compatibility is achieved between top-down pyrogenic and biogenic emissions
derived by GOME-2 and OMI HCHO data, while the flux estimates are found to be
weakly dependent on changes in key uncertain parameters in the performed
sensitivity inversions.
This study identifies several important large regions where the differences
between bottom-up and top-down estimates are particularly important and the
inferred flux estimates from both satellites show a high degree of
consistency, like the Amazon and the Southeastern US. Recent airborne field
measurements in those regions should provide additional constraints and help
close the gap between bottom-up and top-down estimates. The increasing
availability of in situ observations of formaldehyde and related trace gases
can provide a basis for improving and assessing model simulations of diurnal
variations over a range of environmental conditions and interactions between
biogenic and anthropogenic compounds (e.g., ). Furthermore,
planned geostationary satellites have the potential to improve
satellite-based emission estimates by characterizing diurnal variations in atmospheric
constituents . Finally, new cross-section measurements of
isoprene in the infrared open new avenues for the detection of isoprene using
satellites (e.g., IASI) and a direct link to isoprene emissions
.
The Supplement related to this article is available online at doi:10.5194/acp-15-11861-2015-supplement.
Acknowledgements
This research was supported by the Belgian Science Policy
Office through the PRODEX projects ACROSAT and IASI.Flow (2014–2015) and by
the European Space Agency (ESA) through the GlobEmission project (2011–2016).
P.-F. Coheur is senior research associate with FRS-FNRS. The authors would like to
thank the two anonymous reviewers for their careful reading and constructive
criticism.
Edited by: C. H. Song
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