ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-10133-2016Nine years of global hydrocarbon emissions based on source inversion of OMI formaldehyde observationsBauwensMaitemaite.bauwens@aeronomie.beStavrakouTrissevgeniMüllerJean-FrançoisDe SmedtIsabelleVan RoozendaelMichelvan der WerfGuido R.WiedinmyerChristineKaiserJohannes W.https://orcid.org/0000-0003-3696-9123SindelarovaKaterinaGuentherAlexhttps://orcid.org/0000-0001-6283-8288Royal Belgian Institute for Space Aeronomy, Avenue Circulaire 3,
1180 Brussels, BelgiumVrije Universiteit Amsterdam, Faculty of Earth and Life Sciences, Amsterdam, the NetherlandsNational Centre for Atmospheric Research, Boulder, CO, USAMax Planck Institute for Chemistry, Mainz, GermanyUPMC Univ. Paris 06, Université Versailles St-Quentin, CNRS/INSU, LATMOS-IPSL, Paris, FranceCharles University in Prague, Department of Atmospheric Physics, Prague, Czech RepublicUniversity of California, Irvine, USAMaite Bauwens (maite.bauwens@aeronomie.be)11August20161615101331015814March201631March201623June20168July2016This 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/16/10133/2016/acp-16-10133-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/10133/2016/acp-16-10133-2016.pdf
As formaldehyde (HCHO) is a high-yield product in the oxidation of most
volatile organic compounds (VOCs) emitted by fires, vegetation, and
anthropogenic activities, satellite observations of HCHO are well-suited to
inform us on the spatial and temporal variability of the underlying VOC
sources. The long record of space-based HCHO column observations from the
Ozone Monitoring Instrument (OMI) is used to infer emission flux estimates
from pyrogenic and biogenic volatile organic compounds (VOCs) on the global
scale over 2005–2013. This is realized through the method of source inverse
modeling, which consists in the optimization of emissions in a
chemistry-transport model (CTM) in order to minimize the discrepancy between
the observed and modeled HCHO columns. The top–down fluxes are derived in
the global CTM IMAGESv2 by an iterative minimization algorithm based on the
full adjoint of IMAGESv2, starting from a priori emission estimates provided
by the newly released GFED4s (Global Fire Emission Database, version 4s)
inventory for fires, and by the MEGAN-MOHYCAN inventory for isoprene
emissions. The top–down fluxes are compared to two independent inventories
for fire (GFAS and FINNv1.5) and isoprene emissions (MEGAN-MACC and
GUESS-ES).
The inversion indicates a moderate decrease (ca. 20 %) in the average
annual global fire and isoprene emissions, from 2028 Tg C in the a priori to
1653 Tg C for burned biomass, and from 343 to 272 Tg for isoprene fluxes.
Those estimates are acknowledged to depend on the accuracy of formaldehyde
data, as well as on the assumed fire emission factors and the oxidation
mechanisms leading to HCHO production. Strongly decreased top–down fire
fluxes (30–50 %) are inferred in the peak fire season in Africa and
during years with strong a priori fluxes associated with forest fires in
Amazonia (in 2005, 2007, and 2010), bushfires in Australia (in 2006 and 2011),
and peat burning in Indonesia (in 2006 and 2009), whereas generally increased
fluxes are suggested in Indochina and during the 2007 fires in southern
Europe. Moreover, changes in fire seasonal patterns are suggested; e.g., the
seasonal amplitude is reduced over southeast Asia. In Africa, the inversion
indicates increased fluxes due to agricultural fires and decreased maxima
when natural fires are dominant. The top–down fire emissions are much better
correlated with MODIS fire counts than the a priori inventory in regions with
small and agricultural fires, indicating that the OMI-based inversion is
well-suited to assess the associated emissions.
Regarding biogenic sources, significant reductions in isoprene fluxes are
inferred in tropical ecosystems (30–40 %), suggesting overestimated basal
emission rates in those areas in the bottom–up inventory, whereas strongly
positive isoprene emission updates are derived over semiarid and desert
areas, especially in southern Africa and Australia. This finding suggests
that the parameterization of the soil moisture stress used in MEGAN greatly
exaggerates the flux reduction due to drought in those regions. The isoprene
emission trends over 2005–2013 are often enhanced after optimization, with
positive top–down trends in Siberia (4.2 % year-1) and eastern Europe (3.9 % year-1),
likely reflecting forest expansion and warming temperatures, and negative
trends in Amazonia (-2.1 % year-1), south China
(-1 % year-1), the United States (-3.7 % year-1), and
western Europe (-3.3 % year-1), which are generally corroborated
by independent studies, yet their interpretation warrants further
investigation.
Introduction
Complementary to bottom–up methodologies for deriving emissions estimates, inverse modeling has
the potential to improve those estimates through the use of atmospheric
observations of trace gas compounds, in particular over regions undergoing fast economic
development and facing intense air pollution problems, like eastern China
but also on the global scale
. Pollutants like CO and NO2 are directly detected
from satellite and their emissions have been inferred using inversion techniques on different
scales (e.g., ). The detection of formaldehyde
columns from satellite sensors measuring in the UV-visible spectral window opened
the way for the derivation of fluxes of non-methane volatile organic compounds (NMVOCs), a broad
class of formaldehyde precursors emitted by vegetation, fires, and anthropogenic activities
. These compounds have a profound impact on air quality and
climate, owing to their influence on OH levels and the methane lifetime and to their role as
precursors of ozone and secondary organic aerosols . The accurate
estimation of their fluxes is therefore of utmost importance.
Natural emission from vegetation is the dominant volatile organic compound (VOC) source. The global
annual flux is estimated at ca. 1000 Tg VOC, with isoprene accounting for
half of this emission . Despite a
general consensus on the isoprene emission patterns, including their
dependence on temperature and light density responsible for their marked
diurnal and seasonal variations, these emission estimates come, however, with
large uncertainties, associated with the strong variability of emission
factors and the extrapolation of sparse measurements to larger scales. An
uncertainty of a factor of 2 in global and regional isoprene fluxes was
reported based on a compilation of numerous literature studies
, whereas emission models were found to be strongly
sensitive to choices of input variables, leading to even wider uncertainty,
of ca. 200–1000 Tg C year-1 globally .
The global biomass burning fluxes are estimated by bottom–up inventories to
be ca. 1300–2200 Tg C on a yearly basis, which corresponds to
40–100 Tg VOC year-1 using emission factors from the compilation of
or . These
estimates, however, depend on assumptions made in fire emission models
regarding fuel loading and consumption efficiency and on the quality of land
cover maps and fire proxies from satellite
.
Formaldehyde is a high-yield product in the oxidation of a large majority of
NMVOCs. Isoprene alone is responsible for approximately 30 % of the
global HCHO burden according to model estimates , whereas
the contribution of vegetation fires is globally small (3 %) but can be
locally very important. Spaceborne vertical columns of HCHO retrieved from
GOME, SCIAMACHY, the Ozone Monitoring Instrument (OMI), and GOME-2 sensors have been used to constrain the VOC
budget on different scales (e.g.,
). Top–down flux
estimates deduced from two satellite sensors with different overpass times
showed a good degree of consistency over the Amazon and
globally . The latter study using GOME-2 (09:30 LT) and
OMI (13:30 LT) HCHO observations in 2010 reported a good agreement between
the inversion results over most areas and identified large regions where the
derived emissions were highly consistent (e.g., Amazonia, southeastern US).
Encouraged by those results, and relying on a multiyear record of HCHO
columns observed by the OMI sensor, we use inverse modeling to derive
top–down pyrogenic and biogenic VOC estimates over 2005–2013. The satellite
data offer an unparalleled opportunity to bring new insights in our
understanding of emissions and their quantification, to infer long-term
seasonal and interannual flux variability, and to detect potential emission
trends that may not be well represented in bottom–up inventories. To this
purpose, we use a global chemistry-transport model (CTM), coupled with an inversion module and a
minimization algorithm adjusting the emissions used in the model, in order to
achieve an optimal match between the modeled and the observed HCHO columns
while accounting for errors in the a priori emissions and the HCHO
observations. The optimized fluxes are compared with independent bottom–up
pyrogenic and biogenic emission inventories as well as with previous
literature studies. The methodology is briefly presented in
Sect. , and an overview of the results is discussed in
Sect. . The top–down fluxes and comparisons to bottom–up
inventories over big world regions are discussed thoroughly in
Sects. – and emission trends in
Sect. . Conclusions and final remarks are presented in
Sect. .
Methods
We used formaldehyde observations retrieved from the OMI spectrometer aboard
the Aura mission and fully documented in a recent study .
The retrievals are based on an improved DOAS algorithm that reduces the
effect of interferences between species and ensures maximum consistency
between the OMI and GOME-2 columns. The current data version (v14) uses an
iterative algorithm to remove spikes in the residuals of the slant columns
and a procedure based on the background normalization to remove striping
artifacts due to calibration problems .
In addition to the destriping procedure, in order to reduce the effect of the
OMI row anomaly issue affecting the spectra after 2007
(http://www.knmi.nl/omi/research/product/rowanomaly-background.php),
the OMI rows presenting higher levels of noise and fitting residuals than the
average were systematically removed from the dataset .
Although this filtering leads to a loss of coverage, the resulting dataset is
more appropriate for addressing trend studies, as explained in
.
The IMAGESv2 global model calculates the concentrations of 131 transported
and 41 short-lived trace gases with a time step of 6 h at
2∘× 2.5∘ resolution between the surface and the
lower stratosphere. The effect of diurnal variations is accounted for through
correction factors on the photolysis and kinetic rates obtained from model
simulations with a time step of 20 min, which are also used to calculate the
diurnal shapes of formaldehyde columns required for the comparison with
satellite data. A detailed model description is provided in
. Meteorological fields are obtained from ERA-Interim
analyses of the European Centre for Medium-range Weather Forecasts (ECMWF).
The model uses anthropogenic NOx, CO, SO2, NH3, and total NMVOC
emissions from the Emission Database for Global Atmospheric Research
(EDGAR4.2, http://edgar.jrc.ec.europa.eu), which is overwritten by the
EMEP inventory (http://www.ceip.at/ms) over Europe and by the REASv2
inventory over Asia. The NMVOC speciation is obtained from
REASv2 over Asia and from the RETRO inventory elsewhere.
The emissions over the US are scaled according to the NEI national totals for
all years between 2005 and 2013
(http://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data).
Biomass burning emissions are taken from the latest version of the Global
Fire Emissions Database, GFED4s (July 2015), which includes the contribution
of small fires based on active fire detections (;
).
The GFED data are available on a daily basis at 0.25∘× 0.25∘ resolution from 1997 through the present at
http://www.globalfiredata.org. Those emissions are distributed
vertically according to .
A priori isoprene emissions are obtained from the MEGAN-MOHYCAN model
for all years of the study period at a
resolution of 0.5∘× 0.5∘
(http://tropo.aeronomie.be/models/isoprene.htm). Besides the emission
dependence on leaf temperature, photosynthetically active radiation (PAR),
leaf area, and leaf age, the model accounts for the inhibition of isoprene
emissions in very dry soil conditions through a dimensionless soil moisture
activity factor (γSM) expressed as a function of volumetric
soil moisture content obtained from the ERA-Interim
reanalysis. The parameterization of γSM bears large
uncertainties, as it is based on scarce (and sometimes contradictory) field
data, and its implementation can lead to very different results depending on
the choice of database for soil moisture data . It reduces the emissions by ca. 20 % globally
according to MEGAN-MOHYCAN, with strongest effects (up to factor of 3 or
more) over Australia and southern Africa and to a lesser extent over
northern Africa (Sahel), the western US, and the Middle East.
Global distributions of mean 2005–2013 HCHO columns for January and
June observed by OMI (upper panels), modeled using emissions (middle panels)
and inferred after optimization (lower panels). The columns are expressed in
1015 molec. cm-2. The observed monthly averages exclude scenes
with cloud fractions higher than 40 % and land fractions lower than
20 %, as well as data with a retrieval error higher than 100 %. The
four lower panels illustrate the model–data difference before and after
optimization for January and July.
The chemical degradation mechanism of pyrogenic NMVOCs is largely described
in , with only minor modifications. This mechanism
includes an explicit treatment for 16 pyrogenic formaldehyde precursors. The
emissions of other pyrogenic compounds is represented through a lumped
compound (OTHC) with a simplified oxidation mechanism designed in order to
reproduce the overall formaldehyde yield of the explicit NMVOC mix it
represents. The oxidation mechanism for isoprene is based on
, modified to account for the revised kinetics of isoprene
peroxy radicals according to the Leuven Isoprene Mechanism version 1 (LIM1)
, as well as for the chemistry of the isoprene epoxides
(IEPOX) following the Master Chemical Mechanism (MCMv3.2; http://mcm.leeds.ac.uk/MCMv3.2/). Using a box model, the formaldehyde yield in isoprene
oxidation by OH is calculated to be
2.4 mol mol-1 in high
NOx (1 ppbv NO2, after 2 months of simulation) and
1.9 mol mol-1 for 0.1 ppbv NO2. It should be stressed that the
isoprene mechanism still bears important uncertainties at low-NOx
conditions, as both the oxidation products of the isoprene epoxides and the
isomerization products of isoprene peroxy radicals have complex degradation
mechanisms that are still far from being well elucidated despite recent
progress . Note that suppressing the isomerization
channel in the isoprene degradation resulted in only slightly higher model
HCHO columns over isoprene-rich regions .
The mismatch between the CTM and the observations, quantified by the cost function J,
J(f)=12((H(f)-y)TE-1(H(f)-y)+fTB-1f),
is minimized through an iterative quasi-Newton optimization
algorithm, which is based on the calculation of the partial derivatives of
J with respect to the input variables. In our case the input variables are
scalars f=(fj), such that the optimized flux can be expressed as
Φiopt(x,t)=∑j=1mefjΦi(x,t),
with Φi(x,t) being the initial flux depending on space (latitude,
longitude) and time (month) and m being the emission categories or processes. In
Eq. (), H(f) denotes the model acting on the variables,
y the observation vector, E and B the
covariance matrices of the errors in the observations and on the a priori
parameters f, respectively, and T means the transpose of
the matrix. The partial derivatives of J with respect to f are
calculated by the discrete adjoint of the IMAGESv2 chemistry-transport model
(CTM) . The derivation of monthly pyrogenic and
biogenic fluxes is carried out on a global scale at the resolution of the model
(2∘× 2.5∘), as described in detail in
. The inversions are performed separately for all years of
the study period (2005–2013), and about 60 000 flux parameters are
optimized per year globally.
Upper panel: mean (2005–2013) annual biomass burning emission
estimates in Tg C/grid per year according to the a priori inventory GFED4s
and to the OMI-based biomass burning emissions. Lower panel: mean
(2005–2013) annual isoprene emission estimates in Tg isoprene per grid cell
per year according to the a priori MEGAN-MOHYCAN inventory and from the
OMI-based inversion.
The covariance matrix of the observational errors is assumed to be diagonal. The
errors are calculated as the squared sum of the retrieval error and a
representativity error set to 2 × 1015 molec. cm-2. The
assumed error in the a priori biogenic and pyrogenic fluxes is factor of 3.
This choice reflects the high variability of the biomass burning emission source
and the strong uncertainties associated with the biogenic emissions, as
demonstrated by the large range of literature emission estimates
. The spatiotemporal correlations among the a
priori errors in the flux parameters are defined as in .
About 20–40 iterations are needed to reach convergence, which is attained
when the gradient of the cost function is reduced by a factor of 1000 with
respect to its initial value. The cost function generally decreases by ca.
45–55 % in comparison to its initial value.
Figure illustrates a comparison between observed monthly mean
HCHO column densities over 2005–2013 and monthly columns simulated by the
IMAGESv2 model sampled at the time and location of the satellite measurement.
The observed monthly averages exclude scenes with cloud fractions higher than
40 % and land fractions lower than 20 %, as well as data with a
retrieval error higher than 100 %. The number of effective observational
constraints is highest in the first years of the OMI mission (ca. 17 000 per
year) and declines by about 15 % after 2009 due to instrumental
degradation effects , whereas the data availability is
higher during the summer than in the winter in the Northern Hemisphere (ca.
1600 vs. 1200 measurements per month). The satellite columns are freely
available at the BIRA-IASB website (http://h2co.aeronomie.be). The
OMI-based emission fluxes presented in this study are available at the
GlobEmission web portal (http://www.globemission.eu).
Mean a priori and OMI-based emission estimates compared to
independent emission inventories for open biomass burning and isoprene
emissions calculated for different world regions and globally. Regions are
defined in Fig. . The means are taken over the period of data
availability, i.e., over 2005–2013 for all inventories, except for
MEGAN-MACC (2005–2010) and GUESS-ES (2005–2009). NH: Northern Hemisphere;
SH: Southern Hemisphere.
NorthSouthEuropeNHSHRussiaSoutheastAustraliaGlobalAmericaAmericaAfricaAfricaAsiaBiomass burning emissions (burned biomass in Tg C year-1) GFED4s105319314186841302371042028OMI-based8627335320530112203951653GFAS187328223334312642461261938FINNv1.511245234278415114579222006GFED4842311727947997156951438Isoprene emissions (Tg isoprene year-1) MEGAN MOHYCAN321416.850299.43638343OMI-based26978.43528113136272MEGAN-MACC341737.810367128094570GUESS-ES4414318.17760206326452Overview of the results
The source optimization leads to a good overall agreement with the OMI
observations (Fig. ), in particular in the tropics, as a
result of the high signal-to-noise ratio in the observations at these
latitudes. The a posteriori columns remain close to the a priori at high
latitudes, mainly due to lower data availability and higher observational
errors at these latitudes . The inferred mean HCHO columns
over the study period are generally decreased by 20–25 % over the Amazon
and equatorial Africa, whereas a mean decrease of about 13 % is found in
the southeastern US during summertime (cf. Supplement, Fig. S1). The HCHO
columns are increased in a few regions after inversion, especially during
biomass burning events. The annually averaged global distribution of
pyrogenic and isoprene emissions over 2005–2013 before and after
optimization is illustrated in Fig. . Figure
displays the extent of the regions over which comparisons will be discussed.
Bottom–up and top–down emission estimates are summarized in
Tables and .
Definition of big and small regions used in this study. Big regions
are N America (13–75∘ N, 40–170∘ W), S America
(60∘ S–13∘ N, 90∘ W–30∘ E), Europe
(37–75∘ N, 15∘ W–50∘ E), NH Africa
(0–37∘ N, 20∘ W–65∘ E), SH Africa
(0–40∘ S, 20∘ W–65∘ E), Russia
(37–75∘ N, 50–179∘ E), SE Asia
(10∘ S–37∘ N, 65–170∘ E), and Australia
(10–50∘ S, 110–179∘ E). Small regions are the SE US
(26–36∘ N, 75–100∘ W), Amazonia (5–20∘ S,
40–75∘ W), W Europe (37–71∘ N,
10∘W–20∘ E), E Europe (37–71∘ N,
20–50∘ E), northern Africa (0–16∘ N,
15∘ W–35∘ E), southern Africa (15–35∘ S,
10–55∘ E), Siberia (57–75∘ N, 60–140∘ E), south
China (18–32∘ N, 109–122∘ E), Indochina
(9–30∘ N, 94–109∘ E), Indonesia
(10∘ S–7∘ N, 90–140∘ E), N Australia
(10–24∘ S, 110–150∘ E), and S Australia
(24–38∘ S, 110–155∘ E).
Global a priori and OMI-based emission estimates per year. Fire
estimates are expressed in Tg C year-1, isoprene in Tg of isoprene per
year.
The OMI-based fire flux estimates are compared with two independent
inventories: GFAS and FINNv1.5. The Global Fire Assimilation System (GFAS) is
based on the assimilation of fire radiative power observed from the MODIS
instruments aboard the Terra and Aqua satellites and
provides daily global fire emission estimates at 0.5∘× 0.5∘ and 0.1∘× 0.1∘ resolution for 2003
onwards (http://eccad.sedoo.fr). The Fire Inventory from NCAR (FINN)
version 1.5 is an updated version of the FINN daily global high-resolution
inventory . In addition to GFED4s, we also used
GFED4. Both version have adopted lower fuel consumption rates than the
previous version GFED3 to better match field observations
, but in GFED4s this decrease is compensated for by the
addition of small (s) fire-burned area. It is available at
http://www.globalfiredata.org. The average 2005–2013 global burned
biomass is estimated at 1938, 2006, and 1438 Tg C year-1 in GFAS,
FINNv1.5 and GFED4, respectively (Table ; Fig. S2).
The isoprene emission estimates are compared to two bottom–up inventories: MEGAN-MACC and GUESS-ES (Fig. S3). MEGAN-MACC relies on
the MEGANv2.1 model for biogenic volatile organic compounds (BVOC) and is
based on the MERRA reanalysis fields . The emissions are
provided at 0.5∘× 0.5∘ resolution and on a monthly
basis from 1980 through 2010. The GUESS-ES isoprene inventory is based on the
physiological isoprene emission algorithm described by
and updated by . It is coupled to the dynamic global
vegetation model LPJ-GUESS and is driven by the CRU (Climatic
Research Unit) monthly meteorological fields at
1∘× 1∘ resolution between 1969 and 2009. Both
inventories are available from the ECCAD data portal
(http://eccad.sedoo.fr). The mean isoprene emission amounts to 452 and
to 570 Tg year-1, according to the GUESS-ES and to the MEGAN-MACC
inventory, respectively (Table ), and both lie much higher
than the a priori MEGAN-MOHYCAN inventory (343 Tg year-1 average over
2005–2013). The large discrepancy between MEGAN-MACC and MEGAN-MOHYCAN
datasets, both relying on the MEGAN emission model and the
same version of basal emission factors (version 2011), can be explained to a
large extent by (i) the neglect of soil moisture stress effects in
MEGAN-MACC, (ii) a reduction by a factor of 4.1 of the basal emission factors
for forests in Asia in MEGAN-MOHYCAN as suggested by
field observations in Borneo , and (iii) the use of the
crop distribution database of in MEGAN-MOHYCAN, along
with the necessary adjustment of the other plant functional type
distributions, leading overall to larger crop extent and lower total
emissions.
The average global fire flux, expressed as burned biomass, is reduced from
2028 Tg C year-1 (GFED4s) to 1653 Tg C year-1 after optimization
(Table ). Note that the inversion provides updated VOC
emissions of HCHO precursors. However, to ease the comparison with other
inventories, VOC emissions are converted to carbon emissions through the use
of emission factors obtained from the compilation of (with
2011 updates). It should be acknowledged that the top–down estimates given
here for fuel consumption may be affected by errors in the emission factors
as well as by errors in the formaldehyde yields per VOC. The strongest
emission decreases are induced over Africa (23 %), South America, and
southeast Asia (15 %), whereas in Europe the fire fluxes are 12 %
higher than in GFED4s. The reduced top–down emission agrees within 15 %
with the GFED4 inventory (1438 Tg C year-1) and is ca. 18 % lower
than the GFAS and FINN global estimates. The lower a posteriori emissions in
Africa are supported by the independent inventories, and the flux updates in
Europe and Russia are in good agreement with the FINN fluxes. At tropical
latitudes, the estimates from the independent inventories often exhibit large
discrepancies, underscoring the large uncertainty of this source, while the
top–down emissions lie generally within their range (Table ;
Fig. ).
Interannual variability expressed as coefficient of variation,
defined as the standard deviation of the emissions divided by the mean of the
emissions, given for the a priori, for the OMI-based emission estimates, and
for the independent emission inventories of biomass burning (upper panel),
and isoprene emissions (lower panel) over the big regions defined in
Fig. .
Temporal correlation between monthly MODIS fire counts, GFED4s, and
OMI-based fluxes over the regions selected based on literature evidence for
the occurrence of small fires. The regions are shown on the MODIS land cover
map in Fig. S4.
RegionCoordinatesFire typeMODIS vs. GFED4sMODIS vs. OMI-basedN Africa4–16∘ N, 15∘ W–15∘ Eagriculturala0.890.96Maranhão6∘ S–2∘ N, 44–52∘ Wagriculturala0.560.91Mato Grosso7–15∘ S, 50–60∘ Wsmall-scale deforestationb0.950.97SE US30–36∘ N, 75–100∘ Wagriculturala0.360.65N China30–40∘ N, 111–122∘ Eagriculturala0.660.85Indochina6–27∘ N, 87–110∘ Eagriculturala and small-0.840.95scale deforestationdIndonesia10∘ S–5∘ N, 93–130∘ Eagriculturala and peatc0.850.89NW India29–33∘ N, 70–79∘ Eagricultural firesa0.750.87Russia52–60∘ N, 55–90∘ Wagriculturala and peatc0.810.94Eq. Africa14∘ S–2∘ N, 10–25∘ Eagriculturale0.960.99E Australia20–40∘ S, 145–155∘ Eagriculturale0.550.86Madagascar12–26∘ S, 43–50∘ Eagriculturale0.900.96
a Region dominated by cropland according to the MODIS
land cover change . b Region with a high number of
small deforestation fires . c Region with peat fires
selected based on . d Region with a high number of
small deforestation fires . e Region where GFED4s
emissions are predominantly associated with small fires
.
The OMI-based fire emissions present a marked interannual variability,
between a minimum of 1383 Tg C in 2013 and a maximum of 1966 Tg C in 2007
(Table ). Figure illustrates the coefficient
of variability, defined as the standard deviation of the emissions divided by
the mean, which is a measure of the interannual variability of the emissions
. The GFED4s coefficient is lowest over Africa (less than
0.15) and highest in South America, southeast Asia, Russia, and Australia
(0.35–0.57). The low variability over Africa can be explained by the
dominance of intense savanna fires that are highly regular throughout the
years. According to the source attribution of GFED4s, deforestation fires are
by far the prevailing source, responsible for 80 % of the total emission
in South America, while the rest is due to savanna burning occurring in
northeastern South America. The coefficient of variation of South American
deforestation fires amounts to 0.74, pointing to the strong effect of climate
variability, caused by, e.g., the strong El Niño–Southern Oscillation
(ENSO), on the fire occurrence in the Amazon , and the rapid decline
in deforestation rates over 2005–2013 . In addition, the
estimated coefficient for savanna fires (0.41) is substantially higher than
for the African savannas due to the strong variability of fire burning in northern South America . In Australia, savanna, grassland,
and shrubland fires are responsible for the high interannual variability of
the GFED4s inventory (0.42). In southeast Asia the contribution of peat
burning to the total fire flux varies strongly from year to year
(0–38 %) and drives the high coefficient of variability (0.45)
. After inversion, the coefficient of variability is
reinforced over Europe and Southern Hemisphere (SH) Africa but is reduced in the tropics,
especially over southeast Asia and South America, where the decreased
top–down variability is supported by comparisons with GFAS and FINN
(Fig. ). This interannual variability of the optimized fluxes
will be thoroughly discussed in the following sections
(Sects. –).
Updates (percentage change from the a priori) in annually averaged
biomass burning emissions suggested by the flux inversion for all years of
the study period.
Updates (percentage change from the a priori) in annually averaged
isoprene emissions inferred by the optimization for all years of the study
period.
The global mean 2005–2013 isoprene emission is reduced from 343 to
272 Tg year-1 after inversion (Table ), with the
largest reductions inferred in Northern Hemisphere (NH) Africa and South America (ca. 30 %) and in the southeastern US (35 %). In contrast to the emission decrease
suggested by satellite, the isoprene fluxes estimated by MEGAN-MACC and
GUESS-ES are substantially higher, by 100 and 66 %, respectively. The
interannual variation of the isoprene fluxes is low in all regions, with the
coefficient of variability close to 0.04 in the tropics and up to 0.07 in
extratropical regions. The satellite columns suggest stronger interannual
variability over all regions, except in South America where it is slightly
reduced. The interannual variation of isoprene fluxes is low for all
inventories, generally stronger in MEGAN-MACC (up to 0.1) and weaker in
GUESS-ES (Fig. ).
The monthly variation of the a priori and the OMI-based emissions is compared
directly to MODIS Aqua (MYD14CM, 13:30 LT) fire counts
(http://reverb.echo.nasa.gov) over 12 smaller regions selected
based on literature evidence for the occurrence of small fires
(Table ; Fig. S4). Higher spatial and temporal correlations
are calculated after the inversion in all selected areas, especially over
agricultural regions, like the southeastern US, eastern Australia, and
Maranhão, where the correlation improves significantly from 0.36 to
0.65, from 0.55 to 0.86, and from 0.56 to 0.91, respectively. This shows that
satellite HCHO observations do detect the contribution of small fires. It
also explains the improved correlation of OMI-based emissions with GFAS and
FINN in South America, northern Africa, and southeast Asia, regions where this
contribution is important .
Interannual variation of burned biomass (in Tg C year-1) over
2005–2013 from the a priori inventory (black), the satellite-based estimates
(OMI in red), and from other bottom–up inventories (GFED4 in green, GFAS in
orange, FINN in blue) over small regions defined in Fig. .
Units are Tg C year-1. The GOME-2-inferred estimate is shown as magenta
circle.
Interannual variation of isoprene fluxes over 2005–2013 from the a
priori inventory (black), the satellite-based estimates (OMI in red),
MEGAN-MACC (in green), and GUESS-ES (in orange) over regions (red boxes)
defined in Fig. . Units are Tg of isoprene per month. The
GOME-2-inferred estimate is shown as magenta circle.
The ratio of the optimized to the a priori annual fluxes for biomass burning
and isoprene emissions is presented in Figs. and
, respectively. The interannual flux variation is displayed in
Figs. and , and the seasonal variation of the
fluxes over different regions (Sects. –) is shown in Figs. and –. We present
detailed results for regions where the satellite observations suggest
important changes relative to the a priori fluxes.
Amazonian emissions
The OMI columns suggest important fire flux decreases during years with
strong a priori fluxes, by 16 % in 2005, 22 % in 2007, and 32 % in
2010. The inferred flux reduction in 2010 is corroborated by earlier
inversion studies constrained by GOME-2 HCHO columns ,
MOPITT CO observations , and a multisensor-based emission
estimate above Mato Grosso . The top–down interannual fire
variability is marked but less pronounced compared to the a priori, with the
lowest emission inferred in 2009 (80.2 Tg C) and the highest in 2007
(387.4 Tg C; Fig. ), and it is corroborated by the GFAS and FINN
inventories (Fig. ). The time and duration of the fire season
is not modified by the optimization (Fig. ). The OMI-derived
fluxes display the same pronounced seasonality as GFED4s, with fire emissions
peaking between August and September and a rapid decline in October and
November, as found in previous studies .
The independent inventories, however, indicate generally higher fluxes than
the top–down fluxes from October to January (Fig. ).
Seasonal and interannual variation of biomass burning emissions and
isoprene emissions from bottom–up and top–down estimates over Amazonia
(Fig. ). Units are Tg C per month for biomass burning fluxes
and Tg of isoprene per month for biogenic emissions. The annual emission flux
per inventory is given as inset numbers.
Regarding isoprene, the inversion infers generally lower fluxes than the a
priori inventory for all years of the study period, with a 38 % mean
annual reduction over 2005–2013, as illustrated in Figs. and
. The top–down annual isoprene flux ranges between 59 Tg in
2013 and 70 Tg in 2007, and the a priori interannual and seasonal variability
is generally preserved after inversion
(Figs. , ) and is similar in all inventories, with
minimal emissions during the wet-to-dry season transition (April–June) and
higher fluxes during the dry season (July–October). The peak-to-trough ratio
is about a factor of 2 for the a priori and optimized fluxes, whereas it is
weaker in the GUESS-ES inventory (1.6) and stronger in MEGAN-MACC (2.4). During
the wet-to-dry transition season (April–June), top–down estimates from
GOME-2 and OMI show better consistency than in the dry season
(Fig. , ). An all-year-round emission
decrease in most bottom–up inventories was also required in order to
reconcile the GEOS-Chem model with SCIAMACHY and OMI HCHO columns
. The strong seasonal variation and low emissions during the
wet-to-dry transition are most likely due new leaf growth and lower flux
rates from young leaves .
Comparison of a priori (black) and satellite-based (red) isoprene
fluxes with ground-based flux measurements (colored numbered squares). The a
priori and a posteriori isoprene fluxes are averaged over the full period
from 2005 to 2013 for the grid. To ensure meaningful comparison, the
ground-based flux measurements are corrected for the diurnal variation in
isoprene fluxes; cf. Table S1 for more details.
Figure shows a comparison of modeled isoprene fluxes with
flux measurements from 12 field campaigns performed in the Amazon. The
comparison accounts for the diurnal variations in the fluxes through
correction factors used to scale the measured fluxes to daily averages (cf.
Table S1). Direct comparisons between modeled fluxes and field measurements
should, however, be considered with caution mainly due to the coarse
resolution of the modeled emissions but also to the fact that flux
measurements were often performed outside the study period (2005–2013). The
observed isoprene fluxes exhibit strong local differences within the forest
(up to 5 mg m-2 h-1, ), as well as significant differences from one day
to another (up to 0.5 mg m-2 h-1;
), whereas they may exhibit differences of
up to 1 mg m-2 h-1 associated with the use of different
measurement techniques . Overall, the emission
reduction inferred by the satellite observations lies within the variability
of the field measurements, while the discrepancies between the observed
fluxes are often larger than the differences between the a priori and a
posteriori fluxes. The field studies generally agree on higher fluxes during
the dry and the dry-to-wet transition season between July and December
, while a recent field campaign suggests much lower fluxes (by
ca. a factor of 3) compared to the top–down estimates, most likely related to a
local effect of leaf flushing at the measurement location .
African emissions
In northern Africa, the biomass burning source is reduced by the inversion by
15–38 % for the different years and lies closer to GFED4, GFAS, and FINN
estimates (Table ; Fig. ). In this region, both
natural and agricultural fires peak in December, but the agricultural fire
season, from September to May, lasts longer than the season of natural fires,
which generally occurs between November and March . The OMI
observations suggest a ca. 50 % emission decrease in the fire peak season,
which is supported by comparisons with GFAS and FINN inventories
(Fig. ), and a moderate increase from February to April when
the agricultural fires are dominant and when the fraction of small fires is
largest according to GFED4s. Higher emissions from February to April are also
supported by GFAS and FINN, suggesting an even stronger shift in the fire
season, with higher fire emissions lasting until May. The reduced emission
amplitude and the longer burning season in northern Africa are corroborated
by an inversion study using CO columns from the MOPITT instrument
.
In Africa south of the equator, the OMI-based fire source is 23 % lower
than the bottom–up estimate and lies closer to the estimates of GFED4, GFAS, and FINN (Table ; Fig. ). In terms of seasonal
variation, the natural fires open the fire season between April and October,
followed by agricultural fires lasting from June to November .
The inversion infers 21 % lower emissions in the beginning of the fire
season, when fires are predominantly natural, a reduction by 43 % during
the fire peak between July and September, and 20 % higher emissions
than GFED4s in October, when agricultural fires are the prevalent source
(Fig. ). The GFED4s inventory allocates the maximum of the
small-fire fraction to the peak of the fire season , resulting
in an enhanced emission peak in July–August, rather than in September, as
suggested by the OMI observations. This seasonality shift of the burning
season was also reported in past inversion studies constrained by SCIAMACHY
and GOME-2 HCHO and MOPITT CO observations
.
As Fig. but for Africa.
Southern Hemisphere Africa can be divided into two regions based on the fire
source updates suggested by OMI (Fig. ). In its northern part,
reduced emissions are systematically derived for all years, by up to
65 %, with regard to the a priori flux, whereas in its southern part (southern Africa in Fig. ), the emissions exhibit a stronger
variability, increasing significantly until 2010 but remaining closer to the
a priori in the subsequent years, as illustrated in Fig. . The
a posteriori emissions during the peak fire season in September are found to
be up to a factor of 3 higher than FINN and 50 % higher than GFAS and
GFED4. The largest top–down flux in this region is inferred in September
2008, estimated to be 50 % higher than the a priori, due to record-high
wildfires in Mozambique, South Africa, and Swaziland in that year
.
The OMI observations suggest a decrease in isoprene fluxes over the African
continent by ca. 20 % for all years of the target period, from
79 Tg year-1 in the a priori to 63 Tg year-1, as shown in
Table . This decrease is very similar to the result obtained
from an inversion study constrained by the NASA OMI HCHO retrieval product
reporting an emission reduction in African isoprene fluxes, from
87 Tg year-1 in the a priori to 68 Tg year-1 through 2005–2009
. In the latter study, the flux decrease was strongest over
equatorial and northern Africa, in very good agreement with the updates shown
in Fig. . In a follow-up inversion study also based on OMI
observations, invoked a reduction in MEGAN emission factors
for broadleaf trees and shrub (ca. factor of 2) and woody savannas (20 %)
in Africa in order to reconcile the model with the observations, whereas the
reported comparisons with ground-based measurements suggested that even lower
isoprene flux rates may be necessary.
The isoprene fluxes in northern Africa exhibit a weak interannual variability
(Figs. , ). The OMI observations point to a mean
(2005–2013) decrease of 26 % in this region with respect to the
bottom–up estimate. The geographical extent of the emission updates
(Fig. ) is in agreement with previous satellite-based results
using SCIAMACHY and GOME-2 HCHO columns
. As seen in Fig. , the
seasonality of isoprene emissions in northern Africa is characterized by two
emission maxima, driven by the two equatorial rainy seasons occurring from
March to May and from August to November. The satellite columns indicate a
change in the seasonal profile, from two equally strong emission maxima in
April–May and in October to a peak in March and a weaker second peak in
October–November (Fig. ). This agrees with the seasonality
derived from GOME-2 observations and is similar to the seasonality change
reported by . The stronger emissions in the first half of the
year are also consistent with the independent inventories, whereas the
secondary peak is better represented in the GUESS-ES inventory.
The isoprene emissions in southern Africa peak during the Southern Hemisphere
summer, when both temperature and precipitation rates are higher
(Fig. ). Both MEGAN-MACC and GUESS-ES emission estimates are
about a factor of 2 higher than the top–down estimates. The discrepancy with
MEGAN-MACC is partly explained by the neglect of the soil moisture stress
effect (γSM) in the standard version of the MEGAN-MACC model.
Its inclusion in MEGAN-MACC was found to have a strong impact, leading to a
flux decrease by 50 % on a global scale and even stronger decreases in
Africa and South America . Interestingly, the inversion
suggests a large increase in isoprene emissions (up to a factor of 2)
southward of 15 ∘S and particularly in the very dry southwestern
part of the continent (west of ca. 30∘ E), where the soil moisture
stress effect is strongest in the MEGAN-MOHYCAN emissions
(Fig. and Fig. 2 in ). The spatial
coincidence of the largest emission updates inferred by the inversion with
the areas where the soil moisture stress effect is strongest is a first
indication that its parameterization in MEGAN overestimates the impact of
very low soil moisture on the emissions in dry subtropical environments like
southern Africa (also Australia; see Sect. ). A second,
even stronger indication is provided by the interannual variability of the
emission updates in southwestern Africa (15–35∘ S,
10–30∘ E) shown on Fig. . These updates are indeed
found to be well correlated (r=0.81) temporally with the factor by which
the emissions are reduced due to the soil moisture activity factor
γSM. In other words, the emission increments are largest when
and where γSM is lowest.
MEGAN simulates the isoprene response to soil moisture stress with a simple
parameterization that shuts off isoprene emission when soil moisture drops to
the level where plants can no longer draw moisture from the soil, known as
the wilting point. While the MEGAN soil moisture stress effect uses a simple
concept, the implementation is difficult due to the need to accurately model
soil moisture, soil wilting point, and plant rooting depth.
evaluated the MEGAN response to soil moisture stress by comparison to
measured whole canopy isoprene fluxes and found that the algorithm performed
poorly with the default soil wilting point but worked well when a more
accurate value was used.
Interannual evolution of the factor by which the annual isoprene
flux is reduced due to soil moisture stress vs. the isoprene flux increment
inferred from OMI data (in %) in southwest Africa (top) and southern
Australia (bottom).
As Fig. but for southeast Asia.
Emissions in southeast Asia
The fire season in southeast Asia is characterized by a first peak in March,
associated with aboveground vegetation burning in Indochina, and a
second peak in August to October caused by peat combustion occurring in
Indonesia (Fig. ). The GFED4s fluxes vary
considerably across the years, ranging between a minimum of 123 Tg C (in
2011) and 277 Tg C (in 2006). The top–down estimates remain generally close
to the a priori, except in 2006 and 2009, when the satellite observations
suggest a significant decrease in the fluxes associated with peat burning in
Indonesia by almost a factor of 3 (Fig. ).
The optimized fluxes generally increase in March and decrease from August to
October, while the amplitude of the seasonal pattern is reduced, with the
emissions in March being generally larger than the peat burning emissions in
August. In addition, the higher a posteriori correlation with monthly MODIS
fire counts in Indochina (Table ) indicates an improved
representation of the seasonal natural fires in March–April and agricultural
waste burning in April–May .
In Indonesia, the fire season extends from June to November and comprises
intense peat burning, in particular during extreme drought conditions caused
by El Niño . The GFED4s estimates are generally
lower than 100 Tg C year-1 but significantly higher for El Niño
years, e.g., 2006 (350.3 Tg C) and 2009 (191.6 Tg year-1). The inferred
flux drop in 2006 and 2009 is supported by GFAS and FINN, but in all other
years both FINN and GFAS are relatively close to GFED4s. The lower 2006 flux
suggested by the observed columns is corroborated by an independent carbon
emission estimate based on burned area in a small region of Borneo in 2006
(Central Kalimantan, approximately 13 % of the Indonesian peatland)
reporting peat fire emissions of 49 Tg C during the 2006 El Niño episode
. This estimate is about half of the GFED4s value
(109 Tg C) and closer to the OMI-based estimate of 33 Tg C for the same area
and year. Note, however, that this independent estimate does not account for
aboveground biomass burning.
As mentioned in the previous sections, the updated isoprene emissions are
systematically decreased in tropical regions, by about 40 % on average in
Amazonia and equatorial Africa (Fig. ), pointing to
potentially overestimated emission factors used in the MEGAN model for
tropical forests. In contrast to these regions, the emission reduction for
the tropical rainforests of southeast Asia is much weaker (< 20 %; Figs. , ) due to the lower basal emission rates
incorporated in MEGAN-MOHYCAN based on OP3 campaign
measurements in the rainforest of Borneo . The relatively
small discrepancy between the model and the satellite HCHO columns in
southeast Asia supports the use of lower isoprene flux rates for the Asian
rainforests.
In China, most of the fires are agricultural and their emissions are
generally low, except for the North China Plain (Fig. ,
). The isoprene fluxes in China are also reduced after
optimization, from 7.3 Tg year-1 in MEGAN-MOHYCAN to
5.8 Tg year-1 on average over the study period, but the decrease is
stronger in south China, ranging between 27 and 45 % depending on the
year. The emissions peak in summertime and present weak interannual
variability (Fig. ), with a maximum in 2007
(2.6 Tg year-1) and a minimum in 2010 (1.7 Tg year-1; Fig. ). The OMI-based flux in 2010 is in good agreement with an
earlier estimate inferred from GOME-2 HCHO observations
(2.4 Tg year-1; Fig. ) .
As Fig. but for northern and southern
Australia.
Australian emissions
Northern Australia is a major fire-prone area, where bushfires occur during
many months every year . The peak of the fire season is
observed between September and November, but its magnitude depends strongly
on the year. The fire season begins between April and June, with the
beginning of the dry season, is reinforced by the hot temperatures and
winds of the subsequent months, and lasts until December. The OMI data
suggest top–down fluxes close to the a priori in all years, except for 2011,
when the emission maximum is decreased by about 25 % with respect to
GFED4s (Fig. ), whereas the estimates from GFAS and FINN in
this region differ by more than a factor of 10. In southern Australia
(Fig. ), the fire fluxes are generally half those in northern
Australia, and bushfires are again the main fire type in this region. This
region, and in particular the state of Victoria, sometimes experiences
extreme fire events, like the 2006–2007 bushfires, which were some of the worst
on record, and the “Black Saturday” bushfires in February 2009. The
satellite columns of HCHO lead to a significant reduction
(Fig. ) in the fire emission during the aforementioned major
fire events in comparison to the GFED4s inventory, in good agreement with the
FINN estimates.
As Fig. but for Europe and the southeast
US.
The optimization indicates negative isoprene updates in the tropical and
subtropical ecosystems of northern Australia, which are dominated by woodland
and grasslands, and generally positive flux increments in the southern part
of the continent, where temperate forests and grasslands are prevalent
(Figs. , ). The mean reduction over 2005–2013
in northern Australia amounts to ca. 20 % with respect to the a priori
(24.4 Tg year-1) and is supported by the inversion study based on
GOME-2 HCHO columns as shown in Fig. ,
pointing to possibly overestimated emission factors assumed in MEGAN for
tropical ecosystems. In southern Australia, the a posteriori isoprene fluxes
are increased by about 20 % on average over the study period, from 12.5
to 15 Tg year-1, and show small interannual and seasonal variability
(Fig. ). Although the MEGAN-MACC emissions are much higher than
the other inventories over Australia, a sensitivity calculation accounting
for the soil moisture stress activity factor in the MEGAN-MACC model resulted
in a substantial flux decrease of about 70 % with respect to the
reference MEGAN-MACC simulation , stressing the
important role of soil moisture stress in these very dry environments. As for
southern Africa, the OMI-based inversion over southern Australia enhances the
emissions where and when γSM reaches its lowest values
(Figs. and ). As discussed above, the poor
performance of the parameterization could be partly due to misrepresentations
of driving variables (soil moisture content) or soil characteristics (wilting
point, rooting depth). The use of satellite-derived soil moisture or
solar-induced fluorescence could be a promising way
for improving the soil stress estimation in the future.
Global distribution of annual isoprene emission trends over
2005–2013 according to the a priori (left) and top–down inventory (right)
expressed in percentage year-1.
Midlatitude emissions
In Europe, the fire season peaks in summertime and a secondary peak is also
recorded in spring, mainly due to emissions from agricultural waste burning
(Fig. ). The optimized fluxes lie generally close to the a
priori except in 2006 and 2007, when the OMI observations point to higher
fluxes (by 40–50 %) than in GFED4s during the emission peak. The strong
fluxes in April–May 2006 and in summer 2007 were due to numerous agricultural
fires that occurred in the Baltic countries, western Russia, Belarus, and
Ukraine and to intense biomass burning in southern Europe.
The increase in the top–down estimates in 2007 is in line with the reported
increase based on IASI CO columns . The top–down estimate
agrees well with GFED4s during the devastating fires in the Moscow area in
July–August 2010, whereas previous studies reported values which were a
factor of 2 , 3 , and 10
higher than the older GFED3 inventory , which was about
60 % lower than GFED4s in this region.
Regarding isoprene fluxes over Europe, the satellite observations suggest an
average increase by 15 % in western Europe (from 2.9 to
3.4 Tg year-1) and by 33 % in eastern Europe (from 3.9 to
5.2 Tg year-1), whereas the inferred increase is significantly
stronger during extremely hot summers, like in 2007 and 2010. Indeed, in July
2007 Greece experienced the hottest summer on record since 1891
, with temperature anomalies of +5 ∘C compared to
the 1961–1990 mean, and in July 2010, the hottest summer since 1500 was
recorded in western Russia, with temperature anomalies of +6 ∘C
with respect to the 1961–1990 mean
(http://www.ncdc.noaa.gov/temp-and-precip).
The concurrence of pyrogenic and isoprene emissions in the mid- and
high latitudes of the Northern Hemisphere during summertime is, however, an
inherent difficulty in the derivation of top–down emissions by inverting for
HCHO columns. HCHO being an intermediate compound in the oxidation of both
pyrogenic and biogenic hydrocarbons, it cannot be excluded that the HCHO
column enhancements associated with higher isoprene emissions have in reality
a pyrogenic origin and vice versa. The inversion scheme relies strongly on
the a priori emission distributions and errors in the retrievals, and,
thereby, errors in the geolocation of fire hot spots in the bottom–up
inventories could propagate as errors in the source attribution, in
particular for intense fire events associated with summer heat waves.
In the southeastern US, a major isoprene-emitting region, the top–down fluxes are
systematically reduced compared to the initial inventory, by 35 % on
average, with the strongest decrease (50 %) inferred in 2013. Similarly
to the a priori, the a posteriori estimates peak in 2011 and are lowest in
2013. This variability is primarily related to temperature changes, with
recorded temperature anomalies of +3 ∘C in 2011 and
-1.5 ∘C in 2013 with respect to the 1961–1990 mean
(http://www.ncdc.noaa.gov/temp-and-precip/). The use of GOME-2 HCHO
columns to constrain the inversion in 2010 results in an
excellent agreement with the OMI-based fluxes (Figs. and
), whereas both optimizations suggest a slightly modified
seasonal profile, with a primary peak in June and a secondary one in August. The
need for lower emissions in the southeastern US compared to the MEGAN model has
been put forward by past studies based on satellite observations of HCHO from
GOME, SCIAMACHY, and OMI sensors . The
MEGAN-MACC and GUESS-ES estimates are in very good agreement with the a
posteriori fluxes in terms of magnitude, although in some years the peak
emission is delayed by 1 month (Fig. ).
Emission trends
The global distribution of isoprene emission trends over 2005–2013 according
to the bottom–up emission inventory and as suggested by the inversion of
satellite data is displayed in Fig. . Although deriving
long-term emission trends from satellite data may be very useful for
diagnosing global and regional change, particular caution is required when
interpreting the results, since physical changes in the satellite instruments
over time may result in artificial drifts in the observations. In the case
of OMI HCHO columns, special efforts were made to reduce the effects of the
row anomaly issue
(http://projects.knmi.nl/omi/research/product/rowanomaly-background.php)
on the retrieved HCHO columns, in order to ensure the suitability of the data
for addressing trend studies . Nevertheless, it appears
difficult to avoid any time-dependent instrumental effect possibly impacting the interannual variability of emissions reported in this section.
Amazonia experienced a rapid decline in pyrogenic emissions, estimated to be -7 % year-1 in the a priori GFED4s inventory and
-8 % year-1, in the OMI-based emissions as a result of the trend
in OMI columns calculated during the dry season (-3.2 % year-1 in
August–September). This trend in HCHO columns was attributed to a strong
decline in deforestation rates in the Amazon and
especially in Mato Grosso and Rondônia, where the cover loss in evergreen
broadleaf forests decreased by more than 80 % between 2002 and 2009
. The isoprene emission trend over Amazonia, which is close to
negligible (-0.2 % year-1) in the a priori inventory
(Figs. and ), becomes negative after
optimization (-2.1 % year-1). The derivation of biogenic emission
trends in this region is made difficult by the magnitude and strong
interannual variability of biomass burning. However, a decline in isoprene
emissions is supported by the negative trend (-1.3 % year-1) in
the observed HCHO columns during the wet season (November–April), when
biomass burning plays only a very minor role. This result is difficult to
interpret. Recent findings based on satellite surface reflectance data (more
precisely, normalized difference vegetation index, or NDVI, data) point to
diminished vegetation greenness since 2000 due to a precipitation decline
across large parts of Amazonia, especially northern Brazil .
However, most of these changes occurred between 2000 and 2005, whereas the
precipitation rates and NDVI values were comparatively more stable
afterwards, and the leaf area index from MODIS Collection 5 (MOD15A2
composite, http://modis.gsfc.nasa.gov/data/dataprod) either increased
(by less than 0.5 % year-1) or showed no trend over 2005–2013 over
most of Amazonia (see Fig. S5).
Over northern Africa, during the fire season (November–February) a decreasing
trend of about 3 % year-1 over the study period is derived for the
OMI-based fire fluxes (Fig. ), close to the GFED4s trend
(-3.2 % year-1), whereas the corresponding trend of FINN
(-2.4 % year-1) is somewhat weaker. This trend is most likely
related to negative trends observed in burned area in northern Africa
, owing to land use changes (conversion of savannah
into cropland) and to changes in precipitation, driven by the El
Niño–Southern Oscillation .
In Siberia, the strongly positive isoprene emission trend of the bottom–up
inventory (3.8 % year-1) (Fig. ) is a result of the
warming temperature trends in this region
(0.12 ∘C year-1
over 55–75∘ N, 40–120∘ E, based on ECMWF ERA-Interim
temperature data over 2005–2013). The model incorporates both the direct
effect of warming on the MEGAN temperature response of the emissions and the
indirect effect through the increase in leaf area index (LAI), which reaches
3 % year-1 in northern Siberia (Fig. S5). The inversion leads to an
even higher trend (4.2 % year-1), induced by the strongly positive
trend in the HCHO observations (4.2 % year-1) over 2005–2013 in
this region, suggesting a stronger response of isoprene emissions to warming.
This result is in line with reported ecosystem measurements in the Arctic
exhibiting a higher emission response of biogenic emissions than observed at
more southern latitudes . Higher temperatures may also
favor the extension of forests inducing even higher isoprene emissions
. According to MODIS land cover data, the forest fraction
in this region has increased from 31 % in 2005 to 36 % in 2012
.
Opposite a priori isoprene trends are calculated in western and eastern
Europe over the study period, -2.5 and 3.2 % year-1,
respectively, mostly related to the temperature and solar radiation trends.
The OMI observations corroborate these trends, showing a negative trend in
western Europe (-1.1 % year-1) and a positive trend in eastern
Europe (0.4 % year-1). The calculated trends after optimization are
moderately enhanced: -3.3 and 3.9 % year-1 in western and eastern
Europe, respectively. Besides climate parameters, land use changes may also
contribute to the increasing column and emission trend in eastern Europe.
Based on MODIS land cover data , the forest fraction
increased at a faster pace in eastern than in western Europe (1.1 % year
vs. 0.9 % year-1) and the crop fraction decreased more rapidly
(-0.5 % year-1) in eastern than in western Europe
(-0.4 % year-1).
Over the southeastern US, the slightly negative trend in the summertime
isoprene fluxes in the a priori (-0.3 % year-1) becomes much more
pronounced after inversion (-4 % year-1), induced by the downward
trend in the OMI HCHO columns (-2.5 % year-1) over 2005–2013
. Except for this trend, the interannual variability of the
top–down emissions over this region is similar to the a priori
(Fig. ). The long-term decline could be in part an artifact
resulting from the well-documented downward trend in NOx abundances over
the United States , which could significantly
decrease formaldehyde production over time if the yield of HCHO per isoprene
molecule is substantially lower at low NOx level than at high NOx. The
ground-level NO2 concentrations have decreased by as much as a factor of
2 over the eastern US based on OMI and in situ measurements between 2005 and
2012 . The NOx dependence of the HCHO yield is taken into
account in the calculations presented in this study, but the modeled
decrease in planetary boundary layer (PBL) NOx level in the eastern US is lower (ca. -30 %)
than observed (-50 %) during 2005–2012. Furthermore, the low-NOx
oxidation mechanism remains incompletely characterized, especially regarding
the further degradation of primary oxidation products, leaving open the
possibility of a significant overestimation of the HCHO yield at low NOx,
even though a recent analysis of airborne measurements over the southeast US
indicated that state-of-the-art oxidation mechanisms can reproduce the NOx
dependence of prompt HCHO formation inferred from the measurements
. If confirmed, an overestimation of the HCHO yield at low
NOx could also help to explain the negative trend in top–down isoprene
emission over western Europe (Fig. ). More importantly, it
would imply a general underestimation of our top–down emissions in low-NOx
environments and tropical forests in particular.
In south China, the negative summertime trend (-0.7 % year-1) in
HCHO columns drives a change in the sign of the 2005–2013 isoprene trend: from 0.1 % year-1 in the a priori to -1.6 % year-1 in
the OMI-based fluxes. The very small a priori emission trend results from a
combination of compensating effects: on the one hand, declining trends in the
ERA-Interim photochemically active radiation (PAR)
(-0.33 % year-1) and temperature
(-0.03 K year-1), and
on the other hand, an increasing trend in leaf area index
(1 % year-1, cf. Fig S5) and a decline in crop extent in south
China, suggested by the land use database of and
supported by MODIS land cover data . However, a recent land
cover database suggests that the extent of crops has increased in eastern
China in the last 30 years . In addition, the declining trend
in PAR was also derived from ERA-Interim data complemented by surface
radiation measurements . The crop expansion and declining PAR
were proposed to cause a negative isoprene trend in south China
and likely explain the observed negative trend in HCHO.
Conclusions
Global distributions of pyrogenic and biogenic VOC fluxes between 2005 and
2013 were derived using the adjoint inversion scheme built on the IMAGESv2
global CTM and HCHO column abundances retrieved from the OMI sounder. The
inversion suggests a moderate decrease (ca. 20 %) in the global average
emissions of both pyrogenic and biogenic VOCs relative to the a priori
emissions used in the model. The main findings of this study are presented
below.
The global top–down fire fluxes exhibit strong interannual variability, ranging between
ca. 1400 Tg C year-1 (2011) and 2000 Tg C year-1 (2007). The a
priori interannual variability is generally well preserved, but the inferred
estimates are ca. 250 to 450 Tg C lower than the a priori, depending on the
year, with the largest decreases suggested over Africa, South America, and
southeast Asia (23 %). The top–down emissions are better correlated with
MODIS than GFED4s fire counts in regions with small fires, indicating that
the associated emissions may be too low in GFED4s and that they can be
derived by the OMI-based inversion.
The inversion suggests (i) important fire flux decreases (15–30 %) in Amazonia during
years with strong a priori emissions, (ii) about a 50 % emission decrease
during the peak fire season in northern and southern Africa, (iii) generally
increased emissions in Indochina and decreased fluxes in Indonesia during
intense fire events related to El Niño years, (iv) a significant flux
reduction during the major bushfires in Australia, and (v) flux increases
during the devastating fires in southern Europe in 2007.
Changes in fire seasonal patterns are suggested, in particular in southeast
Asia and Africa. In southeast Asia, the seasonal amplitude is reduced after inversion, with
enhanced emissions due to aboveground vegetation burning in March and weaker emissions due
to Indonesian peat burning in August. The inversion suggests generally increased fluxes due to
agricultural fires over Africa and decreased emission maxima due to natural fires.
Significant reductions in isoprene fluxes are inferred in tropical ecosystems
(30–40 % in Amazonia and northern Africa), suggesting overestimated
basal emission rates in these areas. The top–down fluxes generally increase
over Eurasia, especially during heat waves in summer (e.g., western Russia in
2010), suggesting a possibly stronger emission response to high temperatures
than currently assumed.
The inversion suggests large isoprene emission increases (up to 100 % locally) over areas
most affected by the soil moisture stress parameterization in MEGAN, in
particular in southern Africa and southern Australia. The inferred isoprene
increments present a strong interannual correlation with
1/γSM, i.e., the factor by which isoprene emissions are
reduced due to soil moisture stress in MEGAN (r≥0.7), indicating that
the soil moisture parameterization leads to overly decreased isoprene fluxes.
The isoprene emission trends are found to be often enhanced after inversion.
Positive trends in top–down isoprene emissions are inferred in Siberia
(4.2 % year-1) and eastern Europe (3.3 % year-1), likely
reflecting forest expansion and the warming trend. Negative trends are
derived in Amazonia (-2.1 % year-1), south China
(-1 % year-1), the United States (-3.7 % year-1), and
western Europe (-3.9 % year-1). The top–down trends should be
considered with caution due to possible drifts in the satellite data. In
several instances, however, they are supported by independent evidence from
literature studies. Trends in NOx emissions may play a significant role
given their possibly large influence on formaldehyde yields, which remain
imperfectly characterized and deserve more attention, especially at low
NOx.
For simplicity and to avoid excessive computational costs, a detailed error
assessment of the a posteriori emission estimates is not addressed in this
work. Nevertheless, sensitivity inversions conducted in an earlier study,
also based on OMI columns for 2010, have shown that the inferred fluxes were
generally weakly dependent on the choice of key model and inversion
parameters and lay within 7 % of the standard inversion results
. Recent developments in the representation of vertical
profiles of smoke released by open fires , in the
partitioning of burned biomass into emitted trace gases , and
in the spatiotemporal variability of emission factors
indicate additional sources of uncertainty that could
impact the top–down fluxes and should therefore be carefully assessed in
future studies.
Data availability
The OMI HCHO column data are publicly accessible at
http://h2co.aeronomie.be (De Smedt and Van Roozendael, 2016). The
OMI-based inventories are freely available at the GlobEmission web portal
(http://www.globemission.eu; Stavrakou et al., 2016b) and at the
BIRA-IASB emission portal (http://emissions.aeronomie.be; Stavrakou et
al., 2016c). The MEGAN-MOHYCAN bottom-up inventory is also available at
http://emissions.aeronomie.be.
The Supplement related to this article is available online at doi:10.5194/acp-16-10133-2016-supplement.
Acknowledgements
This research was supported by the Belgian Science Policy Office through the
PRODEX projects ACROSAT, by the European Space Agency (ESA) through the
GlobEmission DUE project (2011–2016), and by the MARCOPOLO project
(2014–2016) funded by the European Commission within the Seventh Framework
Programme (grant agreement: 606593). Edited
by: A. Pozzer Reviewed by: two anonymous referees
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