Post on 16-Dec-2015
J.-F. Müller, J. Stavrakou
I. De Smedt, M. Van Roozendael
Belgian Institute for Space Aeronomy, Brussels, Belgium
AGU Fall Meeting 2006, Friday 15 December
Pyrogenic and biogenic emissions
of NMVOCs Inferred from
GOME formaldehyde data
HCHO yields from pyrogenic and biogenic NMVOCs
Preliminary estimation of global HCHO production from biomass burning
IMAGESv2 CTM and the GOME HCHO columns
Grid-based inverse modelling with the adjoint and the error correlation setup
Results
Plan of the presentation
HCHO production by NMVOCs
Emission factors (in g of species per kg dry matter)
for pyrogenic species emitted from various types of fires, Andreae and Merlet,
2001
For the most emitted NMVOCs,use their explicit chemical mechanisms
from MCMv3.1 (Saunders et al, 2003) in a box model and solve with the KPP chemical
solver. Box model simulations start at 6:00 h under high-NOx conditions
(1 ppb NO2 )
Calculation of HCHO production by a NMVOC :
P(HCHO) = P(NMVOC) * Yield *
MW(HCHO) / MW(NMVOC)
“Ultimate” HCHO yields from the oxidation of NMVOCs are calculated after 10-30 days:
Yfinal=(HCHO produced) / C(NMVOC)
“Short-term” yields are calculated as:Yst=(HCHO produced after 1 day) / C0(NMVOC)
Biomass burning emissions of NMVOCs
Total : 65 Tg / year
others22%
CH3COOH14%
CH3OH12%
HCOOH8%
C2H48%
CH2O7%
2,3-butanedione5%
arom5%
CH3CHO4%
C2H64%
acetone4%
C3H63%
M EK2%
C2H22%
based on emission factors from Andreae and Merlet, GBC, 2001
Total : 48 Tg / year
C2H4
18%
CH3OH
16%
others
15%
CH3COOH
10%
HCHO
9%
C2H6
6%
2- 3- butanedione
5%
C3H6
5%
acetone
5%
CH3CHO
4%
MEK
3%
butenes
2%
C3H8
1%
arom
1%
HCHO Production from biomass burning
Total : 27 Tg / year
C2H431%
HCHO16%
others16%
C3H69%
2,3-butanedione9%
CH3CHO7%
CH3OH4%
CH3COOH2%
butenes2%
MEK1%
acr1%
arom2%
After several months
After 1 day
IMAGESv2 CTM
48 long-lived & 22 short-lived
chemical species
50 x 50 res., 40 sigma-pressure vertical levels
monthly mean ECMWF/ERA40
fields for 1997-2001 - oper. analyses for
2002
ERA40 convective fluxes for 1997-2001, climatological mean
for 2002 KPP solver used for off-line diurnal
cycle calculations
EDGARv3 for 1997 Natural emissions from
GEIA95, Biomass burning : van der Werf GFEDv1 (1997-2001) or
GFEDv2 (1997-2004)
Updated degradation mechanisms of lower
alkanes and alkenes, 2,3-
butanedione and MEK
C5H8 oxidation : MIM (Pöschl et al., 2000) - Short-term yield of HCHO from C5H8 : 0.47 C-1 under high and 0.4 under low NOx conditions
Ultimate HCHO yield at high NOx: 0.54 C-1 similar to MCM (0.5), but 20% higher than the GEOS-Chem yield (Palmer et al, 2006), which was found to be consistent with aircraft observations over the U.S. (Millet et al., 2006)
12 explicit NMVOCs : 80% of the total HCHO production, C4H10 emissions account for the remaining 20%
Muller and Stavrakou, 2005 http://www.oma.be/TROPO
GOME HCHO data
slant columns retrieved from GOME spectra using the WinDOAS technique developed at BIRA-IASB
no cloud filtering
fitting window chosen carefully to avoid artefacts over desert areas and reduce background noise
vertical columns derived from vertically resolved AMF calculation with DISORT
vertical HCHO profiles taken from IMAGESv2 for the month/year/geolocation of the satellite ground pixel
http://www.temis.nl, De Smedt et al., in prep.
Prior modelled HCHO vs. GOME column for 1997
GOME data are used in the inversion only when the constribution of pyrogenic and biogenic emissions exceeds 50% of the total modelled HCHO column for a given grid cell and month
H : model operator acting on the control
variables
y :
observations
fB : 1st guess
values of the control variables
E : observation error covariance matrix
B : control variables error covariance matrix
f : control variables vector
For what values of f is the cost function minimal?
Cost function : measure of the bias between the model and the observations
J(f)=½Σi (Hi(f)-yi)T E-1(Hi(f)-yi) + ½ (f-fB)TB-1(f-fB)
Observations
Gradient of the cost function
Calculation of new parameters f with a descent algorithm
Minimum of J(f) ?
Forward CTM Integration from t0 to t
Transport & chemistry
Cost function J(f)
Adjoint model Integration from t to t0
Adjoint transportAdjoint chemistry
Adjoint cost function
Current information
Control variables f
yes
no
Optimized
variables
Inverse modelling with the adjoint
optimize the fluxes emitted from every model grid cell every month from Jan. 1997 to Dec. 2002 ( ~120000 parameters)
source-specific correlations among prior errors on the flux parameters B non-diagonal
distinguish between biomass burning and biogenic emissions
The grid-based inversion method
The error correlation setupThe error correlation setup
errors on pyrogenic emissions : 100%, biogenic : 80%
spatial correlations decrease with geographical distance between the grid cells, decorrelation length : 500 km for pyrogenic, 1500 km for biogenic
they are further reduced when the fire or ecosystem type differ
errors from different years are uncorrelated for pyrogenic, but assumed correlated for biogenic emissions (0.5)
linearly decreasing correlations between different months are assumed on errors of both emission categories (weak for pyrogenic, strong for biogenic emissions)
Optimization results - Africa
remarkable agreement between the model and the data over Africa
systematically enhanced columns in the beginning of each year over the Central African Republic when using GFEDv2 are not supported by the data, but better agreement found between a posteriori and observations when GFEDv1 is used
prior using GFEDv2
optimized using GFEDv2
prior using GFEDv1
optimized using GFEDv1
Optimization results - Indonesia
over Sumatra, the inversion performs much better in 1997 when the GFEDv2 inventory is used – the low GFEDv1 prior emissions, especially in October 1997, are in contradiction with the enhanced HCHO columns observed by GOME
over Borneo, the inversion reduces slightly the GFEDv2 pyrogenic emissions
slight differences between the inferred emissions in both optimizations
prior using GFEDv2
optimized using GFEDv2
prior using GFEDv1
optimized using GFEDv1
Optimization results - Amazonia
significant differences between the two biomass burning inventories over Northeastern Brazil during the dry season
using GFEDv1 : very small emission updates required to match the observations
using GFEDv2 : strong increase by a factor of 4 of isoprene emissions necessary to compensate for the very low prior biomass burning emissions
prior using GFEDv2
optimized using GFEDv2
prior using GFEDv1
optimized using GFEDv1
over Western Amazonia, large reduction of isoprene emissions, little sensitivity to biomass burning prior
Optimized/prior emission ratios
using GFEDv1 as prior
using GFEDv2 as prior
using GFEDv1 as prior
using GFEDv2 as prior
Biom. burning emission ratio – Sept. 1997 Biom. burning emission ratio – Sept. 1997
Biogenic emission ratio – Sept. 1997 Biogenic emission ratio – Sept. 1997
Results over other regions and globally…
The optimization brings the biogenic emissions closer to the MEGAN inventory over
• China - strong reduction, factor of 2
• Australia, ca. 40% increase
• Europe and Eastern U.S.
• Western Amazonia and Indochina – factor of 2 decrease during the wet season
Reduction by ca. 40% of the isoprene emissions over the southeastern U.S :
in agreement with Abbot et al. 2003 using GEOS-Chem, when we account for differences in the HCHO yield from isoprene of the two studies
The inversion brings the model closer to the observations
• the cost reduces by 2.5 after 20 iterations, the gradient reduces by 300
• global biogenic NMVOC sources reduced by ca. 20% ( 0-20%) and global pyrogenic emissions are decreased by about 2-8% (0-15%) when using GFEDv1 (GFEDv2)
Issues to be addressed next
What if the MEGAN emission inventory is used as prior ?
What are the posterior errors on the inferred emissions ?
What is the impact on the CO budget ?
Comparison with independent HCHO observations, and with isoprene and methanol campaign measurements
Extend the HCHO data series beyond 2002 (e.g. SCIAMACHY/GOME2)