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)