J.-F. Müller and T. StavrakouIASB-BIRA

Avenue Circulaire 3, 1180 Brussels

jfm@aeronomie.be

AGU Fall meeting, Dec. 2007

Multi-year emission inversion for reactive gases using the adjoint

model method

Inversion methodology

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Prior emission distributions :

base functions (one per grid cell, category and month)

fj are the emission parameters, which minimize the cost function:

Optimized emissions :anthropogenic

biomass burningbiogenic

Correlations for anthropogenic emission errors

En,k = total emission of country n in subcategory k (=1,… 7)

σ En,k = standard error for this country/subcategory

Φi = total flux emitted in grid cell I

En,k = total emission of country n in subcategory k (=1,… 7)

σ En,k = standard error for this country/subcategory

Φi = total flux emitted in grid cell I= fraction of flux Φi due to country n and subcategory k

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Anm = 1 when n=m, = 0 if n and m belong to different large regions (Western Europe, Eastern Europe, FSU, etc.)

Cijnm = 1 when i=j, <1 otherwise

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Subcategories for NOx:1. Road transport2. Power generation3. Fossil fuel use in industry4. Biofuel (residential)5. Cement6. Non-road land transport7. Other

Basic assumption: errors on emissions from different subcategories, or from different large regions of the world are uncorrelated.

+ Temporal correlations

Correlations for pyrogenic and BVOC emission errors

+ Temporal correlations

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niij dxxB

Plant types considered in the MEGAN model for isoprene:1. Needleleaf evergreen

2. Needleleaf deciduous

3. Broadleaf deciduous

4. Broadleaf evergreen

5. Shrub

6. Grass

7. Crops

Basic assumptions: errors on biogenic emissions decrease exponentially with geographical distance; and the errors on emissions from different vegetation types are uncorrelated.

dij = geographical distance between grid cells i and j

decorrelation lengths : 500 km for pyrogenic / 3000 km for biogenic

xin : fraction of the flux Φi emitted by vegetation type n

(forest/savanna for pyrogenic, 7 plant functional types for biogenic VOC emissions)

dij = geographical distance between grid cells i and j

decorrelation lengths : 500 km for pyrogenic / 3000 km for biogenic

xin : fraction of the flux Φi emitted by vegetation type n

(forest/savanna for pyrogenic, 7 plant functional types for biogenic VOC emissions)

Gradient of the cost function

Calculation of new parameters f with a descent algorithm Minimum of J(f) ?

Observations

Forward CTM Integration from t0 to t

Transport

Chemistry

Cost function J(f)

Adjoint model Integration from t to t0

Adjoint transport

Adjoint chemistry

Adjoint cost function

Checkpointing

Control variables f

yes

no

Optimized control parameters

Minimizing the cost

> 20 iterations needed to decrease the norm of the gradient by factor of ~100

70 chemical compounds, 5°x5° resolution, 40 σ-p levels (Stavrakou and Müller, JGR, 2006)

Updated oxidation mechanism for isoprene and pyrogenic NMVOCs, so that the HCHO yields match MCM-derived values

Monthly wind fields from ECMWF, impact of wind variability represented as horizontal diffusion

Daily ECMWF fields for convective fluxes, PBL mixing, cloud fields, T and H2O

Biomass burning emissions : GFED versions 1 and 2 (Van der Werf et al., 2003, 2006)

Biogenic isoprene emissions from MEGAN model driven by ECMWF meteorological fields (Müller et al., ACPD, 2007)

10-year simulations, plus spin-up

The IMAGES (v2) model

10-year inversion of NOx emissions based on GOME/SCIAMACHY NO2 columns (TEMIS dataset): cf. presentation A34B-04 by Stavrakou et al. on Wednesday afternoon Use averaging kernels

10-year inversion of biogenic and pyrogenic NMVOC emissions based on GOME/SCIAMACHY HCHO columns (new dataset developed by I. De Smedt and M. Van Roozendael, IASB-BIRA) the HCHO retrievals use HCHO vertical profile shapes from IMAGES model

In both cases, the observations are binned onto the CTM grid and monthly averaged accounting for the actual sampling times of the observations at each location

Optimisations

Results: NOx

Optimized / prior emission ratio for anthropogenic NOx

(here, July 2000)

Inferred anthropogenic emission trend 1997-2006, %/year

Results: Biogenic VOCs

Biogenic emission ratio for July 1997 when GFEDv1 is used

when GFEDv2 is used

factor of 2 decrease over

the Eastern U.S.

when GEOS-Chem isoprene mechanism is used

Large increase in Southern Africa,

esp. over shrubland

Comparison with aircraft campaigns

Improving biomass burning inventories?

Prior, GFEDv2

Prior, GFEDv1

Optimized, GFEDv2

Optimized, GFEDv1

Bad timing and amplitude in GFEDv1, optimization fails

Strong overestimation in GFEDv2

Strong underestimation in GFEDv2, optimization wrongly increases biogenic emissions to compensate

Both the chemical observations and the prior information on the emissions (distributions, errors) determine the optimization results

Ideally, emission models should be coupled to the CTM and incoporated in the optimization system; but even then, characterization of the error co(variances) remain difficult

Multi-year emission inversions make possible to estimate the interannual variability of the emissions (e.g. for biomass burning) and their long-term trends (for anthropogenic NOx)

Anthropogenic emission trends can be determined from 10-year NO2 dataset – caution is needed due to the indication of temporal drifts in the data

Biogenic NMVOC emissions determined from the HCHO retrievals developed at IASB-BIRA (De Smedt &Van Roozendael) are generally lower than previously estimated based on another HCHO retrieval and on the GEOS-Chem model – most of the difference is apparently related to the retrievals

Intercomparisons of the HCHO retrievals are clearly needed! Biomass burning inventories can be evaluated and even improved

based on HCHO retrievals

Conclusions