J.-F. Müller and T. Stavrakou IASB-BIRA Avenue Circulaire 3, 1180 Brussels [email protected]
description
Transcript of J.-F. Müller and T. Stavrakou IASB-BIRA Avenue Circulaire 3, 1180 Brussels [email protected]
J.-F. Müller and T. StavrakouIASB-BIRA
Avenue Circulaire 3, 1180 Brussels
AGU Fall meeting, Dec. 2007
Multi-year emission inversion for reactive gases using the adjoint
model method
Inversion methodology
m
jj txtxG
10 ),(),(
m
jjjj txfftxG
1
),()exp(),,(
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
km
kEm
kn
kEn
mn
kmj
kni
nmnmij
kij EE
xxACB,
,
,
,
,
,,7
1
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
knix
,
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
)/)(/)(/exp( jjiiijnj
n
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