Download presentation

Presentation is loading. Please wait.

Published byNorma McBride Modified about 1 year ago

1
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

2
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

3
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 NO 2 ) 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: Y final =(HCHO produced) / C(NMVOC) “Short-term” yields are calculated as: Y st =(HCHO produced after 1 day) / C 0 (NMVOC)

4
Biomass burning emissions of NMVOCs based on emission factors from Andreae and Merlet, GBC, 2001

5
HCHO Production from biomass burning After several months After 1 day

6
IMAGESv2 CTM 48 long-lived & 22 short-lived chemical species 5 0 x 5 0 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 C 5 H 8 oxidation : MIM (Pöschl et al., 2000) - Short-term yield of HCHO from C 5 H 8 : 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, C 4 H 10 emissions account for the remaining 20% Muller and Stavrakou, 2005 http://www.oma.be/TROPO

7
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.nlhttp://www.temis.nl, De Smedt et al., in prep.

8
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

9
H : model operator acting on the control variables y : observations f B : 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)=½Σ i (H i (f)-y i ) T E -1 (H i (f)-y i ) + ½ (f-f B ) T B -1 (f-f B ) Observations Gradient of the cost function Calculation of new parameters f with a descent algorithm Minimum of J(f) ? Forward CTM Integration from t 0 to t Transport & chemistry Cost function J(f) Adjoint model Integration from t to t 0 Adjoint transport Adjoint chemistry Adjoint cost function Current information Control variables f yes no Optimized variables Inverse modelling with the adjoint

10
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 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)

11
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

12
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

13
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

14
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 Biogenic emission ratio – Sept. 1997

15
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)

16
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)

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google