Constraining global isoprene emissions with GOME formaldehyde column measurements Changsub Shim, Yuhang Wang, Yunsoo Choi Georgia Institute of Technology.

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Presentation transcript:

Constraining global isoprene emissions with GOME formaldehyde column measurements Changsub Shim, Yuhang Wang, Yunsoo Choi Georgia Institute of Technology Paul Palmer, Dorian Abbot Harvard University Kelly Chance Harvard-Smithsonian Center for Astrophysics

NO 2 NO OH CO O2O2 hv H2OH2O HO 2 O3O3 ISOPRENE C 5 H 8 Most dominant biogenic hydrocarbon Global budget is highly uncertain. Emission dependence - Temperature, - Vegetation type, - Leaf Mass - Light intensity, etc… Global Atmospheric Isoprene

HCHO for constraining isoprene It is a high-yield byproduct of the isoprene oxidation & VOCs It also has a short lifetime (order of hours) HCHO atmospheric columns have been measured by a satellite instrument (GOME) at 337 ~ 356 nm HCHO is a good proxy for isoprene by remote sensing! ( Chance et al., 2000; Palmer et al., 2003) Objectives Obtaining better global isoprene emissions based on GOME HCHO measurements (Sep ~ Aug. 1997)

Application of Inverse Modeling 8 regions for Inverse modeling  High Signal-to-noise ratio HCHO GOME observations  Account for ~65% of global a priori isoprene emissions Tropical rain forest Grassy lands Savanna Tropical seasonal forest Mixed deciduous Farm land & paddy rice Dry evergreen Regrowing wood (natural + artificial) Drought deciduous Other biogenic source Biomass burning emission Industrial emission State vectors (Source parameters) Isoprene

Application of Inverse Modeling (10 biogenic state vector distribution) V1: Tropical rain forestV2: Grass & shrub V3: Savanna V4: Tropical seasonal forest & thorn woods V5: Mixed deciduousV6: Farm land & paddy rice V7: Dry evergreenV8: Regrowing wood V9: Drought deciduousV10: Other biogenic source

Inverse modeling ( Bayesian Least Squares, Rodgers, 2000) y = Kx + e y : observations (GOME HCHO) x : defined source parameters: GEOS-CHEM K : Jacobian matrix (sensitivity of x to y :GEOS- CHEM) e : error term GEOS-CHEM v Resolution: 4 o x5 o - GEOS-STRAT (26 vertical layers) The solution,

Results (Annual HCHO columns)

Results:Monthly mean HCHO for 8 regions A priori A posteriori GOME Month : Sep96 Aug97

Results (Annual isoprene emissions)

Discrepancy over northern equatorial Africa.

Results: Annual isoprene emissions Continent Weighted Uncertainty(%) Isoprene Annual Emissions ( Tg C yr-1) A PrioriA PosterioriA PrioriA PosterioriGEIA N. America Europe East Asia India S. Asia S. America Africa Australia Total

The impact of a posteriori isoprene emissions

In order to constrain global isoprene emissions, source parameters for 10 vegetation groups, biomass burning, and industrial emissions are considered in inverse modeling over 8 regions with high signal-to-noise ratios in GOME measurements. Global a posteriori isoprene annual emission is higher by 50% to 566 Tg/yr (a priori : 397 Tg/yr). The a posteriori global isoprene annual emissions are generally higher at mid latitudes and lower in the tropics when compared to the GEIA inventory There is a significant discrepancy between the seasonality of GOME measured and GEOS- CHEM simulated HCHO columns over the northern equatorial Africa. We attribute this problem to the incorrect seasonal cycle in surface temperature used in GEOS-CHEM. As a result, isoprene emissions over the region are overestimated. The a posteriori results suggest higher isoprene base emissions for agricultural land and tropical rain forest and lower isoprene base emissions for dry evergreen The a posteriori biomass burning HCHO sources increase by a factor of 2 – 4 in most regions with significant emissions except for India. The industrial HCHO sources are higher by ~20% except for East Asia and India (~60%). The a posteriori uncertainties of emissions, although greatly reduced, are still high (~90%) reflecting the relatively large uncertainties in GOME retrievals. This higher isoprene emissions reduces the global mean OH concentration by 11%. The corresponding CH 3 CCl 3 lifetime is increased to 5.7 years. Conclusions

Acknowledgements We thank Alex Guenther for his suggestion of conducting inverse modeling on a regional basis. We thank Daniel Jacob and Robert Yantosca for their help. We also thank Mark Jacobson for his suggestions. The GEOS-CHEM model is managed at Harvard University with support from the NASA Atmospheric Chemistry Modeling and Analysis Program. This work was supported by the NASA ACMAP program.

Results ( Regional Statistics ) GOME Weighted uncertainties 2 Correlation coefficient(R) 3 Model bias (%)Isoprene emission (Tg C/yr) Regions Ω (%) 4 PRIPOSTPRIPOSTPRIPOSTPRIPOSTGEIA North America Europe East Asia India Southeast Asia South America Africa Australia Global

Results (Emission Type) N. AmericaEuropeE. AsiaIndiaS. AsiaS. AmericaAfricaAustralia PriposPriPosPriposPriPosPriposPripospriPospripos V V V V V V V V V RV BB IND Total