1. 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

2. 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

3. 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. 1996 ~ Aug. 1997)

4. 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

5. 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

6. 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 v5.05 - Resolution: 4 o x5 o - GEOS-STRAT (26 vertical layers) The solution,

7. Results (Annual HCHO columns)

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

9. Results (Annual isoprene emissions)

10. Discrepancy over northern equatorial Africa.

11. 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 291 287 280 285 298 337 332 302 69 96 63 122 110 75 102 96 43 19 28 11 37 95 103 36 50 30 43 15 55 125 189 53 43 14 22 17 60 178 133 32 Total370560499

12. The impact of a posteriori isoprene emissions

13. 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

14. 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.

15. Results ( Regional Statistics ) GOME Weighted uncertainties 2 Correlation coefficient(R) 3 Model bias (%)Isoprene emission (Tg C/yr) Regions Ω (%) 4 PRIPOSTPRIPOSTPRIPOSTPRIPOSTGEIA North America59291690.84 -14.3-3.6-3.622.225.721.4 Europe69287960.520.60-29.9-11.99.512.06.1 East Asia56280630.630.75-39.2-18.617.424.824.812.8 India592851220.570.56-33.2-18.410.514.415.2 Southeast Asia542981100.660.69-35.8-19.420.229.129.138.2 South America54337750.580.64-31.8-12.679.4106.4163.5 Africa533321020.560.54-46.3-23.660.3103.3105.7 Australia69302960.520.56-40-24.833.350.631.1 Global600.68-35375566503

16. Results (Emission Type) N. AmericaEuropeE. AsiaIndiaS. AsiaS. AmericaAfricaAustralia PriposPriPosPriposPriPosPriposPripospriPospripos V1 -------1.557.913.817.926.4-- V2 6.1 2 8.52.35.48.121.82.56.51.6 8.64.32.710.34.82.9 V3 11--------15.621.410.514.61.25 V4 --------4.66.98.516.24.5 -- V5 7.813.35.710.312.51.3--3.24.8----1.6 V6 5.68.94.816.34.912.88.37.44.914.31.2 2.1 -1.8 V7 4.63.21.22--1.9 1.2 2.6 1.4 14.510.1 V8 22.618.13.74.11.35.27.513.58.3 1.6 3.4 -5.9 V9 ------2.43.43.51.71.81.66.915.24.34.7 RV 30.74034.63828.548.53.48.33.711.79.917.98.421.816.235.9 BB --1.8 3.4149.910.85.611.32.311.34.28.8-- IND 67.27.25.46.44.88.15.27.74.25.1111.2 -- Total 1251419011598146881088911898129781216791