1. 1 Global Observations of Sulfur Dioxide from GOME Xiong Liu 1, Kelly Chance 1, Neil Moore 2, Randall V. Martin 1,2, and Dylan Jones 3 1 Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA 2 Dalhousie University, Halifax, NS, Canada 3 University of Toronto, Toronto, Ontario, Canada The 36 th COSPAR Scientific Assembly Beijing, China, July 19, 2006

2. 2 Outline Introduction SO 2 Retrieval Algorithm Examples of High SO 2 from Volcanic and Anthropogenic Sources Global Distributions of SO 2 from GOME Summary and Future Outlook

3. 3 Introduction SO 2 : a colorless gas with irritating odor Natural (e.g., volcano activity, biomass burning) Anthropogenic (e.g., fossil fuel combustion) Lifetime: from several hours (clouds) to several days (no clouds) to several weeks (stratosphere) A major pollutant Aggravates respiratory and cardiovascular diseases Primary contributor to acid rain (a serious problem in China with an annual economic loss of $13 billion) Play important roles in climate change Precursor for background sulfate aerosols Injection from strong volcanic eruptions (e.g., Pinatubo in 1991) to the stratosphere: affects the climate for several months to several years

4. 4 DOAS Fitting Window TOMS Satellite Remote Sensing of Sulfur Dioxide Satellite remote sensing: continuous global monitoring TOMS: detect volcanic eruptions (detection limit: 4-6 DU) [Krueger, 1983; Krueger et al., 1995] GOME: detect both volcanic & anthropogenic SO 2 [Eisinger & Burrows, 1998; Khokhar et al., 2005] using the DOAS technique (detection limit: 0.5-1 DU) Challenges [Khokhar et al., 2005]: interferences from Instrumental artifacts (e.g., diffuser plate structures) Ozone

5. 5 GOME SO 2 Retrieval Algorithm Spectral fitting + optimal estimation + LIDORT Part of our ozone profile retrieval algorithm [Liu et al., 2005]: Derive ozone profiles at 24 layers (4-6 layers in the troposphere) using the optimal estimation technique Fitting windows: 289-307 nm, 325-340 nm (not optimized for SO 2 ) Perform detailed treatments of wavelength/slit and radiometric calibrations Perform extensive forward modeling of ozone, clouds (GOMECAT), aerosols (SAGE/GEOSCHEM/GOCART), surface albedo, trace gases (SO 2, NO 2, HCHO, BrO) with LIDORT and model the first- order Ring effect with a single-scattering model Fitting residuals in the Huggins bands: typically < 0.1% Spatial Resolution: 960  80 km 2

6. 6 GOME SO 2 Retrieval Algorithm Fit SO 2 weighting functions (cross sections  effective AMF) and directly derive SO 2 vertical column density Significant AMF variation: 30% between 315 and 328 nm for 0-2 km Take advantage of extensive on-line calculation of ozone weighting functions: negligible extra computation Weight altitude-dependent AMFs by GEOS-CHEM SO 2 profiles

7. 7 GOME SO 2 Retrieval Algorithm Optimal estimation with dynamic a priori error Use GEOS-CHEM monthly mean SO 2 as a priori/initial value to regularize retrievals Assume 100% a priori error: too tight a constraint for high SO 2 Update a priori error to the retrieved SO 2 (usually occurs in the first iteration) if ret(SO 2 ) > 1.5 apriori(SO 2 )

8. 8 GOME SO 2 Retrieval Algorithm Retrieval errors: for SO 2 > 0.5 DU Precision: 27% / 0.3 DU (0.8  10 16 molecules. cm -2 ) Retrieval errors: 50% / 0.5 DU (1.3  10 16 molecules. cm -2 )

9. 9 SO 2 from Nyamuragira Volcano in October 1998

10. 10 SO 2 Over China in July 1997

11. 11 Global Distribution of SO 2 in October 1998 Monthly mean Cloud fraction < 60% Surface albedo < 50% Mapped to 2.5º  2º grid Remove a few unreasonably extremely high values over non- volcanic regions

12. 12 Much larger retrieved anthropogenic SO 2 than GEOS-CHEM values: not using SO 2 optimal fitting window or other retrieval problems??? Global Distribution of SO 2 in July 1997

13. 13 Global Distribution of SO 2 (07/1995-06/2003)

14. 14 Summary and Future Outlook The vertical column density of SO 2 is simultaneously retrieved with ozone profile from GOME ultraviolet measurements, constrained by GEOS-CHEM simulations with variable a priori errors. SO 2 from both volcanic and anthropogenic sources can be detected. Further investigate and improve the quality of current retrievals Develop a stand-alone SO 2 algorithm (e.g., fitting weighting functions, utilize a priori knowledge) using the optimized SO 2 fitting window

15. 15 Acknowledgements Supported by NASA and the Smithsonian Institution ESA and DLR Cluster machine and its support at Harvard- Smithsonian CFA