1. Global transport and radiative forcing of biomass burning aerosols Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology, Pasadena Evan Lyons, James Randerson University of California, Irvine The 3rd GEOS–Chem Users' Meeting Harvard University, April 12, 2007

2. Objectives and outline Large uncertainties in the estimation of aerosol radiative forcing. Large uncertainties in the estimation of aerosol radiative forcing. -0.1~-0.9 W/m 2 for direct forcing (IPCC,2007).-0.1~-0.9 W/m 2 for direct forcing (IPCC,2007). -0.3~-1.8 W/m 2 for indirect forcing.-0.3~-1.8 W/m 2 for indirect forcing. Purpose: Combine satellite observations and chemical transport models to further constrain quantification of aerosol (particularly the biomass burning aerosols) radiative forcing. Purpose: Combine satellite observations and chemical transport models to further constrain quantification of aerosol (particularly the biomass burning aerosols) radiative forcing. First step: Estimate the global aerosol direct radiative effect using Multi- angle Imaging SpectroRadiometer (MISR) observations. First step: Estimate the global aerosol direct radiative effect using Multi- angle Imaging SpectroRadiometer (MISR) observations. Better aerosol retrievals over land.Better aerosol retrievals over land. First attempt at estimating aerosol direct radiative effect on a global basis (over both ocean and land) using satellite observation based approach.First attempt at estimating aerosol direct radiative effect on a global basis (over both ocean and land) using satellite observation based approach. Ongoing modeling study: GEOS-Chem simulations of aerosols using different GFEDv2 biomass burning emissions. Ongoing modeling study: GEOS-Chem simulations of aerosols using different GFEDv2 biomass burning emissions. Diurnal cyclesDiurnal cycles Synoptic variationSynoptic variation Injection heightInjection height

3. Introduction to MISR Multi-angle multi-channel spectroradiometer on board satellite TERRA Multi-angle multi-channel spectroradiometer on board satellite TERRA Global Mode: Global Mode: 275 m sampling resolution for nadir camera and red band of other cameras275 m sampling resolution for nadir camera and red band of other cameras 1.1 km for the other channels1.1 km for the other channels 400-km swath400-km swath Global coverage: 9 days at equator, 2 days at polesGlobal coverage: 9 days at equator, 2 days at poles Continuous data retrieval since Feb 2000. Continuous data retrieval since Feb 2000. Major products used: Major products used: TOA albedo (2.2x2.2 km 2 )TOA albedo (2.2x2.2 km 2 ) AOD (17.6x17.6 km 2 )AOD (17.6x17.6 km 2 ) Cloud mask (1.1x1.1 km 2 )Cloud mask (1.1x1.1 km 2 ) BHRPAR (1.1x1.1 km 2 )BHRPAR (1.1x1.1 km 2 ) All products are re-sampled to 17.6x17.6 km 2 for this studyAll products are re-sampled to 17.6x17.6 km 2 for this study

4. Method Albedo ~ AOD regression TOA Broadband Albedo (with aerosol) Aerosol Optical Depth Cloud mask BHRPAR Nadir viewCloud mask AOD TOA albedo 1°x 1° grid MISR observations

5. Global distribution of AOD, albedo, and BHRPAR (July, 2002) 26 BHRPAR bins: 0~0.1: each 0.01 interval 0.1~0.4: each 0.02 interval Above 0.4: 1 level

6. Albedo~AOD correlation over ocean Albedo~AOD correlation for 10°x5° grids. The slopes indicate the ability of aerosols to affect TOA radiative flux. Alternative method: do global regression for each solar zenith angle. a e f b c d g

7. Albedo~AOD correlation over land A East US A B C B Central Africa C Saharan desert Global correlation Albedo~AOD correlation for 10°x5° grids

8. Aerosol direct radiative effect (a)(b) (a) Clear-sky and (b) all-sky aerosol direct radiative effect (W/m 2 ) for July 2002.

9. Aerosol direct radiative effect Direct ARE (Clear sky) (W/m 2 ) Direct ARE (All sky) (W/m 2 ) Global-4.70-1.49 Over ocean -4.54-1.95 Over land -4.88-1.18 Source Direct ARE (W/m 2 ) Spatial coverage Temporal coverage Satellite data source Zhang and Christopher, 2005 -6.4 ± 2.6 Cloud-free oceans 09/2000-08/2001 CERES, MODIS Christopher and Zhang, 2002 -6 Cloud-free oceans 09/2000 CERES, MODIS Loeb and Kato, 2002 -4.6 ± 1 Cloud-free tropical oceans 01/1998-08/1998, 03/2000 CERES, TRMM VIRS Loeb and Manalo- Smith, 2005 -5.5, -3.8 Cloud-free oceans 03/2000-12/2003 CERES, MODIS -2.0, -1.6 All-sky oceans From this study (July, 2002): From previous satellite-based studies:

10. Uncertainties Satellite retrieval of aerosol, albedo and surface properties. Satellite retrieval of aerosol, albedo and surface properties. Cloud contamination. Cloud contamination. Diurnal variability. Diurnal variability. TOA albedo narrow-to-broadband conversion. TOA albedo narrow-to-broadband conversion. Surface heterogeneity. Surface heterogeneity.

11. Diurnal cycle effect on GEOS-Chem aerosol simulation 07/2004 Ongoing modeling study Simulation conditions Model: GEOS-Chem v7-03-06 Meteorology: GEOS-4 Simulation type: Offline aerosol simulation Simulation period: 06/2004 ~ 08/2004 Biomass burning emissions: Global Fire Emissions Database version 2 (GFEDv2) with 8 day time interval with diurnal cycle without diurnal cycle

12. Diurnal cycle effect on Central Africa Ongoing modeling study With diurnal cycle, major emissions occur when the PBL is high. The vertical mixing causes faster dilution and the dissipation of pollutants. The accumulation of aerosols during local night is weaker. C emissions BCPI concentration difference (with diurnal cycle - without) Local noon

13. Diurnal cycle effect on Alaska and Northern Canada Emissions from source: BCPI concentration in nearby grid: Ongoing modeling study When the biomass burning emission is very strong and PBL is low, the dissipation effect is weaker. For some regions near the strong source, the transport is more important than the local emission. C emissions Local noon BCPI concentration difference (with diurnal cycle - without)

14. Conclusions and future work Conclusions Conclusions By using MISR datasets, first satellite-based attempt to estimate global aerosol direct radiative effect over both ocean and land has been made.By using MISR datasets, first satellite-based attempt to estimate global aerosol direct radiative effect over both ocean and land has been made. Aerosols have different impacts on TOA albedo in different regions due to different aerosol properties and surface types.Aerosols have different impacts on TOA albedo in different regions due to different aerosol properties and surface types. Global mean result of aerosol radiative effect over ocean is well in the range of other studies in literature.Global mean result of aerosol radiative effect over ocean is well in the range of other studies in literature. By including diurnal cycle of biomass burning emissions in GEOS-Chem simulation, aerosol concentrations at surface may increase or decrease, depending on the source type and intensity, the boundary layer height, and the relative importance of transport and local emissions.By including diurnal cycle of biomass burning emissions in GEOS-Chem simulation, aerosol concentrations at surface may increase or decrease, depending on the source type and intensity, the boundary layer height, and the relative importance of transport and local emissions. Future work Future work Extend the satellite-based estimation of aerosol direct radiative effect to include seasonal and inter-annual variability.Extend the satellite-based estimation of aerosol direct radiative effect to include seasonal and inter-annual variability. Study how synoptic variability of biomass burning emissions and the inclusion of smoke injection height will affect the global distribution of aerosols, and the implication to the aerosol radiative forcing.Study how synoptic variability of biomass burning emissions and the inclusion of smoke injection height will affect the global distribution of aerosols, and the implication to the aerosol radiative forcing.

15. Acknowledgment MISR data were obtained from the NASA Langley Atmospheric Sciences Data Center (http://eosweb.larc.nasa.gov/). MISR data were obtained from the NASA Langley Atmospheric Sciences Data Center (http://eosweb.larc.nasa.gov/).http://eosweb.larc.nasa.gov/ We used Global Fire Emissions Database version2 (van der Werf et al.,2006) resampled to an 8day time step using MODIS fire hot spots (Giglio et al., 2003). We used Global Fire Emissions Database version2 (van der Werf et al.,2006) resampled to an 8day time step using MODIS fire hot spots (Giglio et al., 2003). GEOS-Chem model is managed by the Atmospheric Chemistry Modeling Group at Harvard University. GEOS-Chem model is managed by the Atmospheric Chemistry Modeling Group at Harvard University.