Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology, Pasadena, CA California Institute of Technology, June 05, 2007

Overview Aerosol direct radiative effect (Scattering, Absorption) Aerosol forcing (anthropogenic) Aerosol radiative effect (natural + anthropogenic) Method to estimate global aerosol direct radiative effect Model-based, radiative transfer calculation Satellite-based -0.1~-0.9 W/m 2 for annual mean global aerosol direct forcing (IPCC,2007). Previous satellite based studies CERES (Clouds and the Earth’s Radiant Energy System)  TOA fluxes ! Coarse resolution (~10x10 km 2 ) MODIS (Moderate Resolution Imaging Spectroradiometer)  Aerosols ! Has difficulty in retrieving aerosol properties over bright land This study Use MISR (Multi-angle Imaging SpectroRadiometer) to quantify global aerosol shortwave direct radiative effect (SWDRE), on both land and oceans.

Introduction to MISR (Multi-angle Imaging SpectroRadiometer) On board satellite TERRA 9 view angles at earth surface: ±70.5º. ±60.0º, ±45.6º, ±26.1º, nadir Four spectral bands at each angle: 446 nm (Blue) 558 nm (Green) 672 nm (Red) 866 nm (NIR) Global Mode: 275 m sampling resolution for nadir camera and red band of other cameras 1.1 km for the other channels 400-km swath Global coverage: 9 days at equator, 2 days at poles Continuous data retrieval since Feb 2000.

MISR products used MISR products and images Nadir viewCloud mask AOD TOA albedo 1°x 1° grid MISR images TOA albedo (2.2x2.2 km 2 ) AOD (17.6x17.6 km 2 ) Cloud mask (1.1x1.1 km 2 ) BHRPAR

Method

Global distribution of MISR 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

Global TOA albedo~AOD correlation over ocean

List of selected regions Regions over landRegions over ocean Each region has 10°x5° area

TOA albedo~AOD correlation over ocean regions The slopes indicate the ability of aerosols to affect TOA radiative flux.

Alternative method: do global regression for each solar zenith angle. TOA albedo~AOD correlation over remote ocean regions Global regression over ocean for each SZA

Global TOA albedo~AOD correlation over land

List of selected regions Regions over landRegions over ocean Each region has 10°x5° area

Albedo~AOD correlation over land A East US

Albedo~AOD correlation over land A East US Central Africa

Albedo~AOD correlation over land A East US Sahara desert

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

Aerosol direct radiative effect Aerosol DRE (Clear sky) (W/m 2 ) Aerosol DRE (All sky) (W/m 2 ) Global Over ocean Over land SourceAerosol DRE (W/m 2 )Spatial coverageTemporal coverageSatellite data source Zhang and Christopher, ± 2.6Cloud-free oceans 09/ /2001CERES, MODIS Christopher and Zhang, Cloud-free oceans 09/2000CERES, MODIS Loeb and Kato, ± 1Cloud-free tropical oceans 01/ /1998, 03/2000 CERES, TRMM VIRS Loeb and Manalo- Smith, , -3.8Cloud-free oceans 03/ /2003CERES, MODIS -2.0, -1.6All-sky oceans From this study (July, 2002): From previous satellite-based studies:

Correlation between aerosol SWDRE and 0.56  m AOD Correlation between aerosol SWDRE and AOD (a) Over ocean (b) Over land

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

Use SVM classifier to calculate smoke aerosol effect - method SVM: Support Vector Machine SVM classifiers Clouds Aerosols Smoke Dust Other Ice/Snow Water Land Clouds Land Water Smoke Dust

Use SVM classifier to calculate smoke aerosol effect - result (a) Clear-sky aerosol SWDRE from smoke aerosols (W/m 2 ) (b) MODIS wild fire occurrence from Fire Information for Resource Management System (NASA/U of Maryland, 2002)

Use SVM classifier to calculate smoke aerosol effect - uncertainty Threshold for differentiating ‘Aerosols’ and ‘Surface’ pixels is arbitrary, which may cause the underestimation of total number of ‘smoky’ pixels. Since many ‘Surface’ pixels actually have some aerosol loading, the albedo for ‘Surface’ pixels is highly overestimated. The SVM scene classification is still in provisional quality, and the aerosol sub-classification validation has not yet been completed. Due to the above reasons, we chose not to use SVM scene classifiers in the global aerosol SWDRE estimation.

Conclusions and future work Conclusions By using MISR datasets, first satellite-based attempt to estimate global aerosol direct radiative effect over both ocean and land has been made. Aerosols and TOA albedo show different correlations in absorptive aerosol dominated region and non-absorptive aerosol dominated region. Over land, the slope of AOD ~ TOA albedo decreases as BHRPAR increases, indicating the aerosol scattering and absorbing effect on TOA albedo is smaller over brighter surfaces. Future work Extend the approach to include seasonal and inter- annual variability.

Acknowledgment MISR data were obtained from the NASA Langley Atmospheric Sciences Data Center ( MISR data were obtained from the NASA Langley Atmospheric Sciences Data Center (