# Presentation: 682115

## 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/m2 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 km2)
- 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 and images

- _MISR products used_

- Nadir view

- Cloud mask

- AOD

- TOA albedo

- 1°x 1° grid

- _MISR images_

- TOA albedo
- (2.2x2.2 km2)

- AOD
- (17.6x17.6 km2)

- Cloud mask
- (1.1x1.1 km2)

- BHRPAR
- (1.1x1.1 km2)

## 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 land

- Regions 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.

## TOA albedo~AOD correlation over remote ocean regions

- _Alternative method_: do global regression for each solar zenith angle.

- Global regression over ocean for each SZA

## Global TOA albedo~AOD correlation over land

## List of selected regions

- Regions over land

- Regions over ocean

*Each region has 10°x5° area*

## Albedo~AOD correlation over land

- A
- East US

- 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/m2) for July 2002.

## Aerosol direct radiative effect

|  | Aerosol DRE (Clear sky) (W/m2) | Aerosol DRE (All sky) (W/m2) |
| --- | --- | --- |
| Global | -4.70 | -1.49 |
| Over ocean | -4.54 | -1.95 |
| Over land | -4.88 | -1.18 |

| Source | Aerosol DRE (W/m2) | 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:

## Correlation between aerosol SWDRE and 0.56 m AOD

- 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/m2)

- (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 (_[http://eosweb.larc.nasa.gov/](http://eosweb.larc.nasa.gov/)__)._