Global transport and Global transport and radiative forcing of biomass radiative forcing of biomass

burning aerosols burning aerosols Yang Chen, Qinbin Li, Ralph KahnYang Chen, Qinbin Li, Ralph Kahn

Jet Propulsion LaboratoryJet Propulsion LaboratoryCalifornia Institute of Technology, PasadenaCalifornia Institute of Technology, Pasadena

Evan Lyons, James RandersonEvan Lyons, James RandersonUniversity of California, IrvineUniversity of California, Irvine

The 3rd GEOS–Chem Users' MeetingThe 3rd GEOS–Chem Users' MeetingHarvard University, April 12, 2007Harvard University, April 12, 2007

Objectives and outlineObjectives 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-0.1~-0.9 W/m22 for direct forcing (IPCC,2007). for direct forcing (IPCC,2007).• -0.3~-1.8 W/m-0.3~-1.8 W/m22 for indirect forcing. for indirect forcing.

PurposePurpose: Combine satellite observations and chemical transport models to : Combine satellite observations and chemical transport models to further constrain quantification of aerosol (particularly the biomass burning further constrain quantification of aerosol (particularly the biomass burning aerosols) radiative forcing.aerosols) radiative forcing.

First stepFirst step: Estimate the global aerosol direct radiative effect using Multi-: Estimate the global aerosol direct radiative effect using Multi-angle Imaging SpectroRadiometer (MISR) observations.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 First attempt at estimating aerosol direct radiative effect on a global basis (over

both ocean and land) using satellite observation based approach.both ocean and land) using satellite observation based approach.

Ongoing modeling studyOngoing modeling study: GEOS-Chem simulations of aerosols using : GEOS-Chem simulations of aerosols using different GFEDv2 biomass burning emissions.different GFEDv2 biomass burning emissions.• Diurnal cyclesDiurnal cycles• Synoptic variationSynoptic variation• Injection heightInjection height

Introduction to MISRIntroduction to MISR Multi-angle multi-channel Multi-angle multi-channel

spectroradiometer on board spectroradiometer on board satellite TERRAsatellite TERRA

Global Mode:Global Mode:• 275 m sampling resolution for 275 m sampling resolution for

nadir camera and red band of nadir camera and red band of other camerasother cameras

• 1.1 km for the other channels1.1 km for the other channels• 400-km swath400-km swath• Global coverage: 9 days at Global coverage: 9 days at

equator, 2 days at polesequator, 2 days at poles

Continuous data retrieval since Continuous data retrieval since Feb 2000.Feb 2000.

Major products used:Major products used:• TOA albedo (2.2x2.2 kmTOA albedo (2.2x2.2 km22))• AOD (17.6x17.6 kmAOD (17.6x17.6 km22))• Cloud mask (1.1x1.1 kmCloud mask (1.1x1.1 km22))• BHRPAR (1.1x1.1 kmBHRPAR (1.1x1.1 km22))• All products are re-sampled to All products are re-sampled to

17.6x17.6 km17.6x17.6 km22 for this study for this study

MethodMethod aerosolnoaerosolwithTOAskyclear IF __

fractionCloudFF skyclearskyall _1

Albedo ~ AOD regression

TOA Broadband Albedo

(with aerosol)

Aerosol Optical Depth

Cloud mask

BHRPAR

Nadir view Cloud mask

AOD TOA albedo

1°x 1° grid

MISR observations

Global distribution of AOD, albedo, Global distribution of AOD, albedo, and BHRPAR (July, 2002)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

Albedo~AOD correlation over oceanAlbedo~AOD correlation over oceanAlbedo~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

fb c

dg

Albedo~AOD correlation over landAlbedo~AOD correlation over land

AEast US

A

BC

BCentral Africa

CSaharan desert

Global correlation

Albedo~AOD correlation for 10°x5° grids

Aerosol direct radiative effectAerosol direct radiative effect(a) (b)

(a) Clear-sky and (b) all-sky aerosol direct radiative effect (W/m2) for July 2002.

Aerosol direct radiative effectAerosol direct radiative effect

Direct ARE (Clear sky) Direct ARE (Clear sky) (W/m(W/m22))

Direct ARE (All sky) Direct ARE (All sky) (W/m(W/m22))

GlobalGlobal -4.70-4.70 -1.49-1.49Over oceanOver ocean -4.54-4.54 -1.95-1.95Over landOver land -4.88-4.88 -1.18-1.18

SourceSource Direct ARE (W/mDirect ARE (W/m22)) Spatial coverageSpatial coverage Temporal coverageTemporal coverage Satellite data sourceSatellite data sourceZhang and Zhang and Christopher, 2005Christopher, 2005

-6.4 ± 2.6-6.4 ± 2.6 Cloud-free oceansCloud-free oceans 09/2000-08/200109/2000-08/2001 CERES, MODISCERES, MODIS

Christopher and Christopher and Zhang, 2002Zhang, 2002

-6-6 Cloud-free oceansCloud-free oceans 09/200009/2000 CERES, MODISCERES, MODIS

Loeb and Kato, Loeb and Kato, 20022002

-4.6 ± 1-4.6 ± 1 Cloud-free Cloud-free tropical oceanstropical oceans

01/1998-08/1998, 01/1998-08/1998, 03/200003/2000

CERES, TRMM VIRSCERES, TRMM VIRS

Loeb and Manalo-Loeb and Manalo-Smith, 2005 Smith, 2005

-5.5, -3.8 -5.5, -3.8 Cloud-free oceans Cloud-free oceans 03/2000-12/2003 03/2000-12/2003 CERES, MODIS CERES, MODIS

-2.0, -1.6 -2.0, -1.6 All-sky oceans All-sky oceans

From this study (July, 2002):

From previous satellite-based studies:

UncertaintiesUncertainties Satellite retrieval of aerosol, albedo and surface Satellite retrieval of aerosol, albedo and surface

properties.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.

Diurnal cycle effect on Diurnal cycle effect on GEOS-Chem aerosol simulationGEOS-Chem aerosol simulation

07/2004

Ongoing modeling studyOngoing 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

Diurnal cycle effect on Central AfricaDiurnal cycle effect on Central AfricaOngoing modeling studyOngoing 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

Diurnal cycle effect on Alaska and Northern CanadaDiurnal cycle effect on Alaska and Northern CanadaEmissions from source:

BCPI concentration in nearby grid:

Ongoing modeling studyOngoing 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 emissionsLocal noon

BCPI concentration difference(with diurnal cycle - without)

Conclusions and future workConclusions and future work ConclusionsConclusions

• By using MISR datasets, first satellite-based attempt to estimate global By using MISR datasets, first satellite-based attempt to estimate global aerosol direct radiative effect over both ocean and land has been aerosol direct radiative effect over both ocean and land has been made.made.

• Aerosols have different impacts on TOA albedo in different regions due Aerosols have different impacts on TOA albedo in different regions due to different aerosol properties and surface types.to different aerosol properties and surface types.

• Global mean result of aerosol radiative effect over ocean is well in the Global mean result of aerosol radiative effect over ocean is well in the range of other studies in literature.range of other studies in literature.

• By including diurnal cycle of biomass burning emissions in GEOS-Chem By including diurnal cycle of biomass burning emissions in GEOS-Chem simulation, aerosol concentrations at surface may increase or simulation, aerosol concentrations at surface may increase or decrease, depending on the source type and intensity, the boundary decrease, depending on the source type and intensity, the boundary layer height, and the relative importance of transport and local layer height, and the relative importance of transport and local emissions.emissions.

Future workFuture work• Extend the satellite-based estimation of aerosol direct radiative effect Extend the satellite-based estimation of aerosol direct radiative effect

to include seasonal and inter-annual variability.to include seasonal and inter-annual variability.• Study how synoptic variability of biomass burning emissions and the Study how synoptic variability of biomass burning emissions and the

inclusion of smoke injection height will affect the global distribution of inclusion of smoke injection height will affect the global distribution of aerosols, and the implication to the aerosol radiative forcing.aerosols, and the implication to the aerosol radiative forcing.

AcknowledgmentAcknowledgment MISR dataMISR data were obtained from the NASA Langley were obtained from the NASA Langley

Atmospheric Sciences Data Center (Atmospheric Sciences Data Center (http://eosweb.larc.nasa.gov/http://eosweb.larc.nasa.gov/).).

We used We used Global Fire Emissions Database version2Global Fire Emissions Database version2 (van (van der Werf et al.,2006) resampled to an 8day time step using der Werf et al.,2006) resampled to an 8day time step using MODIS fire hot spots (Giglio et al., 2003).MODIS fire hot spots (Giglio et al., 2003).

GEOS-Chem modelGEOS-Chem model is managed by the Atmospheric is managed by the Atmospheric Chemistry Modeling Group at Harvard University. Chemistry Modeling Group at Harvard University.