Direct aerosol radiative effects based on combined A-Train observations

Jens Redemann, Y. Shinozuka, J. Livingston, M. Vaughan, P. Russell, M.Kacenelenbogen, O. Torres, L. Remer

BAERI – NASA Langley - NASA Ames – SRI – NASA Goddard

http://geo.arc.nasa.gov/AATS-website/

email: Jens.Redemann-1@nasa.gov

Outline Goal: To devise a new, methodology to derive direct aerosol radiative

effects - Faerosol(z) based on CALIOP, OMI and MODIS

Motivation, data sets and role of field observations

Methodology for combining CALIOP, OMI and MODIS data

Checking consistency of input data

Proof of concept for 4-month data set – Jan., Apr., Jul., Oct. 2007

Impact of input data sets

Comparisons to AERONET and CERES flux products

Conclusions

MODIS

OMI

CALIOP

Goal: To use A-Train aerosol obs to constrain aerosol radiative properties to calculate Faerosol(z)

Myhre, Science, July 10, 2009: 1)Observation-based methods too large2)Models show great divergence in regional and vertical distribution of DARF.3)“remaining uncertainty (in DARF) is probably related to the vertical profiles of the aerosols and their location in relation to clouds”.

Target:Faerosol(z) + Faerosol(z)

Constraints/Input:- MODIS AOD (7/2 ) + AOD- OMI AAOD (388 nm) + AAOD- CALIPSO ext (532, 1064 nm) + ext- CALIPSO back (532 , 1064 nm) + back

Goal: To use A-Train aerosol obs to constrain aerosol radiative properties to calculate Faerosol(z)

Retrieval:ext (, z) + extssa (, z) + ssa

g (, z) + g

MODIS aerosol models: 7 fine and 3 coarse mode distribution models define size and refractive indices of bi-modal log-normal size distribution → 100 combinationsFree parameters: Nfine, Ncoarse

Issues to consider- Differences in data quality land/ocean- Impact of model assumptions- Spatial variability- Aerosols above & near clouds

Rtx code

Methodology: Role of field observationsUse suborbital observations to:

1) Guide choices in aerosol models

2)Test retrievals of aerosol radiative properties

3)Test calculated radiative fluxes

4)Study spatial variability = uncertainty involved in extrapolating to data-sparse regions (e.g., above clouds)

See poster A43A-0127 Kacenelenbogen et al.

Re 4): Shinozuka and Redemann, ACP, 2011

Solution space: expansion from over-ocean MODIS models

ARCTAS data are corrected after Virkkula [2010].

Role of suborbital observations: 1) Test realism of aerosol models

7 fine + 3 coarse modesSSA and EAE Lidar Ratio and EAE

ix̂ix : retrieved parameters

: observables

: uncertainties in obs.

: weighting factors

Current choices in retrieval method:1)Metric / error / cost function

2)4 Observablesxi = AOD 550nm (±0.03±5%)

AOD 1240 nm (±0.03±5%) - MODIS AAOD 388 nm ±(0.05+30%) -

OMI 532 ±(0.1Mm-1sr-1+30%), -

CALIOP

3)Minimize X and select the top 3% of solutions that meet for all i

2/12

ˆ

ˆ

i

ii

ii x

xxw

ix̂

iw

iii xxx ˆ|ˆ|

Example of successful retrieval from actual collocated MODIS, OMI, CALIOP (V3) data: Oct. 23, 2007

Consistency issues: AOD comparisons (CALIOP V3)

Eight months of data: January, April, July and October 2007 and 2009

Use CALIOP 5/40km-avg. (V3/V2) aerosol extinction profiles, and 5km

aerosol and cloud layer products

Find all instantaneously collocated, MODIS MYD04_L2 (10x10km)

aerosol retrievals traversed by 5km/40km CALIPSO track

Judicious use of data quality flags

Break down geographically → zonal mean AOD

See Redemann et al., ACPD for details

See Kacenelenbogen et al., ACP 2011

for potential explanations for CALIOP-MODIS

differences

Latitudinal distribution of AOD differences between MODIS and CALIOP V3

MODIS-CALIOP AOD

MODIS-CALIOP AOD

Latit

ude

Latit

ude

Redemann et al., ACPD

Main findings, ocean:1.bias differences of 0.03 - 0.04 (with CALIOP<MODIS for all months), 2.RMS of 0.09 – 0.12 3.r2 is ~ 0.4-0.5 …all after judicious use of quality flags

Shinozuka and Redemann, ACP, 2011

OMAERUV (Torres group) OMAERO (KNMI group)

AOD 380nm AOD 380nm

ssa 380nm ssa 380nm

Consistency issues: Choice of OMI data

OMAERO data collocated with MODIS and CALIOP is a reasonable representation of global OMAERO data

OMAERUV data collocated with MODIS and CALIOP is a poor representation of global OMAERUV

OMAERO data collocated with MODIS and CALIOP is a reasonable representation of global OMAERO data

OMAERUV data collocated with MODIS and CALIOP is a poor representation of global OMAERUV

Target:Faerosol(z) + Faerosol(z)

Constraints/Input:- MODIS AOD (7/2 ) + AOD- OMI AAOD (388 nm) + AAOD- CALIPSO ext (532, 1064 nm) + ext- CALIPSO back (532 , 1064 nm) + back

Retrieval of aerosol radiative properties from A-Train observations

Retrieval:ext (, z) + extssa (, z) + ssa

g (, z) + g

MODIS aerosol models: 7 fine and 3 coarse mode distributions define standard deviation and refractive indices of bi-modal log-normal size distribution → 100 combinationsFree parameters: Nfine, Ncoarse

Rtx code

10-2

10-1

100

101

102

0

0.5

1

1.5

2

2.5

Diameter (m)

dN /

dlo

gD

Number

10-2

10-1

100

101

102

0

5

10

15

20

Diameter (m)

dV /

dlo

gD

Volume

123456789

123456789

Target:Faerosol(z) + Faerosol(z)

Constraints/Input:- MODIS AOD (7/2 ) + AOD- OMI AAOD (388 nm) + AAOD- CALIPSO ext (532, 1064 nm) + ext- CALIPSO back (532 , 1064 nm) + back

Retrieval of aerosol radiative properties from A-Train observations

Retrieval:ext (, z) + extssa (, z) + ssa

g (, z) + g

MODIS aerosol models: 7 fine and 3 coarse mode distributions define standard deviation and refractive indices of bi-modal log-normal size distribution → 100 combinationsFree parameters: Nfine, Ncoarse

Rtx code

10-2

10-1

100

101

102

0

0.5

1

1.5

2

2.5

Diameter (m)

dN /

dlo

gD

Number

10-2

10-1

100

101

102

0

5

10

15

20

Diameter (m)

dV /

dlo

gD

Volume

123456789

123456789

Comparison:CERES Fclear

Airborne Fclear

Comparison:AERONET AOD, ssa, gAirborne test bed data

AERONET Inversion collocation with MODIS-OMI(OMAERO)-CALIPSO

AERONET V2.0 (circles): •~68,200 collocated MODIS-OMI-CALIPSO obs in 4 months of data•93-96% of those have valid MOC retrieval•576 of those have collocated AERONET AOD measurements (±100km, ±1h)•Only 45 of those have collocated AERONET SSA (V2/L2) retrieval

The top 3% X for all 4 months.

Xavg=0.1640 Xstd= 0.0968Xavg+Xstd=0.2608

AOD and SSA retrievals from MODIS – OMI - CALIPSO

AOD~540nm

SSA~540nm

24h avg direct radiative forcing from MODIS – OMI - CALIPSO

Surface

TOA

ConclusionsA. MODIS-CALIOP AOD comparisons: bias differences of 0.03 - 0.04 (CAL<MOD),

RMS differences of 0.09 – 0.12 after judicious use of quality flags.

B. A methodology for the retrieval of aerosol radiative properties from MODIS AOD, OMI AAOD and CALIOP 532 has been devised. Proof of concept study complete for January+April+July+October 2007:– Results sensitive to choice of OMI data (OMAERUV vs. OMAERO)– OMAERUV possibly more accurate, but data collocated with

MODIS+CALIOP not representative of global OMAERUV data set– Tests with collocated AERONET observations sparse– Tests with CERES irradiance measurements difficult to interpret

C. Next questions to answer:1) What is the trade-off between uncertainty and spatial sampling in input satellite data

sets for calculating aerosol-induced changes in TOA or surface fluxes, i.e., how much uncertainty is involved in spatial extrapolation to data-sparse regions?

2) How to test the “consistency” between various satellite input data sets?

3) How do uncertainties in satellite data propagate to retrievals and flux estimates?

D. Next steps to take:– Continue to investigate spatial variability in suborbital data to extend the MOC

retrievals to aerosol above cloud (AAC) based on reduced data set

– Extend study to use more MODIS channels and more OMI retrievals

– Constrain OMI AAOD retrievals with CALIOP height input

– Compare vertical distribution of direct forcing to models