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Direct aerosol radiative effects based on combined A-Train observations

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

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Direct aerosol radiative effects based on combined A-Train observations

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  1. 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

  2. Outline • Goal: To devise a new, methodology to derive direct aerosol radiative effects - DFaerosol(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

  3. Goal: To use A-Train aerosol obs to constrain aerosol radiative properties to calculate DFaerosol(z) CALIOP MODIS OMI • Myhre, Science, July 10, 2009: • Observation-based methods too large • Models show great divergence in regional and vertical distribution of DARF. • “remaining uncertainty (in DARF) is probably related to the vertical profiles of the aerosols and their location in relation to clouds”.

  4. Goal: To use A-Train aerosol obs to constrain aerosol radiative properties to calculate DFaerosol(z) Constraints/Input: - MODIS AOD (7/2 l) + dAOD - OMI AAOD(388 nm) + dAAOD - CALIPSO ext (532, 1064 nm) + dext - CALIPSO back (532 , 1064 nm) + dback • Issues to consider • - Differences in data quality land/ocean • Impact of model assumptions • Spatial variability • Aerosols above & near clouds MODIS aerosol models: 7 fine and 3 coarse mode distribution models define size and refractive indices of bi-modal log-normal size distribution → 100 combinations Free parameters: Nfine, Ncoarse Retrieval: ext (l, z) + dext ssa (l, z) + dssa g (l, z) + dg Target: DFaerosol(z) + d DFaerosol(z) Rtx code

  5. Methodology: Role of field observations • Use suborbital observations to: • Guide choices in aerosol models • Test retrievals of aerosol radiative properties • Test calculated radiative fluxes • 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

  6. Solution space: expansion from over-ocean MODIS models

  7. Role of suborbital observations: 1) Test realism of aerosol models ARCTAS data are corrected after Virkkula [2010].

  8. 7 fine + 3 coarse modes SSA and EAE Lidar Ratio and EAE

  9. Current choices in retrieval method: • Metric / error / cost function • 4 Observables • xi = AOD 550nm (±0.03±5%) • AOD 1240 nm (±0.03±5%) - MODIS • AAOD 388 nm ±(0.05+30%) - OMI • b532±(0.1Mm-1sr-1+30%), - CALIOP • Minimize X and select the top 3% of solutions that meet for all i : retrieved parameters : observables : uncertainties in obs. : weighting factors

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

  11. 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

  12. Latitudinal distribution of AOD differences between MODIS and CALIOP V3 Main findings, ocean: bias differences of 0.03 - 0.04 (with CALIOP<MODIS for all months), RMS D of 0.09 – 0.12 r2 is ~ 0.4-0.5 …all after judicious use of quality flags Latitude MODIS-CALIOP AOD Latitude MODIS-CALIOP AOD Shinozuka and Redemann, ACP, 2011 Redemann et al., ACPD

  13. Consistency issues: Choice of OMI data OMAERUV (Torres group) OMAERO (KNMI group) AOD 380nm AOD 380nm ssa 380nm ssa 380nm

  14. 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

  15. 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

  16. Retrieval of aerosol radiative properties from A-Train observations Constraints/Input: - MODIS AOD (7/2 l) + dAOD - OMI AAOD(388 nm) + dAAOD - CALIPSO ext (532, 1064 nm) + dext - CALIPSO back (532 , 1064 nm) + dback MODIS aerosol models: 7 fine and 3 coarse mode distributions define standard deviation and refractive indices of bi-modal log-normal size distribution → 100 combinations Free parameters: Nfine, Ncoarse Retrieval: ext (l, z) + dext ssa (l, z) + dssa g (l, z) + dg Rtx code Target: DFaerosol(z) + d DFaerosol(z)

  17. Retrieval of aerosol radiative properties from A-Train observations Constraints/Input: - MODIS AOD (7/2 l) + dAOD - OMI AAOD(388 nm) + dAAOD - CALIPSO ext (532, 1064 nm) + dext - CALIPSO back (532 , 1064 nm) + dback MODIS aerosol models: 7 fine and 3 coarse mode distributions define standard deviation and refractive indices of bi-modal log-normal size distribution → 100 combinations Free parameters: Nfine, Ncoarse Comparison: CERESFclear AirborneFclear Retrieval: ext (l, z) + dext ssa (l, z) + dssa g (l, z) + dg Rtx code Target: DFaerosol(z) + d DFaerosol(z) Comparison: AERONETAOD, ssa, g Airborne test bed data

  18. 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

  19. The top 3% X for all 4 months. Xavg=0.1640 Xstd= 0.0968 Xavg+Xstd=0.2608

  20. AOD and SSA retrievals from MODIS – OMI - CALIPSO AOD ~540nm SSA ~540nm

  21. 24h avg direct radiative forcing from MODIS – OMI - CALIPSO Surface TOA

  22. Conclusions • 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. • A methodology for the retrieval of aerosol radiative properties from MODIS AOD, OMI AAOD and CALIOP b532 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 • Next questions to answer: • 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? • How to test the “consistency” between various satellite input data sets? • How do uncertainties in satellite data propagate to retrievals and flux estimates? • 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

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