1. Direct aerosol radiative forcing based on combined A-Train observations – challenges in deriving all-sky estimates Jens Redemann, Y. Shinozuka, M.Kacenelenbogen, M. Vaughan, R. Ferrare, C. Hostetler, R. Rogers, P. Russell, J. Livingston, O. Torres, L. Remer NASA Ames – BAERI – NASA Langley – SSAI – SRI – NASA Goddard – UMBC http://geo.arc.nasa.gov/sgg/AATS-website/ email: Jens.Redemann-1@nasa.govJens.Redemann-1@nasa.gov

2. Goals and Motivation MODIS OMI CALIOP Goal: To use A-Train aerosol obs to constrain aerosol radiative properties to calculate observationally- based  F aerosol (z) and its uncertainty   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

3. Target:  F aerosol (z) +  F aerosol (z) Constraints/Input: - MODIS AOD (550, 1240 nm) +  - OMI AAOD (388 nm) +  - CALIPSO ext (532, 1064 nm) +  ext - CALIPSO back (532 nm)  Mm -1 sr -1  Retrieval: ext (, z) +  ext ssa (, z) +  ssa g (, z) +  g Aerosol models: 7 fine and 3 coarse mode models and refractive indices for bi-modal log-normal size distribution → 100 combinations Free parameters: N fine, N coarse Rtx code Comparison: CERES F clear Airborne F clear Comparison: AERONET AOD, ssa, g Airborne test bed data Approach   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

4. Input &Spatial Sampling Constraints/Input: - MODIS AOD (7/2 ) +  AOD - OMI AAOD (388 nm) +  AAOD - CALIPSO ext (532, 1064 nm) +  ext - CALIPSO back (532, 1064 nm) +  back   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

5. OMAERUV (Torres group)OMAERO (KNMI group) AOD 388nm ssa 388nm Representativeness of input - 1   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

6. Representativeness of input - 2 OMAERO data collocated with MODIS and CALIOP is a reasonable representation of global OMAERO over ocean OMAERUV data collocated with MODIS and CALIOP is a poor representation of global OMAERUV over ocean   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

7.   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions Metric: Simple weighted cost function : retrieved : observables : unc. in obs. : weighting factors

8. AOD & ssa comparisons to AERONET - Ocean ssa 441nm Positive bias in input ssa data is removed in MOC retrieval AOD 500nm   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

9. ssa 550nm AOD 550nm AOD and ssa distribution   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

10. AOD and ssa distribution ssa 400nm 550nm 2200nm AOD 400nm 550nm 2200nm   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

11. MODIS snow-free, gap-filled albedo parameters (8-day res.) Black-sky albedo (0.3-5  m) at solar noon, Day 185, 2007   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

12. Maps of TOA and surface clear sky  F aerosol TOA Surface   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

13. Comparisons of  F aerosol to previous results Seasonal clear-sky  F aerosol results at TOA and SFC from models and observations [W/m 2 ] after CCSP, adapted from Yu et al. 2006.   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

14. [Kacenelenbogen et al., in prep.] CALIOP Aerosol Above Cloud observations CALIOP AAC AOD HSRL AAC AOD day and night RMSE: 0.07 Bias: 3.68 x 10 -18 Lack of correlation CALIOP – HSRL AAC AOD and ~68% of points outside the ±40% envelope 60 50 40 30 20 10 0 CALIOP, October 2007 CALIOP detects AAC in ~23% of the cases where the HSRL detects AAC CALIOP underestimation of AAC mostly due to tenuous aerosol layers under the CALIOP detection threshold R 2 =0.27 N=151 HSRL AAC CALIOP AAC 86 flights 2006-09

15. Conclusions This data set: – –Provides a PURELY observational estimate of clear-sky  F aerosol (z) that compares favorably with previous observationally-based estimates and better with model based estimates at TOA – –Is based on stringently quality-screened, instantaneously collocated level 2 MODIS AOD (7/2 ), OMI AAOD at 388nm, and CALIOP aerosol backscatter at 532nm – –Is based on aerosol models that are consistent with suborbital obs – –Uses OMAERUV over land and OMAERO over ocean (because of representativeness of their subsampling) – –Contains 12 months (2007) of aerosol extinction, ssa and g as f(t, lat, long,, z) – –Agrees better with AERONET in terms of ssa(441nm) than input OMI+MODIS data – –Uses CALIOP layer heights to constrain OMAERUV AAOD – –Provides uncertainty estimates based on range of aerosol models that are consistent with observation within their uncertainties Future work: – –Utilize MODIS DB and/or MAIAC data – –Cover all of the available collocated MODIS, OMI, CALIOP data (June 2006-Dec. 2008) – –All-sky  F aerosol by running retrievals with limited input   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Representativeness of input   Sample retrieval   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

16. ADDITIONAL SLIDES

17. Aerosol models: Based on field observations, optimized to span observed range of ssa vs. EAE and lidar ratio vs. EAE   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Aerosol models   Metric   Sample retrieval   Representativeness of input   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

18. Minimize X and select top 3% solutions with : retrieved parameters : observables : uncertainties in obs. : weighting factors Metric: Simple weighted cost function x i = AOD 550nm (±0.03±5%) AOD 1240 nm (±0.03±5%) - MODIS AAOD 388 nm ±(0.05+30%) - OMI  532 ±(0.1Mm -1 sr -1 +30%), - CALIOP   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Aerosol models   Metric   Sample retrieval   Representativeness of input   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

19. Sample retrieval   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Aerosol models   Metric   Sample retrieval   Representativeness of input   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions

20.   Goals & Motivation   Approach   Technical details   Input & Spatial sampling   Aerosol models   Metric   Sample retrieval   Representativeness of input   Results   AOD & ssa comparisons to AERONET   AOD and ssa distribution   Maps of TOA and surface DARF   Comparisons of DARF to previous results   Conclusions AOD & ssa comparisons to AERONET – Land (Dark Target) Positive bias in input ssa data is removed in MOC retrieval ssa 441nm AOD 500nm