1. Direct aerosol radiative forcing based on combined A-Train observations and comparisons to IPCC-2007 results Jens Redemann, Y. Shinozuka, M. Vaughan, P. Russell, M. Kacenelenbogen, J. Livingston, O. Torres, L. Remer BAERI – SRI - NASA Langley - NASA Ames – NASA Goddard - UMBC http://geo.arc.nasa.gov/AATS-website/ email: Jens.Redemann-1@nasa.govJens.Redemann-1@nasa.gov

2. Goals and Motivation   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 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

3. Target:  F aerosol (z) +  F aerosol (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: 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   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

4.   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 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

5. 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

6. 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

7. 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

8. OMAERUV (Torres group)OMAERO (KNMI group) AOD 388nm ssa 388nm   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 Representativeness of input - 1

9.   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 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

10.   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 - Ocean ssa 441nm Positive bias in input ssa data is removed in MOC retrieval AOD 500nm

11.   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 Positive bias in input ssa data is removed in MOC retrieval ssa 441nm AOD 500nm

12.   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 ssa 550nm AOD 550nm AOD and ssa distribution

13.   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 and ssa distribution ssa 400nm 550nm 2200nm AOD 400nm 550nm 2200nm

14.   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 Maps of TOA and surface DARF – MODIS snow-free, gap-filled albedo parameters (8-day res.) Black-sky albedo (0.3-5  m) at solar noon, Day 185, 2007

15.   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 Maps of TOA and surface DARF TOA Surface

16.   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 Comparisons of DARF to previous results Seasonal clear-sky DARF results at TOA and SFC from models and observations [W/m 2 ] after CCSP, adapted from Yu et al. 2006.

17.   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 Conclusions This data set: – –Provides a PURELY observational estimate of clear-sky DARF(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 DARF by running retrievals with limited input