Long-term Aerosol Data Records

1. Long-term Aerosol Data Records Presented by Istvan Laszlo

2. Requirement, Science, and Benefit Requirement/Objective • Research area: • Document and understand changes in climate forcings and feedbacks, thereby reducing uncertainty in climate projection • Priority research activities: • Produce reference data sets that provide improved climate information • Use these data sets to develop integrated historical analyses of the global climate system • Quantify the climate roles of the radiatively important trace atmospheric species such as fine particles (aerosols), ozone, and chemically active greenhouse gases Science • What is the quantitative role of aerosols in climate ? Benefit • The National Weather Service air-quality forecasters, and their customers • Decision makers on climate change policies • General public engaged in climate change debate will have: • better aerosol products lead to reduction in the uncertainty as to the quantitative role of aerosols in climate forcing, and • better products and solid science that improves the decision tools for climate change

3. Aerosol Retrieval Principles andInstruments(1) Observation Retrieved AOD Observation Residual 1 Residual 2 Retrieved AOD and model • Basis: • Clear-sky satellite observed reflectance, R carries information about aerosol: • R = Raerosol + Ratmosphere-other + Rsurface • subscripts aerosol, atmosphere-other, and surface denote contributions by aerosol, atmospheric species other than aerosol, and surface, respectively. • Aerosol reflectance increases with increasing amount of aerosol (as measured by optical depth, AOD) • The spectral dependence of aerosol reflectance is a function of aerosol type • Retrieval: • Separate out Raerosol from R by calculating the other terms from ancillary information • Estimate AOD by matching aerosol reflectance calculated for varying AOD with observed Raerosol (single- and multi-channel retrievals) • Estimate aerosol type by performing b) for different aerosol models and by selecting the model providing the “best” match. • Illustration of multi-channel aerosol retrieval • AOD and model is from the “minimum” residual between observed and calculated spectral reflectances. • Residual 2 < Residual 1, so retrieved AOD ≈ 1.0 and aerosol model is model 2. aerosol model 1 aerosol model 2 TOA reflectance in red band TOA reflectance in blue band 3

4. Aerosol Retrieval Principles andInstruments(2) Main challenges Clear radiances used → cloud screening Aerosol signal is small → calibration Surface contribution can be large → accurate characterization of surface Complex, spatially and temporally varying structure/shape/composition of aerosol Under-determined problem from current operational satellites with only 1-3 SW channels.→need multi-spectral, multi-angle observation of polarized radiances Selected major passive instruments used: AVHRR: on NOAA Polar Platforms since 1981 Strength: flown over a long period of time Weakness: no onboard calibration of shortwave channel; significant drift of early orbits; limited number of channels for aerosol retrieval MODIS: on NASA EOS Polar Platforms since 2000 Strength: onboard calibration; stable orbit; multiple channels for estimating aerosol type Weakness: current record is short for trend analysis low signal at low AOD brightness is reduced over bright surface retrieval is uncertain around crossover point Simulated reflectance spectra at the top of the atmosphere under aerosol-free, spherical dust, and nonspherical dust. 4

5. Analysis of Aerosol Data (1) Research: Analyzed spatial and temporal variability of AVHRR aerosol record Data : Pathfinder Atmospheres - Extended (PATMOS-x) aerosol record: Aerosol optical depth (AOD) retrieved over ocean from AVHRR GAC radiances AVHRR radiances are tied to MODIS radiances (Heidinger et al., 2002). Spatial resolution: 0.5x0.5-degrees Temporal coverage: 1981-2008 Used NESDIS independent (single) channel aerosol retrieval algorithm (Ignatov & Stowe, 2002) Examined spatial distribution of AOD. Generated time series of global and regional averages. mean AOD for 4/2004-5/2008 5

6. Analysis of Aerosol Data (2) Recent accomplishments: Estimated (linear) trend (-0.01/decade) and significance (trend/std >2) Analysis of regional rather than global trend is more meaningful. Regional long-term trend is quite variable Mid-low-latitude biomass burning aerosol sources have increased in the recent past Major pollution aerosol sources have decreased in the recent past, except for SE Asia, where they have increased Significance of trend varies with region Trend Significance 6

7. Analysis of Aerosol Data (3) Recent accomplishments (contd.): Estimated aerosol radiative effect (“forcing”) Contributing to the GEWEX Aerosol Assessment (NASA, NOAA, Academia, Japan) Analyzed consistency of single-channel (AVHRR) and multi-channel (MODIS) algorithms At low and high AOD retrievals tend to be more inconsistent results of differences in aerosol models and surface effects Results are important for building linkages between the heritage and newer sensors and products. 7

8. Challenges and Path Forward Science challenges Accounting for orbit drift of past NOAA satellites Retrieval of AOD over land has larger uncertainty Separation of natural and anthropogenic aerosols Differences in aerosol data estimated from various sensors prevent creation of a consistent record Next steps Understand and resolve differences between various AOD records Use aerosol data from recent/future passive multi-channel, multi-angle, polarized radiance measurements and active measurements (VIIRS, APS, CALIOP, etc. / ACE) to “calibrate” the AVHRR product, and Use surface-based sun photometer record as an additional time series a consistent vicarious radiometric calibration source Use advanced techniques to estimate trend, confidence, and number of years needed for trend detection. Transition path Create a long-term aerosol record in collaboration with NCDC, and aerosol groups at NASA and Academia – served via internet data portal Provide long-term aerosol data to the GEWEX Aerosol Assessment project – accessible via internet and described in assessment report 8