EGU ’08 Highlights

1. EGU ’08 Highlights Vijay Natraj

2. Sensing Methane Emissions from Space • Second most important anthropogenic greenhouse gas • contributes 0.48 W/m2 to total anthropogenic radiative forcing of 2.43 W/m2 by well-mixed greenhouse gases • indirect effect of about 0.13 W/m2 through formation of other greenhouse gases, notably tropospheric ozone and stratospheric water vapor • Global budget relatively well constrained (550 ± 50 Tg/yr) but partitioning among sources highly uncertain • Recent finding that plants can also directly emit methane in substantial amounts • requires repartitioning of other known sources such as wetland emissions • SCIAMACHY measurements used to retrieve methane globally with high sensitivity to surface • CO2 column retrievals used to convert CH4 column densities to column-averaged mixing ratios • Large scale methane enhancements due to man-made (e.g., rice agriculture) as well as natural (e.g., wetlands) emissions clearly identified • Most pronounced CH4 signal from source regions over India and South East Asia • broadly consistent with model simulations • Higher CH4 abundances over tropical Africa and tropical America • not well constrained by ground-based network • hitherto underestimated CH4 emissions from tropical landmasses

3. Measurements of Tropospheric CO2 Concentrations Using AIRS and SCIAMACHY for 2004 • Sensitivity • AIRS: 9 - 14 km • SCIAMACHY: 0 - 4 km • Both AIRS and SCIAMACHY show seasonal variations in CO2 concentration • January, April: high CO2 in northern hemisphere • July, September: northern hemisphere CO2 reduces due to absorption by new vegetation growth • Phase lag for AIRS-retrieved data • time required for CO2 to mix through troposphere • Greater variability in SCIAMACHY data • fluxes at the surface • AIRS data produces smooth seasonal cycle • CO2 well mixed in upper troposphere

4. Measurements of Tropospheric CO2 Concentrations Using AIRS and SCIAMACHY for 2004 • Seasonal cycles vary between regions • vegetation, population, latitudinal location • amplitude much larger in lower than upper troposphere • northern and southern hemisphere ~6 months out of phase • SCIAMACHY peak amplitude observed 1-2 months before AIRS • Subtracting AIRS data from SCIAMACHY data enhances surface CO2 variations • clear CO2 uptake signal for July-September • South America and Africa show regions of high CO2 in the lower troposphere • decreased vegetation during winter months • forest fires perhaps?

5. Joint Retrieval of Aerosol Load and Surface Reflectance Using MSG/SEVIRI Observations • Problem: discrimination of signal reflected by surface from that scattered by aerosols • Solution: surface and aerosol retrieved simultaneously • Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations • Meteosat Second Generation (MSG) satellite • imaging radiometer • 12 spectral channels (4 Vis/NIR, 8 IR) • continuous imaging, 15 min repeat cycle • 0.6, 0.8 and 1.6 μm channels used • Retrieved parameters: • aerosol:  (550 nm) • surface: ρ0,k,Θ,ρc (for each channel)

6. Joint Retrieval of Aerosol Load and Surface Reflectance Using MSG/SEVIRI Observations • BRDF formulation • product of 4 terms • amplitude: ρ0 • convexity/concavity: MI(k) • forward/backward scattering: FG(Θ) • hot spot: H(ρc) • MI(k) • modified Minnaert function • k = 1: Lambertian • k > 1: decreasing with viewing angle (bell shaped) • k < 1: increasing with viewing angle (bowl shaped)

7. Joint Retrieval of Aerosol Load and Surface Reflectance Using MSG/SEVIRI Observations

8. Joint Retrieval of Aerosol Load and Surface Reflectance Using MSG/SEVIRI Observations • Aerosol Types • spherical • organized by ratio between large and small particles • asymmetry factor crucial • non-absorbing • moderately absorbing • strongly absorbing • non-spherical • organized by imaginary part of refractive index • single scattering albedo crucial • small • medium • large • Validation against MODIS retrievals and AERONET data

9. Satellite-Derived Direct Aerosol Effect of Aerosols Above Clouds • Aerosol below cloud: cloud prevents aerosol-radiation interaction => small negative forcing • Aerosol on cloud: indirect effect • Aerosol above cloud: cloud acts like increasing surface albedo => potentially large positive forcing • Identification of aerosols above clouds • Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) • distinguish between aerosol and cloud layers • retrieve vertical position • OMI UV-Aerosol Index (UVAI) • sensitive to absorbing aerosols • UVAI > 0.9 used • MODIS liquid cloud fraction (LCF) • LCF > 0.2 used • results consistent with expectation • China, southern Africa (biomass burning) • high-latitude regions (snow/ice cover, large SZAs) • Global data equatorwards of 60° latitude at a resolution of 0.25°x0.25° for 2005 used • Local planetary albedo (LPA) computed from CERES data • Linear reduction of LPA for OMI UVAI = 0.9-2.1