EGU ’08 Highlights

Vijay Natraj

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

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

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?

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)

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)

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

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

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