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Satellite Observations of Tropospheric Composition: Current and Future Research

Satellite Observations of Tropospheric Composition: Current and Future Research. Paul Palmer, University of Leeds www.env.leeds.ac.uk/~pip. Model values for preindustrial ozone. }. European mountain-top observations [Marenco et al., 1994]. Ozone exceedances of 90 ppbv, summer 2003 (#days).

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Satellite Observations of Tropospheric Composition: Current and Future Research

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  1. Satellite Observations of Tropospheric Composition: Current and Future Research Paul Palmer, University of Leeds www.env.leeds.ac.uk/~pip

  2. Model values for preindustrial ozone } European mountain-top observations [Marenco et al., 1994] Ozone exceedances of 90 ppbv, summer 2003 (#days) Observed rise in ozone background at northern midlatitudes 60 50 0-1; 1-5; 5-10; >10 40 30 20 10 0 1870 1890 1910 1930 1950 1970 1990 Correlation of high ozone with temperature is driven by: 1) Stagnation, 2)Biogenic hydrocarbon emissions, 3)Chemistry

  3. hv O3 NO2 NO OH HO2 HC+OH  HCHO + products Tropospheric O3 is an important climate forcing agent NOx, HC, CO Level of Scientific Understanding Natural VOC emissions (50% isoprene) ~ CH4 emissions. IPCC, 2001

  4. Pierce et al, JGR, 1998 EPA BEIS2 MEGAN 2.6 Tg C [1012 atom C cm-2 s-1] 3.6 Tg C Bottom-up Isoprene emissions, July 1996 E = A∏iγi Emissions (x,y,t); fixed base emissions(x,y); sensitivity parameters(t) Guenther et al, JGR, 1995 GEIA 7.1 Tg C Guenther et al, ACP, 2006

  5. Global Ozone Monitoring Experiment (GOME) &the Ozone Monitoring Instrument (OMI) Launched in 2004 • GOME (European), OMI (Finnish/USA) are nadir SBUV instruments • Ground pixel (nadir): 320 x 40 km2 (GOME), 13 x 24 km2 (OMI) • 10.30 desc (GOME), 13.45 asc (OMI) cross-equator time • GOME: 3 viewing angles  global coverage within 3 days • OMI: 60 across-track pixels  daily global coverage • O3, NO2, BrO, OClO, SO2, HCHO, H2O, cloud properties

  6. 0.5 2 2.5 1 0 1.5 [1016 molec cm-2] GOME HCHO columns Biogenic emissions Biomass burning July 2001 Data: c/o Chance et al * Columns fitted: 337-356nm * Fitting uncertainty < continental signals

  7. May Jun Jul Aug Sep 1996 1997 1998 1999 2000 2001 GOME HCHO column [1016 molec cm-2] 0.5 2 2.5 1 0 1.5 Palmer et al, JGR, 2006.

  8. hours hours HCHO h, OH OH kHCHO ___________ HCHO EVOC = (kVOCYVOCHCHO) WHCHO Isoprene a-pinene propane 100 km Distance downwind VOC source Relating HCHO Columns to VOC Emissions VOC Local linear relationship between HCHO and E EVOC: HCHOfromGEOS-CHEM and MCM models Palmer et al, JGR, 2003.

  9. NOx = 1 ppb NOx = 0.1 ppb MCM HCHO yield calculations 0.5 Isoprene C5H8+OH(i) RO2+NOHCHO, MVK, MACR (ii) RO2+HO2ROOH ROOH recycle RO and RO2 Cumulative HCHO yield [per C] Higher CH3COCH3 yield from monoterpene oxidation  delayed (and smeared) HCHO production HOURS 0.4 Parameterization (1ST-order decay) of HCHO production from monoterpenes in global 3-D CTM  pinene ( pinene similar) DAYS Palmer et al, JGR, 2006.

  10. GEOS-CHEM Global 3D CTM PAR, T Emissions Modeling Overview MCM: parameterized HCHO source from monoterpenes and MBO MODEL BIOSPHERE MEGAN (isoprene) Canopy model Leaf age LAI Temperature Fixed Base factors GEIA Monoterpenes MBO Acetone Methanol Monthly mean AVHRR LAI

  11. Seasonal Variation of Y2001 Isoprene Emissions May Aug Jun Sep 1012 atom C cm-2s-1 Jul 7 0 3.5 MEGAN GOME MEGAN GOME • Good accord for seasonal variation, regional distribution of emissions (differences in hot spot locations – implications for O3 prod/loss). • Other biogenic VOCs play a small role in GOME interpretation Palmer et al, JGR, 2006.

  12. May Jun Jul Aug Sep 1996 1997 1998 1999 2000 Relatively inactive 2001 [1012 molecules cm-2s-1] 10 0 5 GOME Isoprene Emissions: 1996-2001 Palmer et al, JGR, 2006.

  13. Surface temperature explains 80% of GOME-observed variation in HCHO G98 fitted to GOME data GOME HCHO Slant Column [1016 molec cm-2] G98 Modeled curves NCEP Surface Temperature [K] Palmer et al, JGR, 2006. Time to revise model parameterizations of isoprene emissions?

  14. Tropical ecosystems represent 75% of biogenic NMVOC emissions 1996 1997 1998 1999 2000 2001 What controls the variability of NMVOC emissions in tropical ecosystems? Kesselmeier, et al, 2002 Importance of VOC emissions in C budget? GOME HCHO column, July

  15. OMI, 24/9-19/10, 2004 13x24 km2 monoterpene emission of Apeiba tibourbou 1500 40 [°C] 1000 30 (µmol m-2 s-1) PAR temperature 500 20 10 6 limonene myrcene 5 b-pinene a-pinene 4 sabinene [sum of monoterpenes] emission rate (C) G93 for isop. (µg g-1 h-1) 3 2 1 0 4 4 (mmol m-2 s-1) transpiration assimilation (C) (mg g-1 h -1) 2 2 0 0 12:00 00:00 00:00 12:00 00:00 06:00 18:00 06:00 18:00 local time [hh:mm] Challenges: Cloud cover, biomass burning, and lack of fundamental understanding of NMVOC emissions… TES data @ 6km, 11/04 O3 Improved cloud-clearing algorithms and better spatial resolution data help. CO A more integrated approach to understanding controls of NMVOCs, e.g., surface data, lab data, MODIS Firecount O3-CO-NO2-HCHO-firecount correlations import to utilize when looking at the tropics TES data c/o Bowman, JPL

  16. “Expect harmful levels of ozone and PM2.5 over the next couple of days; please keep small children and animals inside. Transatlantic pollution represent 20% of today’s ozone.” O(10s km) O(few kms) O(1 km) Beta NO2 column data, OMI, August 2004 NAEI NOX emissions as NO2, 2002 Resolution of new satellite data allows UK air quality monitoring from space For example, NO2 Horizontal spatial scales

  17. Many scientific milestones on the way to operational NCWP UKCA: global 3-D coupled chemistry-aerosol-climate model UM mesoscale version of UKCA over UK (JCMM, UKMO) Air-quality-climate links Improved understanding of surface fluxes, aerosol-chemistry processes, and dynamics… Numerical chemical weather prediction (NCWP)  Public consumption Data assimilation/inverse modelling tools for interpreting satellite data. Eg, estimating inter-species error covariance, DATA

  18. TheOrbitingCarbonObservatory(OCO) “First global space-based measurements of CO2 with the precision and spatial resolution needed to quantify carbon sources and sinks” Launch in 2008 2-year mission • Spectroscopic observations of CO2 (1.61 m and 2.06 m) and O2(0.765 m)to estimate the column integrated CO2 dry air mole fraction, • XCO2= 0.2095 x (column CO2) / (column O2) • Precisions of 1 ppm on regional scales • Global coverage in 16 days (nadir 1x1.5 km footprint) • JPL-based instrument: PI D. Crisp; Deputy PI: C. Miller (Crisp et al, 2004)

  19. Geostationary and L1 mission concepts 0.8 L1 0.6 0.4 0.2 0.0 • Continuous mapping of tropospheric columns of O3, AOD, CO, HCHO, NO2, SO2 at km-scale resolution • Continental-scale for Geo, full sunlit disk for L1 • GeoTROPE and Cameo, Janus GEO x x/100 sun * L1 Midday sun-glint screening SEVIRI, September 1999 0.55µm AOD. C/o Kerridge @ RAL L1 worked successfully for SOHO – solar physics satellite

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