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Applications of space-borne Carbon-monoxide measurements in Atmospheric Chemistry and Air Quality
Maarten Krol, Wageningen University / SRON / IMAU
Jos de Laat (KNMI) & Annemieke Gloudemans & Ilse Aben (SRON) Jan Fokke Meirink (IMAU/KNMI) & Guido van der Werf (VU)
Research Question
How can satellite measurements help to improve our knowledge on the CO budget?
Emission Catagory Tg/year Uncertainty
Fossil + BioFuel 571 35-60%
Tropical Fires 170 70%
Savanna Fires 268 70%
Extra-Tropical Fires 29 70%
Biogenic 160 60%
Oxidation (NMHC) 734 60%
Oxidation (CH4) 796 20%
Total 2748
Stavrakou & Muller, 2006
Main Sink: oxidation by the OH radical
De Laat et al. GRL 2006
IMLM v6.3September 2003-August 2004
De Laat et al. GRL 2006
SCIAMACHY CO
• NIR (like TROPOMI)• Surface Sensitivity• Large Noise Errors:• Ice on detector• Weak Lines• Low NIR Albedo
Averaging reduces noise related errors!
Gloudemans et al. GRL 2006
1: Sampling model:at right place & time
2: Inaccurate measurementsget smaller weight
Biomass burningTracer studies
Considerable contributionfrom longe-range transporte.g. from South America
“Excess” CO column
de Laat et al., JGR, 2007
Improved Biomass Burning estimates
de Laat et al., JGR, 2007
Error estimate: 0.05-0.1x1018 #/cm2
SCIAMACHY CO over oceans
IMLM 7.3 September 2003 - December 2005 Over Land: CC < 20% Over Ocean: Cloud top > 800 hPa TM4 vs. SCIAMACHY
Modeled distribution consistent with SCIAMACHY observations
TM4 on average too low (NH) Measurements over clouds!
Remarks on modelling:
Models needed for quantitative analysis Data-assimilation:
estimate “uncertain parameters” (emissions, initial composition)
satellite applications: must ingest large amounts of data (SCIAMACHY, TES, MOPITT)
All data sources have their own errors and biases: bias correction is required (e.g. ECMWF)
Available techniques
Ensemble Kalman Filter (e.g. CarbonTracker)
4D-VAR (e.g. talk Ilse Aben, ECMWF) Application to CO underway...
TRANSCOM meeting: 2-6 June 2008, Utrecht
Conclusions
Development of models and assimilation techniques important for quantitative use satellite data
SCIAMACHY CO: promising development Sensitivity down to Earth surface TROPOMI CO: higher resolution, more
cloudfree pixels, 5x better sensitivity