1 / 14

CO over South America

CO over South America. Modeling inter annual variability of biomass burning emissions. Pim Hooghiemstra & Maarten Krol 28 November 2011 – TM meeting. Motivation. Inter annual variability is observed in MOPITT columns over the Amazon region. Goal.

callia
Download Presentation

CO over South America

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CO over South America Modeling inter annual variability of biomass burning emissions PimHooghiemstra & Maarten Krol 28 November 2011 – TM meeting

  2. Motivation • Inter annual variability is observed in MOPITT columns over the Amazon region.

  3. Goal • Robust monthly total CO emissions on high resolution for 2006 – 2010 over South America • Using TM5-4D-Var with 1ox1o zoom over South America • Prior from EDGARv4.1, GFED3.1, TM4-Crete • MOPITT columns / NOAA surface observations

  4. Prior simulation vs observations • Emissions seem too low a priori (large region) • Over main BB regions, prior is too high in 2007 and 2010

  5. Posterior fit with MOPITT • 4D-Var system brings model simulation close to observations. Fit is very good for large regions, a bit less for small regions.

  6. Emissions 1ox1oregion • Large variability in emissions from year to year • Emissions increase for all months

  7. Spatial patterns • 2007 & 2010: Emissions go down for deforestation regions Brazil • Other years, emissions increase (both BB and other sources)

  8. Validation with NOAA Much too high => assimilate NOAA obsalso

  9. Bias correction • A bias correction is needed to fit NOAA and MOPITT

  10. Fit with data • Fit with NOAA improves greatly! • Fit with MOPITT remains good!

  11. Effect on inferred emissions • Effect is only small

  12. Sensitivity studies • Emission estimates are robust • Using GFED3-2009 for 2010 makes a large difference • NOAA only inversion yields too high biomass burning emissions in September and too low background emissions

  13. Conclusions (1) • Robust IAV in BB emissions: Peak month emissions vary from 28 Tg CO in 2009 to 67 Tg CO in 2007. • BB emissions of GFED3.1 are too high in main deforestation regions in Brazil in 2007 & 2010. In other years, GFED3.1 seems a bit too low.Remark: difficult to quantify, we only optimize total. What about errors in the model?

  14. Conclusions (2) • Inferred emissions sensitive to total prior BB emission. • Bias correction is necessary to fit both MOPITT & NOAA observations. No significant difference in optimized emissions and fit with MOPITT, fit with NOAA improves significantly.

More Related