Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model. - PowerPoint PPT Presentation

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Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

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Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.
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Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model.

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  1. Interannual variability in CO and ozone as seen by TES and MLS and the GMI Combo model. Jennifer A. Logan, Inna Megretskaia, Lin Zhang, and the GMI, TES, and MLS teams Harvard University NASA/Goddard JPL TES meeting, Feb. 24, 2009.

  2. The Global Modeling Initiative (GMI) ‘Combo’ Model • Combo = tropospheric + stratospheric chemical mechanism • GEOS-4 meteorological fields • 2° x 2.5° resolution • Aura4 simulation, 2004-2007 • Model output is available to the community for Aura science • Output saved along satellite track at overpass times • GFED2 biomass burning emissions for 2004-2007 • MEGAN inventory for biogenics • Lightning NOx – regional scaling to average OTD/LIS lightning spatial patterns (Allen and Pickering). • GEOS-Chem similar, without stratospheric chemistry.

  3. Satellite data • TES V003 • Validated with ozonesondes by Lin Zhang, similar high bias to V002, 3-10 ppb. • Filters applied to remove “C-shaped” ozone profiles (Lin Zhang) • Omit data with cloud optical depth >2 for pressure <750 hPa • TES AKs and prior applied to model output • Uniform prior used for 30°N-30°S • MLS data V2.2 • MLS AKs applied to model (makes very little difference)

  4. Outline • Use TES CO to evaluate model performance in lower troposphere to gain insight into reasons for discrepancies with MLS in the upper troposphere • Can the model match the interannual variability in tropical CO and ozone, and if not, why not?

  5. GMI model compared to CO at NOAA/GMD surface sites in the tropics, 2005-2006 Data Model

  6. GMI model and MOZAIC aircraft data for CO in the tropics Cairo Abidjan Delhi Caracas Data Model – 2005 Model - 2006 Locations with enough aircraft data for model evaluation

  7. Jul Aug. Sep Oct. Nov. CO in the tropics at ~700 hPa, July-Nov. 2005SH biomass burning season. TES model model-TES Too much export in easterlies in lower trop., convection N. of equator – similar problems with MOPITT comparisons (Junhua Liu, GEOS-Chem runs) Fire emissions too low over Africa

  8. Jul Aug. Sep Oct. Nov. CO in the tropics at ~700 hPa, July-Nov. 2005SH biomass burning season. TES model model-TES Too much export in easterlies in lower trop., convection N. of equator – similar problems with MOPITT comparisons (Junhua Liu, GEOS-Chem runs) Fire emissions too low over Africa

  9. Jul Aug. Sep Oct. Nov. CO in the tropics at ~150 hPa, July-Nov. 2005 MLS model model-MLS Observed max. over India is missing in model. Model is biased low everywhere, except where is it too high in equatorial band – same problem in LT Model maximum from convection is one month late, implying not enough convection in October.

  10. Time series over region of Asian maximum in UT CO 2005 2006 2007 The UT maximum at 150 hPa in June-August is too late in the model. Same problem at 215 hPa. Papers on high CO seen by MLS over the Himalayas, and effect of Asian monsoon: Li et al., 2005, Fu et al., Randel et al., Park et al. 2008, 2009

  11. Jul Aug. Sep Oct. Nov. CO in the tropics at ~700 hPa, July-Nov. 2006 TES model model-TES Similar features to 2005: too much export in equatorial easterlies too low BB emissions over Africa too high CO near Andes (this appeared with switch to MEGAN biogenic emissions) Huge difference in BB emissions from Indonesia (Logan et al. 2008, Nassar et al. 2009)

  12. CO over South America in LT (TES) and UT (MLS) Model lower than MLS in UT, peaks one month late in 2005 and 2006. Suggest a problem with timing of convection, since LT looks good. 2005 2006 2007 Bench warm-up Good match with TES in LT in 2005-2006, GFED too high in 2007 Model w/AK suggests lower CO in Aug. and Sept. 2005 – caused by lack of sensitivity. GFED CO emissions TES Model w. AK Model w/out AK

  13. S. America - CO from MOPITT and TES in the lower trop. 2005 2006 2007 MOPITT data also show 2006 had lowest CO over S. America in BB season.

  14. CO over Southern Africa in LT and UT 2005 2006 2007 GFED emissions too low over S. Africa, but timing looks OK. UT max. is a month too late over S. Africa also. GFED emissions similar each year GFED CO emissions

  15. Dec. Jan Feb. Mar. Apr. CO in the tropics at ~700 hPa, Dec. 2005-April 2006NH biomass burning season. TES model model-TES High CO near the Andes Largest differences related to BB emissions in N. Africa in March – April.

  16. Dec. Jan Feb. Mar. Apr. CO in the tropics at ~700 hPa, Dec. 2005-April 2006 MLS model model-MLS

  17. Dec. Jan Feb. Mar. Apr. CO in the tropics at ~700 hPa, Dec. 2006-April 2007 BB emissions from N. Africa appear to be too high, or transport out of source region too strong. BB CO is transported south and west

  18. CO time series over Equatorial Africa 2005 2006 2007 Model CO decreases a month too soon in LT, implying emissions in February are too low. Model UT maximum in Feb.-April similar to timing in MLS data. But since CO decreases too soon in the LT, caution is needed in interpreting the MLS comparison. x GFED CO emissions (N. Africa)

  19. CO time series over Indonesia 2005 2006 2007 See Nassar et al. (2009) for detailed discussion of Indonesia in late 2005 and 2006 (El Nino).

  20. CO tape recorder, 10ºN- 10ºS MLS Means for 2005 subtracted from time series GMI Combo CO from Indonesian fires The GMI Combo model looks pretty good. Interannual variability driven by CO fire emissions, especially from Indonesia. Interannual variability in emissions in NH fire season apparent (Jan.-April). Update of Schoeberl et al. (GRL, 2006), see also Combo model study of Duncan et al. (JGR, 2007), with GCM met. fields.

  21. Issues with V003 ozone data (and V002) Some retrievals had “C-shaped” profiles, identified in V002 by Helen Worden, in validation with IONS data over N. America. Test devised to remove them. The original C-test removed some valid looking profiles in the tropics over e.g., North Africa. Lin Zhang devised a better test, based on validation of V003 data. See example to left.

  22. Jul Aug. Sep Oct. Nov. Ozone in the tropics, July-November, 2005 TES model Model - TES The worst model agreement globally is in the S. Atlantic in Sept.-Nov. (sonde data shows the same) The problem is confined to Atlantic sector. Outflow to Indian Ocean is OK

  23. Jul Aug. Sep Oct. Nov. Ozone in the tropics, July-November, 2006 TES model Model - TES Discrepancies are much smaller in 2006. TES is lower in 2006, and model is higher.

  24. Jul Aug. Sep Oct. Nov. July-November, interannual variability in TES data 2005 2006 2007

  25. Ozone in Oct 2006, 6º-14ºS TES vertical resolution ~6 km! GMI S. Amer. Africa GMI Ascension Island sondes, 8°S Problem is in LT not UT. Ozone too low over Africa, so outflow from Africa does not supply S. Atlantic with enough ozone in easterlies

  26. Ozone over South America, Nov. 2004-Dec. 2008 In the model, ozone is related to lightning NOx (but not so simple) LIS data show more lightning in 2006 than 2005. TES data and OMI/MLS data show higher ozone in the South Atlantic region in 2005 and 2007. Independent data from OMI/MLS confirms the IAV in the TES ozone data. 200 hPa 500 hPa Lightning NOx Sept. in red Sauvage et al., Martin et al. – lightning NOx is main source of ozone in the tropics

  27. Ozone over Southern Africa, Nov. 2004-Dec. 2008 Lightning NOx Oct.

  28. Dec. Jan Feb. Mar. Apr. Ozone in the tropics, Dec. 2005-April 2006 Discrepancies smaller than in Sept. – Nov.

  29. North Africa

  30. Indonesia For a detailed analysis of this region using GEOS-Chem, and the effects of the El Nino in late 2006 (and the huge fires in Borneo), see Nassar et al. (2009).

  31. Tropospheric ozone column from TES and OMI/MLS OMI/MLS products: OMI total O3 column - MLS strat. ozone OMI scans, so has better global cover than MLS. Variability in TES and OMI/MLS products is essentially the same. TES column (integrated profile) Schoeberl product (uses trajectories to fill in MLS) Ziemke/Chandra product

  32. Conclusions • Interpretation of MLS CO in upper trop. requires careful analysis of CO data in the lower trop., as errors in LT propagate to the UT. • Over S. America, S. Africa GEOS-4 max. convection appears to be a month too late, but hard to tell for N. Africa as errors in LT CO. • Need to look at convective mass fluxes in model • TES reveals interannual variability in tropical ozone, but model has problems matching this in S. Atlantic, likely due to lightning NOx. • See poster by Junhua Liu for analysis of model meteorology and NOx in 2005/2006 • TES and OMI/MLS products show similar variability