Folkert Boersma

1. Folkert Boersma Reducing errors in using tropospheric NO2 columns observed from space

2. Blond et al. (2007) SCIAMACHY EMEP Main use of satellite observations: estimating emissions of NOx • What is so uncertain about emissions? • quantities • locations • times • trends But we can see the NOx sources from space chem= 4-24 hrs Emissions

3. Satellite observations • Pros • sensitivity to lower troposphere • improving horizontal resolution • global coverage • Cons • daytime only • column only • clouds • sensitivity to forward model parameters assumptions

4. Retrieval method

5. Retrieval method • 3-step procedure • obtain slant column along average light path • separate stratospheric and tropospheric contributions • convert tropospheric slant column in vertical column In equation: Ns, Ns,st, Mtr are all error sources

6. Retrieval method aerosols surface pressure

7. ‘State-of-science’ van Noije et al., ACP, 6, 2943-2979, 2006

8. Systematic differences van Noije et al., ACP, 6, 2943-2979, 2006

9. Stratospheric column Accounting for zonal variability or not? 41.5°N E. J. Bucsela – NASA GSFC Model information Reference Sector

10. Stratospheric column Without correction errors up to 11015 molec.cm-2 March 1997

11. Stratospheric column • Alternative: limb-nadir matching • Limb observes zonal variability • Stratospheric column estimate may introduce offsets from limb-technique Courtesy of E. J. Bucsela – NASA GSFC A. Richter et al.– IUP Bremen

12. Stratospheric column • In summary • Reference sector method questionable • Assimilation & nadir-limb correct known systematic errors • Assimilation self-consistent; uncertainty ~0.2×1015 • Validation needed • - SAOZ network (sunrise, sunset) • Brewer direct sun (Cede et al.) in unpolluted areas

13. Air mass factor Retrieval method Tropospheric air mass factor Mtr- Computed with radiative transfer model and stored in tables Mtr = f(xa,b) xa = a priori tropospheric NO2 prf b = forward model parameters - cloud fraction - cloud pressure - surface albedo - aerosols ( - viewing geometry)

14. Air mass factor errors A priori profile • Large range in sensitivities between 200 & 1000 hPa, especially in the BL • Low sensitivity in lower troposphere for dark surfaces • Clear pixel, albedo = 0.02 • Clear pixel, albedo = 0.15 • Cloudy pixel with fcl = 1.0, pcl = 800 hPa Eskes and Boersma, ACP, 3, 1285-1291, 2003

15. Air mass factor errors A priori profile from CTMs • Shapes reasonably captured by CTMs • Effect of model assumptions on BL mixing lead to errors <10-15% • Models are coarse relative to latest retrievals Martin et al., JGR, 109, D24307, 2004

16. Jun-Aug 2004 SCIAMACHY NO2 MOZART-2 AMF Air mass factor errors Effect of choice of CTM on retrieval MOZART-2 (2°2°) vs. WRF-CHEM (0.2°0.2°) A. Heckel et al. (IUP Bremen)

17. Jun-Aug 2004 SCIAMACHY NO2 WRF-Chem AMF Air mass factor errors Effect of choice of CTM on retrieval Effect ~10% A. Heckel et al. (IUP Bremen)

18. Air mass factor sensitivities M = wMcl+ (1-w)Mcr Cloud fraction Boersma et al., JGR, 109, D04311, 2004 Cloud pressure Albedo

19. AMF errors – surface albedo M = M/asf asf asf = 0.02 (GOME-TOMS) (%)

20. AMF errors – cloud fraction M = M/fcl fcl fcl = 0.05 (FRESCO) (%)

21. AMF errors – cloud pressure M = M/pcl pcl pcl = 50.0 (FRESCO) (%)

22. Martin et al., JGR, 108, 4537, 2003 Air mass factor errors - aerosols • If NO2 present, then also aerosol • Aerosols affect radiative transfer dep. on particle type

23. Indirect correction through M=wMcl+(1-w)Mcr Direct correction Air mass factor errors - aerosols • Aerosols affect radiative transfer • Cloud fraction sensitive to aerosols ( = +1.0  fcl +0.01)

24. Air mass factor errors – surface pressure • Surface pressure from CTMs (2°× 3°) • Strong differences with hi-res surface pressures GOME SCIAMACHY Schaub et al., ACPD, 2007

25. Error top-10 • Cloud fraction errors ~30% • Surface albedo ~15% + resolution effect? • Vertical profile ~10% + resolution effect? • Aerosols ~10%? More research needed • Cloud pressure ~5% • Surface pressure depends on orography

26. Is there a recipe for reducing all these errors? 1. Better estimates of forward model parameters A good example: surface pressures (Schaub et al.) What should be done: - a validation/improvement of surface albedo databases - a validation/improvement of cloud retrievals - investigate effects aerosols on (cloud) retrievals - validation vertical profiles - higher spatial resolution (sfc. albedo, pressure, profile)

27. Is there a recipe for reducing all these errors? 2. How do we know if better forward model parameters improve retrievals? We need an extensive, unambiguous and well-accessible validation database Testbed for retrieval improvements: - in situ aircraft NO2 (Heland, ICARTT, INTEX) - surface columns (SAOZ, Brewer, (MAX)DOAS) - in situ profiles (Schaub/Brunner) - surface NO2 (regionally)

28. Is there a recipe for reducing all these errors? • 3. Towards a common algorithm/reduced errors? • Difficult! • Without testbed, verification of improvements is hard • Improvements for one algorithm may deteriorate other algorithms, depending on retrieval assumptions • Improved model parameters may work for some regions and some seasons, but not for others

29. Is there a recipe for reducing all these errors? • 3. Towards a common algorithm/reduced errors? • Worth the try! • Systematic differences can be reduced (emission estimates) • Requires ‘scientific will’ – enormous task • Collection of validation set • Flexible algorithms digesting various model parameters • Intercomparison leading to recommendations • Fits purpose ACCENT/TROPOSAT