1. Folkert Boersma Reducing errors in using tropospheric NO 2 columns observed from space

2. What is so uncertain about emissions? quantities locations times trends Main use of satellite observations: estimating emissions of NO x But we can see the NO x sources from space Emissions EMEP SCIAMACHY Blond et al. (2007)  chem = 4-24 hrs

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

4. Retrieval method

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

6. Retrieval method aerosols surface pressure

7. IUP BremenDalhousieKNMI/BIRA N s,st Ref. sector scaled to SLIMCAT strat. Ref. SectorData-assimilation in TM4 Cloud fractionFRESCO <0.2 cloud fraction; only cloud selection, no further correction GOMECATFRESCO Cloud pressureNot usedGOMECATFRESCO AlbedoGOME TOMS/GOME Profile shapeMOZART-2 run for 1997, monthly averages on 2.8 x 2.8 ° GEOS-Chem (2  x2.5  ) TM4 (3  x2  ) Temperature correction NoBased on U.S. std. atmosphere Based on ECMWF T-profiles AerosolsBased on LOWTRAN Based on GEOS- Chem No ‘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. Accounting for zonal variability or not? E. J. Bucsela – NASA GSFC 41.5°N Stratospheric column Model information Reference Sector

10. Without correction errors up to 1  10 15 molec.cm -2 Stratospheric column March 1997

11. Alternative: limb-nadir matching Limb observes zonal variability Stratospheric column estimate may introduce offsets from limb- technique Courtesy of E. J. Bucsela – NASA GSFC Stratospheric column 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 ~0.2×10 15 Validation needed - SAOZ network (sunrise, sunset) - Brewer direct sun (Cede et al.) in unpolluted areas

13. Retrieval method Tropospheric air mass factor M tr - Computed with radiative transfer model and stored in tables M tr = f(x a,b) x a = a priori tropospheric NO 2 prf b = forward model parameters - cloud fraction - cloud pressure - surface albedo - aerosols ( - viewing geometry) Air mass factor

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

15. A priori profile from CTMs Air mass factor errors 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. Effect of choice of CTM on retrieval Air mass factor errors MOZART-2 (2°  2°) vs. WRF-CHEM (0.2°  0.2°) Jun-Aug 2004 SCIAMACHY NO 2 MOZART-2 AMF A. Heckel et al. (IUP Bremen)

17. Effect of choice of CTM on retrieval Air mass factor errors Effect ~10% Jun-Aug 2004 SCIAMACHY NO 2 WRF-Chem AMF A. Heckel et al. (IUP Bremen)

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

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

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

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

22. If NO 2 present, then also aerosol Aerosols affect radiative transfer dep. on particle type Air mass factor errors - aerosols Martin et al., JGR, 108, 4537, 2003

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

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

25. Error top-10 1.Cloud fraction errors~30% 2.Surface albedo~15% + resolution effect? 3.Vertical profile~10% + resolution effect? 4.Aerosols~10%? More research needed 5.Cloud pressure~5% 6.Surface pressuredepends 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 NO 2 (Heland, ICARTT, INTEX) - surface columns (SAOZ, Brewer, (MAX)DOAS) - in situ profiles (Schaub/Brunner) - surface NO 2 (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