Quantitative retrievals of NO 2 from GOME

1. Lara Gunn1, Martyn Chipperfield1, Richard Siddans2 and Brian Kerridge2 School of Earth and Environment Institute of Atmospheric Sciences Quantitative retrievals of NO2 from GOME 1. University of Leeds 2. Rutherford Appleton Laboratory

2. School of Earth and Environment Institute of Atmospheric Sciences Introduction • NO2 from GOME has been widely studied • Still the potential for a more accurate retrieval • Constrain the stratosphere (Chemical Data Assimialtion) • Use cloud and aerosol data from ATSR-2 (GRAPE)

3. Input Parameters (Atmospheric Profiles, GRAPE and GOME snr and slant columns) Radiative Transfer Model (Calculates Photon Path Lengths) Estimate of scaling factor and albedo Retrieval Model Optimal Estimation Calculate slant column and surface albedo New estimate of scaling factor and albedo Output (Tropospheric VCD, errors)

4. Input Parameters • Atmospheric Profiles • Stratosphere • Troposphere

5. Stratosphere • SLIMCAT 3D CTM with chemical data assimilation of long-lived species. • Data Assimilation from 1992 on of HALOE CH4, O3, HCl, H2O. • Detailed stratospheric chemistry scheme including heterogeneous reactions. • 7.5o x 7.5o x 24 levels (surface - 60km) • Forced using 6-hourly L60 ECMWF analyses (ERA-40 until 2001)

6. Troposphere • TOMCAT monthly mean profiles • Off-line tropospheric chemistry model forced by ECMWF winds • 64 longitudes 32 latitudes (T21) grid over 31 levels • Model description see Arnold et al. 2005

7. Input Parameters GRAPE

8. Cloud and Aerosol Data (GRAPE) • GRAPE Global Retrieval of ATSR cloud Parameters and Evaluation (NERC – RAL – Oxford) • State-of-the-art retrieval for the whole ATSR2 dataset. • Cloud optical depth, height, temperature and aerosol particle size, type, optical depth

9. Input Parameters GOME sun normalised radiance GOME slant columns - gdp and sao

10. Input Parameters (Atmospheric Profiles, GRAPE and GOME snr and sc) Radiative Transfer Model (Calculates Photon Path Lengths) Estimate of scaling factor and albedo Retrieval Model (Dual) Optimal Estimation Calculate slant column and surface albedo New estimate of scaling factor and albedo Output (Tropospheric VCD, errors)

11. Retrieval Model • Optimal Estimation theory xi+1=xi+(SE-1+KiTSE-1Ki)-1[KiTSE-1(y-F(xi))-Sa-1(xi-xa)] • xi – state vector [scaling factor, albedo] • y – measurement vector [slant column, sun normalised radiance]

12. Input Parameters (Atmospheric Profiles, GRAPE and GOME albedo) Radiative Transfer Model (Calculates Photon Path Lengths) Estimate of scaling factor and albedo Retrieval Model Optimal Estimation Calculate slant column and surface albedo New estimate of scaling factor and albedo Output (Tropospheric VCD, errors)

13. Radiative Transfer Code • Optimized version of GOMETRAN • Scattering cross sections, atmospheric profiles • Phase functions are calculated at Oxford • Simulates spectrum of radiance received by GOME • Calculates ‘weighting functions’ (derivatives with respect to the parameters retrieved) • Clouds as a scattering layer

14. Input Parameters (Atmospheric Profiles, GRAPE and GOME snr / sc) Radiative Transfer Model (Calculates Photon Path Lengths) Estimate of scaling factor and albedo Retrieval Model (Dual) Optimal Estimation Calculate slant column and surface albedo New estimate of scaling factor and albedo Output (Tropospheric VCD, errors)

15. Output

16. Show NO2 enhancements where excepted Background values are strongly negative Maybe due to profiles used in model

17. Show NO2 enhancements where excepted Background values are strongly negative Concs are too high Why are there bits missing??? 69

18. Stratosphere Problems

19. Gradients in the subtropics have increased Assimilated winds (here ERA-40) known to cause too much horizontal mixing causing age of age to be too old (Schoeberl et al, 2003) ppbv CH4 Free running (Run A) • Two Experiments • Free running model • Model constrained by chemical data assimilation of 4 species (CH4, HCl, H2O and O3) • Sequential sub optimal Kalman filter is used to assimilate HALOE observations of CH4, H2O, O3 and HCl. • Species are constrained by conservation of compact correlations in the model • (referencesKhattatov et al 2002, Chipperfield et al 2003) Pressure (hPa) latitude ppbv CH4 Assimilation (Run B) latitude

20. How does assimilation of a single long-lived tracer (CH4) and O3 lead to improvements in modelled NO2? • N2O is altered due to the preservation of its compact correlation with CH4 • NOy is altered through compact NOy:N2O correlation. • NOy is partitioned into the component species by model chemistry. • Changed O3 (assimilation) also affects NOy partitioning (e.g. NO:NO2 ratio) Assimilation Scheme

21. Key Obs Run A Run B ATMOS 3 SS100 10.3 N 16.3 W ATMOS 3 SR49 71.1 S 150.3 E Short-lived Species Validation Pressure (hPa) Pressure (hPa) NO2 vmr (ppbv) NO2 vmr (ppbv)

22. Retrieval Model Problems

23. Correct the stratosphere Quantify the errors Conclusions – Future work • NO2 tropospheric VCD background negative • NO2 tropospheric VCD are too high • Stratospheric column calculation could be to blame!