1. Perspectives for a stratospheric NO2product from OMI

2. Dutch OMI NO2 algorithm: data assimilation TM4 • NO2 in TM4 updated by OMI observations • Observations over polluted areas obtain little weight • ECMWF meteo captures stratospheric dynamics

3. Validation of OMI stratospheric NO2 • Independent data • NDACC • SAOZ • Remote locations • Techniques • - Twilight DOAS • - Errors: radiative transfer, time adjustment (~20%) • - Direct Sun FTIR • - Errors: a priori profiles, X-section (~30%)

4. SZA=90 AMF18 SAOZ SAOZ data Scoresby Celarier et al. [2008] OMI overpass Chemical box-model (SLIMCAT) converts twilight measurements to column value close to OMI overpass

5. Intercomparison SAOZ & FTIR Sodankyla: very good agreement Jungfraujoch: reasonable after Demoulin replaced FTIR polluted a priori NO2 profile with AFGL Izana: modest due to (Gil) “problems in illumination of the detector and stray light wrongly estimated” Improved Izana SAOZ-data: +15% Better match with FTIR and OMI

6. Bias < 1×1015 molec.cm-2 RMS < 13% DOMINO > SP Seasonal bias? Evaluation of OMI stratospheric NO2 data • DOMINO • Standard Product

7. Evaluation of OMI stratospheric NO2 data Average over all ground-based techniques

8. Evaluation of other KNMI stratospheric NO2data Hendrick et al, ACP, 2012

9. Evaluation of other KNMI stratospheric NO2data Hendrick et al, ACP, 2012

10. Preliminary conclusions • Jungfraujoch: KNMI GOME(-2), SCIAMACHY: no bias • Jungfraujoch: DOMINO v1+0.5 1015molec. cm-2 • *** conclusions for version 1 (2010) *** • |SP – ground| < 0.2 ×1015 molec.cm-2 • |DP – ground| < 0.3 ×1015 molec.cm-2 Can we learn anything from these data?

11. FTIR DOMINO SP Standard Product (wave fitting to 24-hr data composite) Day-to-day variability MARCH 2005 9 12 14 17 21 DOMINO captures collapse of polar vortex

12. Total Ozone DOMINO SP Pot. Vorticity Temperature 9 12 14 17 21

13. Diurnal variation • Use OMI’s wide swath: multiple overpasses per day • Infer stratospheric increase rate 70 N OMI observes diurnal increase in stratospheric NO2

14. Annual periodicity Annual periodicity QBO Vortex Vortex Vortex Vortex Vortex Trends in OMI stratospheric NO2

15. Trends in OMI stratospheric NO2 NH SH Mid-latitudes: trend dominated by seasonality Tropics: trend dominated by Quasi-Biennual Oscillation (QBO) QBO: oscillation (23-34 months) in equatorial zonal winds modulates vertical transport of NOy in Tropics

16. Stratospheric NO2 [1015 molec/cm2] Trends in OMI stratospheric NO2 Fitting model: Offset + linear trend + 4,6,12 month harmonic term + QBO QBO: index (NCEP) and composite of 18, 24, 30 month-harmonics

17. Trends in OMI stratospheric NO2 Fitting model: same (no ENSO, no 11-yr Solar Cycle)

18. When can we detect long-term trends? Lauder NDACC station 1981-1999 +6 %/decade Liley et al.: +5%/decade Fit model: Offset Trend 4,6,12 month harmonics QBO Solar Index ENSO El Chicon & Pinatubo After Liley et al. 2000

19. When can we detect long-term trends? Lauder NDACC station 2000-2010 -6 %/decade Trend depends on period considered Not possible to establish effect of N2O trend on NO2

20. Trends over Lauder: ground-based vs. OMI NDACC: +3%/decade OMI: +1%/decade Variability in summer maxima Satellite observations complement ground-based data record Need longer records: include GOME, SCIAMACHY, and GOME-2

21. Summary and outlook • Satellite observations complement ground-based data record in monitoring stratospheric NO2 • For long-term trend analyses: need longer records • include GOME(-2) & SCIAMACHY • Test stratospheric CTMs: • Day-to-day variability in stratospheric NO2 associated with stratospheric wave activity • Diurnal variation of stratospheric NO2 • QBO and trends