1. Correlation properties of global satellite and model ozone time series Viktória Homonnai, Imre M. Jánosi Eötvös Loránd University, Hungary Data: LATMOS/CNRS

2. RECONCILE Reconciliation of essential process parameters for an enhanced predictability of arctic stratospheric ozone loss and its climate interactions 17 partners from 9 countries

3. Activities Aircraft campaign Match campaign Laboratory experiments Modelling activities https://www.fp7-reconcile.eu/reconcileaircraft.html

4. Activities Aircraft campaign Match campaign Laboratory experiments Modelling activities https://www.fp7-reconcile.eu/reconcilematch.html https://www.fp7-reconcile.eu/reconcilelabexp.html

5. Activities Aircraft campaign Match campaign Laboratory experiments Modelling activities Chemistry-Transport Model Chemistry-Climate Model our task: model validation for correlation properties A CLaMS simulation of vortex evolution over the 2009/10 winter https://www.fp7- reconcile.eu/reconcilemodel. html

6. Methods Spectral analysis semi-annual annual QBO Spectral weight determination :

7. Quasi-biennial oscillation quasi-periodic oscillation of the equatorial zonal wind in the stratosphere mean period: 28-29 months red: westerly winds blue: easterly winds http://ugamp.nerc.ac.uk/hot/ajh/qboanim.movie Baldwin, M. P., et al. (2001), The quasi-biennial oscillation, Rev. Geophys., 39(2), 179–229

8. Methods Detrended fluctuation analysis (DFA)  integrated time series : y(k)  local trend: y n (k)  root-mean-square fluctuation:  slope of the linear fit on log-log scale  scaling exponent: α  α >0.5  long-term correlation  same information as autocorrelation function and Fourier spectrum  advantage: treat weak stationarity well

9. Empirical data Previous studies: spectral and detrended fluctuation analysis (DFA) of TOMS total column ozone (TO) data in 1978-1993 periods (Nimbus-7 satellite) Present studies: spectral analysis and DFA of NIWA TO database between 1978 and 2011 NIWA: global, daily, satellite- based data with spatial and temporal interpolation (vs. TOMS); offsets and drifts are corrected with ground-based measurements

10. Comparison of the two empirical datasets Spectral analysis TOMS Nimbus-7NIWA QBO peak annual peak semi-annual peak

11. Comparison of the two empirical datasets Detrended fluctuation analysis NIWA TOMS Nimbus-7

12. Model data LMDz-REPROBUS Chemistry-Climate Model Spatial resolution: 2.5° in latitude, 3.75° in longitude, 31 vertical levels (pressure coordinate) Temporal resolution: monthly mean data from 1960-2006 volume mixing ratio (vmr) data of ozone It was calculated total column ozone (TCO) from vmr:

13. Monthly data vs. Daily data Fourier-spectrum: in daily data there is a long tail → normalization! semi-annual annual QBO

14. Monthly data vs. Daily data DFA: offset because of the different window sizes (x-axis) and the different average fluctuations (y-axis), but after shift is the same

15. Comparison of the empirical and model datasets Spectral analysis Spectral weight of the semi-annual peak Shifted and stronger peak over the Indian ocean Strong peak in Tibet NIWA monthly CCM

16. Comparison of the empirical and model datasets Spectral analysis Spectral weight of the annual peak Equatorial area is different NIWA monthlyCCM

17. Comparison of the empirical and model datasets Spectral analysis Spectral weight of the QBO peak No QBO peak in the CCM NIWA monthly CCM

18. Quasi-biennial oscillation Big challenge  we need large spatial resolution, tropical convection, effects of gravity waves Baldwin, M. P., et al. (2001), The quasi-biennial oscillation, Rev. Geophys., 39(2), 179–229

19. QBO in the CCMs Spontaneous QBO QBO nudging SPARC Report on the Evaluation of Chemistry Climate Models, June 2010

20. Comparison of the empirical and model datasets Detrended fluctuation analysis 1 grid point tropics vs. extratropics tropics CCM NIWA monthly NIWA daily extratropics CCM NIWA monthly NIWA daily

21. Comparison of the empirical and model datasets Detrended fluctuation analysis Global map of the α exponent values NIWA monthlyCCM

22. Summary  Comparisons: two empirical datasets empirical vs. model output   QBO: not simple to build into a global climate model  Annual peak is stronger over the Equator in the CCM  DFA might be related to nonlinearity  good agreement next step in validation

23. Thank you for your attention!