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Correlation Properties of Global Satellite and Model Ozone Time Series

This study investigates the correlation properties of global satellite and model ozone time series using spectral analysis and detrended fluctuation analysis (DFA). The aim is to validate model simulations and improve predictability of arctic stratospheric ozone loss and its climate interactions. The study compares empirical data from satellite observations and model output, highlighting differences and areas for further validation.

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Correlation Properties of Global Satellite and Model Ozone Time Series

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

  2. RECONCILEReconciliation 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. MethodsSpectralanalysis Spectral weight determination: QBO annual semi-annual

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

  8. MethodsDetrended fluctuation analysis (DFA) • integrated time series : y(k) • local trend: yn(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 datasetsSpectral analysis NIWA TOMS Nimbus-7 QBO peak annual peak semi-annual peak

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

  12. Model data • LMDz-REPROBUS Chemistry-ClimateModel • 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! annual QBO semi-annual

  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 datasetsSpectral analysis NIWA monthly CCM • Spectral weight of the semi-annual peak • Shifted and stronger peak over the Indian ocean • Strong peak in Tibet

  16. Comparison of the empirical and model datasetsSpectral analysis NIWA monthly CCM • Spectral weight of the annual peak • Equatorial area is different

  17. Comparison of the empirical and model datasetsSpectral analysis CCM NIWA monthly • Spectral weight of the QBO peak • No QBO peak in the 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 QBO nudging Spontaneous QBO SPARC Report on the Evaluation of Chemistry Climate Models, June 2010

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

  21. Comparison of the empirical and model datasetsDetrended fluctuation analysis NIWA monthly CCM • Global map of the α exponent values

  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

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