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

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

Activities Aircraft campaign Match campaign Laboratory experiments Modelling activities

Activities Aircraft campaign Match campaign Laboratory experiments Modelling activities

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 reconcile.eu/reconcilemodel. html

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

Quasi-biennial oscillation quasi-periodic oscillation of the equatorial zonal wind in the stratosphere mean period: months red: westerly winds blue: easterly winds Baldwin, M. P., et al. (2001), The quasi-biennial oscillation, Rev. Geophys., 39(2), 179–229

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

Empirical data Previous studies: spectral and detrended fluctuation analysis (DFA) of TOMS total column ozone (TO) data in 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

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

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

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 volume mixing ratio (vmr) data of ozone It was calculated total column ozone (TCO) from vmr:

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

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

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

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

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

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

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

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

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

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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