correlation properties of global satellite and model ozone time series viktória homonnai, imre m....
TRANSCRIPT
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 https://www.fp7-reconcile.eu/reconcileaircraft.html
Activities
• Aircraft campaign
• Match campaign
• Laboratory experiments
• Modelling activities
https://www.fp7-reconcile.eu/reconcilematch.htmlhttps://www.fp7-reconcile.eu/reconcilelabexp.html
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
MethodsSpectral analysis
semi-annual
annualQBO
Spectral weight determination:
Quasi-biennial oscillation
quasi-periodic oscillation of the equatorial zonal wind in the stratospheremean period: 28-29 months
red: westerly windsblue: easterly winds
http://ugamp.nerc.ac.u
k/hot/ajh/qboanim
.movie
Baldwin, M. P., et al. (2001), The quasi-biennial oscillation, Rev. Geophys., 39(2), 179–229
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
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
Comparison of the two empirical datasetsSpectral analysis TOMS Nimbus-7NIWA
QBO peak
annual peak
semi-annual peak
Comparison of the two empirical datasetsDetrended 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 1960-2006
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 datasetsSpectral 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 datasetsSpectral analysis
Spectral weight of the annual peak Equatorial area is different
NIWA monthly CCM
Comparison of the empirical and model datasetsSpectral 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 datasetsDetrended 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 datasetsDetrended fluctuation analysis
Global map of the α exponent values
NIWA monthly CCM
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