Dynamical Influence on Inter-annual and Decadal Ozone Change @iup.physik.uni-bremen.de Sandip...

Dynamical Influence on Inter-annual and Decadal Ozone Change

http://www.iup.physik.uni-bremen.desandip@iup.physik.uni-bremen.de

Sandip Dhomse, Mark Weber, J.P.

Burrows

Universtät Bremen FB1, Institüt für

Umweltphysik (iup)

Outline

• Introduction• Data used• Inter-annual variability• Decadal variability• Tele-connection patterns

(Introduction)• Summary and Conclusion• Outlook

sandip@iup.physik.uni-bremen.de 11/11/03

No abrupt change in chemical composition

Introduction

Sun - at the same position

Total Ozone is higher in NH (spring) than SH (spring)

Low TOZ

In tropics

Relatively High TOZ in tropics during SH spring

Wave activity controls

• Stratospheric

circulation

• Stratosphere

Troposphere

Exchange

Holton et al., 1995

SH winter- colder

Mean Winter temp. (shaded) And zonal wind (contour) for NH and SH

SH – polar jet stronger

Tropopause colder during NH winter

Planetary waves

Planetary waves are large-scale distortions to the mean flow

The flow (black) meanders across latitude circles (blue).

Examples of wave patterns

Wave 1 pattern

Wave 2 pattern

Brewer-Dobson circulation

•Breaking planetary waves apply a FORCE to the winds-

decelerates the speed

•Pressure gradient force- remains unaffected

•Corioli’s force - REDUCED so there is a net force towards the

•Air RISES over the equator, drifts steadily POLEWARD

(while meandering around the latitude circles) and SINKS at

the poles

Two components of EP flux are calculated as,(Andrews,et.al 1987)

)''/'']/)cos()cos({[cos

)''/''(cos

uwvuuafaF

uvvuaF

And its divergence is

)cos()cos( 1

• Eliassen Palm (EP) flux vector is a measure of the upward propagating momentum carried by planetary waves

• The divergence of EP flux gives the volume where momentum is deposited

Measuring the wave activity

Heat flux

Momentum flux

Measuring the wave activity

)))(((''

TTvvmeanTv

vvuumeanvu

zonalzonal

''TvF z

Data used

Meteorological data set • ERA40 - 23 pressure levels• ERA15 - 17 pressure levels• UK Met. Office - 22 pressure levels • ECMWF - 21 pressure levels• NCEP - 17 pressure levels

Ozone data set• GOME - total ozone data • TOMS - total ozone data

sandip@iup.physik.uni-bremen.de 11/11/ 03

Inter-annual variation of flux and Ozone

High flux - increase in Brewer-Dobson circulation - more transport

Record high lower stratosphericheat flux on 20th/21stSeptember (ERA40 1960-2002)

Splitting of the polar vortex on 26th September 2002

First major stratospheric warming in SH

wave activity and winter gain in ozone

Weber et al. 2003

High correlation between winter heat flux (wave activity) and spring/fall ozone ratio

Winter ozone gain in Antarctic 2002 presents an intermediate case between other Antarctic winters and cold Arctic winters (higher contribution from transport)

High Heat flux –Low PSC

Does dynamics the same? Or Chemistry is changing?

Model Differences

ERA40 +ERA15

Decadal Variation?

And Difficulties in trend analysis

Northern Hemisphere

Tropics (50 hPa)

One of longest westerly phases of QBO (1993-1996)

tropics

Mid-latitudes

No SSW since last 17 years in Jan.

-1.2 K/decade in Nov. and Dec.

SH – pre-satellite period – Problem?

PSC volume using ERA40 data

T <195 K (PSC volume)

SH (JAN-DEC)NH (JUL-JUN)

Days (Jul-Jun)

195 K- PSC temperature

SH –colder temperature

Days (Jan-Dec)

No significant trend in heat flux

Different models – different trends

No significant trend in any dataset in heat flux as well as EP Flux

Summary

• Strong correlation between seasonal heat flux and total ozone in March.

• No significant trend on seasonal scale in heat flux.

• Maximum cooling trend is in November and January ( -1.2 K/decade) , but there is not trend in heat flux.

• Different models, different periods lead to different trends.

Bremen- 10/23/2003

Arctic Oscillation

From National Geographic Magazine March-2000

Arctic Oscillation

source : http://www.ldeo.columbia.edu

Conclusion

• There are no significant trends in 2D (latitude & altitude) analysis of the data.

• 3D analysis of data will be useful to find the dependence of ozone on different tele-connection patterns.

• EOF analysis is good tool for 3D analysis.

Outlook

• Study ozone dependence on different tele-connection pattern using EOF technique.

• Find out different patterns in the ozone variability using GOME vertical profile data (neural network) , TOMS, SAGE, POAM, ozonesonde datasets.

Thank you very much for your kind attention!!!

Dynamical Influence on Inter-annual and Decadal Ozone Change @iup.physik.uni-bremen.de Sandip...

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