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Preliminary results of the seasonal ozone vertical trends at OHP France

. Preliminary results of the seasonal ozone vertical trends at OHP France. Maud Pastel, Sophie Godin- Beekmann Latmos CNRS UVSQ , France. Previous study. Multiple regression analysis using QBO 10 , 30 hpa , NAO, SFX, HF, AOD 550nm (1985 to 2010) .

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Preliminary results of the seasonal ozone vertical trends at OHP France

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  1. • Preliminaryresults of the seasonalozone vertical trends at OHPFrance Maud Pastel, Sophie Godin-Beekmann Latmos CNRS UVSQ , France NDACC Lidar Working Group, 4-8 Nov 2013, TMF, California

  2. Previousstudy Multiple regressionanalysisusing QBO 10 , 30 hpa, NAO, SFX, HF, AOD 550nm (1985 to 2010) 2 methods : PiecewiseLinear Trend ( PWLT) Equivalent Effective StratosphericClorine Merged profiles: LIDAR v4, MLS, HALOE v19, SAGE II v6, OHP Soundings R2 as a function of altitude and month • Similartrend resultsobtainedbetweenPWLT withturnaround in 1997 and EESC trend models • Ozone recovery visible on verticalprofile time series but signal barelysignificant Nair et al , ACP 2013

  3. Presentstudy (Preliminary) New Lidar data New satellites versions Times series up to 2012 included Update proxiesuntil 2012 included Use additionnalproxies Seasonalanalysis

  4. Stratospheric profiles measurements GOZCARDS (Global OZoneChemistryAnd Relatedtrace gas Data records for the Stratosphere) Merged of SAGE II, HALOE, Aura MLS, UARS MLS and ACE-FTSdata sets • LIDAR data (new version: v 5.0) have been reprocessedfrom 1985 untilnowwith the sametemperatureand pression profiles in order to gethomogenous data. Data available on the NDACC data base (ames format, soon in HDF) • For each comparaisons with satellites, LIDAR data have been convertedinto the same vertical resolution

  5. Monthlymean times series LIDAR SAGE II Consistencybetween SAGE II and GOZCARDS GOZCARDS • ODIN issystematicalylowerthan the LIDAR with a important biasfrom 28 to 40 km • Only MIPAS present a positive bias (of 4.6 %) from 35 to 45 km MLS GOMOS ODIN MIPAS

  6. Data quality(Relative drift in %/yr) SAGE II GOMOS ODIN Avg=0.69%%/yr Avg=-0.20%/yr Avg=0.01%/yr MLS MIPAS GOZCARDS Avg= -0.05%/yr Avg=0.46%/yr Avg=0.12%%/yr Drift generallywithin± 0.5%.y-1 in 25 – 40 km rangeexcept Aura MLS and MIPAS Long-termmeasurements stable at OHP latitude band ( non significant drifts except MIPAS)

  7. Anomalies times series % Between 16 to 21 km , all instruments presentstrong variations except GOZCARDS LIDAR, MLS and GOZCARDS present the smallest variations

  8. Spring (MAM) time seriesanomaly in % SAGE II GOMOS LIDAR ODIN GOZCARDS MLS

  9. Summer (JJA) time seriesanomaly in % SAGE II GOMOS LIDAR ODIN GOZCARDS MLS

  10. Autumn (SON) time seriesanomaly in % LIDAR SAGE II GOMOS ODIN GOZCARDS MLS

  11. Winter (DJF) time seriesanomaly in % SAGE II GOMOS LIDAR ODIN GOZCARDS MLS

  12. Regressionanalysis Proxiesusedfrom 1985 to 2013 • EESC and PWLT Monthlymodel • using multiple proxies • (autocorrelationtakenintoAccount) • Proxiesused: • - QBO (30 & 10 hPa) • NAO index • F10.7 cm Solar flux • Heat flux at 100 hPaaveraged • over 45-75°N • Aerosolsopticalthicknessat 550 nm • Tropopause altitude above OHP QBO Solar flux NAO Heat flux Aerosols Tropopause Applied on LIDAR and the merged of all the satellites with the lidar

  13. LIDAR Variability due to model proxies • QBO significantmainly in • wintermonths (easterly phase) • Aerosols: significant • atall month and • Altitudes • Solar flux: significant • in summer in midstratosphere • NAO mainly • signifcant in winter • Heat flux and tropopause: • significantmainlyin lower • Stratosphere

  14. Regressionanalysis LIDAR Merged of all the data Strong variations: LIDAR residualabove 40 km ( seasonal variation ?) Merged data below 18 km

  15. O3Variabilityexplained LIDAR Merged of all the data Variability of O3 lessexplainedabove 35 km in Spring and Summer Below 20 km , variability more explainedwith LIDAR data except in October

  16. Ozone vertical distribution trends LIDAR Merged of all the data Post turnaround trends Pre- Turnaround trends

  17. Spring ozone vertical distribution trends Merged of all the data LIDAR Post turnaround trends Pre-turnaround: LIDAR PWLT and EESC significantaround 15-20 km Pre- Turnaround trends

  18. Summer ozone vertical distribution trends Merged of all the data LIDAR Post turnaround trends Pre-turnaround: LIDAR PWLT and EESC significantaround 15-20 km Pre- Turnaround trends

  19. Autumn ozone vertical distribution trends Merged of all the data LIDAR Post turnaround trends Pre-turnaround: PWLT and EESC significant: LIDAR: 30-45 km Merged : 24-45 km Pre- Turnaround trends • Both data set: • Similar trends with EESC and PWLT for post-turnaroundperiod for both data exceptbelow 20 km

  20. Winter ozone vertical distribution trends Merged of all the data LIDAR Post turnaround trends Both data: Pre-turnaround: PWLT and EESC significant From 24-45 km Pre- Turnaround trends • Similar trends with EESC and PWLT for post-turnaroundperiod for both data

  21. Conclusions Evaluation of long-term ozone trend at OHP using multiple regressionanalysis for the period1985 – 2013 LIDAR, SAGE II, GOZCARDS and MLS present the smallest anomalies for 1985 to 2013 All Satellites anomalies agreewellwith the lidar, withaveragebiases of lessthan± 5%, in the 20–40 km range Significantpre-turnaround trend depending on the season Post-turnaroundincrease but mainlyunsignificant Outlook Introduction of Umkehrand SBUV II in the presentstudy Used the equivalent latitude in the regressionanalysis ( mightexplain the significantpre-turnaround trend during the Winter period

  22. Thankyou for your attention Thanks to GOZCARDS team ( NSA , Jet Propulsion Laboratory The NASA LangleyResearchCenter (NASA-LaRC) for provinding SAGEII data Dr. Alexandra LaengatKarlsruher Institut fur Technologie (KIT) for MIPAS data Dr. Joachim URBAN atChalmersUniversity of Technology (GOTHENBURG) for ODIN data Dr Alain Hauchecorneat LATMOS ( France) for GOMOS data Dr Lucien Froideveaux ( NSA , Jet Propulsion Laboratory) for AURA MLS data. for providing the data

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