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Merging total ozone data from different uv-vis satellite sensors: GOME / SCIAMACHY / GOME-2

Merging total ozone data from different uv-vis satellite sensors: GOME / SCIAMACHY / GOME-2 M. Coldewey-Egbers DLR, D. Loyola DLR , W. Zimmer DLR , C.Lerot BIRA , M. Van Roozendael BIRA , J.-C. Lambert BIRA , M. Dameris DLR , H. Garny DLR , P. Braesicke UCAM ,

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Merging total ozone data from different uv-vis satellite sensors: GOME / SCIAMACHY / GOME-2

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  1. Merging total ozone data from different uv-vis satellite sensors: GOME / SCIAMACHY / GOME-2 M. Coldewey-EgbersDLR, D. LoyolaDLR, W. ZimmerDLR, C.LerotBIRA, M. Van RoozendaelBIRA, J.-C. LambertBIRA, M. DamerisDLR, H. GarnyDLR, P. BraesickeUCAM, D. BalisAUTH, and M. KoukouliAUTH WMO, Geneva, January 25th, 2011

  2. Outline • Motivation • GOME / SCIAMACHY / GOME-2: • Total ozone retrieval & intercomparison • Merging algorithm • Intercomparison with other datasets: • Total ozone and ozone trends • Workshop questions • Summary and outlook

  3. Ozone Long-Term Monitoring with European Sensors GOME SCIA. OMI GOME-2 GOME-2 GOME-2 S5p S4 June 1995 S5 ~35 years E39C-A (SCN-B2d), Stenke et al., ACP, 2009 GOME/SCIAMACHY/GOME-2, Loyola et al., IJRS Montreal Protocol special issue, 2009

  4. Instrument Overview: GOME, SCIAMACHY, and GOME-2 • passive remote sensing spectrophotometers • satellites fly in sun-synchronous and near polar orbit at a height of ~790km (*) GOME global coverage lost in June 2003 • Operational Algorithm: GOME Data Processor 4.x (DOAS fit + iterative AMF/VCD) • Independent Geophysical Validation

  5. Monthly Mean Total Ozone: 60°N – 60°S

  6. GOME – Total Ozone Long-term Stability Update of Fig.(1) in M. Coldewey-Egbers et al., Applied Optics, Vol. 47(26), 2008

  7. Intercomparison: zonal means GOME vs. SCIAMACHY (2002-2009) GOME vs. GOME-2 (2007-2009)

  8. Merging Algorithm: SCIAMACHY 1. Latitudinal Correction For each month (Jan. to Dec.), averaged over 2002 to 2009, for latitudes Φ from 90°N to 90°S. 2. Add time dependent offset For each individual month from June 1995 to December 2009, averaged over 60°N to 60°S.

  9. GOME-type Total Ozone – Essential Climate Variable

  10. Intercomparison: Data Sets Satellite Observations GTO-ECV_v0: GOME-1, SCIAMACHY, and GOME-2, 1995-2009, 1°lat x 1°lon, Loyola et al., IJRS, 2009. (http://atmos.caf.dlr.de/gome/gto_ecv.html) NASA-MOD: TOMS, SBUV(/2), and OMI, 1978-2009, 5°lat x 10°lon, Stolarski and Frith, 2006. (http://acdb-ext.gsfc.nasa.gov/Data_services/merged/mod_data.public.html) Chemistry Climate Models E39C-A: ECHAM4.L39(DLR)/CHEM/-ATTILA, 1960-2050, 3.75°lat x 3.75°lon, Stenke et al., 2008. UMUKCA-UCAM: Unified Model / UK Chemistry and Aerosols Module – University of Cambridge, 1960-2100, 2.5°lat x 3.75°lon, Morgenstern et al., 2009. Ground-Data:32 Brewer and 47 Dobson Stations, 1995-2008, 5°lat x 5°lon, Balis et al., 2007.

  11. Total Ozone Comparison – Zonal Means

  12. Trends: Statistical Model Monthly mean total ozone time series model: Vyushin et al., JGR 112, 2007 “Impact of long-range correlations on trend detection in total ozone”. Residual Linear Trend (1) Solar Flux 10.7cm (3) QBO at 30 and 50hPa (2x3) Seasonal Cycle (8) Overall Mean (1) Total Ozone at Month m (June 1995 to December 2009) sin() and cos() terms for seasonal dependence

  13. Anomalies: 1980-2040 Trends: 1995–2009 60°N-60°S 60°N-30°N 30°N-30°S 30°S-60°S Trend [%/decade (±2σ)]

  14. Trends: Zonal Means 1995-2009

  15. Trends: GTO-ECV Global 1995-2009 Trend 2-sigma error Significance Number of years

  16. Workshop questions • is your data set suitable for assessing long-term changes? YES • how internally consistent is it? • what is the evidence that it is internally consistent? Trends: GTO-ECV Trends: GOME only

  17. Workshop questions (2) • how can it be used to evaluate other data sets? GTO_ECV_v0 MOD UMUKCA-UCAM E39C-A 1995-2029

  18. Workshop questions (3) • can it be used in conjunction with other data sets to provide a long (20-30 year) record? • Past and future missions can be added (see outlook) • what has been learnt that is relevant in assessing other data sets? • Provide not only data but also associated errors • Internal consistency • Validation with ground based data • Comparison with similar data sets • Comparison of trends

  19. Outlook The generation of a long-term total ozone ECV data record from combined European missions will be continued in the framework of the ESA Climate Change Initiative • Newest retrieval algorithm GDP 5: GODFIT instead of DOAS • GOME/ERS-2 data reprocessed with GDP5 to be released in 2Q/2011 • Optimised version of the GDP5 algorithm will be applied to all 3 european sensors • Refined merging algorithm including error calculation • Add future missions • GOME-2/MetOp-B (2012), GOME-2/MetOp-C • Sentinel Series (S5p, S4, S5) • Add past missions (optional work in cooperation with USA) • Merge MOD and GTO-ECV

  20. http://atmos.caf.dlr.de/gome/gto-ecv.html

  21. GDP 4.x – Algorithm Summary and Milestones • Two steps GDOAS approach (M. van Roozendael et al., JGR 2006) DOAS fit for ozone slant column and effective temperature Iterative AMF/VCD computation using a single wavelength • Improved O3Retrieval Molecular Ring Correction parameterised On-the-fly RTM simulations using LIDORT v3.3 (R. Spurr, 2003) Cloud Correction: OCRA&ROCINN v2.0 (D. Loyola et al., TGRS 2007) • Independent Geophysical Validation (D. Balis et al., JGR 2007) • Milestones: 2004 GDP 4.0operationally with GOME 2006 GDP 4.0operationally with SCIAMACHY (C. Lerot et al., AMT 2009) 2007 GDP 4.1operationally with GOME-2 2010 GDP 4.4GOME-2 reprocessed (D. Loyola et al., accepted, JGR 2011) Intra-cloud ozone, sun-glint and scan angle dependency corrections • Daily composites (0.33° x 0.33°) and monthly averages (1° x 1°)

  22. Trends: GTO-ECV 1995-2009 seasonal dependence NH Winter NH Summer

  23. Global mean trend (60°N-60°S): 1995-2040

  24. Trends: Comparison with Ground Data GTO-ECV vs. GROUND GTO-ECV vs. MOD ρ=0.33 ρ=0.74

  25. Summary • GOME-type Total Ozone - Essential Climate Variable(GTO-ECV) available since 2009:Monthly-mean total ozone data record (06/1995 to 12/2009) generated by merging GDP 4.x data from GOME/ERS-2, SCIAMACHY/ENVISAT, and GOME-2/MetOp-A. • Global Total Ozone Trend Analysis: Significant positive trends for the global mean ozone and in some regions of the northern hemisphere.

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