Xiong Liu ( Harvard-Smithsonian Center for Astrophysics Collaborators: Kelly Chance, Christopher Sioris, Robert Spurr, Thomas Kurosu (CFA) Randall Martin (Dalhousie Univ., Canada) Mike Newchurch (University of Alabama in Huntsville) May Fu, Jennifer Logan, Daniel Jacob, Paul Palmer (Harvard University) PK Bhartia (Atmospheric Chemistry and Dynamics Branch, NASA GSFC ) Rob Chatfield (Atmospheric Chemistry and Dynamics Branch, NASA AMES ) Beijing, China May 26, 2004 First Directly-Retrieved Global Distribution of Tropospheric Column Ozone from GOME

2 Outline n Introduction to Ozone and Tropospheric Ozone n Satellite-based Tropospheric Ozone Retrievals n Algorithm Description n Intercomparison with TOMS, Dobson, and Ozonesonde observations n Examples of Daily Retrievals n Global distribution of Tropospheric Column Ozone and comparison with the 3D GEOS-CHEM model n Summary and Future work n Atmospheric Measurements and Studies at Harvard- Smithsonian CFA (Time allowed)

3 Ozone Stratopause Ozone layer Tropopause Courtesy of Randall Martin nFirst discovered by Schönbein [1840], a reactive oxidant in the atmosphere. nFirst quantitative observation: early 20 th century in Europe (e.g., Dobson, Götz) nFirst discovery of ozone hole by Farman et al. [1985] nFirst attempts to understand ozone in the 1930s. Until the last years, the stratospheric ozone chemistry (NOx, HOx, ClOx) is well understood. nNoble chemistry prizes were awarded to Paul Crutzen, Mario Molina, and Sherwood Rowland in 1995.

4 Tropospheric Ozone CO, VOCs, NO x HO 2 OH NO 2 H2O2H2O2 O3O3 hv, H 2 O HNO 3 OH CO, VOCs NO x HO x Simplified Tropospheric O 3 Chemistry Courtesy of Randall Martin nKey species in climate, air quality, and tropospheric chemistry uMajor Greenhouse gas, 15-20% of climate radiative forcing uPrimary constituents of photochemical smog uLargely controls tropospheric oxidizing capacity NO

5 Residual-based Satellite Tropospheric Ozone Retrievals nWhy satellite observations: global coverage nChallenge: only 10% of Total column Ozone (TO) nResidual-based approaches: TO – Strat. Column Ozone (SCO) uTropospheric ozone residual: TOMS minus SAGE/SBUV/HALOE/MLS [Fishman et al., 1990, 2003, Ziemke et al., 1998, Chandra et al., 2003] u Cloud/clear difference techniques: [Ziemke et al., 1998; Newchurch et al., 2003, Valks et al., 2003] u Modified residual method [Kim et al., 1996; Hudson et al., 1998] u Topographic contrast method [Jiang et al., 1996, Kim and Newchurch et al., 1996, Newchurch et al., 2003] uScan-angle method (Special) [Kim et al., 2001] nLimits: poor spatiotemporal resolution and large spatiotemporal variability of SCO  mostly climatological or tropics nLimb/Nadir matching: TO/SCO from same instrument or satellite

6 Launched April 1995 on ERS-2 Nadir-viewing UV/vis/NIR  nm  nm Footprint 320 x 40 km 2 10:30 am cross-equator time Global coverage in 3 days ESA Global Ozone Monitoring Experiment

7 GOME Radiance Spectrum and Trace Gases Absorption Atmospheric trace gas absorptions detected in satellite spectra

8 Physical Principles of Ozone Profile Retrieval (UV/Vis.) nWavelength-dependent O 3 absorption & dependence of Rayleigh scattering provide discrimination of O 3 at different altitudes from backscattered measurements. nTemperature-dependent ozone absorption in the Huggins bands provides additional tropospheric ozone information. Hartley & Huggins bands ( nm)Huggins bands ( nm) Chappuis bands ( nm) Chance et al., 1997

9 Ozone Profile Retrieval from GOME nDirect tropospheric ozone retrieval: daily global distribution of tropospheric ozone without other SCO measurements or deriving SCO nSeveral groups [Munro et al., 1998; Hoogen et al., 1999; Hasekamp et al., 2001; van der A et al, 2002; Muller et al., 2003] have developed ozone profile retrieval algorithms from GOME: each of them demonstrates that limited tropospheric ozone information can be detected. nHowever, global distribution of tropospheric column ozone has not been published from these algorithms uRequire accurate and consistent calibrations. uNeed to fit the Huggins bands to high precision. uTropospheric column ozone is only ~10% of total column ozone uLimited Vertical Resolution

10 Algorithm Description n Ill-posed problem: non-linear optimal estimation [Rodgers, 2000] Y: Measurement vector (e.g., radiances) X, X i, X i+1 : State vector (e.g. ozone profile) X a : a priori state vector K : Weighting function matrix, sensitivity of radiances to ozone Sa: A priori covariance matrix Sy: Measurement error covariance matrix

11 Algorithm Description — Radiative Transfer Simulation RcRc RoRo I B,o I B,c PcPc RsRs dd n Radiative transfer model: LIDORT [Spurr et al., 2001] u Model Ring effect with a first-order single-scattering model u Radiance polarization correction with a look-up table n Forward model inputs u SAGE strat. [Bauman et al., 2003] & GOCART trop. aerosols [Chin et al., 2002] uDaily ECMWF T profiles and NCEP Ps uClouds: Lambertian surfaces uCloud-top pressure from GOMECAT [Kurosu et al., 1999] uCloud fraction derived at nm with surface albedo database [Kolemeijer et al.,2003] uWavelength dependent albedo (2-order polynomial) from nm to take account of residual aerosol and cloud effects

12 Algorithm Description Perform external wavelength and radiometric calibrations u Derive variable slit widths and shifts between radiances/irradiances u Co-add adjacent pixels from nm to reduce noise u Perform undersampling correction with a high-resolution solar reference u Fit degradation for nm on line in the retrieval Optimize fitting windows: nm, nm Latitude/monthly dependent TOMS V8 climatology Retrieval Grid: 11 layers, use daily NCEP tropopause to divide the troposphere and stratosphere, 2-3 tropospheric layers Tropospheric column ozone: sums of tropospheric partial columns n State Vector: 47 variables (ozone, Ring, surface albedo, undersampling, degradation, wavelength shifts, NO 2, SO 2, BrO) n Spatial resolution: 960×80 km 2

13 Averaging Kernels (DX’/X) VR: 7-12 km (at km) 7-12 km (at 7-37 km) 8-12 km (at km)

14 A Priori Influence A Priori influence in TCO: 15% in the tropics, 50% at high-latitudes

15 Retrieval Errors Precision: 2-8% (< 2DU) in the strat., <12%(5DU) in the troposphere Smoothing: 10% at km, 15% at > 40 km, and 30% at <10 km TO: <2 DU(0.5); 3 DU (1.0%) SCO: <2 DU(1%); 2-5 DU (1-2%) TCO: DU(6-12%); 3-6 DU(12-25%)

16 An Orbit of Retrieved Ozone Profiles Ozone Hole (120 DU) Biomass burning

17 Validation n GOME data are collocated at 33 WOUDC ozonesonde stations during n Validate retrievals against TOMS V8, Dobson/Brewer total ozone, and ozonesonde TCO. n Data mostly from WOUDC n Collocation criteria:  Within ~8 hours, 1.5° latitude and ~500 km in longitude  Average all TOMS points within GOME footprint n Number of comparisons: 4711, 1871, and 1989 with TOMS, Dobson, and ozonesonde, respectively

18 Total Column Ozone Comparison n GOME-TOMS/Dobson: within retrieval uncertainties and saptiotemporal variability.  Means Biases: <6 DU (2%) at most stations  1  : 2-4 DU (1.5%) in the tropics, <6.1 DU (2.4%) at higher latitudes  Means Biases: <5 DU (2%) at most stations  1  : 3-6 DU (<3%) in the tropics, <8-16 DU (<5%) at higher latitudes A Priori Retrieval Dobson TOMS

19 A Priori Retrieval Ozonesonde n GOME-SONDE within retrieval uncertainties.  Biases: <4 DU (15%) except –5.5, 4.4, 5.6 DU (16-33%) at NyÅlesund, Naha, Tahiti  1  : 3-7 DU (13-28%) A Priori Retrieval Ozonesonde n Capture most of the temporal variability nGOME-SONDE within retrieval uncertainties.  Biases: <3.3 DU (15%)  1  : 3-8 DU (12- 27%) Tropospheric Column Ozone Comparison

20 GEOS-CHEM global 3D tropospheric chemistry and transport model Driven by NSAA GEOS-STRAT GMAO met data [Bey et al., 2001] 2  2.5 o resolution/26 vertical levels O 3 -NO x -VOC chemistry Recent anthropogenic, biogenic, natural emissions Synoz flux: 475 Tg O 3 yr -1 from stratosphere A 18-month simulation (June 1996-Nov 1997)

21 Examples of Daily Retrievals

22 Examples of 3-Day Composite Global Maps Biomass burning Mid-latitudes High TCO Band LOW TCO over the Pacific High-latitude high TCO Transport of mid-latitude high TCO air to the tropics

23 Monthly-mean Tropospheric Column Ozone (12/96-11/97)

24 GOME vs. GEOS-CHEM Similar overall structures nGlobal biases: <2±4 DU, r= n SH: <1±2 DU,r= n NH: <4.3±4.6 DU, r=

25 GOME vs. GEOS-CHEM nUsually within 5 DU. nLarge positive bias of DU at some northern tropical and subtropical regions: central America, tropical North Africa, Southeast Asia, Middle East nUsually >0.6. nPoor correlation: central America, equatorial remote Pacific, tropical North Africa and Atlantic, North high latitudes

26 GOME/GEOS-CHEM vs. MOZAIC (Central America) MOZAIC: Data: (vary from location to location) Evaluate GOME/GEOS-CHEM TCO in seasonality

27 GOME/GEOS-CHEM vs. MOZAIC (Southeast Asia) A Priori Retrieval GOES-CHEM MOZAIC

28 GOME/GEOS-CHEM vs. MOZAIC (Accra) A Priori Retrieval GOES-CHEM MOZAIC

29 GOME/GEOS-CHEM vs. MOZAIC (Middle East) A Priori Retrieval GOES-CHEM MOZAIC

30 Summary nOzone profiles and Tropospheric Column Ozone (TCO) are retrieved from GOME using the optimal estimation approach. nRetrieved TO and TCO compare very well with TOMS, Dobson/Brewer, and ozonesonde measurements. nThe retrievals clearly show signals due to convection, biomass burning, stratospheric influence, pollution, and transport, and are capable of capturing the spatiotemporal evolution of TCO in response to regional or short time-scale events. nThe overall structures between GOME and GEOS-CHEM are similar, but some significant positive biases occur at some northern tropical and subtropical regions. n The GOME retrievals usually agree well with the MOZOAIC measurements, to within the monthly variability and some biases can be explained by the reduced sensitivity to lower tropospheric ozone, spatiotemporal variation, and the large spatial resolution of GOME retrievals.

31 Future Work nImprove retrieval algorithms (Chappuis bands, external degradation correction) and complete more than 8-year GOME data record. nApply the algorithm to SCIMACHY, OMI, GOME-2, OMPS, or future geostationary satellite measurements. nIntegrate with the GEOS-CHEM model and other in-situ data, improve our understanding of global/regional budget of tropospheric ozone nTropospheric ozone radiative forcing Acknowledgements n This study is supported by the NASA and by the Smithsonian Institution. n Thank all collaborators. n We thank WOUDC and its data providers (e.g., SHADOZ, CMDL), TOMS, MOZAIC for providing correlative measurements. n We are grateful to NCEP/NCAR ECMWF reanalysis projects. n We appreciate the ongoing cooperation of the European Space Agency and the Germany Aerospace Center in the GOME program.

32 Variable Slit Widths and Shifts

33 Fitting Residuals

34 Aerosol Effects

35 Comparison with SAGE-II (>15 km) n Comparison with SAGE-II V6.2 ozone profiles above 15 km during (5732)  Means Biases: <15%  1  : <10% for the top seven layers and <15% for the bottom layer n Systematic biases in the retrievals but not in the a priori, suggesting residual measurement errors in the GOME level-1 data

36 A Priori Influence (06/7-9/1997) TOMS V8 A Priori GEOS-CHEM A Priori Retrieval with TOMS V8 A Priori Retrieval with GEOS-CHEM A Priori

37 GOME TCO (Dec 96-Nov 97)

38 GOME/GEOS-CHEM vs. MOZAIC