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Resolution-dependent data analysis of UTLS ozone : value and impact of Aura data (HIRDLS-MLS) Valery Yudin, NCAR/ACD. Acknowledgements to Aura Instrument Science Teams and, GFS/NOAA, GEOS/GMAO, and GMI/GSFC-UMBC, SHADOZ groups for data and simulations.

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  1. Resolution-dependent data analysis of UTLS ozone : value and impact of Aura data (HIRDLS-MLS)Valery Yudin, NCAR/ACD . Acknowledgements to Aura Instrument Science Teams and, GFS/NOAA, GEOS/GMAO, and GMI/GSFC-UMBC, SHADOZgroups for data and simulations

  2. Motivation by orbital plots: HIRDLS vs GEOS5 in the extra-tropical UTLS HIRDLS: Jan 23 2006 GEOS5: Jan 23, 2006 T-re PV O3-Data HNO3 O3-An How often and why current ozone analyses fail to reproduce ozone thin layers characterized by negative vertical gradiebts ?

  3. GMAO/GEOS-5.1.0 540x360x72 dz ~250m vs 1km NOAA/GFS-SMOBA 360x180x36 dz ~ 500 m vs 1km GMI-CTM-GEOS4 144x91x45 CAM-Chem-GFS 144x96x28 dz ~ 1 km vs 1km WACCM3-SST/QBO 72x45x76 dz ~ 2 km vs 2km WACCM3-CTM-GEOS5 180x91x72 WACCM3-CTM-with DAS dz ~ 1 km vs 1km Type-1: Column-based O3 data:OMI-TOMS Type-2: Vertical O3 profiles: HIRDLS, MLS, ACE with dx/dz ~ N/f can optimize dynamics; Type-3:Smoothed profiles Nadir-viewing ozone sensors => Layer-averaged or sub-column data (AIRS, TES, METOP…): dX/dZ~ dX/Hp ~[1-5] Type-4: in-situ VP data (no horizontal sampling) Correlative sondes (SHADOZ and WOUDC) to evaluate ozone UTLS streamers, spectra of oscillations. Analyses, models, data and spatial resolutions of observing systems (Fox-Rabinovitz & Lindzen) desirable dz ~N/fdh, for extra-tropical UTLS Analyses and Models Four types of O3 data

  4. Comparing O3-colums (DU): CTM, WACCM-climate, GEOS5 (2006),OMI (2005-08) Anal CTM 2005 2006 CC-GCM DATA 2008 2007 O3-columns are succesfully reproduced 2006 OMI: 2005-2008

  5. Characterization of O3 profiles by Resolution (or Averaging) Kernels:Rows and Images (Sharpness, Values and Properties) MLS: 0-30 km range

  6. HIRDLS (2/3 km) , MLS (2.5 km) , PV (~1km), TES (~2 km) => orbital patterns Data Analysis schemes (DA) is easy to implement when…. a) resolutions of data and models are comparable; b) consistent dynamics of “errors” provide chances to insert the realistic data-driven vertical transport c) Characterization of data by resolution kernels is critical for optimal observing systems. It helps to understand what can be constrained from data Extra-tropical UTLS: Resolution-consistent sampling (dx/dz=N/f) of HIRDLS and MLS matches PV-distributions of GEOS5

  7. Atlantic MLS-HIRDLS orbits, Jan 2006

  8. 23-01-2006, 142oW: HIRDLS O3 and CTM O3 Forecast, blending comparable scales (due to dx/dz ~N/f) Thickness of ozone streamers ~2-3 km WACCM-GEOS5 CTM Forecast

  9. 23/01/06: 12UT, O3: CTMs (CAM-GFS & GMI-GEOS4) and Analyses (GMAO and NOAA) Zonal Mean O3 Lon-Lat O3 at 200 hPa (~11.5 km) CTMs Analyses CTMs Analyses

  10. 142oW, 23/01/2006, 12 UT: PV & O3 Analyses and CTM Spectra of O3-vertical oscillations, UTLS from sondes (sampl.~ 100m) k-3 1-3 km k-2

  11. March 2006: Lamination frequency reproduced by CTM, Analyses and HIRDLS retrievals O3 Profiles CTM GEOS5 (x2.5) HIRDLS

  12. Lamina rate production (P-PV, R-tracer) / Appenzeller & Holton, 1996/ D(Rz)/Dt ~ f/R{R, T} + v.terms D(Pz)/Dt ~ f/R{P, T} + v.terms How often these lamina exist ? Analysis of column-based data => “Assimilation” Vertical Diffusion. ~2-3 km in UTLS can add ~ 10-20 DU, and may affect regional TOR estimations UTLS: Ozone streamers and lamina with sharp vertical gradients, how often MLS and HIRDLS instruments can observe them ? Apr 2006 and 2007, Frequency of lamination events HIRDLS 30% sonde-HIRDLScoincidences =>UTLS lamina MLS

  13. UTLS Lamination frequencies: March 2006 Lamina-range counts

  14. Frequency of O3-laminations seen by HIRDLS, simulated by CTM and produced O3-analyses HIRDLS CTM-GEOS4 GEOS5-Analysis 01.2006 x 2 03.2006 x 2 05.2006 x 2

  15. Examples: Assimilating LS O3 intrusions in UT with vertical resolution of HIRDLS 142oE: 01/23/2006 154o W: Hilo, Hawaii 2-SBUV-layers 7 layers of GEOS-5 CTM DATA 2-SBUV vs 7-GCM High PV->O3 ANALYSIS Independent DATA

  16. Vertical resolutions: data and analysis grids,resolved (visible) and invisible (forbidden-Nyquist) scales

  17. Resolution/Averaging Kernels.Rows and Images: (Sharpness, Values and Properties) MLS: 0-30 km range

  18. A) Preserving vertical O3 short-wavecomponents by resolution-dependent schemes B) Treating “layer-averaged” data as a “point-wise” observations applying “backward” interpolation from the data space => analysis grid Resolution-dependent analyses schemes

  19. Aura mission: Equatorial O3-anomaly (10oS-10oN) Data: MLS & HIRDLS GEOS5 & WACCM-QBO MLS GEOS5 HIRDLS WACCM 2004 2005 2006 2007 2008 2004 2005 2006 2007 2008

  20. H2O-O3 stratospheric analyses GEOS5 vs MLS analysis problems ? (HIRDLS impact !!! What can be seen from ERA-40 and ECMWF) MLS GEOS5 H2O tape recorders O3-stratospheric QBO “sticks”

  21. Equatorial oscillations of O3 and H2O: MLS and GCM:similar shapes of constituent variations(WACCM3-QBOSC runs performed by Katja Matthes, FUB) MLS GCM 2004 2005 2006 2007 2008 2004 2005 2006 2007 2008

  22. Conclusions for Resolution-Dependent Analyses • Characterization of MI data by resolution kernels are needed to advance and derive MI-O3 products (analyses, combined retrievals). • LV sensors deliver data => (dx/dz = N/f ~100) consistent with model dynamics and transport; MLS and HIRDLS even with 14-orbits help to characterize transport in thin layers of the extra-tropical UTLS. • NV sensors (dx/dz ~1-5) report smoothed profiles. They are still column-based data even with DFS ~3-4 (dz > Hp). Their treatment as the point-wise data may degrade dynamics of “leading” vertical scales in the UTLS analyses (O3-lamination frequencies). • Numerics of DAS is important, similar to numerics of transport problem for the MI-O3 analyses. • Main message => Don’t blend observational and simulated information that belong to incomparable vertical scales, constrain only scales visible to the instrument, preserving short-scale strictures of chemicals.

  23. MI-O3 data, analysis and simulations Jan 2006 A priori ?

  24. Why UTLS ? UTLS: Largest variability and uncertainties of O3 Across the Tropopause: Largest and variable O3 gradients, tendency and fluxes; desires toseparate ozone sub-columns and ozone fluxes; Goal of Multi-instrumental O3 data => reduce uncertainties and constrain the climate and day-to-day predictions Several issues for MI-O3 analysis: 1) How to insert and combine O3 data with different spatial resolutions 2) How to design data QC & identify biases 3) Formulate and develop Resolution-dependent data analysis and retrieval schemes: Ozone data analysis in the UTLS CTM CTM 3-5 km layer GEOS5 O3-var, %:01/2005-01/2008

  25. Variations of O3-colums (%):Model, CTM, GEOS-5 (2006), OMI (2005-2008)

  26. 2005-2008: January Monthly-ZM O3 in UTLS: GEOS5/GMAO and MLS/Aura a priori Mid-troposphere data are needed to evaluate O3-forecasts/analyses (AIRS/TES), and chemical forecast system may need comprehensive troposopheric chemistry scheme (?)

  27. Attractive feature of sharp HIRDLS vertical sampling against AMSU-channels~5-10 km “smoothed” Jacobian widths; Analyses schemes: can introduce similar “assimilation” diffusion shown by “ozone”/GEOS5 and GFS assimilation Analysis of NV T-channels may dump short-wave components of T-forecast diffusing them and degrading vertical transport and dynamics predicted models Biases in DA and inverse estimation studies /example of wavy T-biases during winter, Aug 2001/ Dee, 2005 NP STRAT: AMSU-A rad-es SP

  28. Monthly Frequency of ozone lamina, intrusions, etc.. in the UTLS-2006: MLS & GMI-CTM

  29. Resolution of O3-data: research and operational 12-Umkehr Layers SBUV-2 ozone sub-column layers and GFS/NOAA-analysis grid 30 lev Xr-Xa =A(Xt-Xa) Cr =Ca - ACa Layer-based or column-based data =/= Point-wise (or grid-box) data; A = KW 2-layers in UTLS > 10 lev “-” impact MI Aura O3 Data (OMI/TES/MLS/HIRDLS) – Do we need QC tool ? Property of Resolution Kernels –basis for QC (DFS, symmetry, limits) Tropics

  30. Sonde No assim Assim Comparison simulations and assimilation of TES O3 with IONS-06 (Aug) sonde profilesfrom Mark Parrington et al. (2007/2008) AM2-Chem sonde “-” impact model m-s “+” impact TES-assim a-s GEOS-Chem

  31. Importance of Tropospheric Chemistry in Chemistry-Climate Models (WACCM3-example):Tropospheric and UTLS Representation of Ozone in WACCM3 simulations by two chemical mechanisms and comparison with monthly ozone-sonde data 40o N 20o N Red = 115 Species sim. Blue = 57 Species sim. Black = Ozonesonde (Logan, 1999).

  32. Temperature and Nitric Acid Cross-sections support Laminae: 1 April 2006, (Doug Kinnison, Artistic Colors, Apr 10/2006)

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