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COMPARISON BETWEEN FTIR AND CTM MODEL OUTPUT (TM4 and Uni. Oslo)

COMPARISON BETWEEN FTIR AND CTM MODEL OUTPUT (TM4 and Uni. Oslo). B. Dils, S. Dalsoren, M. Van Weele & HYMN FTIR teams. The Model data. TM4 from KNMI: currently covers 2003+2004 6 hourly profiles at FTIR stations (5 gridboxes C,N,S,E,W) both BQT and LPJ CTM from the University of Oslo

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COMPARISON BETWEEN FTIR AND CTM MODEL OUTPUT (TM4 and Uni. Oslo)

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  1. COMPARISON BETWEEN FTIR AND CTM MODEL OUTPUT (TM4 and Uni. Oslo) B. Dils, S. Dalsoren, M. Van Weele & HYMN FTIR teams

  2. The Model data • TM4 from KNMI: • currently covers 2003+2004 • 6 hourly profiles at FTIR stations (5 gridboxes C,N,S,E,W) • both BQT and LPJ • CTM from the University of Oslo • only covers first 269 days of 2004 • 6 hourly profiles at FTIR stations (5 gridboxes C,N,S,E,W) • both BQT and LPJ • LMDz-INCA from LSCE: • no comparison yet • I currently only looked at the C (which contains the FTIR stations geo-location) data. • However FTIR’s slant columns might intersect with neighboring model grids

  3. The FTIR network used in this comparison Spatial coordinates

  4. The FTIR data • HYMN Harmonization strategy • goal: to minimize retrieval setting differences which would result in inter-station biases • one common micro-window set • common spectroscopy database (HITRAN 2004 +”Hase update”) • NCEP pT-input • common a-priori adapted to the sites mean NCEP tropopause height • consistent Tikhonov L1 regularization matrix • stations cover 2003+2004 • Reunion: Aug-Oct 2004 • Paramaribo: Oct-Nov 2004 (no overlap with Oslo data) • DOF = 2-3

  5. Validation • The following steps have been undertaken to minimize any inherent differences between the FTIR and model output: • Temporal collocation criteria: closest CTM to any FTIR measurement • Spatial collocation: The grid cell which contains the FTIR site • Convert CH4 partial columns to volume mixing ratios (for Uni.Oslo only) • Interpolation of CH4 vmrs onto FTIR vertical grid(using pressure data) • Application of the FTIR averaging Kernels (A) onto the CTM data. This yields us what the FTIR would have retrieved if the CTM output represented the true atmospheric statex = (I-A).xaprio + A.xctm

  6. Profile examples (TM4) • No model information above ~50 km • FTIR represents a-priori at these altitudes • At most stations the model seems to underestimate CH4 in the stratosphere • Some FTIR sites (Paramaribo and Reunion) show marked oscillations in the troposphere • the AVK matrix only partially compensates for this • Stronger constraints -> lower DOF

  7. Profile difference examples (TM4) • Same features also visible in the profile difference plots

  8. Profile examples (UIO) • Again, underestimation of stratospheric CH4 • Marked feature in the model profile… real? • No clear visible difference between LPJ and BQT results

  9. Seasonality (TM4) • Tropospheric column = <15 km (needs to be improved) • Seasonality looks stronger in FTIR • Phase shift?

  10. Seasonality (TM4) • Difference in seasonality is also clearly shown in relative (Mod-FTIR/FTIR) difference plots

  11. Seasonality (UIO) • More limited time-frame • Conclusions difficult • At first sight same problem is visible

  12. TM4 Table • TM4>FTIR • Mean Difference Tropo > Total Column • Stratospheric underestimation • Lowest Biases for for High Alt stations

  13. TM4 Table BQT-LPJ difference • BQT has lowest bias (but within error range) • BQT has highest R • All in all differences are minimal

  14. UIO Table • lower bias than TM4 • R tropo > R total column (beware limited dataset) • Overall bias LPJ>BQT • R BQT > LPJ

  15. Conclusions • The limited UiO dataset makes a straightforward TM4 vs UiO comparison difficult • Differences between BQT and LPJ are minimal. • BQT results in higher correlation values for both models • and lower biases for TM4 only • TM4 seems • to underestimate stratospheric CH4 • and overestimate tropospheric CH4 • UiO seems • to underestimate stratospheric CH4 • bias varies for tropospheric CH4 • Seasonality is not perfectly captured

  16. Outlook • LMDz-INCA from LSCE • A complete 2003-2004 UiO dataset would be ideal • If not redo comparison on common timeframe to assess model quality differences • Better parameter for tropopause height (now roughly set to 15 km) • Check if slant FTIR columns intersect other model grids • In depth look on day-to-day variability and seasonality - end-

  17. UIO Table BQT-LPJ difference

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