Harvard University and NASA/GFSC

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Presentation transcript:

Harvard University and NASA/GFSC Evaluation of GMI simulations with TES ozone data: indirect validation of TES Jennifer Logan, Bryan Duncan, Jose Rodriguez, Lin Zhang, Inna Megretskaia, and the TES and GMI Science Teams Harvard University and NASA/GFSC AGU, December 15, 2006

The Global Modeling Initiative (GMI) ‘Combo’ Model ‘Combo’ = tropospheric + stratospheric chemical mechanism (122 species, >300 thermolytic rxns, 78 photolytic rxns) Stratospheric heritage from Goddard model Tropospheric heritage in part from GEOS-Chem Meteorological fields from GEOS-4: DAS, DAS Forecast and GCM 2° latitude x 2.5° longitude resolution ~1 km resolution below 10 hPa (lid at 0.015 hPa) 3 hr updates using 3-hr averages

This model output is available to the community for Aura science. GMI and Aura The Combo model has been run using assimilated meteorological fields for the Aura period (so far, Feb. 2004-Dec. 2005). This model output is available to the community for Aura science. We have been evaluating the Combo simulation for the troposphere and comparing to TES ozone data. Daily ozone fields were archived. Note – these are preliminary results, model updates are in progress. In the next, improved version, daily ozone, CO and other fields will be archived, as required by the Aura teams.

Strategy By evaluating the model with in-situ data, we learn where the model does well and where there are deficiencies. Profile data available for ~50 locations (sondes and MOZAIC). Having “calibrated” the model performance, compare the model and TES ozone. If the model and TES agree where we know the model does well, we are confident that the discrepancies in other regions are “real”. Explore the causes of these discrepancies

Ozone at N. extratropics, DAS and FVGCM The DAS run looks great for tropospheric ozone (see gmi.gsfs.nasa.gov for strat. evaluation by S. Strahan).

Sub-tropics and tropics, DAS and FVGCM DAS very low in BB season (NH and SH) in S. Atlantic

Ozone at 500hPa (MOZAIC data) FVGCM DAS Ozone at 500hPa (MOZAIC data) South U.S. E. Atlantic Middle East S. Asia

DAS vs. in-situ data 500 hPa JULY OCTOBER

TES – DAS comparisons Focus on July to December, 2005. TES data much sparser before July 2005, before routine limb soundings were dropped. TES validation with sondes shows TES is biased high by ~5 ppb at 500 hPa (Nassar et al., Richards et al). Model sampled at TES profile locations on same day, and AKs and prior applied. TES prior from MOZART model Results gridded on 2x2.5 grid Difference [(DAS with AK) –TES] removes prior

Combo-DAS vs. TES ozone, 511 hPa, July 2005 DAS with AK DAS (w. AK/prior) –TES (removes prior)

Are TES comparisons consistent with validation of Combo-DAS using in-situ data? TES comparison in July implies DAS is: Too low in N. tropical Atlantic Too high over U.S. Too low over W. Europe OK in Middle East OK in S. Atlantic TES results consistent with in-situ data where available Indirect validation of TES possible Shows model deficiencies

Combo-DAS vs. TES ozone, 511 hPa, October, 2005

In situ data confirms the underestimate over the S In situ data confirms the underestimate over the S. tropical Atlantic, and in the large scale ozone plume TES comparisons show the extent of the underestimate

Combo-DAS vs. TES, December, 2005

DAS –TES Differences, July – December Regions where model is too high, as well as too low JULY OCT. AUG. NOV SEPT. DEC

They show regions of overestimate of ozone in: Summary The COMBO-DAS looks remarkably like sonde/MOZAIC data in many regions Major discrepancies from sonde/MOZAIC data are in the tropical Atlantic sector, in the biomass burning season TES comparisons show the spatial extent of the underestimate of ozone in: the N. tropical Atlantic in July, less so in August N and S. tropical Atlantic in August Brazil, equatorial Atlantic and Africa in October, November Central Africa in December They show regions of overestimate of ozone in: Eastern Indian Ocean in October - December Next step: Improved emissions of NOx in model, use Forecast fields (Duncan et al. ).