1 / 13

Chemical Data Assimilation and Modeling at the GMAO

Chemical Data Assimilation and Modeling at the GMAO. I. Stajner, S. Pawson, A. daSilva, E. Nielsen, A. Tangborn, H. Hayashi, K. Wargan, LP Chang, C. Weaver, P. Colarco, M. Chin NASA/Goddard Global Modeling and Assimilation Office Atmospheric Chemistry and Dynamics Branch

neron
Télécharger la présentation

Chemical Data Assimilation and Modeling at the GMAO

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chemical Data Assimilation and Modeling at the GMAO I. Stajner, S. Pawson, A. daSilva, E. Nielsen, A. Tangborn, H. Hayashi, K. Wargan, LP Chang, C. Weaver, P. Colarco, M. Chin NASA/Goddard Global Modeling and Assimilation Office Atmospheric Chemistry and Dynamics Branch in collaboration with D. Jacob’s group at Harvard University Chemical data assimilation workshop June 20, 2005

  2. Outline - Assimilation and modeling of: • Ozone • Carbon monoxide • Aerosol - Forecasting - Plans Carbon monoxide forecast at 500 hPa

  3. Ozone assimilation model Limb or occultation MIPAS, MLS POAM, SAGE Approach: combine • 3-dimensional global ozone model with • Satellite ozone data: • Total columns • Stratospheric profiles • Obtain tropospheric ozone profiles as “residuals” • Lower stratosphere is the key region SBUV OMI TOMS tropopause TES

  4. Assimilation of Aura MLS and OMI ozone Mean profiles from: _ SAGE III _ Aura assimilation _ SBUV/2 assimilation Sunset near 67°N Sunrise near 37°S • Assimilation of Aura MLS and OMI data reproduces the mean ozone profile shape in the lower stratosphere and its variability with latitude better than the assimilation of SBUV/2 data. MLS data are available for January 9-13. The comparisons are at SAGE III measurement locations on January 11-13. 3

  5. MLS+OMI assimilation at 100 hPa on January 9, 2005 Variability of lower stratospheric ozone January 11, 2005 • Intrusion of low ozone air at 100 hPa from the Tropics is advected eastward and wrapped around higher ozone over Canada. Circles mark locations where SAGE III ozone was less than 10 mPa on the previous day at sunset. January 12, 2005 Collocated SAGE III and Aura assimilated profiles SAGE III Aura assim. • Spatio-temporal variability of lower stratospheric ozone is well represented in Aura assimilation. January 13, 2005 0 180W 4 5 6 7 8 9 10 11 12 13 14 15 mPa

  6. Comparisons with sondes 52N 5E 52N 20E 46N 7E 64S 56W __ Sondes __ MLS+OMI __ MLS+SBUV total column 10 • Either assimilation with MLS data captures well different profile shapes and variability in the middle and lower stratosphere. • Assimilation of OMI data produces excessively low ozone in the troposphere. This may be due to the way we are using OMI data, or to data themselves. 100 Pressure (hPa) Jan. 11 Jan. 12 Jan. 12 Jan. 9 52N 5E 52N 20E 64S 56W 46N 7E 10 100 Jan. 22 Jan. 19 Jan. 19 Jan. 20 Ozone (mPa)

  7. Tropical tropospheric ozone 324°E 345°E 36°E 112°E 189°E 270°E January mean for SHADOZ sondes (1998-2000) MLS+OMI MLS+SBUV • Both assimilations reproduce climatology at SHADOZ stations. MLS+OMI seems better at Natal (324°E) and Ascension (345°E). • Wave one in tropospheric ozone is captured, with higher ozone values over the Atlantic than over the Pacific ocean. • Tropospheric columns from Aura assimilation agree within ~5 DU with sondes at Nairobi (36°E) and Samoa (189°E). Atlantic Pacific

  8. Asian CO Sources Total CO: 2-day forecast for 7/22/04 CO: NA Biomass Burning CO: NA Fossil Fuels Global Pollution Modeling: Intex-NA Field Mission Using global atmospheric models, with representations of dynamics, physics and chemistry, GMAO is able to simulate distributions of atmospheric trace gases for studies of pollution and impacts on climate. The example shows CO pollution forecasts the Intex-NA (Intercontinental Transport Experiment) mission in Summer 2004. The 2-day forecasts (along a proposed DC-8 flight track) reveal CO pollution throughout the troposphere (above), with contributions (left) from local Fossil-Fuel emissions (bottom), forest fires in the western USA (middle) and Asia (top).

  9. Forecasts of aerosol and CO in real timein support of AVE-Huston • Animations and plots are available from http://code613-3.gsfc.nasa.gov/People/Colarco/AVE_Houston/ave_main.php Peter Colarco

  10. Forecasts of CO from Asia At 35N At 500 hPa • Animations of forecasts of CO from Asia http://code613-3.gsfc.nasa.gov/People/Colarco/AVE_Houston/ave_main.php Peter Colarco

  11. Monthly AOT: ACE Asia (April 2001) Qualitative agreement is seen between the aerosol optical depth in models (left) and observations (right), all capturing Asian dust.

  12. MODIS Radiances FIRST GUESS AOT ANALYSIS AOT GOCART, No Data MODIS Retrieved AOT Assumptions on aerosol properties that are used in 1-D retrieval are consistent with the model Clark Weaver, 613.3

  13. Constituent Data Assimilation in the Troposphere • Products: 3D simulations, analyses, forecasts • Model: • On-line transport within GMAO’s GEOS-4 system • Ozone and CO sources and parameterized chemistry from GEOS-CHEM (Harvard) • Aerosol modules from GOCART (dust, black carbon sulfates, organic carbon, sea-salt) • Data: • O3: SBUV, OMI, MLS, MIPAS, TES, HIRDLS, AIRS • CO: MOPITT, AIRS, TES • Aerosol: MODIS, TOMS,OMI, CALIPSO

More Related