1 / 21

Inverse modeling of North American CO sources during the summer 2004 (ICARTT)

(R. Hudman). Sol ène Turquety – GEOS-CHEM Meeting April 5, 2005. Inverse modeling of North American CO sources during the summer 2004 (ICARTT)

sarai
Download Presentation

Inverse modeling of North American CO sources during the summer 2004 (ICARTT)

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. (R. Hudman) Solène Turquety – GEOS-CHEM Meeting April 5, 2005 • Inverse modeling of North American CO sources • during the summer 2004 (ICARTT) • S. Turquety, D. J. Jacob, R. C. Hudman, J. A. Logan, R. M. Yevich, F. Y. Leung, R. M. Yantosca, C. L. Heald, L. K. Emmons, D. P. Edwards, • and the INTEX Science Team • Anthropogenic emissions: • EPA emission inventories and trends: • Uncertainty on the CO emissions estimated • to ~ 40% by comparison with in situ observations • [Parrish, in preparation] • Global inverse modeling analyses : • North American FF/BF emissions of CO • vary by up to ~ 70% • ICARTT observations: model seem to strongly overestimate CO in the PBL • Biomass burning emissions during the summer 2004 : • 2004: worst fire season in Alaska on record! • Burning in boreal forests of Canada or Siberia can have a large impact on CO on an hemispheric scale, and on air quality in the US in particular • Burning in boreal regions expected to significantly increase as a result of climate change

  2. Bottom-up inventory of the biomass burning emissions in North America during the summer 2004 Solène Turquety – GEOS-CHEM Meeting April 5, 2005 US National Interagency Coordination Center (NICC) Canadian Interagency Forest Fire Center (CIFFC) • Alaska: • > 2.6 million hectares burned • > 8 x 10-year average • Canada: • 15 x average area burned in Yukon Territory (60% of national total) • 6 x average in British Columbia

  3. Biomass burning 2004: Strong signature during ICARTT Solène Turquety – GEOS-CHEM Meeting April 5, 2005

  4. Bottom-up inventory of the biomass burning emissions in North America during the summer 2004 Solène Turquety – GEOS-CHEM Meeting April 5, 2005 MODIS hotspots Daily reports of the area burned from the NIFC A x 60% [W.M. Hao, FSL, Personal comm.] Emissions / unit area Emissions CO 2004 Derive emissions for 10 species, with 1x1 horizontal resolution: NOx, CO, lumped >= C4 alkanes, lumped >= C3 alkenes, acetone, methyl ethyl ketone, acetaldehyde, propane, formaldehyde, and ethane.

  5. Bottom-up inventory of the biomass burning emissions in North America during the summer 2004 Alaska 2004 Logan and Yevich Total emissions CO (Tg) Canada Day since June 1st, 2004 Solène Turquety – GEOS-CHEM Meeting April 5, 2005 • Total emissions North America June 1st – August 31st =10.3 Tg CO • Alaska : 5.7 Tg CO  3 x climatology Logan and Yevich • Canada: 4.5 Tg CO  0.9 x climatology ; Yukon territory: 2.3 Tg CO  4.4 x clim.

  6. Bottom-up inventory of the biomass burning emissions in North America during the summer 2004 Solène Turquety – GEOS-CHEM Meeting April 5, 2005 MOPITT Total CO – summer 2004 GEOS-CHEM Total CO x MOPITT AK (MOPITT – Model)/MOPITT • On average: Underestimate emissions by ~ 25% on average • Area burned? Uncertainty reports… • Emissions / unit area (fuel loads) ? • Injection heights?

  7. Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Importance of injection heights for the 2004 Alaskan-Yukon fires Use TOMS AI as an indicator of the altitude of the aerosol layer: Sensitivity as altitude of the aerosol layer (low sensitivity to the PBL)

  8. Alaska – Yukon fires summer 2004 Total CO emissions Maximum TOMS AI Day since June 1st, 2004 Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Importance of injection heights for the 2004 Alaskan-Yukon fires Pyro-convective cloud from aircraft – June 27, 2004 http://www.cpi.com/remsensing/midatm/smoke.html

  9. Biomass burning 2004: Strong signature during ICARTT Obs. G. W. Sachse GEOS-CHEM NRT Solène Turquety – GEOS-CHEM Meeting April 4, 2005 Biomass burning plume sampled by the DC-8 on flight # 9 (July 18th) GEOS-CHEM NRT

  10. Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Importance of injection heights for the 2004 Alaskan-Yukon fires Compute 5-days trajectories w/ GEOS-4 from TOMS AI to calculate exposure to aerosols (K. Pickering, GSFC, http://croc.gsfc.nasa.gov/intex/) 850hPa 700hPa 500hPa

  11. Vertical distribution 200mb 5 % Upper troposphere 400mb 55 % Free troposphere 40 % Boundary layer PBL Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Importance of injection heights for the 2004 Alaskan-Yukon fires What are the implications for CO inversion? MOPITT Total CO July 17-19, 2004 GEOS-CHEM Total CO x MOPITT AK July 17-19, 2004 (MOPITT – Model)/MOPITT

  12. Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Importance of injection heights for the 2004 Alaskan-Yukon fires What are the implications for CO inversion? GEOS-CHEM BB CO 100 % BL x MOPITT AK GEOS-CHEM BB CO 100 % FT x MOPITT AK GEOS-CHEM BB CO 100 % UT x MOPITT AK

  13. Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Importance of injection heights for the 2004 Alaskan-Yukon fires What is the implications for long range transport? MOPITT Total CO July 22-24, 2004 GEOS-CHEM Total CO x MOPITT AK July 22-24, 2004 (MOPITT – Model)/MOPITT

  14. Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Importance of injection heights for the 2004 Alaskan-Yukon fires What are the implications for long range transport? GEOS-CHEM BB CO 100 % BL x MOPITT AK GEOS-CHEM BB CO 100 % FT x MOPITT AK GEOS-CHEM BB CO 100 % UT x MOPITT AK

  15. AIRS CO for July 18 GEOS-CHEM NRT July 18 MOPITT CO for July 18 Inversion GEOS-CHEM Inversion A priori sources A posteriori sources A posteriori sources Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Ongoing and future work • Using satellite observations to constrain the daily North American biomass burning emissions during the summer 2004 • Magnitude? • Injection height? (?) + TOMS AI? MISR ? Strong variability => daily inversion

  16. AIRS CO GEOS-CHEM NRT MOPITT CO ^ GEOS-CHEM x Inversion A priori sources A posteriori sources Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Ongoing and future work • Inverse modeling of North American anthropogenic emissions of CO using aircraft and satellite measurements (?) xa

  17. Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Ongoing and future work …

  18. Ongoing and future work Observation vector: Observation error State vector: CO sources MOPITT observation Model simulation, with MOPITT AK applied Linear approximation: with K provides the sensitivity of the measurement to the source of interest. Calculated using a tagged CO simulation

  19. Alaska – Yukon fires summer 2004 Total CO emissions Maximum TOMS AI Day since June 1st, 2004 Solène Turquety – GEOS-CHEM Meeting April 5, 2005 Ongoing and future work • Two main signals for the fires: • Alaska – Yukon • Northwest territory Strong daily variability Daily inversion

  20. Inverse modeling of North American CO emissionsconcept and method Difference between observation and simulation Difference between true state and a priori knowledge Maximum a posteriori (MAP) solution [Rodgers, 2000] i.e. solution which minimizes cost function: Observation error A priori error A priori state vector Gain matrix (MOPITT – MODEL) Sequential inversion: observations considered sequentially

  21. Inverse modeling of North American CO emissionsKalman Smoother + Ktxt εt ^ ^ ^ ^ x’(0) x’(t) x’(T) x’(1) ^ ^ ^ ^ ^ ^ ^ x’’(T) x x’’(1) x’ x’’(t) x’’(0) x’’ Inversion of time dependant emissions: Kalman filter(forward)  yobs(0) yobs(1) yobs(T) Forward Kalman filter Update a priori A priori time t = 0 t = 1 t t = T A priori Update a priori Backward Kalman filter Combination forward + backward = Kalman smoother

More Related