1 / 32

Inverse modeling of European sources: Perspectives for aerosols and its precursors

Explore using inverse modeling to estimate aerosol and precursor sources in Europe. Discuss strengths, challenges, and requirements for efficient modeling. Includes examples and perspectives on methane emissions.

mway
Download Presentation

Inverse modeling of European sources: Perspectives for aerosols and its precursors

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. Inverse modeling of European sources: Perspectives for aerosols and its precursors Maarten Krol, Frank Dentener, Peter Bergamaschi, Jean-Philippe Putaud, Frank Raes JRC, Ispra JRC – Ispra

  2. What is inverse modeling? Example: Estimating European emissions of methane Strengths and weaknesses Perspectives for shorter-lived compounds Requirements Outline JRC – Ispra

  3. Model Parameters (P) Model Emission Estimates (P) What is inverse modeling? Output (C(x,t)) Measurements (M(x,t)) Sensitivity: JRC – Ispra

  4. Sensitivity: Minos 2001 Region of influence 1/8/2001 - 19/8/2001 JRC – Ispra

  5. JRC – Ispra

  6. JRC – Ispra

  7. Optimize model parameters by minimizing the difference between model & measurements Minimize cost function: J = Si (Mi(x,t) – Ci(x,t,P))2/ (sMi(x,t))2 + (parameter term) Note: Ci(x,t) depends on P This links to sensitivities S From Sensitivity to Inverse modeling JRC – Ispra

  8. EXAMPLE: Inverse modelling of national and European CH4 emissions using the zoom model TM5 P. Bergamaschi, M. Krol, F. Dentener, and F. Raes EC Joint Research Center, Ispra, Italy in cooperation with several partners: - Institute for Marine and Atmospheric Research, Utrecht, Netherlands - ECN Petten, Netherlands - Umweltbundesamt, Germany - CEA/CNRS, Gif sur Yvette, France - NOAA Climate Monitoring and Diagnostics Laboratory, Boulder, CO, USA JRC – Ispra

  9. TM5 model TM5 model – atmospheric zoom model • offline atmospheric transport model • meteo from ECMWF • global simulation 6o x 4o • zooming 1o x 1o (Europe, …) • http://www.phys.uu.nl/~tm5/ JRC – Ispra

  10. Global and European regions JRC – Ispra

  11. Global and European regions JRC – Ispra

  12. monitoring sites JRC – Ispra

  13. Schauinsland JRC – Ispra

  14. further European sites complete set of 56 sites (year 2001) ftp://ftp.ei.jrc.it/pub/bergamas/CH4BR/ JRC – Ispra

  15. CH4 emission distribution - bottom up JRC – Ispra

  16. CH4 emission distribution - a posteriori JRC – Ispra

  17. a priori / a posteriori emissions JRC – Ispra

  18. revision of German inventory revision of German inventory (EU NIR 2004) 2.40 -> 4.04 Tg CH4/yr (year 2001); revision of whole time series manure management (0.21 -> 1.31 Tg CH4/yr), mainly due to increased CH4 conversion factors from liquid manure management systems Furthermore: frequency distribution of manure management systems by district instead of fixed emission factors for each animal type; incorporation of smaller Bundeslaender, which in previous reports had not been included JRC – Ispra

  19. a priori / a posteriori emissions JRC – Ispra

  20. Forward simulation for Pallas (2002) a priori emission inventory (3 Tg CH4/ yr from Finnish wetlands) yields much too high CH4 mixing ratios during summer JRC – Ispra

  21. JRC – Ispra

  22. Workshop"Inverse modelling for potential verification of national and EU bottom-up GHG inventories " under the mandate of Monitoring Mechanism Committee 23-24 October 2003 JRC Ispra Overview Inverse modelling Different approaches NAME (langrangian) LOTOS TM Global/Regional Environment http://ccu.ei.jrc.it/ccu/ JRC – Ispra

  23. General lack of measurement data to constrain the emissions Often ill-posed Strong dependence on prior estimates Treatment of model errors How well does the model represent the local situation at the measurement site? Transport, chemistry, wet/dry deposition Two general problems inverse modeling JRC – Ispra

  24. Model Parameters (Fixed) Model Emission Estimates (P) Model parameters are fixed and only the emissions are optimized Model uncertainties might be translated in (wrong) emission estimates JRC – Ispra

  25. urban BC modelling uncertainties: JRC – Ispra

  26. Representation of urban situation Data are sparse, from scattered campaigns in various years Wet removal, the main removal process, is uncertain Long term consistent measurements needed Should the focus be on BC or total carbon? Hard to say if BC emission inventories are high/low, because JRC – Ispra

  27. Courtesy: Alex de Meij JRC – Ispra

  28. Careful selection of representative stations Be aware of model errors (perform sensitivity analysis) Reasonably good perspectives to attempt inverse modeling (e.g. of SO2 emission distribution over Europe) Even If long-term data are available: JRC – Ispra

  29. Use of satellite data JRC – Ispra

  30. Use of satellite data JRC – Ispra

  31. Might provide the data source needed Integrates various species and altitudes But: Aerosol water adds to uncertainties Cloud interference Validation of Satellite products needed Role EMEP measurement network Use of satellite data JRC – Ispra

  32. Methane emission estimates look promising Lack of measurement data Role EMEP measurement network satellite measurements Good perspectives for aerosol (precursors) if: Long term measurements (inter-calibration!) Careful data selection Reduce model errors Satellite products might fill data void Aerosol water / cloud issues Validation Satellite products Conclusions JRC – Ispra

More Related