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

3. What is inverse modeling?
Emission Estimates (P)
Model Parameters (P)
Output (C(x,t))
Measurements (M(x,t))
Sensitivity:
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4. Region of influence 1/8/2001 - 19/8/2001
Sensitivity: Minos 2001
Region of influence 1/8/ /8/2001
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5. JRC – Ispra

6. JRC – Ispra

7. From Sensitivity to Inverse modeling
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
JRC – Ispra

8. P. Bergamaschi, M. Krol, F. Dentener, and F. Raes
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, …)
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10. Global and European regions
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11. Global and European regions
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12. monitoring sites
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13. Schauinsland
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14. further European sites
complete set of 56 sites (year 2001) ftp://ftp.ei.jrc.it/pub/bergamas/CH4BR/
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15. CH4 emission distribution - bottom up
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16. CH4 emission distribution - a posteriori
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17. a priori / a posteriori emissions
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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
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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
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21. JRC – Ispra

22. Overview Inverse modelling Different approaches NAME (langrangian)
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
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23. Two general problems inverse modeling
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
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24. Model Parameters (Fixed) Emission Estimates (P)
Model parameters are fixed and
only the emissions are optimized
Model uncertainties might be translated in
(wrong) emission estimates
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25. BC modelling uncertainties:
urban
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26. Hard to say if BC emission inventories are high/low, because
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?
JRC – Ispra

27. Courtesy: Alex de Meij
JRC – Ispra

28. Even If long-term data are available:
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)
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29. Use of satellite data
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30. Use of satellite data
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31. Use of satellite data 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
JRC – Ispra

32. Conclusions 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
JRC – Ispra