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

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

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

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

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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

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

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

Global and European regions JRC – Ispra

Global and European regions JRC – Ispra

monitoring sites JRC – Ispra

Schauinsland JRC – Ispra

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

CH4 emission distribution - bottom up JRC – Ispra

CH4 emission distribution - a posteriori JRC – Ispra

a priori / a posteriori emissions JRC – Ispra

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

a priori / a posteriori emissions JRC – Ispra

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

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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 http://ccu.ei.jrc.it/ccu/ JRC – Ispra

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 JRC – Ispra

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 JRC – Ispra

BC modelling uncertainties: urban JRC – Ispra

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

Courtesy: Alex de Meij JRC – Ispra

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) JRC – Ispra

Use of satellite data JRC – Ispra

Use of satellite data JRC – Ispra

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

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