1. 16th Task Force on Measurement and Modelling Meeting:
Main results of the Eurodelta 3 exercise Phase I on criteria pollutants
Bertrand BESSAGNET (INERIS) on behalf of the EURODELTA team
CONVENTION ON LONG-RANGE
TRANSBOUNDARY AIR POLLUTION
16th Task Force on Measurement and Modelling Meeting:
5 - 8 May 2015 in Krakow

2. B. Bessagnet, A. Colette, F. Meleux, L. Rouïl, A. Ung, F
B. Bessagnet, A. Colette, F. Meleux, L. Rouïl, A. Ung, F. Couvidat (INERIS) P. Thunis (EC JRC) C. Cuvelier (ex. EC JRC) S. Tsyro (Met Norway) R. Stern (FUB) A. Manders, R. Kranenburg (TNO) A. Aulinger, J. Bieser (HZG) M. Mircea, G. Briganti, A. Cappelletti (ENEA) G. Calori, S. Finardi, C. Silibello (ARIANET) G. Ciarelli, S. Aksoyoglu, A. Prévot (PSI) M.-T. Pay, J. M. Baldasano (BSC) M. García Vivanco, J. L. Garrido, I. Palomino and F. Martín (CIEMAT) G. Pirovano (RSE) P. Roberts, L. Gonzalez (CONCAWE) L. White (AERIS EUROPE) L. Menut (LMD, IPSL, CNRS) J.-C. Dupont (IPSL, CNRS) C. Carnevale, A. Pederzoli (UNBS)

3. The Eurodelta III exercize
Two phases:
Simulation of intensive measurement campaigns + EMEP measurements
1 Jun - 30 Jun 2006 (Summer)
8 Jan - 4 Feb 2007 (winter)
17 Sep - 15 Oct 2008 (fall)
25 Feb - 26 Mar 2009 (winter)
Retrospective analysis (2010, 1999, 1990)  Full trend analysis
Common inputs for models : meteorology (IFS except for CMAQ and RCG), emissions (EC4MACS dataset), boundary conditions (MACC), domain (except CMAQ)
Iterative process, with several improvements performed by the modelling teams
One report written for the 2009 campaign and 5 publications ongoing for all campaigns

4. Teams & models RCG : different meteorology
Model acronym in this study
Simulated campaigns
PSI/RSE
CAMx
CAMX
2006, 2007, 2008, 2009
INERIS
CHIMERE
CHIM
HZG
CMAQ
MSC-W - Met.NO
EMEP
TNO
LOTOS-EUROS
LOTO
ENEA
MINNI
FUB
RCG
2008, 2009
RCG : different meteorology
CMAQ : different meteorology and domain
ENS=CAMX,CHIM, EMEP, LOTO, MINNI

5. Wind speed (U10) and Temperature (T2M)
~1 million data
~1 million data

6. Planetary Boundary Layer (PBL)
Various ways to calculate the PBL
Very different PBL over the oceans
Negative biases for all models except for CMAQ (compared to PBL at 12:00)
The most negative biases are observed for MINNI on average in Europe

7. Diurnal cycle at SIRTA (PBL, U10) – CAMX versus Obs
Results for the 2009 campaign
Positive bias particularly important early in the morning for the wind speed
Important negative bias for the PBL during the night
Shift of three hours for the collapse of the PBL

8. Ozone Strong impact of boundary conditions RMSE close to 20 µg m-3
CMAQ gives the lowest correlations for all campaigns
Negative bias at several medium altitude / shore sites
ENS (without RCG and CMAQ)

9. NO2 Similar correlations (0.6-0.7)
CMAQ overestimates the concentrations
CAMX systematically underestimate the concentrations
CMAQ & CHIMERE overshoot at night, all models underestimate in the afternoon

10. SO2
The coefficient of variation is the lowest over emission areas but very high in remote areas like over the oceans
Very different way to simulate the SO2 chemistry and deposition processes in the models.
Diurnal cycle??!!

11. PM10
In general, the models underestimate, MINNI and EMEP have the lowest underestimations (PBL effect?)
Flat diurnal cycle in the observations, slight decrease in the afternoon for the models

12. PM10
Left column: Average PM10 concentrations (µg m-3) of the “ensemble” (ENS) for the 2009 campaign with corresponding observations (coloured dots).
Right column: coefficient of variation of models (no unit) constituting the ensemble with corresponding normalized root mean square errors of the “ensemble” (coloured dots).

13. PM2.5 General underestimations particularly in winter
Correlations between 0.5 and 0.7
Flat profile with a slight increase for the 2006 campaign in the observations – Models see a decrease in the afternoon

14. PM2.5 diurnal cycles

15. Nitrate & OM Systematic underestimation
Coarse nitrate?
Systematic underestimation
Lack of emissions – SOA production
Underestimation of higest values
Robustness of SIA chemistry

16. Diurnal cycles of nitrates & ammonium

17. Some conclusions
Important role of boundary conditions on O3 mean values
The ensemble gives a good picture of PM patterns in Europe with a systematic underestimation
Diurnal cycles show a large impact of the PBL diurnal evolution
Quality of meteorological data
Models underestimate the PBL, for some of them this undertimation could be very important during the night
Wind speed is overestimated on average (problem for low wind speed conditions)
There is a lack of chemical production in the afternoon for PM
The highest PM values are underestimated (role of dust and OM) – lack of sources (condensable organic species)
Some models can be better for one species but this can hide compensation effects on some PM components (next presentation)

19. Ensemble of CO – Coefficient of variation
Low coefficient of variation far from the sources
Vertical mixing is certainly the most influencial variable affecting the dilution/mixing of sources

20. PM2.5
Left column: Average PM2.5 concentrations (µg m-3) of the “ensemble” (ENS) for the 2009 campaign with corresponding observations (coloured dots).
Right column: coefficient of variation of models (no unit) constituting the ensemble with corresponding normalized root mean square errors of the “ensemble” (coloured dots).