1. EURODELTA Preliminary results
A project to evaluate uncertainty in modelling source-receptor relationships used in air quality policy
Preliminary results

2. Objectives are to answer:
Do regional air quality models produce a consistent response to emission changes ?
What is the range of modelled responses to emission changes ?
What are confidence limits for the modelling used in policy ?
Hei Laurence, I would focus on the evaluation of uncertainties, as it is in that session that you present the EURODELTA project.
Therefore, I also would put the TNO inter-comparison as 3rd objective and not 1st.
PR – made some changes to tighten the English – change to active tense and to bring out the test to the EMEP model – suggest drop the performance against data as a bullet because we don’t really talk about this – the JRC slides allow it to be said

3. 5 modelling groups involved in the SR calculations
LOTOS
Emep-Unified
REM3
CHIMERE
PTh: We would propose to add the TM5 model even though they participated only to part of the scenarios.
MATCH
With the support of

4. The modelled scenarios
Base cases : 2000, 2010, 2020
Consistent with the baseline scenarios defined in the CAFE programme
PTh: What about the other set of scenarios? Eurodelta 1 and Italy reductions? In total 28 scenarios

5. The JRC tool

6. Mean Ozone (i) Base case – Emissions year 2000, Met. year 1999 max
Differences between model results of about 10 ppb in the rural areas and over the countries ; largest differences in winter
Model variabiliy of 10 ppb represents about 40% of the ensemble value
Models are closer in highly populated areas and more different in rural areas and over the seas
max
ensemble
min
I would add a picture with the mean Ozone results for 2000 given by the ensemble – and keep the model variability picture
PR clarify reference year
Countries

7. Why such differences between models in base year calculations?
Boundary conditions?
Input of O3 and precursors in the domain
Vertical exchanges?
Paramerisation of vertical mixing – possibly night time as results are overall consistent with differences greater for low O3
Biogenic contributions?
Models calculate biogenic emissions internally

8. Mean Ozone (ii) – Scenario Analysis Emissions in 2020 – Emissions in 2000
All models agree that the effect of emission reductions on mean O3 is small:
ENSEMBLE gives a reduction of about 1-2 ppb (2- 5%) in rural areas,
ENSEMBLE shows an increase in cities of about 2-4 ppb
The variation between models is about 0.5 ppb in rural areas and 1-2 ppb where emissions are higher.

9. mean NO2 decreases as mean O3 rises
ensemble in cells
min-max about ensemble
High
low
pop
Rural areas

10. SOMO35 (i)- Base Case
MODEL VARIABILITY
Variability is larger but quite comparable to what is seen for mean O3 (30-50%)
Larger differences in the UK, in Spain, in the Netherlands and in Northern Italy
Variability appears smaller in the rural areas because model differences at lower O3 (below 35ppb) do not affect SOMO35 but they do affect the mean (in winter for instance)
Expected sensitivity in areas where the concentrations are close to the threshold.

11. SOMO35 (ii) Emissions in 2020 – Emissions in 2000
Ppm.day
ENSEMBLE SHOWS
Reduction of 1 ppm.day (about 30%) between 2000 and 2020 across all EMEP cells
Less significant effect from titration
Variation around the ensemble is about 0.7 to 0.5 ppm.day ( %)

12. Mean PM2.5 Base case - Emissions year 2000, Met. year 1999 max
Model variability is larger than for ozone (more than 50%, 5 g/m3)
Large ( up to 8 g/m3 ) Country to Country differences
Model variations greatest in the Eastern part of Europe and in hot spots in Benelux and Italy.
max
ensemble
min
Countries

13. mean PM2.5 - scenario analysis Emissions in 2020 – Emissions in 2000
ENSEMBLE shows:
reduction of 3-4 g/m3 (~30%) with higher values in populated areas and lower values in rural areas.
Variability between models is ~ g/m3 (~ 50%).
Country differences can still be large

14. Country averaged base-case and reduction scenarios show strong geographical variations..... why ?
base case averaged PM2.5
max
ensemble
min
averaged scenario difference in PM2.5 for emission change
Countries

15. ..... because the different inorganic contributions to PM2.5 vary spatially.
Ammonium predictions are most different in the Po Valley and in Eastern Europe
Sulfate predictions are most different in Eastern Europe
Nitrate predictions are most different in the Benelux countries and in the PO valley

16. The model response to emission changes from 2000 to 2020 shows a different geographical distribution for PM2.5 and for SOMO35

17. PM2.5 overview - 3-4 mg/m3 1-2 mg/m3 50-70%
ENSEMBLE response to 2020 reductions
Model variability
Variability in %

18. Somo35 overview - 1 ppm.day 0.5-0.7 ppm.day 50-70%
ENSEMBLE response to 2020 reductions
Model variability
Variability in %

19. The EMEP model is generally close to the ENSEMBLE both for base case and reduction scenarios in most of the countries
max
ensemble
EMEP
min
mean O3
max
ensemble
EMEP
min
mean PM2.5

20. Source Receptor relationships analysis (in progress)
Emission reductions (total emissions) of 25% and 50% of the main pollutants in Germany, Italy, France and North and Mediterranean Seas:
Compared effects on the neighbouring areas (“LRT”) and on the areas themselves
The air quality improvement with countries emission reductions beyond 2020 is small
Variability between the models of the same order of magnitude as the air quality improvement!
More investigations are needed and planned…..

21. Example: effect of a 50% French Nox reduction on German stations
SOMO35

22. Population decreasing
Effect on SOMO35 in some grid squares in France of Reducing NOx by 50 % in Germany (from 2020 levels)
Population decreasing
Effect on SOMO35 in some grid squares in France of Reducing VOC by 50 % in Germany (from 2020 levels)
Population decreasing

23. Example: effect of a 50% Italian Nox reduction on Austrian stations
PM2.5

24. Initial conclusions (I)
Different models generally agree in their responses to emission reductions for the meteorological years 1999 and 2001:
the overall magnitude of the responses is broadly similar
the geographic spatial distribution is broadly similar
differences between highly populated areas and rural areas are apparent.
ENSEMBLE is relevant for key indicators and the EMEP model predictions are usually close to the ENSEMBLE.
The model variability can be high, up to 50-70% of the response to emission changes for key indicators (e.g. PM2.5).
All models predict that reducing emissions beyond 2020 Current Reduction Plans has a small effect on concentrations.
I have taken away your conclusion that “Model variability averaged over the countries generally higher for the scenarios than for the best case (except for mean O3 ) » because I do not think that it holds (see Table 2 in my notes, where the model variability is not very different between base case and scenarios, but is significantly different for the different indicators – can we not look at the indicators instead, by completing the Table 2, p.e. ?)
***This means that the variability between the models is systematically of the same order of the concentration reductions ! (this sentence is the same as the previous one but it is more negative.. Which one do you prefer to keep?)

25. Initial conclusions (II)
For PM2.5 and SOMO35, the model variability is identified to be largest in:
high NOx emissions areas (Benelux, Northern Italy, UK)
Eastern part of Europe where high reductions of SO2 and NH3 are expected
Big cities (Milan, Paris, Madrid, London, Warsaw….)
So the model variability is largest in the areas of policy interest (high exposure areas like big cities)
Therefore it is policy relevant that the EMEP model is usually close to the ENSEMBLE
I think we should add a transparency

26. Further work (I)
Better understanding of the reasons for the variability of the models
Photochemistry modelling parametrisations
Choice of Boundary Conditions
Vertical exchange
Biogenic emissions
Effect of numerical interpolations
Study of inter-annual variability in EURODELTA
to determine for which air pollution indicators and for which areas in Europe variability between the models is generally driven either by emission reductions or by meteorology
Propo
PTh: we have added biogenic emissions in the list.

27. Further work (II) Assessment of the validity of the ENSEMBLE approach
There is a need for model inter-comparisons excercises like EURODELTA to identify the robustness of one single model results
This work contributes to evaluate the uncertainties in the present policy approach related to the choice of models. We need to investigate how these results on model variability, can be incorporated into IAM framework for policy use.