EFnet: an added value of multi-model simulation

1. EFnet:an added value of multi-model simulation Mikhail Sofiev Finnish Meteorological Institute

2. Content • Introduction: why the ensemble modelling • Examples of single- and multi-model ensembles • EFnet: ensemble forecasting and model development • Conclusions

3. Ensemble modelling: why? • Atmospheric processes are stochastic • The smaller scale and the shorter averaging the higher uncertainty • small-scale processes, as well as some chemical chains of reactions can be chaotic by nature • Deterministic models work poor at small scales, with short averages and complicated chemical chains. • Reason is NOT (well, not only) model weaknesses but rather stochastic nature of the atmosphere • Right form of question: probability terms • Ways to answer the probabilistic questions • make probabilistic models (what about physics?) • run ensembles of existing deterministic model(s)

4. Types of ensembles • Single-model multi-setup ensemble • One deterministic model • Input forcing, initial and/or boundary conditions are perturbed in a “reasonable” way or taken from several sources • Each perturbed set of data is computed in a normal way • Output datasets are considered as realizations of a stochastic process • Example: ECMWF ensemble weather forecast (operational !) • Multi-model ensemble • Several deterministic (and/or other) models are used • Each model uses own input datasets and/or common set(s) • Output datasets are considered as realizations of a stochastic process • Example: EU FP5 ENSEMBLE project, NKS MetNet network, EMEP Pb-1996 model inter-comparison

5. Single-model ensemble: ECMWF Source: www.ecmwf.int

6. Multi-model ensemble: NKS MetNet

7. Multi-model ensemble: EU-ENSEMBLE project Source: Galmarini, 2004

8. Multi-model ensemble: EMEP Pb model inter-comparison Source: Sofiev et al., 1996

9. Multi-model debugging • Source term are the same for both models • Meteorological data are the same but: • Models used own meteo pre-processors Source: Potempski, 2005

10. EFnet: forecasting and model development • Validation of the model results • Operational Quality Assurance QA: • simple, basic statistics, provides the basic quality scores for individual models • Scientific assessment of the model quality: • detailed speciation, precursor analysis, detailed statistics • annual model inter-comparison exercises during high O3, PM seasons • Statistical correction of the model forecasts • Based on past model quality scores – both operational and scientific QA • Model-specific • some models may already have it • depends on the model individual quality score

11. EFnet: forecasting and model development (2) • Ensemble forecast (feasibility study) • Straightforward generation of statistically-sounding ensemble is far beyond the reach of current computers • Multi-model results allow approaching an uncertainty-disclosing problem in air quality prediction problem • Predictability of ozone and PM levels • Points of chaos, multi-track developments, etc • Output: ensemble average (if possible) + uncertainty range

12. Conclusions • Multi-model ensemble is a brand-new tool in air quality assessment • First ensembles were applied in emergency modelling • deterministic models work very poorly • established co-operation, need for mutual backup • comparatively straightforward (cheap) application • Outcome from the ensemble usage • MUCH better stability than individual models • show uncertainty ranges, areas/times of instability, errors or model failures • often agrees with measurements better than individual models • EFnet ensemble approach • Build and understand the multi-model ensemble for O3 and PM • If successful, generate the ensemble air quality forecast