1. IIASA Riku Suutari, Markus Amann, Janusz Cofala, Zbigniew Klimont Wolfgang Schöpp A methodology to propagate uncertainties through the RAINS scenario calculations A study funded by the UK Department of Environment, Food and Rural Affairs

2. IIASA Uncertainty analysis through error propagation Emission model Transfer matricesRegional emissions Dispersion model Critical load model Protection isolinesDeposition estimate Critical load functions Environmental impact

3. IIASA Assumed uncertainties in input parameters Coefficients of variation SO 2 NO x NH 3 Activity rates 1990 2010 0.06-0.20 0.12-0.30 0.05-0.50 0.10-1.00 Emission factors0.05-0.100.075-0.150.15-0.40 Removal efficiencies0.005-0.050.015-0.100.05-0.15 Atmospheric transfer coefficients 0.10-0.15 Critical loads CL max 0.220.12

4. IIASA Uncertainties of intermediate results 95% confidence intervals SO 2 NO x NH 3 Emissions±13 % ±15 % Deposition± 14-17 % Critical loads excess (area of protected ecosystems) -5% - +2.5 %

5. IIASA Probability for protecting ecosystems Gothenburg Protocol 2010

6. IIASA Conclusions General A methodology for uncertainty treatment is now available, but conclusions are still incomplete and preliminary : Quantifications of uncertainties are the most uncertain elements in uncertainty analyses Correlations and distributions difficult to quantify Uncertainties only for specific model output, but not for general ‘model’

7. IIASA Conclusions Uncertainties in the RAINS model chain The potential for error compensation determines uncertainties of emission and deposition estimates Spread of ecosystems sensitivity determines uncertainties in ecosystems protection Uncertainties in ecosystems protection are smaller than many uncertainties of input parameters Uncertainties are determined by data availability and can be influenced by model design Overall largest uncertainties due to sub-grid deposition and limited data about critical loads

8. IIASA Conclusions Uncertainty results in a policy context Do these type of conclusions (quantified uncertainties) help the decision makers? Implications for target setting! Is a new approach required? Error propagation useful for scenario analysis For optimization different approach (robustness!) necessary