UNCERTAINTY ANALYSIS IN OZONE MODELS Dick Derwent rdscientific, Newbury, United Kingdom Task Force on Measurements and Modelling Cyprus May 2010 This work was supported by the UK Defra under contract AQ 0704

PROBABILISTIC EVALUATION How to handle uncertainty within policy analyses and assessments? Model input data are inherently uncertain but how do these uncertainties propagate into the output? GLUE approach Equifinality

EQUIFINALITY PROBLEM Many different parameter sets within a chosen model structure may be acceptable for reproducing observations It may not be possible to find a single optimal representation in a complex model of a given set of observations Keith Beven (2009) Environmental Modelling: An Uncertain Future

WHAT IS THE UNCERTAINTY IN OZONE MODELS DUE TO THE CHEMICAL MECHANISM ALONE? Chemical mechanisms contain rate coefficient and product yield data covering two major areas: the inorganic chemistry of the fast photochemical balance the organic chemistry of the degradation of VOCs which generates ozone Few of the thousands of reactions involved in the organic chemistry have been studied in the laboratory and many are estimated using structure-activity relationships. In contrast, all of the inorganic reactions have been studied in the laboratory.

UNCERTAINTIES IN CHEMICAL MECHANISMS Inorganic chemistry of the fast photochemical balance Organic chemistry of VOC degradation and O3 production RCHO O3 CO, H2 ,CH4, VOCs hν hν HO2 + RO2 OH NO+HO2, O3 ,RO2+NO HNO3 H2O2 ROOH

MONTE CARLO ANALYSIS OF CHEMICAL MECHANISM UNCERTAINTIES USING A PHOTOCHEMICAL TRAJECTORY MODEL Studied PUMA Campaign episode during June 1999 Air mass trajectories from Met Office NAME model Set up PTM using CBM4 (100 reactions) and MCM CRI v2 (1193 reactions) chemical mechanisms assume every rate coefficient has an uncertainty range of ± 30 % perform Monte Carlo analysis using 10,000 model runs

UNCERTAINTIES IN OZONE MODELS DUE TO CHEMICAL MECHANISMS Condensed MCM CRI mechanism UNCERTAINTY DUE TO VOC OXIDATION ALONE UNCERTAINTY DUE TO FAST PHOTOCHEMICAL BALANCE ALONE

UNCERTAINTIES DUE TO CHEMICAL MECHANISMS UNCERTAINTY DUE TO FAST PHOTOCHEMICAL BALANCE ALONE Standard deviation ± 10.4 ppb No difference because both mechanisms use the same rate coefficient data for the fast photochemical balance

UNCERTAINTIES DUE TO CHEMICAL MECHANISMS Standard deviation ± 4.6 ppb ± 4.4 ppb Surprisingly little difference here. This is because most of the RO2 are reacting with NO because the system is VOC-limited.

MONTE CARLO ANALYSIS FOR UNCERTAINTIES IN THE CHEMICAL MECHANISMS ALONE network observations 10,000 runs ± 30 % uncertainties in all reactions or inorganic only 50%ile Standard Deviation 79.0 79.2 80.2 80.3 ± 11.1 ppb ± 10.4 ppb PUMA campaign 26th June 1999

MONTE CARLO ANALYSIS FOR UNCERTAINTIES IN THE CHEMICAL MECHANISMS ALONE Conclusions: there is little difference between the results from the CBM4 and the CRI v2 mechanisms the CRI v2 gives a much more explicit treatment of VOC chemistry, an increase from 6 emitted VOCs in CBM4 to 115 in CRI v2 the vast majority of the chemical mechanism uncertainty comes from the inorganic chemistry that drives the fast photochemical balance there is little increase in uncertainty in going from the compact condensed CBM4 mechanism to the CRI v2 mechanism based on the MCM

MONTE CARLO ANALYSIS OF ALL PTM MODEL UNCERTAINTIES all dry deposition velocities: x 0 – 1 longitude and latitude of position: ± 0.45o x 1-3; ± 0.28o NH3 VOC NOx SO2 CO CH4 emissions: x ± factor of 2 VOC speciation: x ± factor of 2 isoprene emissions: x ± factor of 4 initial conditions: x ± factor of 1.5 boundary layer depth: x ± factor of 2 relative humidity: x ± factor of 2 temperature: ± 0 – 3 oC choice of NAME trajectory: select 1 out of 1000 independent choices all rate coefficients: ± 30 % These are subjective estimates of uncertainties. PUMA Campaign 1999.

MONTE CARLO ANALYSIS TO FIND ACCEPTABLE PARAMETER SETS Fixed PUMA campaign input for 26th June 1999 Monte Carlo sampled input PTM model Generate 10,000 sets of outputs Fail Pass Reject parameter sets Check against observations Store parameter sets 2,602 acceptable parameter sets

PROBABILISTIC UNCERTAINTY ANALYSIS IN A POLICY CONTEXT PTM model PUMA Campaign 26th June 1999 Monte Carlo analysis PTM model with 2,602 acceptable sets of parameters -30 % NOx control case -30 % VOC control case Base case Which set of controls are more likely to reduce peak ozone to below 60 ppb?

PROBABILISTIC ASSESSMENT OF RESPONSES TO 30 % REDUCTIONS IN NOx AND VOC EMISSIONS O3 decreases O3 increases Percentage of runs below 60 ppb 5.4% 9.9% Decrease of 24 ppb required to take O3 down to 60 ppb Area under curves 2602 acceptable parameter sets PUMA Campaign 26th June 1999

PROBABILISTIC SOURCE ATTRIBUTION 2602 acceptable parameter sets PUMA Campaign 26th June 1999

CONCLUSIONS Probabilistic uncertainty assessments are possible using Monte Carlo sampling Provides a way of handling equifinality Approach is practical with simple trajectory model How are these methods to be applied to the large and complex 3-D Eulerian grid models?