Shao-Hang Chu and W.M. Cox US Environmental Protection Agency RTP, NC 27711

1. The Impact of Short-term Climate Variations on Predicted Surface Ozone Concentrations in the Eastern US 2020 and beyond Shao-Hang Chu and W.M. Cox US Environmental Protection Agency RTP, NC 27711

2. Introduction • Models are great tools to test ideas. Their usefulness, however, depends on their ability to simulate the reality and predict the future. • In air quality management, photochemical models are used to test the emission control strategies and predict future concentrations. • However, to clearly identify the effectiveness of our emission control strategies the models are run with a fixed set of meteorology conditions. • For multi-year projections, climate variability may have a significant impact on model predictions. • For this reason, a combined photochemical/statistical modeling approach is developed.

3. A Hybrid Modeling Approach • In this study, the CAMx (v. 3.1) model is used to predict the ozone design value (DV) concentrations at 2020 resulted from all existing and planned emission controls and projected growth. • A Statistical model, the critical design value (CDV) model, is then applied to estimate the impact of short-term climate variability on CAMx predicted ozone DV at 2020 and beyond.

4. Critical Design Value Model • CDV model is a statistic model which can be used to predict the probability of future violation of the NAAQS at any monitoring site with reasonable length of continuous DV data (Chu, 2000). • Its prediction is based on the site specific average pollutant DV concentration, trend and inter-annual variability in the past which is obviously a function of meteorology and emissions. • The inter-annual variability of pollutant concentrations due solely to meteorology fluctuations can be isolated from the observed data (Cox and Chu, 1993,1996).

5. CDV - A Statistical Model The design value (DV) is a 3-year average quantity, it is not unreasonable to assume that it is normally distributed and the probability of DV to exceed any value, X, can be expressed as: F = P[DV>X | DV~N(m,s2)] And a statistical model of a critical design value can be written as: CDV = NAAQS/(1 + tc* CV) where CDV: Critical Design Value – The highest average design value any site could have before a future DV would be predicted to violate the NAAQS with a specific probability NAAQS: Air Quality Standard tc: Critical t – value CV: Coefficient of Variation – A normalized Inter-annual variability of DV

6. CDV and Probabilistic Forecast • CDV model can be used for long-term probabilistic forecast as long as the CV is relatively invariant or changes very slowly in time. • Climate change over a time-scale of several decades is known to be very small; thus, it is a very good candidate for this application. • In this study, we have applied the CDV model to predict the impact of short-term climate variations on ozone attainment at 2020 and beyond.

7. Global Mean Surface Temperature Anomaly (Deg C)Based on IPCC Report

8. Ozone Temperature RelationshipBased on U.S. Data 70 70 8-hr ozone 65 65 Average Ozone, ppb 60 60 55 55 50 50 temperature temperature 1997 1997 1998 1998 1999 1999 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005

9. CDV Model Performance 1:CDV’s Ability To Predict The Current Ozone Non-attainment Areas • The model predicted probability of violation of the NAAQS for each site at 2004 and beyond is based on the statistics of 1990-2003 ozone data. • The red dots represent sites with more than 50% probability of violating the NAAQS beyond 2004. • The grey-shaded areas are the 8-hour ozone non-attainment areas designated in 2005.

11. CDV Model Performance 2:CDV’s Ability to Predict Future Rate of Violation of NAAQS • The CDV model has been used to predict the future rate of violation of ozone NAAQS • The 1st map shows the predicted frequency of violation of the ozone 8-Hr NAAQS based on the statistics of 1982 to 1991 ozone DV Data • The 2nd map shows the actual observed rate of violation of the ozone 8-Hr NAAQS during 1992-2003.

14. CAMx Model Predicted Ozone Design Values in the Eastern U.S. at 2020 Due to Emission Changes • In this study, CAMx (v. 3.1), a state of science photochemical model, has been used to predict the 8-hour ozone DV in the Eastern U.S. at 2020 due to emission changes. (EPA, 2005) • The emission changes are calculated based on current and future emission reductions specified in existing regulations such as CAIR, CAMR and CAVR, etc., over the 2001 inventory and projected future emission growth. • The meteorology conditions used in the CAMx predictions are held constant at 1995 levels.

16. CDV Model Predicted Probability of Violating the Ozone NAAQS at 2020 and Beyond • The CDV model is then applied to estimate the impact of short-term climate variability on the CAMx predicted 8-hour ozone DV in the eastern U.S. at 2020 and beyond. • In the 3 experiments the predicted probability of future violation of the NAAQS is based on the CAMx predicted concentrations and observed DV variations attributed to the inter-annual climate variability in recent years (1990-2004). • The color code indicates the probability category of future violation of the 8-hour ozone NAAQS.

20. Summary and Conclusions • The study has shown that the hybrid modeling approach can take into account the influences of both emission controls and the climate variability in predicting the future ozone conditions. • Comparing the CDV modeling results with CAMx results we find that including the impact of short-term climate variations reveals potential future non-attainment areas that are otherwise not identified by CAMx predictions. • The results can be used by policy makers to identify potential problem areas and improve our current control strategies to reduce risk of violation of the ozone NAAQS in the future. • The high success rate in multi-year predictions suggests that this approach can be used to realistically assess the existing and planned multi-pollutant control strategies.