On the Model’s Ability to Capture Key Measures Relevant to Air Quality Policies through Analysis of Multi-Year O 3 Observations and CMAQ Simulations Daiwen Kang Computer Sciences Corporation, Research Triangle Park, NC, USA Shawn Roselle, Christian Hogrefe, Rohit Mathur, and S. Trivikrama Rao AMAD/NERL, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA

Motivation Inherent uncertainties in model formulation and input data influence the predictions and inferred trends of ambient pollutant levels Given the uncertainties, approaches need to be developed that build on robust characteristics of the model for predicting impacts of emission controls We examine multi-year trends in observed and modeled daily maximum 8-hr (DM8HR) O 3 (influenced by changes in a multitude of processes such as emissions, meteorology, and global background) In practice, however, many processes are held/assumed constant when examining emission control scenarios – Examine multi-year changes in ambient levels under similar synoptic conditions

Observations and Modeling Observations: hourly O 3 mixing ratios extracted from AQS network from Model: CMAQ v4.7 annual simulations from – 24 vertical layers – CB05 Chemical Mechanism – Consistent GEOS-CHEM boundary conditions Domain: 12-km Eastern U.S. Emissions: based on 2002 National Emissions Inventory – Year-specific updates to fires, mobile and EGU point (CEMS data) emissions Analysis Period: May - September

Trends in Observed DM8HR O 3 Distributions, Season (May to September) mean DM8HR O 3 mixing ratios were calculated at each AQS site within the eastern U.S. domain Boxplots represent the distribution of these seasonal means across all sites

Variations and Trends In Means and Standard Deviations of Observed and Modeled DM8HR O 3 Seasonal means and standard deviations (STD) were calculated at each site and then spatially averaged across all sites in the domain Both mean and STD change over time. The model overestimates mean values but underestimates STD.

Percentage Change of the Observed and Modeled DM8HR O3 (ppb) Mean And Standard Deviation for the Northeast Region Mean Concentration Percentage Change (%) Standard Deviation Percentage Change (%) Percentage change is calculated relative to the 2002 value as the base year for this analysis 2005 and 2006 are considered as years of interest for some of the subsequent analyses The model generated percentage changes for both mean and STD generally track the observed changes

Spatial Distribution of Mean DM8HR O 3 (ppb) and Mean Biases (ppb) Observed DM8HR O 3 (ppb) Mean Bias (ppb) Where observed values are higher, the mean biases are lower, and vice versa. The model tends to overestimate lower values but better reproduces higher values

Observed and Modeled Change of 4 th Highest DM8HR O 3 between Two Years in the Eastern US Modeled Change (ppb) Observed Change (ppb) Compared to 2002, in 2005 and 2006, both increases and decreases are observed and modeled for the 4 th highest DM8HR O 3 At a majority of sites, the 4 th highest DM8HR O 3 decreased from their 2002 levels The model generally captured the change well, though the simulated magnitude of the change tends to be smaller

Observed and Modeled DM8HR O 3 Distributions Both observed and modeled DM8HR O 3 can be approximated by a normal distribution Thus, extreme values (such as the 4 th highest) can be derived from the mean and STD Robust “bias corrections” on MEAN and STD magnitude could thus yield accurate estimate of extreme values (and their changes) over time ObservedModeled

Scatter Plots of the 4 th Highest DM8HR O 3 between Original and Re-Sampling from Normal Distribution (µ, σ) Original The derived values from the distributions are in good agreement with the original values for both observations and model simulations The derived values tend to be on the high end, especially where the values are higher Re-sampling

Motivation Inherent uncertainties in model formulation and input data influence the predictions and inferred trends of ambient pollutant levels Given the uncertainties, approaches need to be developed that build on robust characteristics of the model for predicting impacts of emission controls We examine multi-year trends in modeled and observed daily maximum 8-hr (DM8HR) O 3 (influenced by changes in a multitude of processes such as emissions, meteorology, and global background) In practice, however, many processes are held/assumed constant when examining emission control scenarios – Examine multi-year changes in ambient levels under similar synoptic conditions

MSLP for the Six Patterns Determined from the R2 MSLP Dataset Pattern Frequencies, 2002 – 2006 (“UA”: unassigned) Synoptic Weather Pattern Classification

Spatial distribution of Pattern Anomaly (pattern mean – all mean) of DM8HR O 3 Values for Pattern Observed Modeled

Observed and Modeled Change between Two Years’ Mean DM8HR O 3 Weather Pattern Observed Modeled Modeled Change (ppb) Observed Change (ppb) The model underestimates the change in mean DM8HR O 3 for the northeast region

Summary The observed inter-annual DM8HR O 3 variation and trend from are well reproduced by CMAQ simulations – However systematic biases exist in the MEAN and STD values across years The 4 th highest DM8HR O 3 values can be approximated reasonably well over all the simulated years in this study using the MEAN and STD values from the assumed normal distributions – Robust “bias corrections” on MEAN and STD magnitude could thus yield accurate estimate of extreme values (and their changes) over time When classified by weather patterns, the spatial variations of annual mean as well as the inter-annual changes for DM8HR O 3 are well simulated, but the intensity of simulated inter-annual changes is not as strong as that of the observed changes

Future Research Additional analysis for longer time period is necessary when continuous model simulations for more years are available

Acknowledgement Thank Kristen Foley for her insightful review and comments Thank Wyat Appel for managing the CDC PHASE Project CMAQ simulations and make it available for our analysis Thank all those involved in the CMAQ simulations and AQS data processing

Spatial Distribution of Pattern Anomaly (pattern mean – all mean) Values for Pattern Observed Modeled

Observed and Modeled Change between Two Years’ Mean DM8HR O 3 Weather Pattern3 Observed Modeled