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Daiwen Kang Computer Sciences Corporation, Research Triangle Park, NC, USA

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

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Daiwen Kang Computer Sciences Corporation, Research Triangle Park, NC, USA

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  1. On the Model’s Ability to Capture Key Measures Relevant to Air Quality Policies through Analysis of Multi-Year O3 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

  2. 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) O3 (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

  3. Observations and Modeling • Observations: hourly O3 mixing ratios extracted from AQS network from 2001-2010 • Model: CMAQ v4.7 annual simulations from 2002-2006 • 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

  4. Trends in Observed DM8HR O3 Distributions, 2001-2010 • Season (May to September) mean DM8HR O3 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

  5. Variations and Trends In Means and Standard Deviations of Observed and Modeled DM8HR O3 • 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.

  6. Percentage Change of the Observed and Modeled DM8HR O3 (ppb) Mean And Standard Deviation for the Northeast Region Mean Concentration Standard Deviation Percentage Change (%) 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

  7. Spatial Distribution of Mean DM8HR O3 (ppb) and Mean Biases (ppb) Observed DM8HR O3 (ppb) 2002 2005 2006 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

  8. Observed and Modeled Change of 4th Highest DM8HR O3 between Two Years in the Eastern US 2005-2002 2006-2002 Modeled Change (ppb) Observed Change (ppb) Observed Change (ppb) • Compared to 2002, in 2005 and 2006, both increases and decreases are observed and modeled for the 4th highest DM8HR O3 • At a majority of sites, the 4th highest DM8HR O3 decreased from their 2002 levels • The model generally captured the change well, though the simulated magnitude of the change tends to be smaller

  9. Observed and Modeled DM8HR O3 Distributions Observed Modeled • Both observed and modeled DM8HR O3 can be approximated by a normal distribution • Thus, extreme values (such as the 4th 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

  10. Scatter Plots of the 4th Highest DM8HR O3 between Original and Re-Sampling from Normal Distribution (µ, σ) 2002 2005 2006 Original Re-sampling Re-sampling Re-sampling • 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

  11. 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) O3 (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

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

  13. Spatial distribution of Pattern Anomaly(pattern mean – all mean) of DM8HR O3 Values for Pattern1 Observed 2002 2005 2006 Modeled

  14. Observed and Modeled Change between Two Years’ Mean DM8HR O3 (ppb) @ Weather Pattern1 2005-2002 Modeled Observed 2005 - 2002 Modeled Change (ppb) 2006 - 2002 2006-2002 Observed Change (ppb) The model underestimates the change in mean DM8HR O3 for the northeast region

  15. Summary • The observed inter-annual DM8HR O3 variation and trend from 2002-2006 are well reproduced by CMAQ simulations • However systematic biases exist in the MEAN and STD values across years • The 4th highest DM8HR O3 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 O3 are well simulated, but the intensity of simulated inter-annual changes is not as strong as that of the observed changes

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

  17. 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

  18. Spatial Distribution of Pattern Anomaly (pattern mean – all mean) Values for Pattern3 2002 2005 2006 Observed Modeled

  19. Observed and Modeled Change between Two Years’ Mean DM8HR O3 (ppb) @ Weather Pattern3 2005 - 2002 Modeled Observed 2006 - 2002

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