2013 Progress Report: Extreme Air Quality Events Using a Hierarchy of Models: Present and Future

EPA Grant Number: R835205
Title: Extreme Air Quality Events Using a Hierarchy of Models: Present and Future
Investigators: Hess, Peter , Berner, Judith , Grigoriu, Mircea Dan , Mahowald, Natalie M.
Institution: Cornell University , National Center for Atmospheric Research
EPA Project Officer: Chung, Serena
Project Period: June 1, 2012 through May 31, 2015 (Extended to May 31, 2016)
Project Period Covered by this Report: August 30, 2012 through August 30,2013
Project Amount: $746,825
RFA: Extreme Event Impacts on Air Quality and Water Quality with a Changing Global Climate (2011) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Global Climate Change , Water and Watersheds , Climate Change , Air , Water


This grant funds interdisciplinary research to address the following broad questions:
  • Under current conditions, what is the probability of an extreme pollution event?
  • During the next century, how are the probability, frequency, duration, and severity of high pollution episodes likely to change under future emission and climate scenarios?
  • What are the geographic, meteorological, climatological, and chemical conditions that could contribute to extreme pollution episodes in the United States?
  • What parts of the country are particularly sensitive to extreme pollution events now and in the future?
  • How do extreme pollution events relate to heat waves? What are the feedbacks between heat waves and severe pollution events?

Progress Summary:

In the past year, we have initiated a statistical analysis of the extremes in the CASTNET ozone data in order to understand the impact of analysis methodology on estimating extreme values. Refined analysis techniques suggest the tails of the ozone distribution over the United States are heavier than previously thought. This has the implication that extreme ozone concentrations may be more severe than previously reported. In addition extreme value theory is being used to examine the joint extremes of temperature and pollution.
We have also compare extremes in observed and simulated ozone data using both analyzed meteorological fields to drive the Community Atmosphere Model with chemistry (CAM-chem) and self-generated meteorological fields from a general circulation model. According to statistical tests using mixed effect modeling, we finds that over the Northeastern United States most versions of CAM-chem (when corrected for the high bias in simulated ozone) have longer return intervals than suggested by the CASTNET, suggesting the model under predicts extreme events. Over the Western and Southeastern United States, the simulated return intervals are for the most part not statistically different than those obtained using CASTNET measurements.
We have also investigated the impact of two stochastic parameterizations, namely a stochastic kinetic-energy backscatter scheme (SKEBS) and a stochastically perturbed parameterization scheme (SPPT) in CAM. These schemes are routinely used to assess uncertainty in Numerical Weather Prediction, but not yet in climate models. The impact of these schemes on mean bias, climate variability and the tail behavior of dynamically relevant variables is investigated. We find that adding a stochastic parameterization improves the low-frequency variability in the Northern Hemisphere without introducing large biases. The return values signifying extreme events are increased. While large biases over land dominate the difference between simulations and observations, stochastic parameterizations capture extreme events better than the unperturbed model, only after the mean model bias is removed from all simulations.
Analysis of extremes in the IGAC/SPARC Community Climate Modeling Initiative simulations show ozone extremes vary dramatically over the 140 years of the simulation. In particular, Wenxiu has noted a shift from heavy tailed to light tailed distributions around 2040, attributed to the decrease in future projections of U.S. emissions.

Future Activities:

We expect to complete our statistical analysis of CASTNET ozone observations examining the joint ozone-temperatue extremes. Following this analysis, it is expected that we will conduct a more extensive analysis of simulated extreme events using the CCMI simulations. The analysis will include: a) extensive comparison between simulated and measured extremes in both temperature and ozone; b) assessing extremes in a much longer timeseries than the CASTNET data permits; c) assessing how extreme values change under non-stationary conditions including changes in emissions and meteorology. We will continue our investigation of stochastic parameterizations and their impact on extreme events in CAM focusing on summertime blocking and stagnation episodes and extending an evaluation of these parameterizations to air quality.

Journal Articles on this Report : 6 Displayed | Download in RIS Format

Other project views: All 22 publications 10 publications in selected types All 10 journal articles
Type Citation Project Document Sources
Journal Article Berner J, Fossell KR, Ha S-Y, Hacker JP, Snyder C. Increasing the skill of probabilistic forecasts: understanding performance improvements from model-error representations. Monthly Weather Review 2015;143(4):1295-1320. R835205 (2013)
R835205 (Final)
  • Full-text: AMS-Full Text PDF
  • Abstract: AMS-Abstract
  • Journal Article Brown-Steiner B, Hess PG, Lin MY. On the capabilities and limitations of GCCM simulations of summertime regional air quality:a diagnostic analysis of ozone and temperature simulations in the US using CESM CAM-Chem. Atmospheric Environment 2015;101:134-148. R835205 (2013)
    R835205 (2014)
    R835205 (Final)
    R834283 (Final)
  • Full-text: ScienceDirect-Full Text HTML
  • Abstract: ScienceDirect-Abstract
  • Other: ScienceDirect-Full Text PDF
  • Journal Article Clark SK, Ward DS, Mahowald NM. The sensitivity of global climate to the episodicity of fire aerosol emissions. Atmospheric Chemistry and Physics Discussions 2013;13:23691-23717. R835205 (2013)
    R835205 (Final)
  • Full-text: EGU Journals-Full Text PDF
  • Abstract: EGU Journals-Abstract HTML
  • Journal Article Franzke CLE, O'Kane TJ, Berner J, Williams PD, Lucarini V. Stochastic climate theory and modeling. WIREs Climate Change 2015;6(1):63-78. R835205 (2013)
    R835205 (Final)
  • Full-text: Wiley-Full Text PDF
  • Abstract: Wiley-Abstract & Full Text HTML
  • Journal Article Riddick S, Ward D, Hess P, Mahowald N, Massad R, Holland E. Estimate of changes in agricultural terrestrial nitrogen pathways and ammonia emissions from 1850 to present in the Community Earth System Model. Biogeosciences 2016;13(11):3397-3426. R835205 (2013)
    R835205 (Final)
  • Full-text: Biogeosciences-Full Text PDF
  • Abstract: Biogeosciences-Abstract
  • Journal Article Romine GS, Schwartz CS, Berner J, Fossell KR, Snyder C, Anderson JL, Weisman ML. Representing forecast error in a convection-permitting ensemble system. Monthly Weather Review 2014;142(12):4519-4541. R835205 (2013)
    R835205 (Final)
  • Full-text: AMS Journals-Full Text PDF
  • Abstract: AMS Journals-Abstract
  • Supplemental Keywords:

    Global climate, CASTNET, environmental chemistry, statistics, general circulation models, air quality

    Progress and Final Reports:

    Original Abstract
  • 2012 Progress Report
  • 2014 Progress Report
  • Final Report