2012 Progress Report: Extreme Air Quality Events Using a Hierarchy of Models: Present and FutureEPA 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. , Samorodnitsky, Gennady
Current 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: June 1, 2012 through May 31,2013
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?
In the past year, we have initiated a statistical analysis of CASTNET ozone data for extremes in order to understand the impact of non-stationarity on the extremal behavior of the observed datasets. Preliminary results suggest the impact of non-stationarity on the tail behavior of the ozone distribution might be significant. Extreme value theory is being used to examine the joint extremes of temperature and pollution.
We have also compared extremes in observed and simulated ozone data (in the Community Atmosphere Model with chemistry, CAM-chem) using both analyzed meteorological fields to drive the simulation and self-generated meteorological fields. According to statistical tests using mixed effect modeling, we find that over the Northeast United States, most configurations of CAM-chem (when corrected for the high bias in simulated ozone) have longer return intervals than suggested by CASTNET, suggesting the model underpredicts 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 ozone measurements. We have also investigated the impact of various stochastic methods in modifying meteorological biases, variability and extremes in a general circulation model. Results suggest that SPPT (stochastically perturbed parameterization schemes) does not significantly impact the model bias. Results suggest that SKEBS (stochastic kinetic-energy back scatter schemes) increases the temperature extremes at 2 m compared with observations, but the base model overestimates extreme heat in the first place.
In the next year, we expect to complete our statistical analysis of CASTNET ozone measurements. Following this analysis, it is expected that we will conduct a more extensive analysis of simulated extreme events including: (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; and (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.