Long‐term probabilistic forecast of climate impact on air quality: Model development and t* distribution

Abstract

Models are great tools to test ideas. Their usefulness, however, depends on their ability to simulate the current reality and to predict the future. In this study, I have derived a new t* distribution. I show that a statistical model based on the t* distribution of station temporal data is capable of predicting the probability of any future outcome to exceed a specific value using only the currently available sample statistics assuming a normal random variable. In an air quality management application the model has demonstrated categorically an average success rate of over 80% both in simulating the current ozone nonattainment areas and in forecasting the rate of future violation of the 8‐hour ozone National Ambient Air Quality Standards in the United States for up to 12 years. While the predictability of deterministic climate models is still limited by large uncertainties, the probabilistic forecast by this model provides a promising alternative in assessing the climate impact on environment for decades.