Probability Distribution Analysis of Observational Extreme Events and Model Evaluation

Earth’s surface temperatures were the warmest in 2015 since modern record-keeping began in 1880, according to the latest study. In contrast, a cold weather occurred in many regions of China in January 2016, and brought the first snowfall to Guangzhou, the capital city of Guangdong province in 67 years. To understand the changes of extreme weather events as well as project its future scenarios, this study use statistical models to analyze on multiple climate data. We first use Granger-causality test to identify the attribution of global mean temperature rise and extreme temperature events with CO2 concentration. The four statistical moments (mean, variance, skewness, kurtosis) of daily maximum temperature distribution is investigated on global climate observational, reanalysis (1961-2010) and model data (1961-2100). Furthermore, we introduce a new tail index based on the four moments, which is a more robust index to measure extreme temperatures. Our results show that the CO2 concentration can provide information to the time series of mean and extreme temperature, but not vice versa. Based on our new tail index, we find that other than mean and variance, skewness is an important indicator should be considered to estimate extreme temperature changes and model evaluation. Among the 12 climate model data we investigate, the fourth version of Community Climate System Model (CCSM4) from National Center for Atmospheric Research performs well on the new index we introduce, which indicate the model have a substantial capability to project the future changes of extreme temperature in the 21st century. The method also shows its ability to measure extreme precipitation/ drought events. In the future we will introduce a new diagram to systematically evaluate the performance of the four statistical moments in climate model output, moreover, the human and economic impacts of extreme weather events will also be conducted.