Title: Probabilistic Climate Model Evaluation
Presenting Author: Amy Braverman
Organization: Jet Propulsion Laboratory
Co-Author(s): Snigdhansu Chatterjee (University of Minnesota) Megan Heyman (University of Minnesota) Noel Cress (University of Wollongong)

Abstract:
Climate models typically require fast supercomputers to obtain output over decades or longer at high spatial and temporal resolution. Starting values, boundary conditions, greenhouse gas emissions and so forth make the climate model itself an uncertain representation of the current climate system and, by implication, of the future climate system. Modern observational datasets offer opportunities for evaluation of competing climate models. In this talk, we propose evaluation of competing climate models through probabilities, which are unitless and are scaled between zero and one. The probabilities are derived from summary statistics of climate-model output and observational data, through a statistical resampling technique known as the Wild Scale-Enhanced Bootstrap. Here we compare monthly sequences of CMIP5 model output values of average global surface temperature to similar sequences obtained from the well-known HadCRUT4 data set. The summary statistics we choose come from working in the decorrelated and dimension-reduced wavelet space and regressing model output on observations. The dimension-reduced slope and intercept statistics are bootstrapped to allow a probability to be associated with each model.