Evaluating the ability of numerical models to capture important shifts in environmental time series: A fuzzy change point approach. (May 2021) - Hollaway, M.J.; Henrys, P.A.; Killick, R.; Leeson, A.; Watkins, J.
Abstract: Numerical models are essential tools for understanding the complex and dynamic nature of the natural environment. The ability to evaluate how well these models represent reality is critical in their use and future development. This study presents a combination of changepoint analysis and fuzzy logic to assess the ability of numerical models to capture local scale temporal events seen in observations. The fuzzy union based metric factors in uncertainty of the changepoint location to calculate individual similarity scores between the numerical model and reality for each changepoint in the observed record. The application of the method is demonstrated through a case study on a high resolution model dataset which was able to pick up observed changepoints in temperature records over Greenland to varying degrees of success. The case study is presented using the DataLabs framework, a cloud-based collaborative platform which simplifies access to complex statistical methods for environmental science applications. Highlights: New fuzzy changepoint based evaluation method focussed on local scale temporal events. The method factors in uncertainty of changepoint locations in evaluation of timing. Provides framework to evaluate numerical model ability to capture fine scale events.