Ensemble consistency testing using causal connectivity

摘要: Understanding differences in climate model output can be challenging in light of the chaotic nature of the climate system and its inherent variability. While progress has been made in automatically detecting small changes, making quantitative assessments of when changes truly affect the climate state in the modeland thus indicate a potential problem-has remained elusive. We present a first step in this direction based on evaluating changes in the connectivity structure among key model variables. We use a collection (ensemble) of climate model simulations to obtain a graphical model of the relationships of 15 key climate variables and then build a statistical model to probabilistically describe the occurrence of these relationships. This statistical model forms the basis of a test to evaluate new runs. We illustrate our methodology using data from a large publicly available ensemble of climate model simulations.