Explore a 45-minute conference talk on ensemble consistency testing for the Community Earth System Model, presented at the Machine Learning for Climate KITP conference. Delve into the challenges of informing society about future climate changes at regional and local scales, and learn how big data and machine learning algorithms are advancing climate science. Discover the Ensemble Consistency Test (ECT) approach for evaluating differences in climate models, including its procedure, effectiveness, and statistical details. Examine counterexamples, challenges in highly accurate testing, and alternative estimators for different ensemble sizes. Gain insights into the intersection of climate modeling, software quality assurance, and statistical analysis in this comprehensive presentation by Dorit Hammerling from the National Center for Atmospheric Research.