Large-ensemble Monte Carlo: a researcher’s guide to better climate trend uncertainties

Internal climate variability (ICV) often acts in complex ways that bias results from statistical methods, and the climate research community does not have a single established method for addressing these biases. Here we formalize an argument for a technique we call climate model Large-Ensemble Monte-Carlo (LENS-MC) to inform the selection of statistical methods for real-world application. LENS-MC can be used to identify appropriate statistical methods for a wide range of climate variables. Until now, scientists have often made best efforts to select methods based on rather simplistic assumptions about the mathematical properties of ICV. LENS-MC relaxes these assumptions and justifies method selection. In a case study of statistical errors in 20-year trends in global temperature and top-of-atmosphere (TOA) flux series, we compare a number of methods with standard ordinary least squares (OLS). The low bias in trend standard errors is reduced from 72 % to 3 % in global mean temperature, and from 29 % to 4 % in TOA flux. Using the suggested methods, researchers are less likely to mistakenly report significant trends, and LENS-MC could be widely applied to statistical climate analysis for which model output is available, provided that model ICV displays similar structure to observed ICV.