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ERCL研究文章|Large-ensemble Monte Carlo

24 12月 2024 gabriels

Internal climate variability (ICV) often violates the assumptions of statistical methods, and the climate research community does not have an established approach for addressing resulting biases. Here we argue for a technique we call climate model Large-Ensemble Monte-Carlo (LENS-MC) to inform the selection of statistical methods for real-world application. Until now, scientists have often made best efforts to select methods based on assumptions about the mathematical properties of ICV. LENS-MC relaxes these assumptions and justifies method selection, potentially for a wide range of statistical analyses. We demonstrate LENS-MC using a case study of statistical errors in 20 year trends in global temperature and top-of-atmosphere flux series, comparing results with standard ordinary least squares (OLS). OLS commonly underestimates trend uncertainties, resulting in a higher likelihood of falsely reporting statistically significant trends or changes in trends, for example reporting p < 0.05 in 20 year temperature trends when the statistics are actually equivalent to p < 0.56. LENS-MC tests result in the selection of methods that almost eliminate the low bias in OLS trend standard errors. 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 statistical structure, such as in autocorrelation, to observed ICV.

文章介绍

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

Zachary I Glaser, Mark T Richardson and Felix W Landerer

通讯作者：

Mark T Richardson，美国加州理工学院喷气推进实验室

期刊介绍

Environmental Research: Climate

Environmental Research: Climate（ERCL）是一本多学科、开放获取的期刊，致力于解决有关物理科学的重要挑战以及气候系统和全球变化的评估，并在影响/未来风险、复原力、环境减缓、环境适应、环境安全和最广泛意义上的解决方案方面进行努力。我们鼓励所有的研究方法，包括定性、定量、实验、理论和应用方法。