地球系统科学
气候变化
气候模式
强迫(数学)
辐射压力
气候学
气候系统
试验台
计算机科学
环境资源管理
环境科学
地质学
海洋学
计算机网络
作者
C. Deser,Flavio Lehner,Keith B. Rodgers,Toby R. Ault,Thomas L. Delworth,Pedro DiNezio,Arlene M. Fiore,Claude Frankignoul,John C. Fyfe,Hannes Jung,J. E. Kay,Reto Knutti,Nicole S. Lovenduski,Jochem Marotzke,Katharine McKinnon,Shoshiro Minobe,James T. Randerson,James A. Screen,Isla R. Simpson,Mingfang Ting
标识
DOI:10.1038/s41558-020-0731-2
摘要
Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed. Climate change detection is confounded by internal variability, but recent initial-condition large ensembles (LEs) have begun addressing this issue. This Perspective discusses the value of multi-model LEs, the challenges of providing them and their role in future climate change research.
科研通智能强力驱动
Strongly Powered by AbleSci AI