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<p>In recent decades, large ensemble simulation (LENS) or super-large ensemble simulation (SLENS) experiments with climate models, including the simulation of both the historical and future climate, have been increasingly exploited in the fields of climate change, climate variability, climate projection, and beyond. This paper provides an overview of LENS in climate systems. It delves into its definition, initialization, significance, and scientific concerns. Additionally, its development history and relevant theories, methods, and primary fields of application are also reviewed. Conclusions obtained from single-model LENS can be more robust compared with those from ensemble simulations with smaller numbers of members. The interactions among model biases, forced responses, and internal variabilities, which serve as the added value in LENS, are highlighted. Finally, we put forward the future trajectory of LENS with climate or Earth system models (ESMs). Super-large ensemble simulation, high-resolution LENS, LENS employing ESMs, and combining LENS with artificial intelligence, will greatly promote the study of climate and related applications.</p>

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Large Ensemble Simulations of Climate Models for Climate Change Research: A Review

  • 期刊论文

  • 发表日期：

    2025年2月28日

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  • Pengfei Lin,
  • Lu Yang,
  • Bowen Zhao,
  • Hailong Liu,
  • Pengfei Wang,
  • Wenrong Bai,
  • Jing Ma,
  • Jilin Wei,
  • Chenyang Jin,
  • Yuewen Ding

摘要

In recent decades, large ensemble simulation (LENS) or super-large ensemble simulation (SLENS) experiments with climate models, including the simulation of both the historical and future climate, have been increasingly exploited in the fields of climate change, climate variability, climate projection, and beyond. This paper provides an overview of LENS in climate systems. It delves into its definition, initialization, significance, and scientific concerns. Additionally, its development history and relevant theories, methods, and primary fields of application are also reviewed. Conclusions obtained from single-model LENS can be more robust compared with those from ensemble simulations with smaller numbers of members. The interactions among model biases, forced responses, and internal variabilities, which serve as the added value in LENS, are highlighted. Finally, we put forward the future trajectory of LENS with climate or Earth system models (ESMs). Super-large ensemble simulation, high-resolution LENS, LENS employing ESMs, and combining LENS with artificial intelligence, will greatly promote the study of climate and related applications.