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

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Title: Large Ensemble Simulations of Climate Models for Climate Change Research: A Review.
Authors: Lin, Pengfei1,2 (AUTHOR), Yang, Lu1,2 (AUTHOR) yanglu211@mails.ucas.ac.cn, Zhao, Bowen3 (AUTHOR), Liu, Hailong1,4 (AUTHOR) hlliu2@qnlm.ac, Wang, Pengfei1 (AUTHOR), Bai, Wenrong5 (AUTHOR), Ma, Jing6 (AUTHOR), Wei, Jilin1 (AUTHOR), Jin, Chenyang1,2 (AUTHOR), Ding, Yuewen1,2 (AUTHOR)
Source: Advances in Atmospheric Sciences. May2025, Vol. 42 Issue 5, p825-841. 17p.
Subject Terms: *Climate change models, *Atmospheric models, *Climate research, *Artificial intelligence, *Climate change
Abstract (English): 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. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 摘要: 近几十年来, 气候模式的大样本集合模拟 (large ensemble simulation; LENS) 或超大样本集合模拟 (super-LENS; SLENS) 试验, 包括现在和未来气候的模拟, 在气候变化、气候变率、气候预测及其扩展领域得到了广泛应用。 本文综述了气候系统中大样本集合模拟的研究进展, 归纳了其定义、初始化、重要性及相关科学问题。 此外, 在文中还归纳回顾了大样本集合模拟的发展历程、相关理论、方法以及主要的应用领域。 基于单模式大样本集合模拟得出的结论比单模式少样本集合模拟更为可靠。 文章特别强调了大样本集合模拟在模式偏差、强迫响应与内部变率间的相互作用研究中具有新的价值。 最后, 文章展望了大样本集合模拟基于气候模式或地球系统模式的未来发展轨迹。 超大集合模拟、高分辨率大样本集合模拟、结合地球系统模式的大样本集合以及 LENS 与人工智能的融合, 将极大推动未来气候研究及其相关应用的发展。 [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Label: Title
  Group: Ti
  Data: Large Ensemble Simulations of Climate Models for Climate Change Research: A Review.
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  Data: <searchLink fieldCode="AR" term="%22Lin%2C+Pengfei%22">Lin, Pengfei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Lu%22">Yang, Lu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> yanglu211@mails.ucas.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Bowen%22">Zhao, Bowen</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Hailong%22">Liu, Hailong</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> hlliu2@qnlm.ac</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Pengfei%22">Wang, Pengfei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bai%2C+Wenrong%22">Bai, Wenrong</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Jing%22">Ma, Jing</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Jilin%22">Wei, Jilin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jin%2C+Chenyang%22">Jin, Chenyang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ding%2C+Yuewen%22">Ding, Yuewen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Advances+in+Atmospheric+Sciences%22">Advances in Atmospheric Sciences</searchLink>. May2025, Vol. 42 Issue 5, p825-841. 17p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Climate+change+models%22">Climate change models</searchLink><br />*<searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+research%22">Climate research</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 摘要: 近几十年来, 气候模式的大样本集合模拟 (large ensemble simulation; LENS) 或超大样本集合模拟 (super-LENS; SLENS) 试验, 包括现在和未来气候的模拟, 在气候变化、气候变率、气候预测及其扩展领域得到了广泛应用。 本文综述了气候系统中大样本集合模拟的研究进展, 归纳了其定义、初始化、重要性及相关科学问题。 此外, 在文中还归纳回顾了大样本集合模拟的发展历程、相关理论、方法以及主要的应用领域。 基于单模式大样本集合模拟得出的结论比单模式少样本集合模拟更为可靠。 文章特别强调了大样本集合模拟在模式偏差、强迫响应与内部变率间的相互作用研究中具有新的价值。 最后, 文章展望了大样本集合模拟基于气候模式或地球系统模式的未来发展轨迹。 超大集合模拟、高分辨率大样本集合模拟、结合地球系统模式的大样本集合以及 LENS 与人工智能的融合, 将极大推动未来气候研究及其相关应用的发展。 [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s00376-024-4012-2
    Languages:
      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 825
    Subjects:
      – SubjectFull: Climate change models
        Type: general
      – SubjectFull: Atmospheric models
        Type: general
      – SubjectFull: Climate research
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Climate change
        Type: general
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      – TitleFull: Large Ensemble Simulations of Climate Models for Climate Change Research: A Review.
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              Text: May2025
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              Y: 2025
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