The Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2
The Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2
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DOI：
10.5194/gmd-15-6451-2022
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发表时间：
2022-08
影响因子：
5.1
通讯作者：
S. Yeager;N. Rosenbloom;A. Glanville;Xiandu Wu;I. Simpson;Hui Li;M. Molina;K. Krumhardt;Samuel Mogen;K. Lindsay;D. Lombardozzi;W. Wieder;Who M. Kim;J. Richter;M. Long;G. Danabasoglu;D. Bailey;M. Holland;N. Lovenduski;W. G. Strand;Teagan King
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作者：
S. Yeager;N. Rosenbloom;A. Glanville;Xiandu Wu;I. Simpson;Hui Li;M. Molina;K. Krumhardt;Samuel Mogen;K. Lindsay;D. Lombardozzi;W. Wieder;Who M. Kim;J. Richter;M. Long;G. Danabasoglu;D. Bailey;M. Holland;N. Lovenduski;W. G. Strand;Teagan King
摘要。与较短的（亚季节到季节）和较长的（年代际）时间尺度相比，对有影响的地球系统变化的多年期预测潜力的探索相对不足。在这项研究中，我们引入了一个新的初始化预测系统，使用社区地球系统模型版本2 (CESM2)，该系统专门用于探测提前期从1个月到2年不等的潜在和实际预测技能。季节-多年大集合（SMYLE）由2年的后播模拟集合组成，从1970年到2019年每年有4次初始化，集合规模为20。一套完整的输出可用于探索CESM2所代表的所有地球系统组成部分的近期可预测性。研究表明，SMYLE预测El Niño-Southern振荡的技能与其他重要的季节性预测系统相比具有竞争力，在提前期超过12个月的情况下，相关性超过0.5。对预测技术的广泛概述揭示了对大气、海洋、陆地和海冰的季节性异常进行多年有效预测的不同程度的潜力。SMYLE数据集、实验设计、模型、初始条件和相关分析工具都是公开的，为更广泛的社区对环境变化的多年预测研究提供了基础。
Abstract. The potential for multiyear prediction of impactful Earth system change remains relatively underexplored compared to shorter (subseasonal to seasonal) and longer (decadal) timescales. In this study, we introduce a new initialized prediction system using the Community Earth System Model version 2 (CESM2) that is specifically designed to probe potential and actual prediction skill at lead times ranging from 1 month out to 2 years. The Seasonal-to-Multiyear Large Ensemble (SMYLE) consists of a collection of 2-year-long hindcast simulations, with four initializations per year from 1970 to 2019 and an ensemble size of 20. A full suite of output is available for exploring near-term predictability of all Earth system components represented in CESM2. We show that SMYLE skill for El Niño–Southern Oscillation is competitive with other prominent seasonal prediction systems, with correlations exceeding 0.5 beyond a lead time of 12 months. A broad overview of prediction skill reveals varying degrees of potential for useful multiyear predictions of seasonal anomalies in the atmosphere, ocean, land, and sea ice. The SMYLE dataset, experimental design, model, initial conditions, and associated analysis tools are all publicly available, providing a foundation for research on multiyear prediction of environmental change by the wider community.