The Max Planck Institute Grand Ensemble: Enabling the Exploration of Climate System Variability
The Max Planck Institute Grand Ensemble: Enabling the Exploration of Climate System Variability
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DOI：
10.1029/2019ms001639
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发表时间：
2019-08-01
影响因子：
6.8
通讯作者：
Marotzke, Jochem
中科院分区：
文献类型：
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作者：
Maher, Nicola;Milinski, Sebastian;Marotzke, Jochem
马克斯·普朗克研究所大集合 (MPI-GE) 是目前可用的最大的单一综合气候模型集合，拥有 100 名成员用于历史模拟 (1850-2005) 和四种强迫情景。它是目前唯一包含情景代表性浓度路径 (RCP) 2.6 和 1% CO2 情景的大型集成。这些优势使 MPI-GE 成为一个强大的工具。我们概述了 MPI-GE 及其组件，并详细介绍了已完成的实验。我们演示了如何将大型集合中的强制响应与内部变化分开。这种分离使得气候变化下的强迫信号和内部变异性的量化达到了前所未有的精度。然后，我们演示了评估 MPI-GE 的多种方法，并将观察结果置于大型集合的背景下，包括一种用于比较模型内部变异性与估计的观测变异性的新方法。最后，我们提出了四种新颖的分析，这些分析只能使用大型集成来完成。首先，我们使用强迫情景来解决温度和降水是否具有路径依赖性。其次，计算了高噪声大气环流的强迫信号，并确定了对北太平洋和北大西洋地区重要的不同驱动因素。第三，我们使用集合维度来研究全球变暖下大西洋经向翻转环流变异性变化的时间依赖性。最后，以海平面压力为例，演示如何利用 MPI-GE 来估计给定科学问题所需的集合规模，并为未来的集合项目提供见解。
The Max Planck Institute Grand Ensemble (MPI-GE) is the largest ensemble of a single comprehensive climate model currently available, with 100 members for the historical simulations (1850-2005) and four forcing scenarios. It is currently the only large ensemble available that includes scenario representative concentration pathway (RCP) 2.6 and a 1% CO2 scenario. These advantages make MPI-GE a powerful tool. We present an overview of MPI-GE, its components, and detail the experiments completed. We demonstrate how to separate the forced response from internal variability in a large ensemble. This separation allows the quantification of both the forced signal under climate change and the internal variability to unprecedented precision. We then demonstrate multiple ways to evaluate MPI-GE and put observations in the context of a large ensemble, including a novel approach for comparing model internal variability with estimated observed variability. Finally, we present four novel analyses, which can only be completed using a large ensemble. First, we address whether temperature and precipitation have a pathway dependence using the forcing scenarios. Second, the forced signal of the highly noisy atmospheric circulation is computed, and different drivers are identified to be important for the North Pacific and North Atlantic regions. Third, we use the ensemble dimension to investigate the time dependency of Atlantic Meridional Overturning Circulation variability changes under global warming. Last, sea level pressure is used as an example to demonstrate how MPI-GE can be utilized to estimate the ensemble size needed for a given scientific problem and provide insights for future ensemble projects.