CMIP6中大西洋-太平洋遥相关的制度导向因果模型评价

Soufiane Karmouche, E. Galytska, J. Runge, G. Meehl, A. Phillips, K. Weigel, V. Eyring
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引用次数: 7

摘要

摘要气候系统及其时空变化受到长期内部变率模式的强烈影响,如太平洋十年变率(PDV)和大西洋几十年变性(AMV)。当它们在温暖和寒冷阶段交替时,PDV和AMV之间的相互作用在十年到几十年的时间尺度上变化。在这里,我们使用因果发现方法来推导大西洋-太平洋相互作用中的指纹,并研究其相位依赖性变化。根据PDV和AMV的阶段,在第一步的重新分析中识别出具有特征因果指纹的不同机制。在第二步中,进行了面向制度的因果模型评估,以评估参与耦合模型相互比较项目第6阶段(CMIP6)的模型在表示观测到的PDV、AMV及其热带外遥相关之间不断变化的相互作用方面的能力。从重新分析中获得的因果图在分析整个1900-2014年期间以及该期间的几个定义状态期间,例如,当AMV经历其负(冷)阶段时,检测到AMV到PDV的直接相反符号响应。再分析还表明,在PDV的冷期,PDV对AMV有相同的信号反应。CMIP6历史模拟在模拟观察到的因果模式方面表现出不同的技能。通常,与其他方案相比,当PDV和AMV异相时,大集合(LE)模拟显示出更好的网络相似性。此外,两个最大的集合(就成员数量而言)被发现包含与观测结果具有相似因果指纹的实现。对于大多数制度,这些相同的模型在相互比较时显示出更高的网络相似性。这项工作展示了LEs的因果发现如何补充气候变化的可用诊断和统计指标,为气候模型评估提供强大的工具。
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Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6
Abstract. The climate system and its spatio-temporal changes are strongly affected by modes of long-term internal variability, like the Pacific decadal variability (PDV) and the Atlantic multidecadal variability (AMV). As they alternate between warm and cold phases, the interplay between PDV and AMV varies over decadal to multidecadal timescales. Here, we use a causal discovery method to derive fingerprints in the Atlantic–Pacific interactions and to investigate their phase-dependent changes. Dependent on the phases of PDV and AMV, different regimes with characteristic causal fingerprints are identified in reanalyses in a first step. In a second step, a regime-oriented causal model evaluation is performed to evaluate the ability of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the observed changing interactions between PDV, AMV and their extra-tropical teleconnections. The causal graphs obtained from reanalyses detect a direct opposite-sign response from AMV to PDV when analyzing the complete 1900–2014 period and during several defined regimes within that period, for example, when AMV is going through its negative (cold) phase. Reanalyses also demonstrate a same-sign response from PDV to AMV during the cold phase of PDV. Historical CMIP6 simulations exhibit varying skill in simulating the observed causal patterns. Generally, large-ensemble (LE) simulations showed better network similarity when PDV and AMV were out of phase compared to other regimes. Also, the two largest ensembles (in terms of number of members) were found to contain realizations with similar causal fingerprints to observations. For most regimes, these same models showed higher network similarity when compared to each other. This work shows how causal discovery on LEs complements the available diagnostics and statistical metrics of climate variability to provide a powerful tool for climate model evaluation.
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