论文标题
与多个内在时间尺度的耦合气候模型的半自动调整：从Lorenz96模型中学到的经验教训
Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model
论文作者
论文摘要
这项研究的目的是评估历史匹配的潜力（HM），以调整具有多尺度动力学的气候系统。通过考虑玩具气候模型，即两尺度的Lorenz96模型并在完美模型设置中生产实验，我们详细探讨了如何需要仔细测试几种内置选择。我们还展示了在参数范围内引入物理专业知识的重要性，这是运行HM的先验。最后，我们重新审视气候模型调整中的经典过程，该程序包括分别调整慢速和快速组件。通过在Lorenz96模型中这样做，我们说明了合理参数的非唯一性，并突出了从耦合中出现的指标的特异性。本文也有助于通过在每个社区使用的术语之间建立相同概念的术语并提出有希望的协作途径，从而使不确定性量化，机器学习和气候建模的社区桥接社区，从而使气候建模研究受益。
The objective of this study is to evaluate the potential for History Matching (HM) to tune a climate system with multi-scale dynamics. By considering a toy climate model, namely, the two-scale Lorenz96 model and producing experiments in perfect-model setting, we explore in detail how several built-in choices need to be carefully tested. We also demonstrate the importance of introducing physical expertise in the range of parameters, a priori to running HM. Finally we revisit a classical procedure in climate model tuning, that consists of tuning the slow and fast components separately. By doing so in the Lorenz96 model, we illustrate the non-uniqueness of plausible parameters and highlight the specificity of metrics emerging from the coupling. This paper contributes also to bridging the communities of uncertainty quantification, machine learning and climate modeling, by making connections between the terms used by each community for the same concept and presenting promising collaboration avenues that would benefit climate modeling research.
