In a recent study, Coppola et al (2020) assessed the ability of an ensemble of convection‐permitting models (CPM) to simulate deep convection using three case studies. The ensemble exhibited strong dis crepancies between models, which were attributed to various factors. In order to shed some light on the issue, we quantify in this paper the uncertainty associated to different physical parameterizations from that of using different initial conditions, often referred to as the inter nal variability. For this purpose, we establish a framework to quantify both signals and we compare them for upper atmospheric circulation and near‐surface variables. The analysis is carried out in the context of the CORDEX Flagship Pilot Study on Convective phenomena at high resolution over Europe and the Mediterranean, in which the intermediate RCM WRF simulations that serve to drive the CPM are run several times with different parameterizations. For atmospheric circulation (geopotential height), the sensitivity induced by multi‐physics and the internal variability show comparable magnitudes and a similar spatial distribution pattern. For 2‐meter temperature and 10‐meter wind, the simulations with different parameterizations show larger differences than those launched with different initial conditions. The systematic effect over one year shows distinct patterns for the multiphysics and the internal variability. Therefore, the general lesson of this study is that internal variability should be analyzed in order to properly distinguish the impact of other sources of uncertainty, especially for short‐term sensitivity simulations.

中文翻译：

---

区域气候模型中的内部变率与多物理场不确定性

在最近的一项研究中，Coppola 等人 (2020) 使用三个案例研究评估了一组对流允许模型 (CPM) 模拟深对流的能力。合奏在模型之间表现出强烈的差异，这归因于各种因素。为了阐明这个问题，我们在本文中量化了与使用不同初始条件的不同物理参数化相关的不确定性，通常称为内部可变性。为此，我们建立了一个框架来量化这两种信号，并将它们与高层大气环流和近地表变量进行比较。该分析是在关于欧洲和地中海高分辨率对流现象的 CORDEX 旗舰试点研究的背景下进行的，其中用于驱动 CPM 的中间 RCM WRF 模拟以不同的参数化运行多次。对于大气环流（位势高度），由多物理场和内部变异引起的敏感性显示出相似的幅度和相似的空间分布模式。对于 2 米温度和 10 米风，不同参数化的模拟显示出比不同初始条件下发射的模拟更大的差异。一年多的系统效应显示了多物理场和内部可变性的不同模式。因此，本研究的一般教训是，应分析内部可变性，以正确区分其他不确定性来源的影响，尤其是对于短期敏感性模拟。