This study investigates the mechanisms by which small-scale turbulence and cloud physics determine the organization of large-scale convection in radiative-convective equilibrium (RCE), an idealization of the tropical atmosphere. Under uniform forcings similar to typical tropical conditions, the atmosphere in RCE might spontaneously separate into dry and moist regions on scales of 100–1,000 km, with convective clouds aggregating into a cluster in the latter. This phenomenon is known as convective self-aggregation. Herein, we demonstrate that subtle changes in assumptions related to cloud physics and turbulence on scales of 1 km can dictate the emergence or suppression of convective self-aggregation, resulting from a bifurcation of the dynamical system. The bifurcation occurs when a small dry patch forms in the domain and is sustained because it contributes to negative effective diffusivity of the circulation. Cloud-radiation feedbacks and turbulence circulation interactions govern the formation of such dry patches, thereby modulating the bifurcation. This sensitive dependence on subgrid process models might be a fundamental barrier to climate predictability in light of inherent uncertainties in microscale processes. Because without the capability to include exact representations of those processes in climate models, slight differences in the different approximations used by modelers can lead to qualitative changes in climate predictions, at least for some processes.

中文翻译：

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灰区云和湍流参数化对辐射-对流平衡分岔的调制

这项研究调查了小尺度湍流和云物理在辐射-对流平衡 (RCE) 中确定大尺度对流组织的机制，这是热带大气的理想化。在类似于典型热带条件的均匀强迫下，RCE 的大气可能会在 100-1,000 公里的尺度上自发地分为干燥和潮湿的区域，对流云在后者中聚集成一个星团。这种现象称为对流自聚集。在此，我们证明了与云物理和湍流相关的假设的细微变化1 公里可以决定由动力系统分叉导致的对流自聚集的出现或抑制。当在域中形成一个小的干燥斑块并持续存在时，就会发生分叉，因为它有助于循环的负有效扩散。云辐射反馈和湍流环流相互作用控制着这种干斑的形成，从而调节分叉。鉴于微尺度过程的固有不确定性，这种对子电网过程模型的敏感依赖可能是气候可预测性的根本障碍。因为没有能力在气候模型中包含这些过程的精确表示，建模者使用的不同近似值的细微差异可能导致气候预测发生质的变化，至少对于某些过程。