Evaluating and constraining storm-resolving climate models

全球风暴分解模式(GSRM)能够以前所未有的细节水平模拟大气。它们可以直接计算常规气候模型必须参数化的关键大气过程(Hohenegger等人，2023)。这显著提高了模拟的云和降水场的准确性(例如，Hohenegger等人，2020年)，这是减少全球变暖预测中的不确定性的关键一步(Randall等人，2003年)。然而，由于模型设计选择的重大差异，例如剩余未解决的次网格尺度过程的参数化(Zadra等人，2018年)，GSRM模拟中的显著不确定性仍然存在。需要足够的GSRM评估指标，以使研究人员能够进一步改进模型，从而有助于自信地预测未来气候(Palmer，2016)。在这个项目中，我们的目标是为风暴消解模拟开发稳健的评估指标，以指导未来的发展和GSRM的改进。我们的目标是利用GSRM和卫星观测，利用这些指标来更深入地了解控制云形成的物理过程。

Global storm-resolving models (GSRMs) enable simulating the atmosphere at an unprecedented level of detail. They can directly compute key atmospheric processes which conventional climate models must parameterise (Hohenegger et al, 2023). This significantly improves the accuracy of the simulated cloud and precipitation fields (e.g., Hohenegger et al., 2020), which is a key step towards reducing the uncertainty in projections of global warming (Randall et al., 2003).Nonetheless, significant uncertainties in GSRM simulations remain due to substantial differences in model design choices, such as parameterisations of remaining unresolved subgrid-scale processes (Zadra et al., 2018). Adequate evaluation metrics for GSRMs are needed to enable researchers to further improve models and thereby facilitate predicting future climates with confidence (Palmer, 2016). In this project, we aim to develop robust evaluation metrics for storm-resolving simulations to guide future developments and GSRM improvements. We aim to use the metrics to gain a deeper understanding of the physical processes which control cloud formation utilising GSRMs and satellite observations.