Numerical models are essential tools for understanding the complex and dynamic nature of the natural environment. The ability to evaluate how well these models represent reality is critical in their use and future development. This study presents a combination of changepoint analysis and fuzzy logic to assess the ability of numerical models to capture local scale temporal events seen in observations. The fuzzy union based metric factors in uncertainty of the changepoint location to calculate individual similarity scores between the numerical model and reality for each changepoint in the observed record. The application of the method is demonstrated through a case study on a high resolution model dataset which was able to pick up observed changepoints in temperature records over Greenland to varying degrees of success. The case study is presented using the DataLabs framework, a cloud-based collaborative platform which simplifies access to complex statistical methods for environmental science applications.

数值模型是理解自然环境的复杂和动态本质的基本工具。评估这些模型代表现实情况的能力对于它们的使用和未来发展至关重要。这项研究提出了变更点分析和模糊逻辑的结合，以评估数值模型捕获观测中发现的局部尺度时间事件的能力。基于模糊并集的度量标准将变更点位置的不确定性因素考虑在内，以便为观察记录中的每个变更点计算数值模型与现实之间的单个相似性评分。通过对高分辨率模型数据集的案例研究证明了该方法的应用，该数据集能够获得格陵兰岛上温度记录中观察到的变化点，并获得不同程度的成功。