It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

中文翻译：

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利用栅格数据集的时间序列识别海面温度异常演变的面向过程的方法

重要的是要考虑海面温度异常 (SSTA) 的演变在何处、何时以及如何在区域或全球气候变化中发挥重要作用。在何时何地的比较中，要清楚地描述SSTA如何在空间和时间上演化是一个很大的挑战。针对 SSTA 从生成、发展到消散的演变，本文提出了一种从栅格数据集的时间序列识别 SSTA 在空间和时间上的演变的新方法。这种称为 PoAIES 的方法包括三个关键步骤。首先，增强了基于聚类的方法来探索SSTA的时空聚类，将SSTA在某个时间快照的每个聚类作为SSTA的快照对象。第二，SSTA的快照对象在连续时间快照的时空拓扑结构用于将SSTA的快照对象链接成SSTA的演化对象，称为进程对象。这里根据快照对象的重叠区域自动确定链接阈值，只有满足指定链接阈值的快照对象才会链接到一个进程对象中。第三，我们使用基于图的模型来表示 SSTA 的流程对象。一个节点代表 SSTA 的一个快照对象，一条边代表两个快照对象之间的演化。使用来自边的父节点的多个子节点和来自边的子节点的多个父节点，识别边缘的类型（进化关系），其显示其发展、分裂、合并或分裂/合并。