CMAD: Advancing Understanding of Geospatial Clusters of Anomalous Melt Events in Sea Ice Extent

Abstract

Traditional statistical analyses do not reveal the spatial locations and the temporal occurrences of clusters of anomalous events that are responsible for a significant loss of sea ice extent. To address this problem, we present a novel method named Convolution Matrix Anomaly Detection (CMAD). The onset and progression of clusters of anomalous melting events over the Antarctic Sea ice are studied as loss in sea ice extent, which are essentially negative values, where the traditional convolutional operation of the Convolutional Neural Network (CNN) approach is ineffective. CMAD is based on an inverse max pooling concept in the convolutional operation of CNN to address this gap. CMAD is developed to offer a solution without using a neural network, and unlike a full CNN, it doesn’t require any training or testing processes. Satellite images are utilized to establish the loss in the Antarctic region. Our analysis shows that anomalous melting patterns have significantly affected the Weddell and the Ross Sea regions more than any other regions of the Antarctic, consistent with the largest disappearance in sea ice extent over these two regions. These findings bolster the applicability of the inverse max pooling based CMAD in detecting the spatiotemporal evolution of clusters of anomalous melting events over the Antarctic region. The anomalous melting process was first noticed along the outer boundary of the sea ice extent in early September 2022 and gradually engulfed the entire sea ice region by February 2023 - in tandem with the scientific literature. These findings indicate that there is a necessity to delve deeper into the role of the anomalous melting process on sea ice retreat for a better understanding of the sea ice retreat process. The nature of the problem is to detect clusters of contiguous grids of anomalous melting events rather than detecting discrete grid points. CMAD’s ability to perform both data clustering and anomaly detection via the pooling operations allows for a more comprehensive analysis of sea ice melt patterns, facilitating the pinpointing of areas with potentially significant melt events. This method has the potential to apply in other fields of study where anomalous events are detected in clusters. The inverse max pooling concept has successfully detected clusters of anomalous events in sea ice and demonstrated the capability to detect anomalies with 87% accuracy in benchmark data. In contrast to well-established conventional methods such as DBSCAN, HDBSCAN, K-Means, Bisecting K-Means, BIRCH, Agglomerative Clustering, OPTICS, and Gaussian Mixtures, when applied to dynamic multidimensional data, CMADBenchmark (which is a variation of CMAD) exhibits superior capabilities in detecting extreme events. The comparative analysis reveals that CMADBenchmark outperforms these traditional approaches, showcasing its heightened sensitivity and efficacy in capturing significant variations within evolving multidimensional datasets over time. This heightens the detection accuracy positions of CMAD as a valuable tool for discerning extreme events in the context of dynamic and changing multidimensional data.