Interactive Detection and Visualization of Ocean Carbon Regimes

Abstract

Our research focuses on the detection of ocean carbon uptake regimes that are critical in the context of comprehending climate change. One observation among geoscientific data in Earth System Sciences is that the datasets often contain local and distinct statistical distributions posing a major challenge in applying clustering algorithms for data analysis. The use of global parameters in many clustering algorithms is often inadequate to capture such local distributions. In this study, we propose a novel tool to detect and visualize oceanic carbon uptake clusters. We implement a distance-variance selection method (augmented by BIC scores) on agglomerative hierarchical clustering constructed upon a regional multivariate linear regression model set. Instead of relying on a global distance, users can select the local distance and variance thresholds on our tool to detect the connections on the dendrograms that stand as potential clusters by considering both compactness and similarity.