Topology-based feature analysis of scalar field ensembles: An application to climate (change) analysis

Abstract

This paper presents a framework for topological feature analysis in time-dependent climate ensembles. Important climate indices such as the El Nino Southern Oscillation Index (ENSO) or the North Atlantic Oscillation Index (NAO) are usually derived by evaluating scalar fields at fixed locations or regions where these extremal values occur most frequently today. However, under climate change, dynamic circulation changes are likely to cause shifts in the intensity, frequency and location of underlying physical phenomena. In case of the NAO for instance, climatologists are interested in the position and intensity of the Icelandic Low and the Azores High as their interplay strongly influences the European climate, especially during the winter season. To robustly extract and track such highly variable features without a-priori region information, we present a topology-based method for dynamic extraction of such uncertain critical points on a global scale. The system additionally integrates techniques to visualize the variability within the ensemble and correlations between features. To demonstrate the utility of our VTK+TTK-based software framework, we explore a 150-year climate projection consisting of 100 ensemble members and particularly concentrate on sea level pressure fields.