Description: Formed under low temperature – high pressure conditions vast amounts of methane hydrates are considered to be locked up in sediments of continental margins including the Arctic shelf regions[1-3]. Because the Arctic has warmed considerably during the recent decades and because climate models predict accelerated warming if global greenhouse gas emissions continue to rise [3], it is debated whether shallow Arctic hydrate deposits could be destabilized in the near future[4, 5]. Methane (CH4), a greenhouse gas with a global warming potential about 25 times higher than CO2, could be released from the melting hydrates and enter the water column and atmosphere with uncertain consequences for the environment. In a recent study, we explored Arctic bottom water temperatures and their future evolution projected by a climate model [1]. Predicted bottom water warming is spatially inhomogeneous, with strongest impact on shallow regions affected by Atlantic inflow. Within the next 100 years, the warming affects 25% of shallow and mid- depth regions (water depth 〈 600 m) containing methane hydrates. We have quantified methane release from melting hydrates using transient models resolving the change in stability zone thickness. Due to slow heat diffusion rates, the change in stability zone thickness over the next 100 years is small and methane release limited. Even if these methane emissions were to reach the atmosphere, their climatic impact would be negligible as a climate model run confirms. However, the released methane, if dissolved into the water column, may contribute to ocean acidification and oxygen depletion in the water column.

Description: A special focus in data mining is to identify agglomerations of data points in spatial or spatio-temporal databases. Multiple applications have been presented to make use of such clustering algorithms. However, applications exist, where not only dense areas have to be identified, but also requirements regarding the correlation of the cluster to a specific shape must be met, i.e. circles. This is the case for eddy detection in marine science, where eddies are not only specified by their density, but also their circular-shaped rotation. Traditional clustering algorithms lack the ability to take such aspects into account. In this paper, we introduce Vortex Correlation Clustering which aims to identify those correlated groups of objects oriented along a vortex. This can be achieved by adapting the Circle Hough Transformation, already known from image analysis. The presented adaptations not only allow to cluster objects depending on their location next to each other, but also allows to take the orientation of individual objects into considerations. This allows for a more precise clustering of objects. A multi-step approach allows to analyze and aggregate cluster candidates, to also include final clusters, which do not perfectly satisfy the shape condition. We evaluate our approach upon a real world application, to cluster particle simulations composing such shapes. Our approach outperforms comparable methods of clustering for this application both in terms of effectiveness and efficiency.

Description: Mining spatio-temporal correlation patterns for traffic prediction is a well-studied field. However, most approaches are based on the assumption of the availability of and accessibility to a sufficiently dense data source, which is rather the rare case in reality. Traffic sensors in road networks are generally highly sparse in their distribution: fleet-based traffic sensing is sparse in space but also sparse in time. There are also other traffic application, besides road traffic, like moving objects in the marine space, where observations are sparsely and arbitrarily distributed in space. In this paper, we tackle the problem of traffic prediction on sparse and spatially irregular and non-deterministic traffic observations. We draw a border between imputations and this work as we consider high sparsity rates and no fixed sensor locations. We advance correlation mining methods with a Sparse Unstructured Spatio Temporal Reconstruction (SUSTeR) framework that reconstructs traffic states from sparse non-stationary observations. For the prediction the framework creates a hidden context traffic state which is enriched in a residual fashion with each observation. Such an assimilated hidden traffic state can be used by existing traffic prediction methods to predict future traffic states. We query these states with query locations from the spatial domain.