Climatic observables are often correlated across long spatial distances, and extreme events,
such as heatwaves or floods, are typically assumed to be related to such teleconnections …

The increasing interest in complex networks research has been motivated by intrinsic
features of this area, such as the generality of the approach to represent and model virtually …

Global climate models are central tools for understanding past and future climate change.
The assessment of model skill, in turn, can benefit from modern data science approaches …

Quantifying the Strength and Delay of Climatic Interactions: The Ambiguities of Cross
Correlation and a Novel Measure Based on Graphical Models in: Journal of Climate …

Understanding the causes and consequences of, and devising countermeasures to, global
warming is a profoundly complex problem. Network representations are sometimes the only …

Climate and Earth research seeks to identify causal relationships without the advantage of
experimental controls. Algorithmic methods for that purpose have a long history and have …

The quantification of spatio-temporal hydroclimatic extreme events is a key variable in water
resources planning, disaster mitigation, and preparing climate resilient society. However …

Across geosciences, many investigated phenomena relate to specific complex systems
consisting of intricately intertwined interacting subsystems. Such dynamical complex …

We introduce a methodology to construct parsimonious probabilistic models. This method
makes use of information filtering networks to produce a robust estimate of the global sparse …

High-resolution Earth system model simulations generate enormous data volumes, and
retaining the data from these simulations often strains institutional storage resources …