Using Causal Effect Networks to Analyze Different Arctic Drivers of Midlatitude Winter Circulation

Abstract

In recent years, the Northern Hemisphere midlatitudes have suffered from severe winters like the extreme 2012/13 winter in the eastern United States. These cold spells were linked to a meandering upper-tropospheric jet stream pattern and a negative Arctic Oscillation index (AO). However, the nature of the drivers behind these circulation patterns remains controversial. Various studies have proposed different mechanisms related to changes in the Arctic, most of them related to a reduction in sea ice concentrations or increasing Eurasian snow cover.Here, a novel type of time series analysis, called causal effect networks (CEN), based on graphical models is introduced to assess causal relationships and their time delays between different processes. The effect of different Arctic actors on winter circulation on weekly to monthly time scales is studied, and robust network patterns are found. Barents and Kara sea ice concentrations are detected to be important external drivers of the midlatitude circulation, influencing winter AO via tropospheric mechanisms and through processes involving the stratosphere. Eurasia snow cover is also detected to have a causal effect on sea level pressure in Asia, but its exact role on AO remains unclear. The CEN approach presented in this study overcomes some difficulties in interpreting correlation analyses, complements model experiments for testing hypotheses involving teleconnections, and can be used to assess their validity. The findings confirm that sea ice concentrations in autumn in the Barents and Kara Seas are an important driver of winter circulation in the midlatitudes.

Mapping and discrimination of networks in the complexity-entropy plane

“…Beyond this, our framework might also be applicable to networks constructed from non-pairwise interdependencies that are investigated in, e.g, causal effect networks [74,85,86]. The assessment of statistical complexity could help to more objectively choose thresholds for the construction of such networks and complements existing approaches based on, e.g., the assessment of the recurrence network's percolation threshold [76,87,88].…”

<strong>New insights on land surface-atmosphere feedbacks over tropical South America at interannual timescales</strong>

“…This type of interpretation is rather confusing because it goes against the ordinary algebraic interpretation. Diverse similar metrics do exist within the context of information theory, such as the Interaction Information, whose positive (negative) values determine redundancy (synergy) in the interaction between two series [54,55].…”

“…To that end, graphs must be represented through matrices. There is a mathematical connection between graphs and the algebraic properties of such matrices [53,55,78,87]. From this point of view, a graph, k Γ , has two important matrices associated: the adjacency matrix (W) and the Laplacian matrix (L).…”

“…Thus, the most important k singular values to reduce the matrix X are related with [53] review the combined usage of MCA and Graph Theory. [52] and [55] propose using graph models to evaluate climatic interactions in a context of causality. For our purposes, we first classify the interaction of the expansion coefficients, k x , and k y (eqn.…”

New Insights on Land Surface-Atmosphere Feedbacks over Tropical South America at Interannual Timescales

“…According to such interpretation, the concept of feedback with graphs coincides with the type of bidirectional link established in Figure 1 between evaporation and soil moisture; on the contrary, the precipitation-soil moisture link is defined as a coupling or a hierarchical interaction whereby one variable dominates over the other. The structure of links shown in Figure 1, derived from graph theory, provides a clearer understanding of the processes and mechanisms involved in LAFs [53][54][55][56].…”