Granger Causality of Coupled Climate Processes: Ocean Feedback on the North Atlantic Oscillation

Mosedale, Timothy J.; Stephenson, David B.; Collins, Matthew; Mills, Terence C.

doi:10.1175/jcli3653.1

Cited by 171 publications

(136 citation statements)

References 50 publications

(54 reference statements)

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“…The Granger causality is measured in terms of predictability gain that is inferred with linear and stationary stochastic models that are fitted to the analyzed data. It is one of the most popular methods for datadriven causality inference and is very widely deployed in areas ranging from economics (11) to ecology (12) and climate (13). Inspection of Fig.…”

Section: Application To Causality Inference From Biomolecular Moleculmentioning

confidence: 99%

On inference of causality for discrete state models in a multiscale context

Gerber

Horenko

2014

Proc. Natl. Acad. Sci. U.S.A.

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Discrete state models are a common tool of modeling in many areas. E.g., Markov state models as a particular representative of this model family became one of the major instruments for analysis and understanding of processes in molecular dynamics (MD). Here we extend the scope of discrete state models to the case of systematically missing scales, resulting in a nonstationary and nonhomogeneous formulation of the inference problem. We demonstrate how the recently developed tools of nonstationary data analysis and information theory can be used to identify the simultaneously most optimal (in terms of describing the given data) and most simple (in terms of complexity and causality) discrete state models. We apply the resulting formalism to a problem from molecular dynamics and show how the results can be used to understand the spatial and temporal causality information beyond the usual assumptions. We demonstrate that the most optimal explanation for the appropriately discretized/ coarse-grained MD torsion angles data in a polypeptide is given by the causality that is localized both in time and in space, opening new possibilities for deploying percolation theory and stochastic subgridscale modeling approaches in the area of MD.multiscale systems | probabilistic networks | Granger causality | nonstationarity | regularization D iscrete state modeling is a powerful tool in many areas of science such as in computational biophysics [where it is mostly used in a form of Markov state models (1-4)], materials science [e.g., deployed in percolation theory and Ising models (5)], bioinformatics [e.g., as probabilistic Boolean models for analysis and control of complex biological networks (6)], and geosciences [e.g., used in the form of the generalized linear regression models (7)]. A central issue of discrete state modeling is the identification of an optimal model for the discrete quantity of interest y (e.g., being a Boolean variable or a probability measure) expressed as a function of other available discrete quantities x 1 , x 2 , . . . , x n (being also Boolean variables or probability measures) and of all other potentially relevant quantities u (being discrete and/or continuous variables). Inference of causality then implies identification of all x i that have a statistically significant impact on y and distinguishing them from all those x j that are insignificant for y. To give a concrete example, in the context of molecular dynamics variable y may describe a probability for a certain torsion angle (e.g., from the protein backbone) to be in one of the discrete conformational states; x 1 , x 2 , . . . , x n can be the values of probabilities for all torsion angles of this protein in previous times and variable u may represent all of the positions and velocities of individual atoms, simulation settings (e.g., temperature), and force-field and solvent properties, etc. Understanding the causality in this situation will mean, for example, identification of the memory depth (e.g., in the context of Markov state models, where...

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Section: Application To Causality Inference From Biomolecular Moleculmentioning

confidence: 99%

On inference of causality for discrete state models in a multiscale context

Gerber

Horenko

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…We assume that the time series at each grid point can be described by a D-dimensional autoregressive process in the form (Mosedale et al, 2006) …”

Section: The Gc Estimatormentioning

confidence: 99%

“…Typical values of D are about 8 days for SST and 4 days for , depending on the location. GC is a powerful tool for the analysis of climate time series Kaufmann, 1999, 2014;Salvucci et al, 2002;Khokhlov et al, 2006;Mosedale et al, 2006;Mokhov et al, 2011;Attanasio et al, 2012;Pasini et al, 2012). It is especially useful in systems that exhibit feedback and closed loops, for which the lagged correlation can lead to misleading conclusions (Chatfield, 1989;Runge et al, 2014).…”

Section: The Gc Estimatormentioning

confidence: 99%

A study of the air-sea interaction in the South Atlantic Convergence Zone through Granger causality

2014

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Air-sea interaction in the region of the South Atlantic Convergence Zone (SACZ) is studied using Granger causality (GC) as a measure of directional coupling. Calculation of the area weighted connectivity indicates that the SACZ region is the one with largest mutual air-sea connectivity in the south Atlantic basin during summertime. Focusing on the leading mode of daily coupled variability, GC allows distinguishing four regimes characterized by different coupling: there are years in which the forcing is mainly directed from the atmosphere to the ocean, years in which the ocean forces the atmosphere, years in which the influence is mutual and years in which the coupling is not significant. A composite analysis shows that ocean-driven events have atmospheric anomalies that develop first and are strongest over the ocean, while in events without coupling anomalies develop from the continent where they are strongest and have smaller oceanic extension.

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“…Elsner (2006Elsner ( , 2007 applied a GC analysis to sea surface temperature anomalies and global surface temperature anomalies for Atlantic hurricanes. GC has been used to assess the "feedback of daily sea surface temperatures (SSTs) on daily values of the North Atlantic as simulated by a realistic coupled general circulation model (GCM)" (Mosedale et al 2006). They find that SST Granger causes the North Atlantic Oscillation.…”

Section: Granger Causality Overviewmentioning

confidence: 99%

Exploring Granger causality between global average observed time series of carbon dioxide and temperature

Kodra

Chatterjee

Ganguly

2010

Theor Appl Climatol

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Detection and attribution methodologies have been developed over the years to delineate anthropogenic from natural drivers of climate change and impacts. A majority of prior attribution studies, which have used climate model simulations and observations or reanalysis datasets, have found evidence for human-induced climate change. This papers tests the hypothesis that Granger causality can be extracted from the bivariate series of globally averaged land surface temperature (GT) observations and observed CO 2 in the atmosphere using a reverse cumulative Granger causality test. This proposed extension of the classic Granger causality test is better suited to handle the multisource nature of the data and provides further statistical rigor. The results from this modified test show evidence for Granger causality from a proxy of total radiative forcing (RC), which in this case is a transformation of atmospheric CO 2 , to GT. Prior literature failed to extract these results via the standard Granger causality test. A forecasting test shows that a holdout set of GT can be better predicted with the addition of lagged RC as a predictor, lending further credibility to the Granger test results. However, since second-order-differenced RC is neither normally distributed nor variance stationary, caution should be exercised in the interpretation of our results.

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Granger Causality of Coupled Climate Processes: Ocean Feedback on the North Atlantic Oscillation

Cited by 171 publications

References 50 publications

On inference of causality for discrete state models in a multiscale context

On inference of causality for discrete state models in a multiscale context

A study of the air-sea interaction in the South Atlantic Convergence Zone through Granger causality

Exploring Granger causality between global average observed time series of carbon dioxide and temperature

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