Causal Discovery for Climate Research Using Graphical Models

Ebert‐Uphoff, Imme; Deng, Yi

doi:10.1175/jcli-d-11-00387.1

Cited by 147 publications

(106 citation statements)

References 47 publications

Supporting

Mentioning

106

Contrasting

Order By: Relevance

“…We recently introduced a very different type of climate network based on causal discovery theory [Ebert-Uphoff and Deng, 2010, 2012a, 2012b. Hlinka et al…”

Section: Climate Network Based On Causal Discoverymentioning

confidence: 99%

“…We recently introduced a very different type of climate network based on causal discovery theory [Ebert-Uphoff and Deng, 2010, 2012a, 2012b. Hlinka et al [2013] also explore causal-discovery-based methods for climate networks, contrasting methods based on Granger causality and on transfer entropy [Runge et al, 2012].…”

Section: Climate Network Based On Causal Discoverymentioning

confidence: 99%

“…Thus, they do not truly distinguish between direct and indirect causal connections. Furthermore, the results for many of them have been described as being fairly similar to correlation networks, especially for MI networks and phase synchronization networks [Yamasaki et al, 2009].We recently introduced a very different type of climate network based on causal discovery theory [Ebert-Uphoff and Deng, 2010, 2012a, 2012b. Hlinka et al…”

mentioning

confidence: 98%

See 2 more Smart Citations

Weakening of atmospheric information flow in a warming climate in the Community Climate System Model

Deng

Ebert‐Uphoff

2014

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

We introduce a new perspective of climate change by revealing the changing characteristics of atmospheric information flow in a warming climate. The key idea is to interpret large-scale atmospheric dynamical processes as information flow around the globe and to identify the pathways of this information flow using a climate network based on causal discovery and graphical models. We construct such networks using the daily geopotential height data from the Community Climate System Model Version 4.0 (CCSM4.0)'s 20 th century climate simulation and 21 st century climate projection. We show that in the CCSM4.0 model under enhanced greenhouse gases (GHGs) forcing, prominent midlatitude information pathways in the midtroposphere weaken and shift poleward, while major tropical information pathways start diminishing. Averaged over the entire Northern Hemisphere, the atmospheric information flow weakens. The implications of this weakening for the interconnectivity among different geographical locations and for the intrinsic predictability of the atmosphere are discussed. Climate Networks Based on Causal DiscoveryThe traditional idea of climate networks is to define a correlation network of nodes where each node represents a point on a global grid [Tsonis and Roebber, 2004]. Any two nodes are connected if the correlation of a specific atmospheric and/or oceanic variable measured at the two nodes is beyond a threshold [Tsonis and Roebber, 2004;Tsonis et al., 2006]. The introduction of a climate network effectively brought the vast framework of network analysis to climate science and triggered a flurry of research activity in this area, including classification of network type, analysis of network properties such as density of connections, backbone of the network, and effects of natural modes of climate variability (e.g., El Niño) on network properties [Tsonis et al., 2007;Tsonis and Swanson, 2008;Gozolchiani et al., 2008;Yamasaki et al., 2008;Donges et al., 2009;Steinhaeuser et al., 2010]. While correlation networks remain the most common type, three more definitions have recently been proposed. MI networks [Donges et al., 2009] use mutual information, rather than correlation, to decide whether two nodes should be connected. Phase synchronization networks [Yamasaki et al., 2009] use the level of robust phase synchronization between nodes as criterion for connections, and event synchronization networks [Boers et al., 2013] focus on the level of synchronization of extreme events (maxima and minima) in the nodes' time series. While correlation and MI networks emphasize instantaneous connections, synchronization networks focus on connections over time by considering temporal lags. All of the above network definitions, however, decide whether an edge exists between two nodes, X and Y, in the network based only on a test involving those two nodes. Thus, they do not truly distinguish between direct and indirect causal connections. Furthermore, the results for many of them have been described as being fairly similar to corr...

show abstract

“…We recently introduced a very different type of climate network based on causal discovery theory [Ebert-Uphoff and Deng, 2010, 2012a, 2012b. Hlinka et al…”

Section: Climate Network Based On Causal Discoverymentioning

confidence: 99%

Section: Climate Network Based On Causal Discoverymentioning

confidence: 99%

mentioning

confidence: 98%

See 1 more Smart Citation

Weakening of atmospheric information flow in a warming climate in the Community Climate System Model

Deng

Ebert‐Uphoff

2014

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…This motivates the use of more sophisticated measures, known also as causality analysis methods. Indeed, it has been recently suggested that data-driven detection of climate causality networks could be used for deriving hypotheses about causal relationships between prominent modes of atmospheric variability [12,13].…”

Section: Introductionmentioning

confidence: 99%

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information

Hlinka

Hartman

Vejmelka

et al. 2013

Entropy

117

View full text Add to dashboard Cite

Across geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical complex systems can be represented by a directed graph, where each link denotes an existence of a causal relation, or information exchange between the nodes. For geophysical systems such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods. These include bivariate nonlinear methods based on information theory and their linear counterpart. The trade-off between the valuable sensitivity of nonlinear methods to more general interactions and the potentially higher numerical reliability of linear methods may affect inference regarding structure and variability of climate networks. We investigate the reliability of directed climate networks detected by selected methods and parameter settings, using a stationarized model of dimensionality-reduced surface air temperature data from reanalysis of 60-year global climate records. Overall, all studied bivariate causality methods provided reproducible estimates of climate causality networks, with the linear approximation showing Entropy 2013, 15 2024 higher reliability than the investigated nonlinear methods. On the example dataset, optimizing the investigated nonlinear methods with respect to reliability increased the similarity of the detected networks to their linear counterparts, supporting the particular hypothesis of the near-linearity of the surface air temperature reanalysis data.

show abstract

“…Here, we first disentangle the contributions of AMOC and sea-ice variability to GMT variations in unperturbed control runs of the CMIP5 model ensemble using graph-theoretical statistical models (Runge et al, 2012a;Ebert-Uphoff and Deng, 2012, denoted graphical models hereafter) and commonality analysis (). We relate AMOC variability to North Atlantic deep-ocean temperature and salinity and investigate an internal advective feedback mechanism.…”

Section: Introductionmentioning

confidence: 99%

The role of the North Atlantic overturning and deep ocean for multi-decadal global-mean-temperature variability

et al. 2014

View full text Add to dashboard Cite

Abstract. Earth's climate exhibits internal modes of variability on various timescales. Here we investigate multi-decadal variability of the Atlantic meridional overturning circulation (AMOC), Northern Hemisphere sea-ice extent and global mean temperature (GMT) in an ensemble of CMIP5 models under control conditions. We report an inter-annual GMT variability of about ±0.1 • C originating solely from natural variability in the model ensemble. By decomposing the GMT variance into contributions of the AMOC and Northern Hemisphere sea-ice extent using a graph-theoretical statistical approach, we find the AMOC to contribute 8 % to GMT variability in the ensemble mean. Our results highlight the importance of AMOC sea-ice feedbacks that explain 5 % of the GMT variance, while the contribution solely related to the AMOC is found to be about 3 %. As a consequence of multi-decadal AMOC variability, we report substantial variations in North Atlantic deep-ocean heat content with trends of up to 0.7 × 10 22 J decade −1 that are of the order of observed changes over the last decade and consistent with the reduced GMT warming trend over this period. Although these temperature anomalies are largely densitycompensated by salinity changes, we find a robust negative correlation between the AMOC and North Atlantic deepocean density with density lagging the AMOC by 5 to 11 yr in most models. While this would in principle allow for a self-sustained oscillatory behavior of the coupled AMOCdeep-ocean system, our results are inconclusive about the role of this feedback in the model ensemble.

show abstract

Causal Discovery for Climate Research Using Graphical Models

Cited by 147 publications

References 47 publications

Weakening of atmospheric information flow in a warming climate in the Community Climate System Model

Weakening of atmospheric information flow in a warming climate in the Community Climate System Model

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information

The role of the North Atlantic overturning and deep ocean for multi-decadal global-mean-temperature variability

Contact Info

Product

Resources

About