Submitted as: methods for assessment of models 19 Jul 2021

Submitted as: methods for assessment of models | 19 Jul 2021

Review status: this preprint is currently under review for the journal GMD.

Quantifying Causal Contributions in Earth Systems by Normalized Information Flow

Chin-Hsien Cheng1,2 and Simon A. T. Redfern1 Chin-Hsien Cheng and Simon A. T. Redfern
  • 1Asian School of the Environment, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
  • 2Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China

Abstract. To understand the plethora of important processes that are characterized by their complexity, from global pandemics to global climate change, it may be critical to quantify causal contributions between time series variables. Here, we examine an empirical linear relationship between the rate of changing causes and effects with various multipliers. Sign corrected normalized information flow (nIFc) tends to provide the best estimates of causal contributions, often in situations where such causality is poorly reflected by regressions. These include: i) causal contributions with alternating feedback (correlation) sign, ii) significant causal time-lags, iii) significant noise contributions, and iv) comparison among many causes to an overall mean effect, especially with teleconnection. Estimates of methane-climate feedbacks with both observational and Earth system model CESM2 data are given as examples of nonlinear process quantification and model assessment. The relative causal contribution is hypothesized to be proportional to |nIF|, i.e. the ratio between entropy (degree of uncertainty) received from the cause-variable (i.e. information flow, |IF|) and the total entropy change of the effect-variable. Large entropy, associated with noise, deteriorates the estimates of total entropy change, and hence nIF, while the proportional relationship between the relative causal contribution and IF improves.

Chin-Hsien Cheng and Simon A. T. Redfern

Status: open (until 13 Sep 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2021-196', Daniel Fiifi Hagan, 23 Jul 2021 reply
    • AC1: 'Reply on CC1', Simon Redfern, 27 Jul 2021 reply

Chin-Hsien Cheng and Simon A. T. Redfern

Chin-Hsien Cheng and Simon A. T. Redfern


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Short summary
Causality is one of the foundations of scientific understanding and progress. Causality, being one of the foundations of scientific understanding and progress, continues to expand its application in various research disciplines in recent years. For Earth sciences, causation is important for evaluating, constraining, and improving climate models. Here, we explore, the conditions under which information flow works best for quantifying causality and explain why it is advantageous.