Abstract
Abstract. Detecting the direction and strength of the causality signal in
observed time series is becoming a popular tool for exploration of
distributed systems such as Earth's climate system. Here, we suggest that in
addition to reproducing observed time series of climate variables within
required accuracy a model should also exhibit the causality relationship
between variables found in nature. Specifically, we propose a novel
framework for a comprehensive analysis of climate model responses to
external natural and anthropogenic forcing based on the method of
conditional dispersion. As an illustration, we assess the causal
relationship between anthropogenic forcing (i.e., atmospheric carbon dioxide
concentration) and surface temperature anomalies. We demonstrate a strong
directional causality between global temperatures and carbon dioxide
concentrations (meaning that carbon dioxide affects temperature more
than temperature affects carbon dioxide) in both the observations and in
(Coupled Model
Intercomparison Project phase 5; CMIP5) climate model simulated temperatures.
Cited by
6 articles.
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