Spatial-temporal causal modeling for climate change attribution

Abstract

Attribution of climate change to causal factors has been based predominantly on simulations using physical climate models, which have inherent limitations in describing such a complex and chaotic system. We propose an alternative, data centric, approach that relies on actual measurements of climate observations and human and natural forcing factors. Specifically, we develop a novel method to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our method in order to address the attribution of extreme climate events, such as heatwaves. Our experimental results on a real world dataset indicate that changes in temperature are not solely accounted for by solar radiance, but attributed more significantly to CO2 and other greenhouse gases. Combined with extreme value modeling, we also show that there has been a significant increase in the intensity of extreme temperatures, and that such changes in extreme temperature are also attributable to greenhouse gases. These preliminary results suggest that our approach can offer a useful alternative to the simulation-based approach to climate modeling and attribution, and provide valuable insights from a fresh perspective.

A non-linear Granger-causality framework to investigate climate–vegetation dynamics

“…In addition, it is quite likely that neighbouring areas have similar climatic conditions which, in turn, affect vegetation dynamics in a similar manner. We therefore also consider an extension of our framework to exploit spatial autocorrelations, inspired by Lozano et al (2009), who achieved spatial smoothness via an additional penalty term that punishes dissimilarity between coefficients for spatial neighbours. In our analysis, we incorporate spatial autocorrelations at a given pixel by extending the predictor variables of our models with the predictor variables of the eight neighbouring pixels.…”

Discovery of Causal Time Intervals

“…While the original Granger causality test was designed for two time series, several methods [2,18,17] have been proposed to analyze time series data involving many features and to learn a causal graph structure. Following the work [2], [20] detects causality of spatial time series, [18] proposes to use hidden Markov Random Field method, [17] handles extreme values in time series, [4] detects Granger causality from irregular time series, and [5] presents Copula-Granger method to efficiently capture non-linearity in the data. Learning temporal causal graph has been applied to biology applications [25], climate analysis [9], microbiology [19], fMRI data analysis [24], anomaly detection [23], and longitudinal analysis [26].…”

“…In this section, we use a real climate dataset [20] to study causal relationships between temperature and other meteorological factors.…”

“…Various methods have been developed to determine the causality between two time series. Among all causality test methods, Granger causality test [10] is one of the most popular methods [25,2,20,18,17].…”

Discovering spatio-temporal causal interactions in traffic data streams

“…13] and climate change [18,23,8] etc. More specifically, the contributions we make in this paper are:…”