Spatio-Temporal Stochastic Interventions for Climate Change Detection and Attribution

Music City Center 

While physical understanding predicts a causal relationship between greenhouse gas emissions and warming in the global climate, estimation of the exact magnitude of this causal effect is notoriously difficult to constrain. One of the reasons for this high degree of uncertainty is that the climate system's overall sensitivity depends on how the spatial pattern of temperature changes causally affects outgoing temperature. While climate model simulations provide dynamically informed estimates, performing inference on the observations is challenging due to the lack of suitable counterfactuals and the high-dimensional nature of the global climate system. We propose to address these difficulties through the causal inference framework of stochastic interventions, where the interventions are modeled as continuous spatial Gaussian processes on the domain. Representing the interventions a spatial stochastic process allows for the causal effects to be consistently estimated from the limited observational record. Using a Bayesian framework, prior information in the form of climate model simulations is incorporated into the form of the stochastic interventions in order to relax the underlying assumptions with physical information. The robustness of the results are assessed through sensitivity analyses and validation studies using climate models.

Stochastic Interventions

Climate Change

Detection & Attribution

Causal Inference