document: Spatial-temporal causal modeling for climate change attribution 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 .
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