An Automatic Procedure for the Attribution of Extreme Events at the Global Scale: A Proof of Concept for Heat Waves

Abstract Scientific and media attention to extreme events has increased in recent years due to their severity and the damage they cause. Estimating the contribution of climate change to the probability of occurrence and intensity of these events has become a common practice, thanks to initiatives relying on climate science researchers, who diagnose the return period of the event and the contribution of the anthropogenic forcings in the event properties. These initiatives have succeeded in shedding light on the detection and attribution concept but still rely on the commitment of researchers to deal with an ever-increasing number of events. Here, we propose a proof of concept of an automatic attribution method which, in a few seconds, can attribute a heat wave that has occurred anywhere on the globe and estimate its statistical properties both in the past and in the future, using an existing method based on Bayesian statistics that combines past observations and climate models. Our web application currently covers extreme heat events over a period of 3 days and paves the way to a number of relevant applications, such as (i) enabling public or private organizations to become autonomous, i.e., processing the events they wish without waiting for an academic research investigation; (ii) handling events that have received less attention but have had a major impact, particularly in developing countries; and (iii) climate monitoring of extreme events over a specific region. Significance Statement Quantifying the contribution of climate change to the frequency and intensity of extreme events (e.g., heat waves, floods) is an ad hoc exercise, often associated with an academic publication. Here, we propose a new paradigm via an automatic attribution method that, in a few seconds, provides key indicators regarding the contribution of anthropogenic climate change to the properties of hot extreme events that has occurred anywhere on the globe. This approach and its online application enable scientists and stakeholders trained in its use to study the events that interest them and to conduct climate monitoring of past events by refining the statistical indicators as more observations become available.