Whenever an extreme weather or climate-related event occurs, the media and decision-makers ask the question to what extent it is influenced by climate change. For a few years now the scientific community has been able to answer that question for relatively simple extremes: hot and cold extremes, extreme precipitation and drought. This emerging field of climate science is called Extreme Event Attribution and was assessed to yield reliable estimates of changing risks of extreme weather by the US National Academy of Sciences. Scientific studies, going through peer-review are usually published a year or longer after an event occurred, when the public has moved on and questions about rebuilding or relocating have been answered without taking scientific evidence on the role of climate change into account.
The World Weather Attribution (WWA) initiative, a collaboration between climate scientists at the University of Oxford in the UK, KNMI in the Netherlands, IPSL/LSCE in France, and Princeton University and NCAR in the US, ETH Zurich in Switzerland, IIT Delhi in India and climate impact specialists at the Red Cross / Red Crescent Climate Centre (RCCC) around the world, has been founded to change this, and provide robust assessments on the role of climate change in the aftermath of the event. The initiative is led by Drs Friederike Otto at the University of Oxford and Geert Jan van Oldenborgh at KNMI.
Since WWA started in 2014, the group has developed methods to do extreme event attribution quickly but thoroughly. Read a detailed description of the methods and links to the peer-reviewed studies developing these methods.
The first step is to decide which events to analyse. For this, an objective trigger criterium has been developed by the RCCC, although less impactful events of importance to one of the partners can also be tackled. The expected outcome of the attribution analysis plays no role. Given the go-ahead, the meteorological characteristics of the event are defined choosing the metrics that are as salient to the impacts as possible, in collaboration with local experts whenever possible. Long, homogeneous observational time series are analyzed to obtain an estimate of the observed changes (the detection step). To attribute the change to anthropogenic emissions (or not), as many climate models as possible are being used. The performance of these models to determine which represent the extreme realistically is evaluated before computing how the probability of these kind of events changes due to anthropogenic climate change. These estimates are combined with the observed trend in a synthesis that produces a coherent attribution statement. In addition trends in vulnerability and exposure that contributed to the impact are analysed. The methodology has been peer-reviewed and published, and summarised in our article Pathways and pitfalls in extreme event attribution.
Finally, these results are disseminated through media channels, making our expertise available to provide additional explanations and context. If you are interested in receiving our press material please contact Leo Barasi.