Attributing Extreme Weather Events and Mean Climate Change using Dynamical and Event Storylines

The impact of extreme weather events on society, as well as natural systems, have been increasingly damaging. Climate change has altered the frequency and intensity of these extremes. The question remains how quantifiable that influence is, so that society can prepare itself for the future, and reduce possible negative impacts. Storylines are a conditional attribution method, that aids the understanding of climate change influence on extreme weather events, as well as mean climate change, by quantifying the climate signal. Instead of trying to estimate if or when a certain level of warming would happen, storylines show the effect such a climate change level would produce if it occurs. Conditioning on the dynamical part of the climate change signal strongly reduces uncertainties and makes the attribution quantifiable. Generally speaking, there are two types of meteorological storylines, what IPCC AR6 refers to as dynamical and event storylines. Dynamical storylines can be evaluated through statistical analysis based on an ensemble of model simulations and used to characterize physically self-consistent mean climate change. Examples of climate change effect on southern-hemisphere precipitation will show how dynamic storylines can be applied. Event storylines recreate the dynamics of an extreme event in worlds with different plausible climate change backgrounds that are also physically self-consistent. The thermodynamic signal of climate change is quantified for the Russian heatwave of 2010. Overall, the storyline method is an important tool to be added to the standard climate change attribution toolbox.