Quantifying the influence of observed global warming on the probability of unprecedented extreme climate events

Now that observed global warming has been clearly attributed to human activities, there has been increasing interest in the extent to which that warming has influenced the occurrence and severity of individual extreme climate events. However, although trends in the extremes of the seasonal- and daily-scale distributions of climate records have been analyzed for many years, quantifying the contribution of observed global warming to individual events that are unprecedented in the observed record presents a particular scientific challenge. I will describe a modified method for leveraging observations and large climate model ensembles to quantify the influence of observed global warming on the probability of unprecedented extreme events. In this approach, we first diagnose the causes of the individual event in order to understand which climate processes to target in the probability quantification. We then use advanced statistical techniques to quantify the uncertainty in the return period of the event in the observed record. We then use large ensembles of climate model simulations to quantify the distribution of return period ratios between the current level of climate forcing and the pre-industrial climate forcing. I will examine a set of individual extreme events, including the ongoing California drought and the extreme precipitation that created catastrophic flooding in northern India in June 2013. I will conclude with a discussion of agreement and disagreement between our approach and other approaches that have analyzed similar events, including implications for interpretation of the role of human forcing in shaping unprecedented extreme events.