Picking up once again our examination of the most recent IPCC report, we focus this week on “extreme event attribution”, in which climate scientists rush to the scene of any bad weather event and declare that they predicted it all along and greenhouse gases are the culprit. Setting aside our own doubts about the whole business, let’s find out what the IPCC had to say. As before we replace lists of references with (---) and occasionally elide explanatory material with ... but other than that, this is what Section 11.2.3 of the new IPCC report says about the science of attributing extreme weather events to greenhouse gases: “[New] approaches have been developed to answer the question of whether and to what extent external drivers have altered the probability and intensity of an individual extreme event (---). In AR5, there was an emerging consensus that the role of external drivers of climate change in specific extreme weather events could be estimated and quantified in principle, but related assessments were still confined to particular case studies, often using a single model, and typically focusing on high-impact events with a clear attributable signal. However, since AR5, the attribution of extreme weather events has emerged as a growing field of climate research with an increasing body of literature (---), including the number of approaches to examining extreme events (---).”
A commonly-used approach, often called the risk-based approach in the literature and referred to here as the “probability-based approach”, produces statements such as ‘anthropogenic climate change made this event type twice as likely’ or ‘anthropogenic climate change made this event 15% more intense’. This is done by estimating probability distributions of the index characterizing the event in today’s climate, as well as in a counterfactual climate, and either comparing intensities for a given occurrence probability (e.g., 1-in-100 45 year event) or probabilities for a given magnitude (---). … Other more conditional approaches involve prescribing certain aspects of the climate system.... These highly conditional approaches have also been called “storylines” (---) and can be useful when applied to extreme events that are too rare to otherwise analyse or where the specific atmospheric conditions were central to the impact. … However, the imposed conditions limit an overall assessment of the anthropogenic influence on an event, as the fixed aspects of the analysis may also have been affected by climate change. …
The outcome of event attribution is dependent on the definition of the event (---), as well as the framing (---) and uncertainties in observations and modelling. Observational uncertainties arise both in estimating the magnitude of an event as well as its rarity (---). Results of attribution studies can also be very sensitive to the choice of climate variables (---). Attribution statements are also dependent on the spatial (---) and temporal (---) extent of event definitions, as events of different scales involve different processes (---) and large-scale averages generally yield higher attributable changes in magnitude or probability due to the smoothing out of the noise. In general, confidence in attribution statements for large-scale heat and lengthy extreme precipitation events have higher confidence than shorter and more localized events, such as extreme storms, an aspect also relevant for determining the emergence of signals in extremes or the confidence in projections (---).
The reliability of the representation of the event in question in the climate models used in a study is essential (---). Extreme events characterized by atmospheric dynamics that stretch the capabilities of current-generation models (---) limit the applicability of the probability-based approach of event attribution. The lack of model evaluation, in particular in early event attribution studies, has led to criticism of the emerging field of attribution science as a whole (---) and of individual studies (---). In this regard, the storyline approach (---) provides an alternative option that does not depend on the model’s ability to represent the circulation reliably. In addition, several ways of quantifying statistical uncertainty (---) and model evaluation (---) have been employed to evaluate the robustness of event attribution results. For the unconditional probability-based approach, multi-model and multi-approach (e.g., combining observational analyses and model experiments) methods have been used to improve the robustness of event attribution (---).