Dr Nicholas Leach

Abstract:

Since 2004, many methods for event attribution have been developed. Early studies showed that attribution statements are sensitive to the framing of research questions but few large comparisons have been undertaken.Here, we firstly motivate the need for multi-method extreme event attribution, highlighting conceptual differences between methods. In a second part, we present a case study of midlatitude storm Babet (2023) to compare three common storyline attribution methods, alongside a severity-based probabilistic method. We discuss three widely relevant questions which highlight the complementarity and the differences between methods: (1) How has climate change impacted the frequency of the event? (2) How has climate change impacted the event severity? (3) Were the dynamics of the event influenced by climate change and if yes, how?We show that methods differ in the extent to which they reproduce observed weather patterns. This influences attribution statements, and can even change the sign of results for events with uncertain climate signals. We argue that limitations and strengths of methods need to be clearly communicated when presenting event attribution reports to ensure findings can be used reliably by a wide range of stakeholders.

Abstract:

In 2021, Canada experienced one of the most extreme heatwaves ever seen anywhere on the globe. We use a weather forecast model to attribute health impacts to climate change. We simulate the heatwave as a present-day forecast, a preindustrial-counterfactual scenario, and a future-counterfactual scenario. Despite the extremeness of the event, our analysis shows that, under current climate conditions, we could have still seen up to 30% more heat-related deaths than the number observed. We show that between 11 and 15% of the observed human mortality was attributable to climate change during this event, depending on the conditioning of the atmospheric circulation. We also show that, had "the same event" occurred in the future, the mortality toll is nonlinear compared with the warming trend, and so the future attribution would be even more extreme, 16 to 31%. We argue that this method gives particularly reliable impact attribution results and is therefore strongly defensible in decision-making and legal settings.

Abstract:

While it is widely believed that the intense rainfall in summer 2022 over Pakistan was substantially exacerbated by anthropogenic climate change1, 2, climate models struggled to confirm this3, 4. Using a high-resolution operational seasonal forecasting system that successfully predicted the extreme wet conditions, we perform counterfactual experiments simulating pre-industrial and future conditions. Both experiments also exhibit strong anomalous rainfall, indicating a limited role of CO2-induced forcing. We attribute 10% of the total rainfall to historical increases in CO2 and ocean temperature. However, further increases in the future suggest a weak mean precipitation reduction but with increased variability. By decomposing rainfall and large-scale circulation into CO2 and SST-related signals, we illustrate a tendency for these signals to compensate each other in future scenarios. This suggests that historical CO2 impacts may not reliably predict future responses. Accurately capturing local dynamics is therefore essential for regional climate adaptation planning and for informing loss and damage discussions.