Alice Wells, Postdoctoral Fellow, Colorado State University, USA.

Title: Redistributing Risk: Air Quality Mortality Under Different Stratospheric Aerosol Injection Strategies.

Abstract: Research into solar climate intervention, a proposed technique to counteract global warming by reflecting a small fraction of sunlight back into space, has mostly focused on climate responses, with limited assessment of potential global public health impacts. One proposed method, stratospheric aerosol injection (SAI), has the potential to directly affect air quality-related mortality through sulfate aerosol injection into the upper atmosphere. This study quantifies how two SAI strategies with the same global mean temperature target modify ozone- and fine particulate matter (PM2.5)-attributable mortality relative to a baseline climate change scenario. We combined climate model simulations of surface ozone and PM2.5 with an epidemiological health impact assessment framework based on the Global Burden of Disease (GBD) methodology. We evaluated a baseline climate change scenario and two SAI simulations designed to limit warming to 1.5°C above pre-industrial. One injects at 21km at 30°N/S, while the other injects at 15km at 60°N/S. Across all simulations, declining emissions reduce global air pollution-attributable mortality. Both SAI strategies redistribute ozone- and PM2.5-related mortality regionally; however, changes attributable to SAI are small relative to emission-driven reductions. The high-latitude, low-altitude strategy increases PM2.5-related mortality more than the mid-latitude, high-altitude strategy. Ozone-related mortality strongly depends on latitude, linked to the stratospheric ozone response to the aerosol injection. These results highlight the need to consider health impacts in solar climate intervention evaluations.

Freddy Bouchet, Directeur de recherche au CNRS, Centre National de la Recherche Scientifique (CNRS), France.

Title: Rare event simulations, emulators, and machine learning for predicting extreme heat waves and extremes of renewable electricity production.

Abstract: In the climate system, extreme events and tipping points (transitions between climate attractors) are of primary importance for understanding the impacts of climate change and for designing effective adaptation and mitigation strategies. Recent extreme heat waves with severe societal consequences, as well as prolonged periods of very low renewable energy production in electricity systems, are striking examples. A key challenge in studying such phenomena is the lack of available data: these events are inherently rare, and realistic climate models are computationally expensive and highly complex. This data scarcity severely limits the applicability of traditional approaches, whether based on modelling, physics, or statistical analysis. In this talk, I will present new algorithms and theoretical approaches based on rare-event simulations, climate-model emulators, and machine-learning methods for stochastic processes. These methods are specifically designed to predict the probability that an extremely rare event will occur and to produce huge catalogues of dynamical trajectories leading to the event. The approach combines, on the one hand, state-of-the-art AI-based emulators that reproduce the full atmospheric dynamics of climate models, and, on the other hand, rare-event simulation techniques that reduce by several orders of magnitude the computational cost of sampling extremely rare events. To illustrate the performance of these tools, I will present results on midlatitude extreme heat waves and on extremes of renewable energy production, with a particular focus on their implications for the resilience of electricity systems.