Mitigating extreme events through multi-scenario ensemble forecasts and local interventions in the Lorenz 96 model

The prediction and mitigation of extreme weather events are important challenges in science and society. Recently, Miyoshi and colleagues introduced the control simulation experiment framework to examine the controllability of chaotic systems under observational uncertainty. Using this framework, they developed a method to reduce extreme events in the Lorenz 96 model by exploiting the system’s sensitivity to initial conditions, guiding trajectories toward desired outcomes with small control inputs (Sun et al., Nonlin. Processes Geophys., 30, 117-128, 2023). Their method is primarily designed to apply control inputs to all grid variables, reducing the success rate of extreme event mitigation to approximately 60% when the control input is applied to only one site. In this study, we propose a new approach to mitigate extreme events in the Lorenz 96 model through local interventions based on multi-scenario ensemble forecasts. Specifically, we explore effective intervention scenarios by computing the system’s responses to a limited number of feasible interventions. Our method achieves a success rate of 94%, even when interventions are applied to only one site per step. This represents a significant improvement over the ~60% success rate of the previous study, albeit at a moderately higher intervention cost. Furthermore, the success rate increases to 99.4% when interventions are applied to two sites.