Projections of climate change impacts from emissions scenarios are often hindered by the cost of running Earth system models, downscaling outputs, and driving process-based impact models. New generations of statistical, physical, and hybrid climate change emulators aim to reduce these bottlenecks by generating projections of climate change and its associated impacts more efficiently, capturing global, regional, and extreme-event responses. This capability is transforming the assessment of climate change impacts and risks by enabling rapid, policy-relevant analyses for sectors such as natural systems, agriculture, water resources, economics, energy systems and human health.
This session invites contributions of developments in climate change emulation as well as their applications to projecting climate impacts, highlighting opportunities and challenges for robust and computationally efficient climate risk assessments. This includes simple climate models, probabilistic emulation techniques and machine learning approaches, including traditional pattern scaling, as well as benchmarking efforts that aid in interpretability and consistency between emulation methods. To foster interdisciplinary exchange, we also encourage submissions from reduced complexity modeling approaches for climate impact projections.