Emulating the Forced Response of Climate Models with Generative Machine Learning
Global climate models are essential tools to simulate past and potential future pathways of climate change, as well as associated climate impacts. Shared Socioeconomic Pathways (SSPs) describe a range of future scenarios of global economic and demographic development. These SSPs are intrinsically linked to changes in climate forcings -- the external drivers, such as greenhouse gas and aerosol emissions, which change Earth's energy balance over time. These forcings act as boundary conditions in Earth System Models (ESM), providing insight into the potential climatic impacts of each SSP. Running an ESM, however, is extremely computationally expensive, conflicting with the need for large ens...
| Authors | Graham Clyne, Julia Kaltenborn, Peer Nowack et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 May 2026 |
| Last updated at source | 31 August 2026 |
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