Learning Climate Sensitivity from Future Observations, Fast and Slow
In Progress
Project goal
As more observations of global temperature, ocean heat uptake, and atmospheric carbon dioxide concentrations become available, the signal-to-noise ratio for carbon-induced warming will grow relative to internal climate variability. It is an open question how efficiently climate models will be able to use additional observations to constrain climate sensitivity and other policy-relevant aspects of the climate system. In this project, we use a variational technique and the FaIR climate model to probe how quickly we will learn about key uncertain climate process from simulated future observations. We hope to provide time- and emissions-dependent estimates of how climate uncertainty evolves over time.
