A longstanding pursuit in climate science is to better understand Earth’s climate sensitivity, which quantifies how global mean surface temperature responds to changes in radiative forcing. Uncertainty in climate sensitivity arises due to forcing and radiative feedbacks, which are influenced by processes ranging from cloud microphysics and local meteorology to large-scale atmospheric circulation and the spatial pattern of surface temperature changes. This session solicits work across theory, observations, and modeling focusing on climate sensitivity, radiative feedbacks, and the pattern effect. It aims to serve as an exchange platform for atmospheric and oceanic science communities, showcasing the full spectrum of modeling approaches from conceptual frameworks and CMIP ensembles to km-scale simulations and novel machine learning methods.

We welcome contributions related to, but not limited to:
- Process studies of feedbacks from clouds, convection, and moist processes
- The modulation of radiative feedbacks by surface warming patterns (the "pattern effect'')
- Theoretical and conceptual models of climate sensitivity (ECS, TCR)
- Relationships between idealized climate sensitivity measures and climate change projections
- Insights into CMIP6 "hot models'' as well as novel CMIP7 simulations
- Ocean heat uptake, air-sea interactions, and ocean dynamics shaping surface temperature patterns, radiative feedbacks, and transient climate sensitivity
- Carbon-climate feedbacks, carbon-cycle interactions, and Earth system/emission response metrics (TCRE, ZEC, ESS)