The accelerating growth of atmospheric CO2, CH4, and N2O poses major threats to the Earth system and climate, having moved the world closer to breaching international climate targets, such as the Paris Agreement and the Global Methane Pledge. In recent years, evidence is accumulating that warming is driving increased greenhouse gas emissions from natural systems. This emission-driven warming will likely continue and intensify due to climate feedback mechanisms. These biogeochemical feedbacks involve not only well-known processes in the atmosphere, ocean, and cryosphere, but also land-based systems such as permafrost, boreal–tropical forests, dynamic vegetation, wetlands, fire, and inland waters, many of which are tightly linked to one another. However, large uncertainties remain in the quantification of these processes for both warming-induced emissions and the associated temperature implications. Understanding these underlying mechanisms will help us address our remaining questions, such as: 1) Which feedback processes are most relevant for mitigation strategy planning (e.g., via the Transient Climate Response to cumulative Emissions of carbon dioxide (TCRE) or non-CO2 contributions to carbon budgets)? 2) What drives nonlinearity in the future climate trajectories? 3) Is the future climate (ir)reversible? 4) When will the climate be irreversible? 5) How will the climate respond to “overshoot, peak, and decline” scenarios? 6) What are the associated uncertainties across regional to global scales?

This session aims to gather and integrate work that tackles the impacts of warming-induced emissions on the Earth system and the mechanisms underlying them, thereby providing clearer guidance for sustaining human society within the remaining carbon budget. All community efforts (e.g., WIEMIP) toward these goals are strongly encouraged. We welcome all contributions spanning laboratory-based experiments, satellite and field observations, machine-learning emulators, and modeling approaches, including fully-coupled Earth system models and stand-alone models.