Detail Project and Funding

Abstract: State-of-the-art climate models suffer from major limitations, as clouds remain the dominant source of uncertainty. This uncertainty propagates nonlinearly into predictions of global temperature, climate sensitivi ty, and extreme events, undermining confidence in long-term projections. Even under optimistic emission scenarios, current models exhibit long-term temperature uncertainties of up to 1.8°C, alarmingly close to the 2°C threshold regarded as critical for ecosystems and human societies.
SYNONIM addresses this fundamental bottleneck by introducing a physically grounded and predictive description of ice-cloud microphysics, with the aim of elevating the reliability of climate projections to an unprecedented level.
Cloud-related biases largely arise from simplified global assumptions on microphysics, particularly ice particle size and shape. To overcome this limitation, SYNONIM introduces a pioneering theoretical framework inspired by gauge theories from fundamental physics to predict the evolution of ice crystal habits.
For the first time, habit evolution is modeled as a stochastic process driven by temperature and humidity, extending classical growth dynamics and explicitly linking crystal shape variability to measurable atmospheric conditions.
This framework enables the derivation of robust habit distributions that will be used as inputs for advanced cloud retrieval algorithms applied to multi-platform remotely sensed and in situ observations collected over the last 30 years in strategically relevant climatological regions. The resulting global statistics of particle size and shape will provide unprecedented observational constraints on ice-cloud radiative properties.
By integrating these constraints directly into the radiation schemes of selected climate models, SYNONIM will drastically reduce uncertainties in cloud radiative effects, enabling a step change in the predictive capability of climate models and supporting robust strategies for climate mitigation.