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 sensitivity, and extreme events, reducing confidence in long-term projections.
To enhance model performance, both remote and in situ measurements, whether spectrally resolved or not, are strictly necessary. By applying appropriate inversion techniques to these observations, we can derive accurate statistics for optical and microphysical cloud parameters, providing vital data to refine currently over-approximated parameterizations.
The accuracy of these statistics strongly depends on our ability to estimate the distribution of ice crystal shapes, which dictate the cloud's radiative effect. To date, these ice crystal habits cannot be predicted solely from thermodynamic variables like temperature and vapor pressure.
In this seminar, we will explore how decades of synergistic measurements, spanning extreme sites like Antarctica and the Atacama Desert to stratospheric balloons and future lunar infrastructure, combined with a novel theoretical approach based on gauge theories, offer a path to profoundly enhance the predictive capabilities of current climate models.
IFIC seminar organizers