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Condensation is the dominating growth mechanism of small droplets in developing clouds. In this regard, the standard theory of Köhler for droplet formation gives the saturation conditions by which an aerosol particle is in equilibrium with its surrounding environment or, contrarily, grows unboundedly due to condensation. While this theory provides clear-cut threshold supersaturation values for droplet activation, clouds are eminently turbulent, subject to environmental constraints and are composed of large families of droplets competing for water vapor. This work proposes a nonlinear stochastic framework to extend this standard theory in order to account for (i) turbulent effects on droplet de/activation, and (ii) stagnating environmental conditions in the condensational growth equation. The latter effects are modelled by a nonlinear sink term modifying Köhler’s equation and the former by stochastic perturbations driven by white noise. The resulting Brownian models allow for dynamic transitions between haze and cloud droplets under fluctuations of supersaturation, yielding a hysteresis phenomenon when the latter is varying slowly and monotonically like an ascending air parcel. In our Brownian models, particle's size distributions are described by Gibbs states. Comparisons with experimental data obtained from closed chamber clouds are conducted. The results show that predictions made by Gibbs states match to a high-degree of accuracy the experimentally obtained distributions. Concretely, our Brownian models can predict bimodal distributions in which haze and activated droplets coexist, when the standard theory cannot. With such attributes, we believe that our Brownian modelling framework might provide a basis for future developments of cloud droplet activation parameterizations., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The emergence of organized multiscale patterns resulting from convection is ubiquitous, observed throughout different cloud types. The reproduction of such patterns by general circulation models remains a challenge due to the complex nature of clouds, characterized by processes interacting over a wide range of spatio-temporal scales. The new advances in data-driven modeling techniques have raised a lot of promises to discover dynamical equations from partial observations of complex systems. This study presents such a discovery from high-resolution satellite datasets of continental cloud fields. The model is made of stochastic differential equations able to simulate with high fidelity the spatio-temporal coherence and variability of the cloud patterns such as the characteristic lifetime of individual clouds or global organizational features governed by convective inertia gravity waves. This feat is achieved through the model's lagged effects associated with convection recirculation times, and hidden variables parameterizing the unobserved processes and variables., 11 pages, 9 figures

Clouds cover more than half of the Earth’s surface and hence play a central role in the global water cycle and Earth’s energy budget. Their response to global warming contributes greatly to the uncertainty of climate projections. In this study, we will focus on the response to global warming of one the most basic cloud parameters, namely cloud cover, i.e. the portion of the sky that is covered by clouds. Due to the complexity and diversity formed by cloud patterns over the globe as well as the short-term availability of reliable observational satellite datasets, trend estimations of cloud cover have remain inconclusive. We will show in this talk that an empirical orthogonal function analysis on 42 years of reanalysis cloud data (from ECMWF-ERA5), allows nevertheless for extracting a clear trend. Our findings reveal that the corresponding trend mode strongly correlates with the global mean surface temperature and ENSO modes, while exhibiting a clear land-ocean contrast. We show a generally increasing trend in cloud cover over the ocean and a decreasing trend over land. A detailed assessment of 207 ERA5-thermodynamic variables suggests that the reduction in low-level relative humidity is the most plausible explanation for the decreased continental cloud cover. Our findings imply potential stress on the terrestrial water cycle and changes in the energy partition between land and ocean, all associated with global warming., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)