Observational constraints on microphysics processes in deep-convective clouds in dependence of aerosol conditions combining cloud-resolving models and

Deep convective clouds (DCCs) are thermally-driven systems that transport moist warm air vertically from the lower to the upper troposphere. They can be found in the inter-tropical convergence zone (ITCZ) and in the mid-latitudes over continents, where due to the release of convective available potential energy (CAPE), strong updrafts are initiated, forming cumulonimbus clouds. DCCs can be divided into subcategories depending on their size, from a single tower through a cluster of clouds to mesoscale cells spreading hundreds of kilometres wide. However, DCCs are highly important in the climate system regardless of their size; on the one hand, they are responsible for heating the atmosphere by releasing latent heat as they grow and absorbing longwave radiation, but on the other hand, DCCs are responsible for cooling by backscattering shortwave solar radiation. Furthermore, DCCs are highly important both locally and globally. Locally, they govern the water budget, and globally, they drive large-scale circulation, transporting heat, momentum, moisture, and aerosols. In addition, the radiative effect and climate impact of clouds and aerosols are among the most considerable uncertainties in modelling the radiative forcing of the climate system. Aerosols are tiny particles or droplets of liquid which can be found in the atmosphere. Aerosols originate from natural sources (e.g., volcanoes, ocean, forests, and deserts) and human-made sources (e.g., fossil fuels) and play a major role in cloud microphysics. Aerosols directly affect climate since they scatter and absorb (and re-emit) solar radiation (and terrestrial radiation). Indirectly, aerosols affect climate by altering cloud microphysical properties, making them brighter or longer-lasting by acting as cloud condensation nuclei (CCN), and their properties influence hydrometeor type and size distribution; hence aerosols are crucial in cloud microphysics. As CCN, they are the key factor in allowing the formation of cloud droplets by decreasing the relative humidity needed, and as IN, they can initiate crystallisation of supercooled water droplets. In addition, aerosols interact with hydrometeors and other aerosols within clouds and can control rates of the microphysical processes in different scenarios.

In order to resolve the complexity associated with DCCs microphysical processes, a combination of theory, observations and models is used. Hence, to simulate clouds, microphysical processes need to be parameterised. As implied above, microphysical processes are highly nonlinear, and as a result, they need to be parametrised locally. Due to the complex nature of the microphysical processes, various simplifications must be made, and as a result, it produces uncertainties in the simulated cloud structures and precipitation. Lastly, as mentioned above, the representation of cloud microphysics, and especially DCC microphysics in models, is one of the major origins of uncertainty in models, as described in many recent studies. This has two major contributors; the fundamental uncertainty related to the microphysical processes themselves and differences in time and length scales between real processes and their representation in models. The length scale of microphysical processes is in the range of nanometers to centimetres, and the time scale is microseconds to minutes, while in models, the length scale is in the range of kilometres to hundreds of kilometres, and the time scale is usually a few hours. The excess representation-associated uncertainty in high-resolution models of cloud feedbacks to warming and aerosol-cloud interaction is pronounced especially for mixed-phase and ice-phase clouds. Hence, this is vital to fully understand the microphysical processes taking place in DCCs, the relationship between them, and the best way to represent them in global models.