We analyze the importance of cloud top temperature, dust aerosol, sea salt aerosol, and sea ice cover for the thermodynamic phase of low-level, mid-level, and mid to low-level clouds observed by CloudSat/CALIPSO over the Arctic and the Southern Ocean using an explainable machine learning technique. As expected, the cloud top temperature is found to be the most important parameter for determining cloud phase. The results show also a predictive power of sea salt and sea ice on the phase of low-level clouds, while in mid-level clouds dust shows predictive power. Over the Southern Ocean, strong zonal winds coincide with the aerosol distribution. While they can produce high mixing ratios of sea spray at lower levels, the strong zonal winds may prevent the pole-ward transport of dust. Sea ice may prevent the release of sea salt aerosols and marine organic aerosols leading to higher liquid fractions in clouds over sea ice.

One of the largest uncertainties in numerical weather prediction and climate models is the representation of mixed-phase clouds, in which supercooled liquid water and ice can coexist. The aim of our study is to understand how the supercooled liquid fraction (SLF) in clouds with temperature from -40°C to 0°C is related to temperature, geographical location, and cloud type. Our analysis contains a comparison of four satellite-based datasets, one derived from active and three from passive satellite sensors, and focuses on SLF distribution near-globally, but also stratified by hemisphere and continental/maritime regions. Despite the SLF differences found among the datasets, they commonly indicate an increase of SLF with COT, and generally larger SLF in the Southern Hemisphere than in the Northern Hemisphere (up to about 20% difference), with the exception of continental low-level clouds, for which the opposite is true.

The impact of cloud condensation nuclei (CCN) concentration on microphysical processes within thunderstorms and the resulting surface precipitation is not fully understood yet. In this work, an analysis of the microphysical pathways occurring in these clouds is proposed to systematically investigate and understand these sensitivities. Thunderstorms were simulated using convection-permitting (1 km horizontal grid spacing) idealised simulations with the ICON model, which included a 2-moment microphysics parameterization. CCN concentrations were increased from 100 to 3200 CCN/cm3, in five different wind shear environments ranging from 18 to 50 m/s. Large and systematic decreases of surface precipitation (up to 35%) and hail (up to 90%) were found as CCN was increased. Wind shear changes the details, but not the sign, of the sensitivity to CCN. The microphysical process rates were tracked throughout each simulation, closing the mass budget for each hydrometeor class, and collected together into “microphysical pathways”, which quantify the different growth processes leading to surface precipitation. Almost all surface precipitation occurred through the mixed-phase pathway, where graupel and hail grow by riming and later melt as they fall to the surface. The mixed-phase pathway is sensitive to CCN concentration changes as a result of changes to the riming rate, which were systematically evaluated. Supercooled water content was almost insensitive to increasing CCN concentration, but decreased cloud drop size led to a large reduction in the riming efficiency (from 0.79 to 0.24) between supercooled cloud drops and graupel or hail, resulting in less surface precipitation.

Clouds pose a particularly difficult challenge within Earth’s climate system. They are relatively small in spatiotemporal scale but still have a strong influence on radiative fluxes, global circulation, and precipitation patterns. Increasing research attention has been devoted to them over the past 50 years, and we give a summary of the resulting body of scientific literature in this introductory chapter. Articles on clouds and climate are doubling every 8 years, a rate about twice that of scientific publications generally. This expanding number of publications correlates with more citations, but citation rates have also slowed in the most recent decade, despite a growing number of atmospheric science students. We show some basic “science of science” (SciSci) analyses of the clouds and climate literature, such as authorship networks or abstract text mining for techniques, and suggest that further SciSci analyses may help us to process the proliferation of results on clouds and climate and optimize how we do research in the crucial years ahead.