Abstract. We assess the current status of atmospheric ice mass estimates by first critically comparing satellite-based datasets, then examining global circulation and global storm-resolving models. The analysis focuses on the frozen water path, which offers a more consistent measure across modelling and observational datasets than cloud ice or other partial quantities. As a reference, we use three retrievals derived from the CloudSat mission. Despite using the same input data, these retrievals exhibit a significant spread. Still, common biases cannot be ruled out, and we argue that the uncertainty in overall means can be as high as 30 %. A recently developed machine learning product based on passive thermal infrared observations greatly extends the spatial and temporal coverage available for comparisons, but its local precision is limited compared to radar-based retrievals. Global circulation models continue to underestimate frozen water paths compared to the observational benchmark and fail to provide consistent representations of regional temporal changes or the annual cycle. Storm-resolving models, which operate at finer grid spacing and explicitly resolve convective dynamics, show better representation of total ice mass, with variations among them similar to the observational uncertainty. However, several issues were noted, such as apparent deviations in the spatial structures of tropical deep convection, and they differ significantly in their relative amounts of cloud ice, snow, and graupel. Together, these findings reveal progress but highlight continuing uncertainties that limit confidence in projections of cloud-related climate feedbacks.
Eriksson et al. (Tue,) studied this question.