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. We revisit the concept of the cloud vertical structure (CVS) classes we havepreviously employed to classify the planet's cloudiness (Oreopoulos et al.,2017). The CVS classification reflects simple combinations of simultaneouscloud occurrence in the three standard layers traditionally used to separatelow, middle, and high clouds and was applied to a dataset derived fromactive lidar and cloud radar observations. This classification is nowintroduced in an atmospheric global climate model, specifically a version ofNASA's GEOS-5, in order to evaluate the realism of its cloudiness and of theradiative effects associated with the various CVS classes. Such classes canbe defined in GEOS-5 thanks to a subcolumn cloud generator paired with themodel's radiative transfer algorithm, and their associated radiative effectscan be evaluated against observations. We find that the model produces50 % more clear skies than observations in relative terms and producesisolated high clouds that are slightly less frequent than in observations,but optically thicker, yielding excessive planetary and surface cooling. Lowclouds are also brighter than in observations, but underestimates of thefrequency of occurrence (by ∼20 % in relative terms) helprestore radiative agreement with observations. Overall the model better reproducesthe longwave radiative effects of the various CVS classes becausecloud vertical location is substantially constrained in the CVS framework.

An evaluation of clouds and radiation in a large-scale atmospheric model using a cloud vertical structure classification