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Abstract

Four previously identified patterns of meso-scale cloud organization in the trades -- called Sugar, Gravel, Flowers and Fish -- are studied using long-term records of ground-based measurements, satellite observations and reanalyses. A neural network trained to detect these patterns is applied to satellite imagery to identify periods during which a particular pattern is over the Barbados Cloud Observatory. Surface based remote sensing at the observatory is composited and shows that the patterns can be distinguished by differences in cloud macro-physical structures. Variations in total cloudiness among the patterns are dominated by variations in cloud-top cloudiness. Cloud amount near cloud base varies little.







Each pattern is associated with a distinct atmospheric environment whose characteristics are traced back to origins that are not solely within the trades. Sugar air-masses are characterized by weak winds and of tropical origin. Fish are driven by convergence lines originating from synoptical disturbances. Gravel and Flowers are most native to the trades, but distinguish themselves with slightly stronger winds and stronger subsidence in the first case and greater stability in the latter. These results suggest that due to the tight bound of the patterns to wind and air-mass origin, the patterns with the higher cloud fraction, Flowers and Fish, will be disfavoured in a warming climate with more equable sea-surface temperatures and fewer mid-latitudinal disturbances.