Suchergebnisse

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 reanalyzes. A deep neural network trained to detect these patterns is applied to satellite imagery to identify periods during which a particular pattern is observed 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 geometry. 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. The patterns with the higher cloud amounts and more negative cloud‐radiative effects, Flowers and Fish, are selected by conditions expected to occur less frequently with greenhouse warming. ; Key Points: Meso‐scale patterns of trade‐wind clouds are identified with a neural network and characterized based on observations. The four analyzed patterns distinguish themselves by stratiform cloudiness and less by cloudiness at the lifting condensation level. Two patterns are imprinted by tropical, respectively extra‐tropical intrusions. ; European Union's Horizon 2020 Research and Innovation Programme ; NASA ; https://doi.org/10.5281/zenodo.4767674

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Surveys in Geophysics 38 (2017): 1529–1568, doi:10.1007/s10712-017-9428-0. ; Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air–sea interactions and convective organization. ; The EUREC4A project is supported by the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 694768), by the Max Planck Society and by DFG (Deutsche Forschungsgemeinschaft, German Research Foundation) Priority Program SPP 1294.