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Ultraclean Layers and Optically Thin Clouds in the Stratocumulus-to-Cumulus...

Wood, R., O. Kuan-Ting, C. S. Bretherton, H. Mohrmann, B. A. Albrecht, P. Zuidema, V. Ghate, C. Schwartz, E. W. Eloranta, S. Glienke, R. A. Shaw, J. Fugal, and P. Minnis (2018), Ultraclean Layers and Optically Thin Clouds in the Stratocumulus-to-Cumulus Transition. Part I: Observations, J. Atmos. Sci., 75, 1631-1652, doi:10.1175/JAS-D-17-0213.1.

A common feature of the stratocumulus-to-cumulus transition (SCT) is the presence of layers in which the concentration of particles larger than 0.1 mm is below 10 cm23. These ultraclean layers (UCLs) are explored using aircraft observations from 14 flights of the NSF–NCAR Gulfstream V (G-V) aircraft between California and Hawaii. UCLs are commonly located in the upper part of decoupled boundary layers, with coverage increasing from less than 5% within 500 km of the California coast to ;30%–60% west of 1308W. Most clouds in UCLs are thin, horizontally extensive layers containing drops with median volume radii ranging from 15 to 30 mm. Many UCL clouds are optically thin and do not fully attenuate the G-V lidar and yet are frequently detected with a 94-GHz radar with a sensitivity of around 230 dBZ. Satellite data indicate that UCL clouds have visible reflectances of ;0.1–0.2 and are often quasi laminar, giving them a veil-like appearance. These optically thin veil clouds exist for 1–3 h or more, are associated with mesoscale cumulus clusters, and likely grow by spreading under strong inversions. Active updrafts in cumulus (Cu) clouds have droplet concentrations of ;25–50 cm23. Collision–coalescence in the Cu and later sedimentation in the thinner UCL clouds are likely the key processes that remove droplets in UCL clouds. UCLs are relatively quiescent, and a lack of mixing with dry air above and below the cloud may help to explain their longevity. The very low and highly variable droplet concentrations in UCL clouds, together with their low geometrical and optical thickness, make these clouds particularly challenging to represent in large-scale models.

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Modeling Analysis and Prediction Program (MAP)