A Large‐Scale Analysis of Pockets of Open Cells and Their Radiative Impact

Pockets of open cells sometimes form within closed‐cell stratocumulus cloud decks but little is known about their statistical properties or prevalence. A convolutional neural network was used to detect occurrences of pockets of open cells (POCs). Trained on a small hand‐logged data set and applied t...

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Veröffentlicht in:Geophysical research letters 2021-03, Vol.48 (6), p.n/a
Hauptverfasser: Watson‐Parris, D., Sutherland, S. A., Christensen, M. W., Eastman, R., Stier, P.
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Sprache:eng
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Zusammenfassung:Pockets of open cells sometimes form within closed‐cell stratocumulus cloud decks but little is known about their statistical properties or prevalence. A convolutional neural network was used to detect occurrences of pockets of open cells (POCs). Trained on a small hand‐logged data set and applied to 13 years of satellite imagery the neural network is able to classify 8,491 POCs. This extensive database allows the first robust analysis of the spatial and temporal prevalence of these phenomena, as well as a detailed analysis of their micro‐physical properties. We find a large (30%) increase in cloud effective radius inside POCs as compared to their surroundings and similarly large (20%) decrease in cloud fraction. This also allows their global radiative effect to be determined. Using simple radiative approximations we find that the instantaneous global annual mean top‐of‐atmosphere perturbation by all POCs is only 0.01 W/m2. Plain Language Summary The amount of sunlight that reaches, and warms, the surface of the earth is heavily influenced by clouds, in particular marine stratocumulus clouds, a type of low‐lying cloud that forms above cold‐upwelling regions of the ocean. Marine stratocumulus clouds form in two distinct regimes; open‐cells and closed‐cells. Closed‐cell clouds have a higher cloud cover and reflectivity than open‐cell clouds. Small pockets of open cell clouds sometimes form within larger regions of closed‐cell clouds; these are referred to as “pockets of open cells.” Here we use machine learning to detect occurrences of this phenomenon and characterize them in a long‐term satellite data set. This allows their effect on the climate to be determined for the first time. Despite substantial local‐scale changes in cloud properties, we find that their effect on the climate is small. Key Points Convolutional Neural Networks are used to detect 8,491 pockets of open cells in marine stratocumulus between 2005 and 2018 The first comprehensive analysis of their microphysical and climatological properties is presented Their global radiative effect is found to be negligible. Closing all POCs would lead to an instantaneous top‐of‐atmosphere imbalance of only 0.01 W/m2
ISSN:0094-8276
1944-8007
DOI:10.1029/2020GL092213