Abstract
AbstractShallow cloud fields over the subtropical ocean exhibit many spatial patterns. The frequency of occurrence of these patterns can change under global warming. Hence, they may influence subtropical marine clouds’ climate feedback. While numerous metrics have been proposed to quantify cloud patterns, a systematic, widely accepted description is still missing. Therefore, this study suggests one. We compute 21 metrics for 5,000 satellite scenes of shallow clouds over the subtropical Atlantic Ocean and translate the resulting data set to its principal components (PCs). This yields a unimodal, continuous distribution without distinct classes, whose first four PCs explain 82% of all 21 metrics’ variance. The PCs correspond to four interpretable dimensions: Characteristic length, void size, directional alignment, and horizontal cloud top height variance. These dimensions span a space in which an effective pattern description can be given, which may be used to better understand the patterns’ underlying physics and feedback on climate.
Highlights
Shallow cumulus clouds are the most abundant cloud type over the tropical oceans (Johnson et al, 1999), but result from many interacting processes and scales
We compute 21 metrics for 5,000 satellite scenes of shallow clouds over the subtropical Atlantic Ocean and translate the resulting data set to its principal components (PCs)
One typically first encapsulates various aspects of the complex patterns in a few, simple “metrics.” In this paper, we compute 21 commonly used metrics for 5,000 cloud fields observed by satellite over the Atlantic Ocean east of Barbados. We show that these 21 metrics contain a large amount of redundant information: To effectively describe and interpret the cloud patters, one only needs four derived metrics, which capture a cloud field's typical cloud size, the size of connected clear sky patches, the clouds’ degree of directional alignment and spatial variance in cloud top height
Summary
Shallow cumulus clouds are the most abundant cloud type over the tropical oceans (Johnson et al, 1999), but result from many interacting processes and scales. Cloud field organization has been defined by metrics of fractal analysis (Cahalan & Joseph, 1989), directional alignment (Brune et al, 2018), subcritical percolation (Windmiller, 2017), or spatial variance (de Roode et al, 2004; Wood & Hartmann, 2006). While this makes it difficult to objectively define and discuss organization, all these interpretations share the same aim: Quantifying cloud patterns. We demonstrate and discuss our description’s ability to characterize previously diagnosed and novel pattern regimes (Section 3.4), before concluding (Section 4)
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