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Cloud patterns have four interpretable dimensions
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  • Martin Janssens,
  • Jordi Vilà-Guerau de Arellano,
  • Marten Scheffer,
  • Coco Antonissen,
  • Pier Siebesma,
  • Franziska Glassmeier
Martin Janssens
Wageningen University & Research

Corresponding Author:[email protected]

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Jordi Vilà-Guerau de Arellano
Wageningen University Research
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Marten Scheffer
Wageningen University
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Coco Antonissen
TU Delft
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Pier Siebesma
TU Delft
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Franziska Glassmeier
TU Delft
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Shallow 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 paper suggests one. We compute 21 metrics for 5000 satellite scenes of shallow clouds over the subtropical Atlantic Ocean and translate the resulting dataset 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.