TY - JOUR
T1 - Cloud Patterns in the Trades Have Four Interpretable Dimensions
AU - Janssens, Martin
AU - Vilà-Guerau de Arellano, Jordi
AU - Scheffer, Marten
AU - Antonissen, Coco
AU - Siebesma, A. Pier
AU - Glassmeier, Franziska
PY - 2021
Y1 - 2021
N2 - 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 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.
AB - 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 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.
UR - http://www.scopus.com/inward/record.url?scp=85102494320&partnerID=8YFLogxK
U2 - 10.1029/2020GL091001
DO - 10.1029/2020GL091001
M3 - Article
AN - SCOPUS:85102494320
SN - 0094-8276
VL - 48
SP - 1
EP - 11
JO - Geophysical Research Letters
JF - Geophysical Research Letters
IS - 5
M1 - e2020GL091001
ER -