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Data · dataset · 2026

The role of mesoscale cloud morphology in the shortwave cloud feedback

Listed in National Center for Atmospheric Research

A supervised neural network algorithm is used to categorize near-global satellite retrievals into three mesoscale cellular convective (MCC) cloud morphology patterns.

Description

At constant cloud amount, morphology patterns differ in brightness associated with the amount of optically thin cloud features. Environmentally driven transitions from closed MCC to other morphology patterns, typically accompanied by more optically thin cloud features, are used as a framework to quantify the morphology contribution to the optical depth component of the shortwave cloud feedback.

A marine heat wave is used as an out-of-sample test of closed MCC occurrence predictions. Morphology shifts in optical depth between 65 degrees S and 65 degrees N under projected environmental changes (i.e., from an abrupt quadrupling of CO2) assuming constant cloud cover contributes between 0.04 and 0.07 W m(-2) K-1 (aggregate of 0.06) to the global mean cloud feedback.

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Inferred from text
Atmospheric sciences 75%
Provenance · 1 source records, 8 field assertions
SourceKeyLast seenRaw
National Center for Atmospheric Research131784da-9da4-4e1e-92c5-29047eba7c829 d agoJSON v1
FieldAssertionExtractorEvidence
concepts[field].anzsrc:group:3701enrichment · data ucar edutaxonomy-embedding@1.1.0title+keywords+description (75%)
concepts[field].local:field:earth-environmentalmapping · data ucar educonnector:data_ucar_edu@1.0.0
concepts[field].local:field:ocean-atmosphericmapping · data ucar educonnector:data_ucar_edu@1.0.0
created_datesource · data ucar educonnector:data_ucar_edu@1.0.0
descriptionsource · data ucar educonnector:data_ucar_edu@1.0.0/notes
publication_datesource · data ucar educonnector:data_ucar_edu@1.0.0
titlesource · data ucar educonnector:data_ucar_edu@1.0.0/title
updated_datesource · data ucar educonnector:data_ucar_edu@1.0.0