ML4WX

Climate analysis & attribution

The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback

Isabel L. McCoy, Daniel Thompson McCoy, Robert Wood, Paquita Zuidema, Frida A.‐M. Bender

Geophysical Research Letters · · NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), NSF NCAR / UCAR, University of Washington

PaperPDF

In one sentence. A neural network classifies satellite cloud morphology, showing that closed-cell cloud changes contribute about 0.06 W/m²/K to global shortwave cloud feedback under CO2 quadrupling.

Abstract

A supervised neural network algorithm is used to categorize near-global satellite retrievals into three mesoscale cellular convective (MCC) cloud morphology patterns. 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°S - 65°N under projected environmental changes (i.e., from an abrupt quadrupling of CO2) assuming constant cloud cover contributes between 0.04-0.07 W/m2/K (aggregate of 0.06) to the global mean cloud feedback.

Labels

Topic
Climate analysis & attribution, Observations & remote sensing
Type
Application study
Method
CNN / U-Net
Time scale
Climate projections / centennial
Domain
Global
Data
Satellite, CMIP / climate model output
Status
Research
Identifiers
DOI:10.1029/2022gl101042 OpenAlex:W4317397798
Added
2026-10-22

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