Uncovering the Large-Scale Meteorology That Drives Continental, Shallow, Green Cumulus Through Supervised
Tom Dror1, Vered Silverman1, Orit Altaratz1
1Department of Earth and Planetary Sciences Weizmann Institute of Science Rehovot Israel.
Geophysical Research Letters
|July 22, 2022
Summary
Shallow cumulus clouds over vegetated areas, termed green clouds (greenCu), exhibit unique properties. A new dataset reveals these green clouds globally share similar meteorological drivers, improving climate model representation.
Area of Science:
- Atmospheric Science
- Climate Modeling
- Remote Sensing
Background:
- Shallow cumulus clouds (Cu) present significant uncertainties in climate predictions due to representation challenges.
- A specific type of continental shallow convective Cu, named green clouds (greenCu), has distinct morphological properties and appears over vegetated areas globally.
Purpose of the Study:
- To identify and characterize greenCu fields across diverse geographical regions using machine learning.
- To establish a novel satellite-based dataset (greenCuDb) for analyzing greenCu.
- To investigate the large-scale meteorological conditions driving greenCu formation worldwide.
Main Methods:
- Supervised machine learning classification was employed to identify greenCu.
- A high-resolution satellite-based dataset (greenCuDb) of MODIS images was created.
- ERA5 reanalysis data was combined with greenCuDb to generate composites and analyze meteorological conditions.
Main Results:
- GreenCu fields were identified across tropical, mid-, and higher-latitude regions.
- GreenCu were found to be driven by consistent large-scale meteorological conditions globally.
- Characteristic conditions include anomalously warm and humid boundary layers with strong free-tropospheric subsidence.
Conclusions:
- GreenCu exhibit globally consistent formation mechanisms despite geographical variations.
- The established greenCuDb dataset provides a valuable resource for climate research.
- Improved representation of greenCu in climate models is crucial for reducing prediction uncertainties.
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