English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

An automated cirrus classification

MPS-Authors
/persons/resource/persons206668

Brueck,  Matthias
Hans Ertel Research Group Clouds and Convection, The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

acp-18-6157-2018.pdf
(Publisher version), 3MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Gryspeerdt, E., Quaas, J., Goren, T., Klocke, D., & Brueck, M. (2018). An automated cirrus classification. Atmospheric Chemistry and Physics, 18, 6157-6169. doi:10.5194/acp-18-6157-2018.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002E-124F-8
Abstract
Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. In this work, a classification system (Identification and Classification of Cirrus or IC-CIR) is introduced to identify cirrus clouds by the cloud formation mechanism. Using reanalysis and satellite data, cirrus clouds are separated into four main types: orographic, frontal, convective and synoptic. Through a comparison to convection-permitting model simulations and back-trajectory-based analysis, it is shown that these observation-based regimes can provide extra information on the cloud-scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes. Despite having different cloud formation mechanisms, the radiative properties of the regimes are not distinct, indicating that retrieved cloud properties alone are insufficient to completely describe them. This classification is designed to be easily implemented in GCMs, helping improve future model–observation comparisons and leading to improved parametrisations of cirrus cloud processes.