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Volume 18, issue 9
Atmos. Chem. Phys., 18, 6157-6169, 2018
https://doi.org/10.5194/acp-18-6157-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 18, 6157-6169, 2018
https://doi.org/10.5194/acp-18-6157-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 May 2018

Research article | 03 May 2018

An automated cirrus classification

Edward Gryspeerdt1,2, Johannes Quaas2, Tom Goren2, Daniel Klocke3, and Matthias Brueck4 Edward Gryspeerdt et al.
  • 1Space and Atmospheric Physics Group, Imperial College London, London, UK
  • 2Institute for Meteorology, Universität Leipzig, Leipzig, Germany
  • 3Hans-Ertel-Zentrum für Wetterforschung, Deutscher Wetterdienst, Offenbach, Germany
  • 4Max-Planck-Institut für Meteorologie, Hamburg, Germany

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.

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Cirrus clouds can form by a variety of mechanisms, such as orographic uplift, through convective systems or through large-scale rising motions. In this work, an automated classification of cirrus clouds based on satellite and reanalysis data is presented to separate cirrus by these different formation mechanisms. The classification provides information on the ice origin and cloud-scale updraughts that could not be determined using satellite or reanalysis data alone.
Cirrus clouds can form by a variety of mechanisms, such as orographic uplift, through convective...
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