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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 15
Atmos. Chem. Phys., 11, 7483-7490, 2011
https://doi.org/10.5194/acp-11-7483-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 11, 7483-7490, 2011
https://doi.org/10.5194/acp-11-7483-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Aug 2011

Research article | 01 Aug 2011

Cloud macroscopic organization: order emerging from randomness

T. Yuan T. Yuan
  • Joint Center for Environmental Technology, UMBC, Baltimore, MD, USA
  • Laboratory for Atmosphere, NASA Goddard Space Flight Center, Greenbelt, MD, USA

Abstract. Clouds play a central role in many aspects of the climate system and their forms and shapes are remarkably diverse. Appropriate representation of clouds in climate models is a major challenge because cloud processes span at least eight orders of magnitude in spatial scales. Here we show that there exists order in cloud size distribution of low-level clouds, and that it follows a power-law distribution with exponent γ close to 2. γ is insensitive to yearly variations in environmental conditions, but has regional variations and land-ocean contrasts. More importantly, we demonstrate this self-organizing behavior of clouds emerges naturally from a complex network model with simple, physical organizing principles: random clumping and merging. We also demonstrate symmetry between clear and cloudy skies in terms of macroscopic organization because of similar fundamental underlying organizing principles. The order in the apparently complex cloud-clear field thus has its root in random local interactions. Studying cloud organization with complex network models is an attractive new approach that has wide applications in climate science. We also propose a concept of cloud statistic mechanics approach. This approach is fully complementary to deterministic models, and the two approaches provide a powerful framework to meet the challenge of representing clouds in our climate models when working in tandem.

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