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Clouds play a central role in many aspects of the climate system and theirforms and shapes are remarkably diverse. Appropriate representation ofclouds in climate models is a major challenge because cloud processes spanat least eight orders of magnitude in spatial scales. Here we show thatthere exists order in cloud size distribution of low-level clouds, and that itfollows a power-law distribution with exponent γ close to 2. γis insensitive to yearly variations in environmental conditions, but hasregional variations and land-ocean contrasts. More importantly, wedemonstrate this self-organizing behavior of clouds emerges naturally from acomplex network model with simple, physical organizing principles: randomclumping and merging. We also demonstrate symmetry between clear and cloudyskies in terms of macroscopic organization because of similar fundamentalunderlying organizing principles. The order in the apparently complexcloud-clear field thus has its root in random local interactions. Studyingcloud organization with complex network models is an attractive new approachthat has wide applications in climate science. We also propose a concept ofcloud statistic mechanics approach. This approach is fully complementary todeterministic models, and the two approaches provide a powerful framework tomeet the challenge of representing clouds in our climate models when workingin tandem

Cloud macroscopic organization: order emerging from randomness