Open AccessThe Plant–Craig Stochastic Convection Scheme in ICON and Its Scale Adaptivity
Author(s) -
Richard J. Keane,
George C. Craig,
Christian Keil,
Günther Zängl
Publication year - 2014
Publication title -
journal of the atmospheric sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.853
H-Index - 173
eISSN - 1520-0469
pISSN - 0022-4928
DOI - 10.1175/jas-d-13-0331.1
Subject(s) - precipitation , convection , closure (psychology) , scale (ratio) , variable (mathematics) , environmental science , scaling , resolution (logic) , climate model , meteorology , statistical physics , mathematics , geology , physics , computer science , climate change , mathematical analysis , geometry , oceanography , quantum mechanics , artificial intelligence , economics , market economy
The emergence of numerical weather prediction and climate models with multiple or variable resolutions requires that their parameterizations adapt correctly, with consistent increases in variability as resolution increases. In this study, the stochastic convection scheme of Plant and Craig is tested in the Icosahedral Nonhydrostatic GCM (ICON), which is planned to be used with multiple resolutions. The model is run in an aquaplanet configuration with horizontal resolutions of 160, 80, and 40 km, and frequency histograms of 6-h accumulated precipitation amount are compared. Precipitation variability is found to increase substantially at high resolution, in contrast to results using two reference deterministic schemes in which the distribution is approximately independent of resolution. The consistent scaling of the stochastic scheme with changing resolution is demonstrated by averaging the precipitation fields from the 40- and 80-km runs to the 160-km grid, showing that the variability is then the same as that obtained from the 160-km model run. It is shown that upscale averaging of the input variables for the convective closure is important for producing consistent variability at high resolution.