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Effects of a Simple Convective Organization Scheme in a Two‐Plume GCM
Author(s) -
Chen Baohua,
Mapes Brian E.
Publication year - 2018
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1002/2017ms001106
Subject(s) - plume , convection , entrainment (biomusicology) , precipitation , environmental science , buoyancy , diurnal cycle , atmospheric convection , climatology , water vapor , atmospheric sciences , atmosphere (unit) , meteorology , latitude , geology , mechanics , physics , rhythm , acoustics , geodesy
Abstract A set of experiments is described with the Community Atmosphere Model (CAM5) using a two‐plume convection scheme. To represent the differences of organized convection from General Circulation Model (GCM) assumptions of isolated plumes in uniform environments, a dimensionless prognostic “organization” tracer Ω is invoked to lend the second plume a buoyancy advantage relative to the first, as described in Mapes and Neale (2016). When low‐entrainment plumes are unconditionally available (Ω = 1 everywhere), deep convection occurs too easily, with consequences including premature (upstream) rainfall in inflows to the deep tropics, excessive convective versus large‐scale rainfall, poor relationships to the vapor field, stable bias in the mean state, weak and poor tropical variability, and midday peak in diurnal rainfall over land. Some of these are shown to also be characteristic of CAM4 with its separated deep and shallow convection schemes. When low‐entrainment plumes are forbidden by setting Ω = 0 everywhere, some opposite problems can be discerned. In between those extreme cases, an interactive Ω driven by the evaporation of precipitation acts as a local positive feedback loop, concentrating deep convection: In areas of little recent rain, only highly entraining plumes can occur, unfavorable for rain production. This tunable mechanism steadily increases precipitation variance in both space and time, as illustrated here with maps, time‐longitude series, and spectra, while avoiding some mean state biases as illustrated with process‐oriented diagnostics such as conserved variable profiles and vapor‐binned precipitation curves.

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