Effects of a Simple Convective Organization Scheme in a Two-Plume GCM

被引:16
作者
Chen, Baohua [1 ]
Mapes, Brian E. [2 ]
机构
[1] Texas A&M Univ Corpus Christi, Dept Math & Stat, Corpus Christi, TX 78412 USA
[2] Univ Miami, Rosenstiel Sch Marine & Atmospher Sci, 4600 Rickenbacker Causeway, Miami, FL 33149 USA
关键词
HIGH-RESOLUTION SIMULATION; RAIN-PROFILING ALGORITHM; PART I; INTRASEASONAL VARIABILITY; CUMULUS CONVECTION; PRECIPITATION; PARAMETERIZATION; SHALLOW; SENSITIVITY; TRANSITION;
D O I
10.1002/2017MS001106
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
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 Omega 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 (Omega = 51 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 Omega = 0 everywhere, some opposite problems can be discerned. In between those extreme cases, an interactive Omega 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.
引用
收藏
页码:867 / 880
页数:14
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