Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign

By Zhe Feng, Samson Hagos, A. Rowe, Casey D. Burleyson, M. Martini, Simon P. Szoeke. 2015

176 citations recorded by the source corpus.

This paper investigates the mechanisms of convective cloud organization by precipitation‐driven cold pools over the warm tropical Indian Ocean during the 2011 Atmospheric Radiation Measurement (ARM) Madden‐Julian Oscillation (MJO) Investigation Experiment/Dynamics of the MJO (AMIE/DYNAMO) field campaign. A high‐resolution regional model simulation is performed using the Weather Research and Forecasting model during the transition from suppressed to active phases of the November 2011 MJO. The simulated cold pool lifetimes, spatial extent, and thermodynamic properties agree well with the radar and ship‐borne observations from the field campaign. The thermodynamic and dynamic structures of the outflow boundaries of isolated and intersecting cold pools in the simulation and the associated secondary cloud populations are examined. Intersecting cold pools last more than twice as long, are twice as large, 41% more intense (measured with buoyancy), and 62% deeper than isolated cold pools. Consequently, intersecting cold pools trigger 73% more convection than do isolated ones. This is due to stronger outflows that enhance secondary updraft velocities by up to 45%. However, cold pool‐triggered convective clouds grow into deep convection not because of the stronger secondary updrafts at cloud base, but rather due to closer spacing (aggregation) between clouds and larger cloud clusters that form along the cold pool boundaries when they intersect. The close spacing of large clouds moistens the local environment and reduces entrainment drying, increasing the probability that the clouds further develop into deep convection. Implications for the design of future convective parameterization with cold pool‐modulated entrainment rates are discussed.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d4c154ace83c6551c3b33e904b5cb3e69a4411fa

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Citations in the corpus

Could Machine Learning Break the Convection Parameterization Deadlock? (2018, background): “…the main features of tropical climate [ Singh and O'Gorman , 2013] such as the Inter Tropical Convergence Zone (ITCZ) [ Oueslati and Bellon , 2015], or modes of climate variability [ Bush et al. , 2015] such as El Niño or the Madden Julian Oscillation (MJO) [ Kim et al. , 2012; Feng et al. , 2015].”
Physical mechanisms controlling the initiation of convective self‐aggregation in a General Circulation Model (2015, methodology): “In a pioneer study using a two-dimensional Cloud-Resolving Model (CRM), Held et al. [1993] showed that under certain conditions, the atmosphere could spontaneously develop narrow and stationary deep convective regions surrounded by dry subsiding areas.”