Parameterizing Convective Organization to Escape the Entrainment Dilemma

By B. Mapes, Richard B. Neale. 2011

202 citations recorded by the source corpus.

Lateral mixing parameters in buoyancy‐driven deep convection schemes are among the most sensitive and important unknowns in atmosphere models. Unfortunately, there is not a true optimum value for plume mixing rate, but rather a dilemma or tradeoff: Excessive dilution of updrafts leads to unstable stratification bias in the mean state, while inadequate dilution allows deep convection to occur too easily, causing poor space and time distributions and variability. In this too‐small parameter space, compromises are made based on competing metrics of model performance. We attempt to escape this “entrainment dilemma” by making bulk plume parameters (chiefly entrainment rate) depend on a new prognostic variable (“organization,” org) meant to reflect the rectified effects of subgrid‐scale structure in meteorological fields. We test an org scheme in the Community Atmosphere Model (CAM5) with a new unified shallow‐deep convection scheme (UW‐ens, a 2‐plume version of the University of Washington scheme). Since buoyant ascent involves natural selection, subgrid structure makes convection systematically deeper and stronger than the pure unorganized case: plumes of average (or randomly sampled) air rising in the average environment. To reflect this, org is nonnegative, but we leave it dimensionless. A time scale characterizes its behavior (here ∼3 h for a 2o model). Currently its source is rain evaporation, but other sources can be added easily. We also let org be horizontally transported by advection, as a mass‐weighted mean over the convecting layer. Linear coefficients link org to a plume ensemble, which it assists via: 1) plume base warmth above the mean temperature 2) plume radius enhancement (reduced mixing), and 3) increased probability of overlap in a multi‐plume scheme, where interactions benefit later generations (this part has only been implemented in an offline toy column model). Since rain evaporation is a source for org, it functions as a time‐lagged but positive feedback on deep convection development. This evades the entrainment dilemma, since fully developed org–enhanced convection is not overly dilute, avoiding stability bias, while the pioneering updrafts of new convection are suppressed by entrainment, encouraging more large‐scale variability.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a84f3942f6a955181f491395cd206d26e4bcc0ff

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Citations in the corpus

Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events (2018, methodology): “Recent developments include eﬀorts to include the eﬀects of the spatial organization of convection [ Mapes and Neale , 2011] and use of super-parameterization in which CRMs are embedded in GCM grid boxes [ Randall et al. , 2003].”
Multiscale Convective Organization and the YOTC Virtual Global Field Campaign (2012, background): “—, and R. Neale, 2011: Parameterizing convective organization to escape the entrainment dilemma.”
Could Machine Learning Break the Convection Parameterization Deadlock? (2018, background): “One possibility is that this reflects the difficulty in representing mesoscale effects and subcloud layer organization as well as its memory [ Mapes and Neale , 2011; D'Andrea et al. , 2014].”
A Unified Convection Scheme (UNICON). Part I: Formulation (2014, methodology): “Recently, Hohenegger and Bretherton (2011) and Mapes and Neale (2011) explored simulating deep convection by extending the shallow convection scheme developed by PB09.”
The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies (2018, background): “…atmosphere is conducive for organized convection, resulting in less mixing of air with the properties of the large-scale environment (e.g., Bretherton et al., 2004b; Brown & Zhang, 1997; Holloway & Neelin, 2009; Mapes & Neale, 2011; Redelsperger et al., 2002; Sherwood, 1999; Zhang et al., 2003).”
Convective Self-Aggregation in Numerical Simulations: A Review (2017, background): “Third, the moisture feedback could be due to entrainment, since a parcel ascending in a moister environment will be less cooled by entrainment, leading to a larger parcel buoyancy and stronger convection (Tompkins 2001; Holloway and Neelin 2009; Mapes and Neale 2011).”
Influence of the subcloud layer on the development of a deep convective ensemble (2012, background): “Such a model can either be highly idealized in the form of a model of convection with memory (e.g., Mapes and Neale 2011) or can be rooted in a physical model of the cold pool circulation (e.g., Grandpeix and Lafore 2010).”
Prognostic Validation of a Neural Network Unified Physics Parameterization (2018, background): “Moreover,tuningthese parameters (e.g., entrainment rates) often improves mean state bias at the expense of degraded variability (Kim et al., 2011; Mapes & Neale, 2011).”
CORRIGENDUM of the MJO Transition from Shallow to Deep Convection in Cloudsat-Calipso Data and GISS GCM Simulations (2012, unknown): “Such linkages have been proposed elsewhere (Bechtold et al. 2008; Mapes and Neale 2011).”
Deep learning to represent subgrid processes in climate models (2018, background): “Similarly, recurrent neural networks might be used to capture convective memory across time steps (46, 47).”
Observational Evidence for Relationships between the Degree of Aggregation of Deep Convection, Water Vapor, Surface Fluxes, and Radiation (2012, background): “Some attempts to include features of mesoscale convective organization in parameterization schemes have been carried out (Moncrieff 2004; Qian et al. 1998; Redelsperger et al. 2000; Grandpeix and Lafore 2010; Mapes and Neale 2011).”
The Formation of Wider and Deeper Clouds as a Result of Cold-Pool Dynamics (2014, methodology): “The approach of including the effect of cold pools on the widening of clouds bears similarities with parameterizing one aspect of a parameter characterizing convective organization, as, for example, the ‘‘org’’ parameter defined in Mapes and Neale (2011).”
The Tropical Subseasonal Variability Simulated in the NASA GISS General Circulation Model (2012, background): “The systematic change in stability of the tropical atmosphere in response to the change in entrainment rate has also been documented in other models (Mapes and Neale 2011).”
Entrainment and detrainment in cumulus convection: an overview (2013, background): “Recent attempts to unify shallow and deep convective parametrisations have therefore added explicit relations to tighten their entrainment and detrainment rates to precipitation or its evaporation, as a measure of cold pool activity (Hohenegger and Bretherton, 2011; Mapes and Neale, 2011).”
Fifty Years of Research on the Madden‐Julian Oscillation: Recent Progress, Challenges, and Perspectives (2020, background): “What has also long been known is that the same changes in the convection scheme that improves the MJO tend to affect the mean state significantly, often in a negative way (e.g., D. Kim, Sobel, Maloney, et al., 2011; Mapes & Neale, 2011a; W. Wang & Schlesinger, 1999).”