The Cumulus Parameterization Problem: Past, Present, and Future

By A. Arakawa. 2004

784 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/696c87112769700b65392c082c9810ea47e3a5db

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Citations in the corpus

Prognostic Validation of a Neural Network Unified Physics Parameterization (2018, background): “There is a long-standing debate about the validity of these hypotheses (Arakawa, 2004; Emanuel et al., 1994), but moist convection is so complex that it is diﬃcult to deﬁnitively prove any sort of causal relationship.”
Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS (2018, neutral): “…consistently performed better in winter than in summer This is because predictable large-scale stratiform systems dominate in winter (Adler et al., 2001; Ebert et al., 2007; Coifﬁer, 2011), whereas unpredictable small-scale convective cells dominate in summer (Arakawa, 2004; Prein et al., 2015) –”
The Dynamics of Idealized Convection Schemes and Their Effect on the Zonally Averaged Tropical Circulation (2007, background): “In his review of cumulus parameterization, Arakawa (2004) emphasizes the relative insensitivity of GCMs to convection scheme because most schemes can be interpreted as performing a similar kind of relaxation toward moist adiabatic temperature profiles.”
A Stochastic Parameterization for Deep Convection Based on Equilibrium Statistics (2008, background): “Nonetheless, the concept is the basis for most, if not all current, cumulus parameterizations (Arakawa 2004). a. Fluctuations in radiative–convective equilibrium The convecting atmosphere is considered to support an ensemble of convective clouds (updrafts or updraft– downdraft pairs).”
Parameterizing Convective Organization to Escape the Entrainment Dilemma (2011, background): “Coarse-grid atmosphere models like General Circulation Models (GCMs) call for some parameterization of water phase changes and vertical fluxes that occur in unresolved (subgrid scale) convective drafts [Arakawa, 2004].”
A Cumulus Parameterization with State-Dependent Entrainment Rate. Part I: Description and Sensitivity to Temperature and Humidity Profiles (2010, methodology): “Lin and Arakawa (1997a) analyzed the origins of entrainment sources simulated in a twodimensional CRM of 500-m horizontal resolution by an elaborate method using trajectory analysis for a deep convection case.”
Deep learning to represent subgrid processes in climate models (2018, background): “To represent the effects of such sub-grid processes on the resolved scales, physical approximations—called parameterizations—have been heuristically developed and tuned to observations over the last decades (2, 3).”
Simulations of the Atmospheric General Circulation Using a Cloud-Resolving Model as a Superparameterization of Physical Processes (2005, background): “Overall, the problem of cloud parameterization remains still largely unsolved and is in some sense " deadlocked, " as discussed by Randall et al. (2003a) and Arakawa (2004).”
A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges (2015, background): “…source of precipitation in many regions of the world and contributes disproportionally to extreme events such as flash floods and landslides through heavy precipitation associated with mesoscale convective systems, squall lines, and tropical cyclones [e.g., Slingo et al., 1994; Arakawa, 2004].”
ATMOSPHERIC MOIST CONVECTION (2005, methodology): “Nonetheless, the question of exactly how best to frame it, and how much the details matter, is a question of active research (Emanuel et al. 1994, Arakawa 2004).”
The Transition to Strong Convection (2009, background): “This forms the basis of many convective parameterizations (Manabe et al. 1965; Arakawa and Schubert 1974; Randall and Pan 1993; Zhang and McFarlane 1995; Pan and Randall 1998; Moorthi and Suarez 1992; Randall et al. 2003; Arakawa 2004; Zhang and Wang 2006).”
A mass-flux cumulus parameterization scheme for large-scale models: description and test with observations (2012, background): “One of the most important problems is the treatment of entrainment of environmental air into cloud columns (Arakawa 2004; Deb et al. 2006; Wang et al. 2007).”
A Parameterization of Dry Thermals and Shallow Cumuli for Mesoscale Numerical Weather Prediction (2009, methodology): “Arakawa (2004) explains that buoyancy is an important parameter in shallow convection.”
Deep moist atmospheric convection in a subkilometer global simulation (2013, methodology): “Effects of convection, such as the vertical transport of heat and moisture, have been parameterized using dynamic and thermodynamic quantities allocated on grids, so- called cumulus parameterization [Arakawa, 2004 and references therein].”
A Dual Mass Flux Framework for Boundary Layer Convection. Part I: Transport (2009, methodology): “A considerable variety of methods has been developed; a recent review on convective parameterization has been written by Arakawa (2004). One method that has been particularly successful, perhaps because of its simplicity and therefore computational efficiency, is the bulk mass flux approach (Ooyama ”