Structure of the Madden-Julian Oscillation in the Superparameterized CAM

By James Joseph Benedict, David A. Randall. 2009

217 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ea63934f872fc7449939cf9df37f183b6b5b4270

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Citations in the corpus

An Idealized Semi-Empirical Framework for Modeling the Madden–Julian Oscillation (2012, background): “…the subject of both theoretical and observational work (Raymond 2000; Bretherton et al. 2004; Peters and Neelin 2006; Neelin et al. 2009; Muller et al. 2009), and it has been suggested that the simulation of the MJO in global models is sensitive to it (Benedict and Randall 2009; Zhu et al. 2009).”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, background): “…led to markedly improved simulations, for example, of the Asian monsoon, of tropical surface temperatures, and of precipitation and its diurnal cycle, albeit at great computational expense [e.g., Benedict and Randall, 2009; Pritchard and Somerville, 2009a,b; Stan et al., 2010; DeMott et al., 2013].”
The Role of Convective Moistening in the Madden–Julian Oscillation (2009, methodology): “Stephens et al. (2004), Benedict and Randall (2007), and many others create similar composite wave passage plots in their analyses.”
Cracking the MJO nut (2013, background): “[25] A wide range of studies have suggested important roles for a number of various diabatic heating components in initiation and maintenance of the MJO, including shallow convective heating [Zhang and Mu, 2005; Benedict and Randall, 2009; Li et al., 2009; Zhang and Song, 2009], stratiform heating [”
The MJO as a Dispersive, Convectively Coupled Moisture Wave: Theory and Observations (2016, unknown): “…used to study the dynamics of the MJO in re-analysis (Benedict and Randall 2007; Kiranmayi and Maloney 2011; Hannah and Maloney 2014; among others) and GCM simulations (Benedict and Randall 2009; Maloney et al. 2010; Hsu and Li 2012; Hsu et al. 2014; Pritchard and Bretherton 2014; among others).”
A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges (2015, methodology): “This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.”
Global‐scale convective aggregation: Implications for the Madden‐Julian Oscillation (2015, methodology): “We use SP-CAM primarily because of its realistic simulation of the MJO [Benedict and Randall, 2009; Andersen and Kuang, 2012], a rare feature among current GCMs [Lin et al., 2006; Hung et al., 2013].”
Deep learning to represent subgrid processes in climate models (2018, background): “SPCAM drastically improves the realism of the wave spectrum (38), including in our aquaplanet setup (34).”
Fifty Years of Research on the Madden‐Julian Oscillation: Recent Progress, Challenges, and Perspectives (2020, methodology): “…with superparameterized convection largely showed a better performance in MJO simulation than the corresponding model with a conventional parameterization scheme (Benedict & Randall, 2009; Khairoutdinov et al., 2005; D. Kim et al., 2009; Thayer‐Calder & Randall, 2009; H. Y. Zhu et al., 2009).”
Intraseasonal Variability in an Aquaplanet General Circulation Model (2010, background): “Many studies have suggested that a gradual moistening of the lower and middle troposphere, possibly by the action of shallow convection, is an important underlying mechanism that sets the MJO timescale (e.g. Benedict and Randall 2007, 2009; Tian et al. 2010).”
Tracking Pulses of the Madden–Julian Oscillation (2013, background): “Hypothesis I is built upon the abundant literature on the sensitivity of convection to environmental moisture (Brown and Zhang 1997; Raymond 2001; Kuang and Bretherton 2006; Peters and Neelin 2006).”
Structure and Properties of Madden–Julian Oscillations Deduced from DYNAMO Sounding Arrays (2013, background): “…and Hartmann 1993; Hu and Randall 1994; Kemball-Cook and Weare 2001), during which moistening of the lower troposphere is achieved via detrainment from cumulus and congestus clouds (Johnson et al. 1999; Kikuchi and Takayabu 2004) and/or moisture advection (Benedict and Randall 2009; Maloney 2009).”
Could Machine Learning Break the Convection Parameterization Deadlock? (2018, background): “…degree of aggregation, and reduce overly strong land-atmosphere coupling [ Grabowski , 2001; Khairoutdinov et al. 2005; Pritchard and Somerville , 2009; Benedict and Randall, 2009; Pritchard et al. 2011; Randall , 2013; Kooperman et al. , 2016a; 2016b; Sun and Pritchard , 2016; Qin et al. 2018].”
Multiscale Convective Organization and the YOTC Virtual Global Field Campaign (2012, background): “Superparameterization simulates MJO-like systems (e.g., Benedict and Randall 2009), although there is a tendency for the MJO amplitude to be exaggerated and the environment to be too moist compared to observations.”
Atmosphere‐ocean coupled processes in the Madden‐Julian oscillation (2015, methodology): “…parameterization with a cloud-resolving model, a practice known as “superparameterization,” has been shown to be an effective, if computationally expensive, means of producing an MJO in atmosphere-only [Benedict and Randall, 2009] and coupled GCMs [Stan et al., 2010; Benedict and Randall, 2011].”