Moist Static Energy Budget of MJO-like Disturbances in the Atmosphere of a Zonally Symmetric Aquaplanet

By J. Andersen, Z. Kuang. 2012

277 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/ad706eec82aca8f6890ca3f1f655809f45b034eb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. C. DeMott
2. Chidong Zhang
3. E. Maloney
4. David A. Randall
5. N. Klingaman
6. Daehyun Kim
7. Á. Adames
8. Hai Lin

In Global and Planetary Change

1. C. DeMott
2. Chidong Zhang
3. E. Maloney
4. David A. Randall
5. N. Klingaman
6. Daehyun Kim
7. Á. Adames
8. Hai Lin

In Oceanography

1. Chidong Zhang
2. C. DeMott
3. Pierre Baldi
4. E. Maloney
5. Pierre Gentine
6. David A. Randall
7. Á. Adames
8. N. Arnold

Citations in the corpus

• Fifty Years of Research on the Madden‐Julian Oscillation: Recent Progress, Challenges, and Perspectives (2020, background): “To the east of MJO convection, synoptic eddy activity tends to be reduced within anomalousMJO easterlies possibly through the barotropic kinetic energy conversion processes (Andersen & Kuang, 2012; Maloney & Hartmann, 2001; Maloney & Dickinson, 2003).”
• MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis (2017, unknown): “…motivated the recent development of the ‘process-oriented’ MJO simulation diagnostics by the MJOTF and others (Maloney 2009; Kiranmayi et al. 2011; Andersen and Kuang 2012; Kim et al. 2014; Benedict et al. 2014; Chikira 2014; Hannah and Maloney 2014; Arnold et al. 2015; Kim et al. 2015;…”
• Could Machine Learning Break the Convection Parameterization Deadlock? (2018, methodology): “To evaluate this idea, we use a well validated version of the SuperParameterized Community Atmosphere Model (SPCAM3) in a simplified aquaplanet configuration with zonally symmetric SSTs following a realistic meridional distribution [ Andersen and Kuang , 2012].”
• The MJO as a Dispersive, Convectively Coupled Moisture Wave: Theory and Observations (2016, unknown): “…the governing equations are vertically integrated in order to simplify them and explicitly represent diabatic heating in terms of observable surface and top of the atmosphere (TOA) variables, as in Kiranmayi and Maloney (2011), Andersen and Kuang (2012), and Sobel et al. (2014), among many others.”
• Global‐scale convective aggregation: Implications for the Madden‐Julian Oscillation (2015, methodology): “Like the nonrotating aggregation, an MSE budget suggests the model MJO is principally supported by longwave feedbacks, consistent with previous modeling studies [Andersen and Kuang, 2012; Arnold et al., 2013; Hannah and Maloney, 2014; Crueger and Stevens, 2015] and observations [Sobel et al., 2014].”
• Atmosphere‐ocean coupled processes in the Madden‐Julian oscillation (2015, background): “AIR-SEA COUPLING AND THE MJO 1143 and the moisture (or moist static energy) budget of the MJO [Maloney, 2009; Kiranmayi and Maloney, 2011; Andersen and Kuang, 2012; DeMott et al., 2014; Chikira, 2014], which is particularly well suited to quantifying processes relevant to MJO initiation,…”
• Modulation of the boreal wintertime Madden‐Julian oscillation by the stratospheric quasi‐biennial oscillation (2016, background): “This is likely the pathway for the MJO convections as well because the cloud-radiation feedback plays a crucial role in MJO dynamics [Andersen and Kuang, 2011; Chikira and Sugiyama, 2013; Sobel et al., 2014; Kim et al., 2015].”
• Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems. (2019, background): “0 [21] to simulate the climate for two years in aquaplanet configuration [22], where the surface temperatures are fixed with a realistic equator-to-pole gradient [23].”