The Tropical Subseasonal Variability Simulated in the NASA GISS General Circulation Model

By Yonghua Chen, -. S. Yao. 2012

178 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f0ecea16cff5c455c878274a253a682688843906

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. E. Maloney
2. Chidong Zhang
3. C. DeMott
4. N. Klingaman
5. Daehyun Kim
6. Hai Lin
7. L. Harris
8. Á. Adames

In Global and Planetary Change

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

Citations in the corpus

• MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis (2017, unknown): “…appropriate changes to parameterizations, especially that of cumulus convection (Tokioka et al. 1988; Wang and Schlesinger 1999; Maloney and Hartmann 2001; Lin et al. 2008; Zhang and Song 2009; Hannah and Maloney 2011; Kim and Kang 2012; Kim et al. 2012), as summarized in Kim and Maloney (2016).”
• 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].”
• Atmosphere‐ocean coupled processes in the Madden‐Julian oscillation (2015, methodology): “Araligidad and Maloney [2008] studied the sensitivity of MJO convection to latent heat flux using data from two West Pacific buoys, finding that intraseasonal latent heat flux predicted about 20% of contemporaneous precipitation.”
• MJO and Convectively Coupled Equatorial Waves Simulated by CMIP5 Climate Models (2013, background): “…to the cumulus parameterization and include the moisture convective trigger (e.g., Wang and Schlesinger 1999; Lin et al. 2008; Kim and Kang 2012; Kim et al. 2012), diluted CAPE closure (Subramanian et al. 2011; Zhou et al. 2012), interactions between deep and shallow convection (Zhang and Song…”
• The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies (2018, background): “In particular, it has often been reported that models with better MJO simulations tend to produce poor mean climate (e.g., Kim et al., 2012).”
• Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events (2018, background): “Parameterizations of moist convection affect the vertical structure of temperature and humidity in the tropics [Held et al., 2007; Benedict et al., 2013] and the ability of GCMs to simulate the Madden Julian Oscillation and other tropical disturbances [Kim et al., 2012; Benedict et al., 2013].”
• Fifty Years of Research on the Madden‐Julian Oscillation: Recent Progress, Challenges, and Perspectives (2020, background): “Subsequent studies showed that MJO simulations can be improved by increasing convection's sensitivity to free tropospheric water vapor (Arnold et al., 2015; Del Genio et al., 2012; D. Kim et al., 2012; D. Kim & Kang, 2012; D. Kim & Maloney, 2017; H. Zhu & Hendon, 2015).”