MJO and Convectively Coupled Equatorial Waves Simulated by CMIP5 Climate Models
By Meng-Pai Hung, Jialin Lin, Wanqiu Wang, Daehyun Kim, T. Shinoda, S. Weaver. 2013
329 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/5c5b869791cb7859da1f822ca51cc527726d4edb
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Citations in the corpus
- Tracking Pulses of the Madden–Julian Oscillation (2013, background): “This the MJO (Hung et al. 2013) degrades their seasonal to interannual prediction, lessens our confidence in their ability to project future climate, and manifests our lack of full understanding of the MJO.”
- Atmosphere‐ocean coupled processes in the Madden‐Julian oscillation (2015, methodology): “However, most state-of-the-art general circulation models (GCMs) struggle to simulate the MJO structure and frequency [Hung et al., 2013] and its propagation beyond the Maritime Continent [Jiang et al., 2015].”
- Evaluation of CMIP6 DECK Experiments With CNRM‐CM6‐1 (2019, background): “…convectively coupled equatorial waves (i.e., Kelvin, equatorial Rossby, mixed Rossby‐gravity, and inertio‐gravity waves) and the Madden and Julian Oscillation (MJO, Madden & Julian, 1971; Zhang, 2005), still remains a real challenge for most state‐of‐the‐art climate models (Hung et al., 2013).”
- MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis (2017, unknown): “On the other hand, Hung et al. (2013) and Jiang et al. (2015) showed that the MJO propagation speed of some CMIP5 models in the Indian Ocean tends to be slower than observed, but they indicated that this is due to too strong a persistence of precipitation.”
- MJO prediction in the NCEP Climate Forecast System version 2 (2014, background): “As shown in Lin et al. (2006) and Hung et al. (2013), there exists substantial spread in the simulation of the MJO by the contemporary GCMs, reflecting the large impacts of the different numerical parameterizations in the models.”
- Cracking the MJO nut (2013, methodology): “…convection and its interaction with the large-scale environment [Wang, 2011; Khouider et al., 2013], and global climate models have particular difficulties in reproducing this phenomenon [Slingo et al., 1996, 2005; Lin et al., 2006; Kim et al., 2009; Sperber et al., 2011; Hung et al., 2012].”
- Fifty Years of Research on the Madden‐Julian Oscillation: Recent Progress, Challenges, and Perspectives (2020, methodology): “M.‐P. Hung et al. (2013) and Ahn et al. (2017) examined CMIP5 models in terms of their MJO simulation capability.”
- Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, background): “Climate models have improved over the past decades, leading, for example, to better simulations of El Niño, storm tracks, and tropical waves [Guilyardi et al., 2009; Hung et al., 2013; Flato et al., 2013].”
- The MJO as a Dispersive, Convectively Coupled Moisture Wave: Theory and Observations (2016, unknown): “…the global weather–climate system is well documented (Zhang 2005; Lau and Waliser 2011; Zhang 2013) our understanding of the MJO remains incomplete (Wang 2011), and general circulation models (GCMs) simulate the MJO with an unsatisfactory skill (Lin et al. 2006; Kim et al. 2009; Hung et al. 2013).”
- Global‐scale convective aggregation: Implications for the Madden‐Julian Oscillation (2015, background): “Strong mesoscale organization has been observed in tropical field campaigns [e.g., Houze and Betts, 1981] and by satellite [e.g., Tobin et al., 2012], but it remains unclear how closely these forms of organization resemble the simple quasi-circular systems seen in modeling studies.”