Changes in the MJO under Greenhouse Gas–Induced Warming in CMIP5 Models

Rushley, Stephanie S.; Kim, Dae Hyun; Adames, Ángel F.

doi:10.1175/jcli-d-18-0437.1

Cited by 37 publications

(52 citation statements)

References 81 publications

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“…To provide confidence in future MJO projections, we use a subset of CMIP5 models that have been assessed to produce good MJO variability in current climate. Hence, we consider here the superset of CMIP5 models that were assessed in Bui and Maloney (, ) and Rushley et al () to produce good MJO variability in current climate. These include BCC‐CSM1‐1, CMCC‐CMS, CMCC‐CM, CNRM‐CM5, GFDL‐CM3, IPSL‐CM5B‐LR, MIROC5 (with three ensemble members), MRI‐CGCM3, and NorESM1‐M.…”

Section: Methodsmentioning

confidence: 99%

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Transient Response of MJO Precipitation and Circulation to Greenhouse Gas Forcing

Bui

Maloney

2019

Geophysical Research Letters

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Recent studies have shown that Madden‐Julian oscillation (MJO) precipitation anomaly amplitude tends to increase while associated circulations weaken at the end of 21st century in Coupled Model Intercomparison Project phase 5 models under Representative Concentration Pathway 8.5. Transient changes of MJO characteristics earlier in the 21st century have received less attention. In this study, changes of MJO precipitation and circulation amplitude during these interim time periods under Representative Concentration Pathway 8.5 are examined in Coupled Model Intercomparison Project phase 5 models. Multimodel mean changes in MJO precipitation and circulation amplitude are not individually detectable in the early and middle 21st century relative to the historical period (1986–2005). However, robust multimodel mean decreases in the ratio of MJO wind to precipitation anomalies occur even early in the 21st century. This decreased ratio is explained by increasingly large tropical static stability as the climate warms, which under weak temperature gradient balance mandates that a diabatic heating anomaly is balanced by an increasingly weaker circulation anomaly. These results suggest the robustness of weak temperature gradient theory for explaining MJO dynamics, not only in an equilibrium climate but also in the transient response.

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Section: Methodsmentioning

confidence: 99%

“…To address the first challenge, models can be prefiltered to select only those that produce a good MJO before doing projections. Hence, in the current study, we will analyze 11 CMIP5 simulations of the 21st century from models that have been assessed by Bui and Maloney (, ) and Rushley et al () to have a good MJO.…”

Section: Introductionmentioning

confidence: 99%

Transient Response of MJO Precipitation and Circulation to Greenhouse Gas Forcing

Bui

Maloney

2019

Geophysical Research Letters

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“…Arnold et al 23 noticed that intraseasonal OLR variance increased with the increase in tropical SST from his aquaplanet experiment using the NCAR community Atmospheric model. Rushley et al 24 examined the projected changes of MJO in response to greenhouse-gas induced warming during the twenty-first century using five CMIP5 models. They observed that the MJO related precipitation variation is likely to be stronger in future warmer climates.…”

Section: Introductionmentioning

confidence: 99%

Exploring the long-term changes in the Madden Julian Oscillation using machine learning

Dasgupta

Metya

Naidu

et al. 2020

Sci Rep

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The Madden Julian Oscillation (MJO), the dominant subseasonal variability in the tropics, is widely represented using the Real-time Multivariate MJO (RMM) index. The index is limited to the satellite era (post-1974) as its calculation relies on satellite-based observations. Oliver and Thompson (J Clim 25:1996–2019, 2012) extended the RMM index for the twentieth century, employing a multilinear regression on the sea level pressure (SLP) from the NOAA twentieth century reanalysis. They obtained an 82.5% correspondence with the index in the satellite era. In this study, we show that the historical MJO index can be successfully reconstructed using machine learning techniques and improved upon. We obtain a significant improvement of up to 4%, using the support vector regressor (SVR) and convolutional neural network (CNN) methods on the same set of predictors used by Oliver and Thompson. Based on the improved RMM indices, we explore the long-term changes in the intensity, phase occurrences, and frequency of the winter MJO events during 1905–2015. We show an increasing trend in MJO intensity (22–27%) during this period. We also find a multidecadal change in MJO phase occurrence and periodicity corresponding to the Pacific Decadal Oscillation (PDO), while the role of anthropogenic warming cannot be ignored.

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“…Climatological monthly precipitation shows different features of number and phase of the peak among the stations such as local peaks in August and November at Chico, in June and October at Rio Piedras, Chamon in November, and Esperanza; these differences in phases may be due to local-scale complex orographic convection activities induced in the ITCZ migration and intraseasonal oscillations that module the convective activity in the tropics as MJO [27].…”

Section: Methods and Datamentioning

confidence: 99%

“…Seasonal changes of partitioning of net radiation at the Earth’s surface to sensible and latent heats affects the diurnal variation of precipitation through atmospheric stability [25]. On interannual time scale, El Niño–Southern Oscillation (ENSO) [16, 26] and Madden-Julian Oscillation (MJO) affect the diurnal variation by changing the amplitude but not the phase [16, 27]. The upper Río Chagres basin is a sub-basin on the eastern side of the Panama Canal watershed (Fig 1).…”

Section: Introductionmentioning

confidence: 99%

Seasonal changes of the diurnal variation of precipitation in the upper Río Chagres basin, Panamá

Nakaegawa¹,

Pinzón²,

Fábrega³

et al. 2019

PLoS ONE

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This study elucidated the characteristics of climatological seasonal changes in the diurnal variations of precipitation at four ground stations in the upper Río Chagres basin in the Panama Canal watershed. The seasonal changes differed among the stations, although they are located within an area of only 414 km2. Precipitation peaks in the early afternoon at 1500 local standard time (LST) were observed at all the stations. At Chamon, monthly-mean hourly precipitation at every hour exceeded 0.3 mm h–1 throughout November and December. The occurrence of morning precipitation in January and March distinguished the seasonal precipitation pattern at Esperanza from the pattern at the other stations. Analyses of the seasonal changes in the diurnal variation with pattern correlations and rotational empirical orthogonal functions grouped the stations into two pairs: no morning peak at Chico and Río Piedras in the downstream basin and morning peak at Chamon and Esperanza in the upstream basin.

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Changes in the MJO under Greenhouse Gas–Induced Warming in CMIP5 Models

Cited by 37 publications

References 81 publications

Transient Response of MJO Precipitation and Circulation to Greenhouse Gas Forcing

Transient Response of MJO Precipitation and Circulation to Greenhouse Gas Forcing

Exploring the long-term changes in the Madden Julian Oscillation using machine learning

Seasonal changes of the diurnal variation of precipitation in the upper Río Chagres basin, Panamá

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