Tropical intraseasonal oscillation simulated in an AMIP-type experiment by NICAM

Kikuchi, Kimiyo; Kodama, Chihiro; Nasuno, Tomoe; Nakano, Masuo; Miura, Hiroaki; Satoh, Masaki; Noda, Akira; Yamada, Yohei

doi:10.1007/s00382-016-3219-z

Cited by 22 publications

(21 citation statements)

References 94 publications

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“…Meanwhile, in the suppressed to preconditioning phases of the MJO, the horizontal transport of moisture by the high-frequency variability played an important role in moistening the lower to middle troposphere. The above considerations imply that the relatively weak convective signal associated with the MJO in the NICAM simulations (Nasuno 2013;Kodama et al 2015;Kikuchi et al 2017a) can be related to the overdevelopment of the grid-scale convection in the model.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the Near Real-Time Forecasts Using a Global Nonhydrostatic Model during the CINDY2011/DYNAMO

Nasuno

Kikuchi

Nakano

et al. 2017

Journal of the Meteorological Society of Japan

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By comparison with satellite and field observations, the comprehensive performance and potential utility of near real-time forecasts using Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are demonstrated by exploiting the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY2011) / Dynamics of the Madden-Julian Oscillation (DYNAMO) campaign. A week-long forecast was run each day using a regionally stretched version of NICAM, with the finest mesh size of 14 km over the tropical Indian Ocean (IO), throughout the intensive observation period (IOP).The simulated precipitation time series fairly represented the evolution and propagation of the observed Madden-Julian Oscillation (MJO) events, although a 30 % overprediction of precipitation over the IO domain (60 -90°E, 10°S -10°N) was found on average. Frequencies of strong (> 40 mm day −1 ) precipitation were overpredicted, while those of weak precipitation were underpredicted against satellite observations. Compared with the field observations at Gan Island, the biases in precipitation frequency were less obvious, whereas the growth of lower to middle tropospheric dry (~ 1 g kg −1) and warm (~ 1 K) biases were found. Despite these mean biases, temporal variations of the moisture and zonal wind profiles including the MJO events were reasonably simulated.Using the forecast data the moisture and energy budgets during the IOP were investigated. The diagnosis using the 7-day-mean fields captured the observed features of the MJO events. Meanwhile, significant upward transport of moisture by the grid-resolved high-frequency variability was detected throughout the IOP. The relationship between these high-frequency effects and the simulated MJO or mean biases is also discussed.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the Near Real-Time Forecasts Using a Global Nonhydrostatic Model during the CINDY2011/DYNAMO

Nasuno

Kikuchi

Nakano

et al. 2017

Journal of the Meteorological Society of Japan

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“…The BSISO and MJO PC time series for the models were also obtained in a similar manner by projecting the 25–90‐day filtered simulated OLR onto the observed EEOFs. As discussed by Kikuchi et al (), since we are interested in how models are able to accurately simulate the observed spatiotemporal structures of the ISO rather than in isolating the model's preferred spatiotemporal structures of the ISO, we do not use EEOFs derived from model simulations. Because the ISO simulated in the models is usually much weaker in amplitude, the PCs in the models are adjusted by dividing by α,

normalα = \frac{\overset{‖{PC}_{MJO}^{model}‖}{true} + \overset{‖{PC}_{BSISO}^{model}‖}{true}}{\overset{‖{PC}_{MJO}^{obs}‖}{true} + \overset{‖{PC}_{BSISO}^{obs}‖}{true}},

before determining the ISO mode for a given day.…”

Section: Analysis Methodsmentioning

confidence: 99%

Seasonality of Intraseasonal Variability in Global Climate Models

Nakano

Kikuchi

2019

Geophysical Research Letters

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The tropical intraseasonal (30–90 days) oscillation (ISO) displays distinctive behaviors in boreal summer and winter. How well each mode is simulated in climate models has been investigated; however, very few studies have examined whether these modes are simulated in appropriate season. Here we developed diagnostics to assess this aspect and applied these diagnostics to numerous atmosphere‐only and atmosphere–ocean‐coupled models. We found out that all models share serious biases and that they sometimes incorrectly simulate the boreal summer ISO mode even in boreal winter and underestimate the appearance frequency of the boreal summer ISO in boreal summer. Nearly all atmosphere‐ocean‐coupled models show some improvements in the ISO seasonality representation compared to their atmosphere‐only counterparts. It is suggested that good models for simulating the ISO seasonality have good life cycles for each ISO mode and that an accurate reproduction of the seasonal mean low‐level zonal wind is crucial.

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“…Noda et al 2010Noda et al , 2012Pauluis and Garner 2006). Present and future climate simulations using 14-km mesh NICAM were performed (Kodama et al 2015;Satoh et al 2015) and analyzed in terms of intraseasonal variation (Kikuchi et al 2017), tropical synoptic-scale disturbance (Fukutomi et al 2015), tropical cyclones (Satoh et al 2015;Yamada et al 2017), and climate sensitivity (Chen et al 2016). The performance of MJO prediction was statistically confirmed through 14-km mesh, 54-ensemble simulation (Miyakawa et al 2014).…”

Section: Jamstec Model Intercomparision Projectmentioning

confidence: 96%

JAMSTEC Model Intercomparision Project (JMIP)

Kodama

Kuwano-Yoshida

Watanabe

et al. 2019

JAMSTEC Rep. Res. Dev.

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The JAMSTEC Model Intercomparison Project (JMIP) provides a first opportunity to systematically compare multiple global models developed and/or used in JAMSTEC with the aim of moving toward better weather and climate predictions. Here, we evaluate climate simulations obtained from atmospheric models (AFES and MIROC5), atmospheric model with slab ocean (NICAM.12), and fully coupled model (SINTEX-F1 and SINTEX-F2). In these simulations, the sea surface temperature is fixed (for AFES and MIROC5) or nudged (NICAM.12, SINTEX-F1, and SINTEX-F2) to the observed historical one. We focus on the climatology and variability of precipitation and its associated phenomena, including the basic state, the energy budget of the atmosphere, extratropical cyclones, teleconnection, and the Asian monsoon. We further discuss the possible causes of similarities and differences among the five JMIP models. Though some or most of the dynamical and physical packages in the JMIP models have been developed independently, common model biases are found among them. The AFES and MIROC5, and the SINTEX-F1 and SINTEX-F2, show strong similarities. In many respects, NICAM.12 shows unique characteristics, such as the distributions of precipitation, shortwave radiation, and explosive extratropical cyclones and the onset of the Asian summer monsoon. To some extent, the similarities and differences among the JMIP models overlap with those among the Coupled Model Intercomparison Project Phase-5 (CMIP5) models, suggesting that JMIP can be used as a simple and in-depth version of CMIP to investigate the mechanisms of model bias. We suggest that this JMIP framework could be expanded to an intercomparison of weekly-to-seasonal scale weather forecasting; here, more fruitful discussion is expected through intensive collaboration among modeling and observation groups.

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Tropical intraseasonal oscillation simulated in an AMIP-type experiment by NICAM

Cited by 22 publications

References 94 publications

Evaluation of the Near Real-Time Forecasts Using a Global Nonhydrostatic Model during the CINDY2011/DYNAMO

Evaluation of the Near Real-Time Forecasts Using a Global Nonhydrostatic Model during the CINDY2011/DYNAMO

Seasonality of Intraseasonal Variability in Global Climate Models

JAMSTEC Model Intercomparision Project (JMIP)

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