Indo‐Pacific Ocean response to atmospheric intraseasonal variability: 1. Austral summer and the Madden‐Julian Oscillation

Waliser, Duane E.; Murtugudde, Ragu; Lucas, Lisanne E.

doi:10.1029/2002jc001620

Cited by 108 publications

(186 citation statements)

References 112 publications

(179 reference statements)

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“…On the one hand, the (Senguta et al 2001;Waliser et al 2003), oceanic mixed-layer entrainment (Fu et al 2003;Saji et al 2006), and horizontal advection (Han et al 2007). On the other hand, the resultant intraseasonal SST anomaly feeds back to organize MJO convection and associated circulations through enhancing boundary-layer convergence (Wang and Xie 1998;Waliser et al 1999) and surface evaporation (Fu et al 2008).…”

Section: Discussionmentioning

confidence: 99%

Multi-model MJO forecasting during DYNAMO/CINDY period

Lee

Hsu

et al. 2013

Clim Dyn

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The present study assesses the forecast skill of the Madden-Julian Oscillation (MJO) observed during the period of DYNAMO (Dynamics of the MJO)/CINDY (Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011) field campaign in the GFS (NCEP Global Forecast System), CFSv2 (NCEP Climate Forecast System version 2) and UH (University of Hawaii) models, and revealed their strength and weakness in forecasting initiation and propagation of the MJO. Overall, the models forecast better the successive MJO which follows the preceding event than that with no preceding event (primary MJO). The common modeling problems include too slow eastward propagation, the Maritime Continent barrier and weak intensity. The forecasting skills of MJO major modes reach 13, 25 and 28 days, respectively, in the GFS atmosphere-only model, the CFSv2 and UH coupled models. An equal-weighted multi-model ensemble with the CFSv2 and UH models reaches 36 days. Air-sea coupling plays an important role for initiation and propagation of the MJO and largely accounts for the skill difference between the GFS and CFSv2. A series of forecasting experiments by forcing UH model with persistent, forecasted and observed daily SST further demonstrate that: (1) air-sea coupling extends MJO skill by about 1 week; (2) atmosphere-only forecasts driven by forecasted daily SST have a similar skill as the coupled forecasts, which suggests that if the high-resolution GFS is forced with CFSv2 forecasted daily SST, its forecast skill can be much higher than its current level as forced with persistent SST; (3) atmosphere-only forecasts driven by observed daily SST reaches beyond 40 days. It is also found that the MJO-TC (Tropical Cyclone) interactions have been much better represented in the UH and CFSv2 models than that in the GFS model. Both the CFSv2 and UH coupled models reasonably well capture the development of westerly wind bursts associated with November 2011 MJO and the cyclogenesis of TC05A in the Indian Ocean with a lead time of 2 weeks. However, the high-resolution GFS atmosphere-only model fails to reproduce the November MJO and the genesis of TC05A at 2 weeks' lead. This result highlights the necessity to get MJO right in order to ensure skillful extended-range TC forecasting.

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Section: Discussionmentioning

confidence: 99%

Multi-model MJO forecasting during DYNAMO/CINDY period

Lee

Hsu

et al. 2013

Clim Dyn

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“…The same is true with CF1PS showing more than half of h2v 0 h 0 p i in CF1 despite the lack of dSST. Therefore, the column-integrated MSE and moisture budget analyses suggest that, as in the observations (e.g., Waliser et al 1999;Kemball-Cook and Weare 2001;Raymond et al 2003;Stephens et al 2004), the moisture in the lower atmosphere is accumulated over the warm SST during the calm and sunny period. The resulting increase in the column MSE facilitates deep convection, a process consistent with the recharge-discharge paradigm [e.g., Bladé and Hartmann 1993; see also Xu and Rutledge (2014) for DYNAMO observations].…”

Section: R |{Z}mentioning

confidence: 99%

“…Figure 1c shows the diagnostics of the simulated MJO as measured by the real-time multivariate MJO (RMM) index (Wheeler and Hendon 2004). The RMM index, defined as the two leading combined EOFs of outgoing longwave radiation (OLR), with zonal wind at 850 and 200 hPa in the tropics (158S-158N), has been widely used to assess skill of the MJO simulations and forecasts (e.g., Waliser et al 2009;Gottschalck et al 2010), including that for the DYNAMO period (Gottschalck et al 2013;Fu et al 2013;Ling et al 2014). Figure 1c shows the trajectories of the observed (black) and simulated (CF1; red) MJO2 in the phase space for the 30-day integration period starting from 14 November 2011.…”

Section: A Simulated Mjo In Scoarmentioning

confidence: 99%

“…The upper-ocean processes determining mixed-layer heat content, stratification, and mixing all influence the sea surface temperature (SST) and the surface heat flux; the ensuing air-sea interaction is recognized as an important element for initiation and evolution of the MJO (Waliser et al 1999;Zhang et al 2006). The convection and the associated winds and incident solar radiation, in turn, generate intraseasonal variations in SST.…”

Section: Introductionmentioning

confidence: 99%

“…Characterized by large-scale, eastwardpropagating, equatorially trapped, baroclinic oscillations in the tropical wind field at periods of 30-90 days (Madden andJulian 1971, 1994), the MJO has predictability time scales of 10-30 days, far beyond the usual time scales of weather prediction (e.g., Hendon et al 2000;Waliser et al 2003). Although the MJO exerts a profound influence on global weather and climate (Zhang 2005, the complexities of multiscale interaction of the circumequatorial tropical atmospheric circulation with individual cloud systems and upperocean processes make it difficult for the climate models to accurately represent the MJO (e.g., Zhang 2005;Madden and Julian 2005;Lin et al 2006;Hung et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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Coupled Impacts of the Diurnal Cycle of Sea Surface Temperature on the Madden–Julian Oscillation

et al. 2014

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This study quantifies, from a systematic set of regional ocean-atmosphere coupled model simulations employing various coupling intervals, the effect of subdaily sea surface temperature (SST) variability on the onset and intensity of Madden-Julian oscillation (MJO) convection in the Indian Ocean. The primary effect of diurnal SST variation (dSST) is to raise time-mean SST and latent heat flux (LH) prior to deep convection. Diurnal SST variation also strengthens the diurnal moistening of the troposphere by collocating the diurnal peak in LH with those of SST. Both effects enhance the convection such that the total precipitation amount scales quasi-linearly with preconvection dSST and time-mean SST. A column-integrated moist static energy (MSE) budget analysis confirms the critical role of diurnal SST variability in the buildup of column MSE and the strength of MJO convection via stronger time-mean LH and diurnal moistening. Two complementary atmosphere-only simulations further elucidate the role of SST conditions in the predictive skill of MJO. The atmospheric model forced with the persistent initial SST, lacking enhanced preconvection warming and moistening, produces a weaker and delayed convection than the diurnally coupled run. The atmospheric model with prescribed daily-mean SST from the coupled run, while eliminating the delayed peak, continues to exhibit weaker convection due to the lack of strong moistening on a diurnal basis. The fact that time-evolving SST with a diurnal cycle strongly influences the onset and intensity of MJO convection is consistent with previous studies that identified an improved representation of diurnal SST as a potential source of MJO predictability.

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Impact of diurnal forcing on intraseasonal sea surface temperature oscillations in the Bay of Bengal

Thushara

Vinayachandran

2014

JGR Oceans

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The diurnal cycle is an important mode of sea surface temperature (SST) variability in tropical oceans, influencing air-sea interaction and climate variability. Upper ocean mixing mechanisms are significant at diurnal time scales controlling the intraseasonal variability (ISV) of SST. Sensitivity experiments using an Ocean General Circulation Model (OGCM) for the summer monsoon of the year 2007 show that incorporation of diurnal cycle in the model atmospheric forcings improves the SST simulation at both intraseasonal and shorter time scales in the Bay of Bengal (BoB). The increase in SST-ISV amplitudes with diurnal forcing is 0.05 C in the southern bay while it is 0.02 C in the northern bay. Increased intraseasonal warming with diurnal forcing results from the increase in mixed layer heat gain from insolation, due to shoaling of the daytime mixed layer. Amplified intraseasonal cooling is dominantly controlled by the strengthening of subsurface processes owing to the nocturnal deepening of mixed layer. In the southern bay, intraseasonal variability is mainly determined by the diurnal cycle in insolation, while in the northern bay, diurnal cycle in insolation and winds have comparable contributions. Temperature inversions (TI) develop in the northern bay in the absence of diurnal variability in wind stress. In the northern bay, SST-ISV amplification is not as large as that in the southern bay due to the weaker diurnal variability of mixed layer depth (MLD) limited by salinity stratification. Diurnal variability of model MLD is not sufficient to create large modifications in mixed layer heat budget and SST-ISV in the northern bay.

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Indo‐Pacific Ocean response to atmospheric intraseasonal variability: 1. Austral summer and the Madden‐Julian Oscillation

Cited by 108 publications

References 112 publications

Multi-model MJO forecasting during DYNAMO/CINDY period

Multi-model MJO forecasting during DYNAMO/CINDY period

Coupled Impacts of the Diurnal Cycle of Sea Surface Temperature on the Madden–Julian Oscillation

Impact of diurnal forcing on intraseasonal sea surface temperature oscillations in the Bay of Bengal

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