Influence of Indian Ocean Dipole and Pacific recharge on following year’s El Niño: interdecadal robustness

Izumo, Takeshi; Lengaigne, Matthieu; Vialard, Jérôme; Luo, Jing–Jia; Yamagata, Toshio; Madec, Gurvan

doi:10.1007/s00382-012-1628-1

Cited by 111 publications

(81 citation statements)

References 109 publications

(143 reference statements)

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“…Increased ENSO variance corresponds to an enhanced ENSO-IOB correlation in models (Figure 18a). A similar relationship exists for the IOD response to ENSO decadal modulations (Izumo et al 2013). For each model, the decade with the maximum correlation is compared with the decade with the minimum correlation ( Figure 18b).…”

Section: G Time-latitude Variations Along Iosupporting

confidence: 54%

Indian Ocean Variability in the CMIP5 Multimodel Ensemble: The Basin Mode

Xie

Yang

et al. 2013

Journal of Climate

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This study evaluates the simulation of the Indian Ocean Basin (IOB) mode and relevant physical processes in models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Historical runs from 20 CMIP5 models are available for the analysis. They reproduce the IOB mode and its close relationship to El Niño–Southern Oscillation (ENSO). Half of the models capture key IOB processes: a downwelling oceanic Rossby wave in the southern tropical Indian Ocean (TIO) precedes the IOB development in boreal fall and triggers an antisymmetric wind anomaly pattern across the equator in the following spring. The anomalous wind pattern induces a second warming in the north Indian Ocean (NIO) through summer and sustains anticyclonic wind anomalies in the northwest Pacific by radiating a warm tropospheric Kelvin wave. The second warming in the NIO is indicative of ocean–atmosphere interaction in the interior TIO. More than half of the models display a double peak in NIO warming, as observed following El Niño, while the rest show only one winter peak. The intermodel diversity in the characteristics of the IOB mode seems related to the thermocline adjustment in the south TIO to ENSO-induced wind variations. Almost all the models show multidecadal variations in IOB variance, possibly modulated by ENSO.

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Section: G Time-latitude Variations Along Iosupporting

confidence: 54%

Indian Ocean Variability in the CMIP5 Multimodel Ensemble: The Basin Mode

Xie

Yang

et al. 2013

Journal of Climate

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“…Thus, the IndoPacific indices exhibit little or no relation with the NOAA/CPC modes of Northern Hemisphere atmospheric variability. On the other hand, the Indo-Pacific modes are expected to have a closer interrelation (e.g., Torrence and Webster 1999;Annamalai et al 2003;Izumo et al 2014). The values in Table 1 show that the MEI, IOD, and IMI correlate significantly with each other, and the strongest correlation is found between MEI and IMI (r 5 20.43).…”

Section: Climatic Index Characteristicsmentioning

confidence: 96%

Impacts of Climate Modes on Air–Sea Heat Exchange in the Red Sea

et al. 2015

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The impacts of various climate modes on the Red Sea surface heat exchange are investigated using the MERRA reanalysis and the OAFlux satellite reanalysis datasets. Seasonality in the atmospheric forcing is also explored. Mode impacts peak during boreal winter [December-February (DJF)] with average anomalies of 12-18 W m 22 to be found in the northern Red Sea. The North Atlantic Oscillation (NAO), the east Atlanticwest Russia (EAWR) pattern, and the Indian monsoon index (IMI) exhibit the strongest influence on the airsea heat exchange during the winter. In this season, the largest negative anomalies of about 230 W m 22 are associated with the EAWR pattern over the central part of the Red Sea. In other seasons, mode-related anomalies are considerably lower, especially during spring when the mode impacts are negligible. The mode impacts are strongest over the northern half of the Red Sea during winter and autumn. In summer, the southern half of the basin is strongly influenced by the multivariate ENSO index (MEI). The winter mode-related anomalies are determined mostly by the latent heat flux component, while in summer the shortwave flux is also important. The influence of the modes on the Red Sea is found to be generally weaker than on the neighboring Mediterranean basin.

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“…The DMI-monsoon correlation is quite low in the multi observation and reanalysis mean (r = 0.27), and not even statistically significant at the 90 % level in APHRO-DITE (r = 0.10) and GPCC (r = 0.12). Thus, the IODmonsoon link appears relatively weak, as also noted by Izumo et al (2013), even though significant signatures have been identified based on composite analyses . A positive DMI is associated with an easterly wind anomaly just south of the Equator in the Indian Ocean (Fig.…”

Section: Indian Summer Monsoonmentioning

confidence: 99%

“…By contrast, Izumo et al (2013) have suggested that the bienniality of the Indo-Australian monsoon system might be a passive response to the bienniality of the IOD-ENSO system.…”

Section: Introductionmentioning

confidence: 95%

The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations

et al. 2013

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A large spread exists in both Indian and Australian average monsoon rainfall and in their interannual variations diagnosed from various observational and reanalysis products. While the multi model mean monsoon rainfall from 59 models taking part in the Coupled Model Intercomparison Project (CMIP3 and CMIP5) fall within the observational uncertainty, considerable model spread exists. Rainfall seasonality is consistent across observations and reanalyses, but most CMIP models produce either a too peaked or a too flat seasonal cycle, with CMIP5 models generally performing better than CMIP3. Considering all North-Australia rainfall, most models reproduce the observed Australian monsoon-El Niño Southern Oscillation (ENSO) teleconnection, with the strength of the relationship dependent on the strength of the simulated ENSO. However, over the Maritime Continent, the simulated monsoon-ENSO connection is generally weaker than observed, depending on the ability of each model to realistically reproduce the ENSO signature in the Warm Pool region. A large part of this bias comes from the contribution of Papua, where moisture convergence seems to be particularly affected by this SST bias. The Indian summer monsoon-ENSO relationship is affected by overly persistent ENSO events in many CMIP models. Despite significant wind anomalies in the Indian Ocean related to Indian Ocean Dipole (IOD) events, the monsoon-IOD relationship remains relatively weak both in the observations and in the CMIP models. Based on model fidelity in reproducing realistic monsoon characteristics and ENSO teleconnections, we objectively select 12 ''best'' models to analyze projections in the rcp8.5 scenario. Eleven of these models are from the CMIP5 ensemble. In India and Australia, most of these models produce 5-20 % more monsoon rainfall over the second half of the twentieth century than during the late nineteenth century. By contrast, there is no clear model consensus over the Maritime Continent.

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Influence of Indian Ocean Dipole and Pacific recharge on following year’s El Niño: interdecadal robustness

Cited by 111 publications

References 109 publications

Indian Ocean Variability in the CMIP5 Multimodel Ensemble: The Basin Mode

Indian Ocean Variability in the CMIP5 Multimodel Ensemble: The Basin Mode

Impacts of Climate Modes on Air–Sea Heat Exchange in the Red Sea

The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations

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