The IOCAS intermediate coupled model (IOCAS ICM) and its real-time predictions of the 2015–2016 El Niño event

Zhang, Rong Hua; Gao, Chuan

doi:10.1007/s11434-016-1064-4

Cited by 86 publications

(54 citation statements)

References 29 publications

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“…As a result, the warming intensity for the 2015 El Niño event was underestimated in the summer and fall of 2015. Nevertheless, starting from the late summer of 2015, the model was able to adequately capture the warm conditions in late 2015 (Zhang and Gao 2016b).…”

Section: Resultsmentioning

confidence: 99%

“…Figure 2 displays the Niño3.4 SST anomalies predicted using the ICM started from different initial times during the periods 2014-2015 (colored lines). For this standard simulation experiment, a simple initialization scheme was adopted (Zhang and Gao 2016b) in which observed interannual SST anomalies were used to derive an interannual wind stress (τ) field that was used to force the ocean model to yield initial ocean conditions, and the two MPs were taken as their standard values (α Te = 1.0 and α τ = 0.87). Although the ICM can depict the general warming tendency in 2014-2015, obvious discrepancies existed between the observed and predicted SST anomalies.…”

Section: Resultsmentioning

confidence: 99%

“…experiment was conducted using a simple initialization procedure in which only the observed interannual SST anomaly data were used for the ENSO prediction (Zhang and Gao 2016b). Taking a prediction for 1 January 2015 as an example, a temporal succession of daily SST fields was produced using a linear interpolation method from observed monthly SST data from 1980 through December 2014 and the weekly SST data in the first week of January 2015; the observed SST data (Reynolds et al 2002) are available online from the IRI data library.…”

Section: Simulation Experiments and The Optimal Correction Proceduresmentioning

confidence: 99%

“…Other coupled models used to make real-time ENSO predictions have produced similar discrepancies in predictions of the 2015 El Niño event (Zhang and Gao 2016b). For example, many coupled models experienced obvious problems in adequately predicting the onset in the spring of 2015 and the rapid warming during the late spring and summer of 2015 when predictions were initialized in early 2015 (see the IRI website at http://iri.columbia.edu/climate/ENSO/ currentinfo/update.html).…”

Section: A Standard Simulationmentioning

confidence: 99%

“…Currently, various coupled models have been developed and used to make 6-month and longer real-time ENSO predictions in advance and with reasonable success. Nevertheless, coupled models with different degrees of complexity still have systematic biases with large uncertainties in the real-time prediction of ENSO (Jin et al 2008;Luo et al 2008;Zhang and Gao 2016b); for a summary of the model ENSO forecasts, see the International Research Institute for Climate and Society (IRI) website at http://iri.columbia.edu/climate/ENSO/currentinfo/update. html.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An improved simulation of the 2015 El Niño event by optimally correcting the initial conditions and model parameters in an intermediate coupled model

Lei

Gao

2017

Clim Dyn

Self Cite

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Simulation Experiments and The Optimal Correction Proceduresmentioning

confidence: 99%

Section: A Standard Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An improved simulation of the 2015 El Niño event by optimally correcting the initial conditions and model parameters in an intermediate coupled model

Lei

Gao

2017

Clim Dyn

Self Cite

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An Argo‐Derived Background Diffusivity Parameterization for Improved Ocean Simulations in the Tropical Pacific

Zhu

Zhang

2018

Geophysical Research Letters

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Model biases are substantial in ocean and coupled ocean‐atmosphere simulations in the tropical Pacific Ocean, including a too cold tongue and too diffuse thermocline. These biases can be partly attributed to vertical mixing parameterizations in which the background diffusivity depiction has great uncertainties. Here based on the fine‐scale parameterization, the Argo data are used to derive the spatially varying background diffusivity, with a magnitude of O(10−6 m2 s−1) in the most area of tropical Pacific. This new scheme is then employed into the version 5.1 of the Modular Ocean Model‐based ocean‐only and coupled models, resulting in substantial improvements in ocean simulations, including a more realistic cold tongue and equatorial thermocline. The improved simulations can be attributed to the reduced cooling effects induced by weakened equatorial upwelling. Additionally, the subsurface cooling effect is attributed to the reduced heat transfer from the upper layer to the subsurface layer and the convergence of the colder water from off the equator.

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The North Pacific Victoria mode: More effective prediction signal for the Central Pacific El Niño than the North Pacific Oscillation

Mao

2021

Intl Journal of Climatology

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By use of observational reanalysis data and the CMIP5 data, the relationships of the Central Pacific (CP) El Niño and the Victoria Mode (VM)/North Pacific Oscillation (NPO) are compared. The VM/NPO and the CP El Niño are physically related via negative feedback process of air–sea interaction. The VM/NPO has a delay effect on the CP ENSO in the subsequent winter. Nevertheless, the impact of VM on the CP El Niño is more effective relative to the NPO. When the conventional ENSO (hereafter as ENSO) own cycle is removed, the former's correlation with the EMI (El Niño Modoki index) in the Dec(+1) only decreases from 0.48 to 0.31, which is still significant. But the latter's correlation dose not reach 95% confidence level. Composition analysis also shows that during the D(+1)JF(+2), very few SST anomalies could be found over 95% significant level whether in positive or negative NPO phase. While for the VM cases, a large area of the CP ENSO‐like SST anomalies is observed significantly in the tropical Pacific. Especially, for non‐ENSO year, CP El Niño occurs in 50% positive VM cases' following winter. Although in previous studies, the VM is only considered as a transition stage during the process of NPO affecting the CP El Niño, the regression analysis shows that the VM is a partly natural mode, most of its variation is contributed by its internal evolution. The CP El Niño would still occur during positive VM cases even when NPO phase is negative. Their relationship remains stable in the CMIP5 RCP8.5 simulations, that is, the global warming does not interfere with the impact of positive VM on the CP El Niño. In conclusion, the VM may be a more effective prediction signal for the CP El Niño than the NPO.

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The IOCAS intermediate coupled model (IOCAS ICM) and its real-time predictions of the 2015–2016 El Niño event

Cited by 86 publications

References 29 publications

An improved simulation of the 2015 El Niño event by optimally correcting the initial conditions and model parameters in an intermediate coupled model

An improved simulation of the 2015 El Niño event by optimally correcting the initial conditions and model parameters in an intermediate coupled model

An Argo‐Derived Background Diffusivity Parameterization for Improved Ocean Simulations in the Tropical Pacific

The North Pacific Victoria mode: More effective prediction signal for the Central Pacific El Niño than the North Pacific Oscillation

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