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

Lei, Tao; Gao, Chuan

doi:10.1007/s00382-017-3919-z

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…An alternative approach is to obtain the optimal model errors based on the observations and model rather than subjectively or empirically [37,41,46,47]. For example, as demonstrated by Zhang et al (2018), the optimized MPs and ICs are obtained based on the CNOP approach, which is then used to successfully predict the 2015 El Niño event [13].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…As a strong air-sea coupling phenomenon in the earth system, El Niño has genes of quasi-periodicity but features of complexity, diversity, and variability [5,6]. Therefore, although efforts towards the prediction of El Niño have never been stopped [7][8][9][10][11], operational prediction for the sea surface temperature (SST) in the tropical Pacific is still faced with huge challenges (e.g., [12,13]).…”

Section: Introductionmentioning

confidence: 99%

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Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions

Tao

2024

JMSE

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With an El Niño prediction model, an advanced approach of conditional nonlinear optimal perturbation (CNOP) is used to reveal the maximum impacts of the errors occurring in initial conditions (ICs) and model parameters (MPs) on the El Niño predictions. The optimally growing initial errors CNOP-I and parameter errors CNOP-P are obtained, as well as their optimally combined mode (denoted by CNOPs). The comparisons among CNOP-I, -P, and CNOPs show that the El Niño predictions are more sensitive to the uncertainties in the MPs than in the ICs. The CNOP-I mainly affects the short-term prediction (less than 3 months), whereas the CNOP-P tends to induce much larger error over a longer prediction time. Both CNOP-I and CNOP-P can induce larger error growth during spring than during other seasons; that is to say, both of them cause the “spring predictability barrier” (SPB) phenomenon. The spring error growth caused by CNOP-I is mainly attributed to the uncertainties of the ocean advection processes, while that caused by the CNOP-P is controlled by thermodynamics. When the errors in ICs and MPs are simultaneously included in predictions, the resultant CNOPs produce much larger error growth and cause much more significant SPB; furthermore, the corresponding mechanism is dominated by the nonlinear advection processes. This certainly indicates that strong nonlinear interactions between the errors in ICs and MPs enhance the SPB, thus deepening our understanding of El Niño predictability. It is obvious that initial and model errors should be simultaneously given great attention to improve the El Niño prediction level.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions

Tao

2024

JMSE

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“…The CNOP method [26] is used to study the optimal growth initial error (OGE) [27][28][29], which represents a perturbation under a given physical constraint, resulting in the largest nonlinear error evolution at the prediction time. IOCAS ICM (intermediate coupled model, developed at the Institute of Oceanology, Chinese Academy of Sciences) [9] stands out as one of the prediction models listed in Figure 1, which has provided ENSO forecasting for IRI/CPC since August 2015 and successfully predicted a strong El Niño event in 2015/2016 [10,11] and consecutive third La Niña events in 2020/2021, 2021/2022 and 2022/2023 [12,13]. Many researchers have utilized IOCAS ICM for ENSO predictability studies, including the maximal initial error [14,15], prediction barrier [14] and the sensitive area of target observation [16].…”

Section: Introductionmentioning

confidence: 99%

Error Evolutions and Analyses on Joint Effects of SST and SL via Intermediate Coupled Models and Conditional Nonlinear Optimal Perturbation Method

Qin

Yuan

et al. 2023

JMSE

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A seasonal predictability barrier has long been noticed in ENSO forecasting with numerical models. Previous studies explored the impact of seasonal optimal initial perturbation evolutions in sea surface temperature anomalies (SSTA) on ENSO forecasting using the intermediate coupled model (ICM) via the conditional nonlinear optimal perturbation (CNOP) method. In this paper, we investigate the joint effects of SSTA and sea level anomalies (SLA) from the perspective of the optimal growth initial error (OGE). After determining the four seasonal OGEs about SSTA and SLA (i.e., SSTA-OGE, SLA-OGE and Joint-OGE), we first demonstrate the patterns, evolutions and the resulting spring predictability barrier (SPB) of the above OGEs. Then, we analyze the mechanism of OGE evolutions and SPB. Finally, we conduct observing system simulation experiments to determine the best (economic) observation network. Our experimental results indicate that the ENSO evolution error induced by SSTA-OGE and Joint-OGE presents season dependency, but SLA-OGE has no impact on ENSO evolution. Moreover, Joint-OGEs induce error evolutions and the SPB with more significant intensity than SSTA-OGEs and SLA-OGEs. From mechanism analyses, the evolutions of SSTA-OGEs are mainly dominated by Bjerknes feedback. Further, the evolution dynamics of Joint-OGEs primarily contain the continuous heating between the upper ocean combined with Bjerknes feedback and thermal diffusion in response to the discharge process. In addition, comprehensive and economical sensitive areas are identified through Joint-OGE, including the central-eastern equatorial Pacific and the western and north-eastern tropical Pacific boundary, which contribute to the ENSO prediction benefits reaching 58.31% on average.

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“…Several papers have demonstrated that statistically and dynamically downscaled anomalies are similar for temperature, but vary for precipitation (Pierce et al, 2012;Tang et al, 2016). Recently, a number of studies have attempted to correct regional model biases by combining statistical and dynamical techniques, resulting in improvements to climate simulations (Dobler and Ahrens, 2008;Colette et al, 2012;Walton et al, 2015;Zhang et al, 2017;Zhang et al, 2018b).…”

Section: Introductionmentioning

confidence: 99%

Skilful two‐month‐leading hybrid climate prediction for winter temperature over China

Dai

Fan

2020

Intl Journal of Climatology

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Two skilful 2‐month‐leading hybrid downscaling prediction schemes in October for winter surface air temperature (SAT) over China are proposed in this paper. The schemes are based on the year‐to‐year increment approach and the coupled climate patterns between the winter SAT over China and its predictors. Observed North Pacific sea surface temperature (SST) from the preceding July to September, Arctic sea ice concentration (SIC) in the preceding August and winter sea level pressure (SLP) over pan Eurasia from version 2 of the Climate Forecast System (CFSv2) are selected as the predictors based on the fundamental physics. Individual‐predictor schemes (IP‐schemes), that is, SLP‐scheme, SST‐scheme, SIC‐scheme, indicate that these predictors exhibit prediction skills in different regions. Multi‐predictor scheme I (MP‐schemeI) is developed by combining three predictors. However, MP‐schemeI shows limited skill in predicting SAT over Northeast China (NECTA), due to the limited skill of CFSv2 over the extratropics. Thus, MP‐schemeII is established, in which a hybrid downscaling model for NECTA is constructed. These two MP‐schemes have comparable prediction skill over China, but MP‐schemeII outperforms MP‐schemeI over NEC. The temporal (spatial) anomaly correlation coefficient (ACC) increases from 0.23 (0.15) in MP‐schemeI to 0.36 (0.21) in MP‐schemeII, and the ratio of the same sign of anomalous years (Anomalous‐RSS) improves from 39.1% to 56.5% over NEC. For the winter SAT over China, the MP‐schemes greatly enhance the prediction compared with CFSv2 outputs and the IP‐schemes. The temporal (spatial) ACC increases from 0.29 (0.05) in CFSv2 to 0.52 (0.29) in the MP‐schemes, and the station‐average root‐mean‐square error decreases by about 48.0% compared with CFSv2. Moreover, the RSS (Anomalous‐RSS) of the winter SAT over China is 55.9% (47.4%) in CFSv2 and 64.7% (63.2%) in the MP‐schemes. This indicates that the MP‐schemes perform better in predicting anomalous winters.

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

Cited by 10 publications

References 36 publications

Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions

Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions

Error Evolutions and Analyses on Joint Effects of SST and SL via Intermediate Coupled Models and Conditional Nonlinear Optimal Perturbation Method

Skilful two‐month‐leading hybrid climate prediction for winter temperature over China

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