Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

Zhao, Yushun; Liu, Zhengyu; Zheng, Fei; Jin, Yishuai

doi:10.1175/mwr-d-18-0199.1

Cited by 13 publications

(18 citation statements)

References 29 publications

(41 reference statements)

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“…Here, we use NINO 3.4 (5°S to 5°N, 170–120°W) index to represent ENSO activities. In the control run, the model shows the roughly 4‐year ENSO periodicity through the lagged correlation and a distinct ENSO SPB feature (Figure S1), which is consistent with Zhao et al (2019).…”

Section: Methods Model and Datasupporting

confidence: 89%

“…We use an intermediate coupled ocean‐atmosphere model—the latest version of Cane‐Zebiak model (Lamont‐Doherty Earth Observatory, Version 5, LDEO5; Chen et al, 2004; Zhao et al, 2019) to simulate the unstable ENSO mode. The atmospheric model is built on the steady‐state, linear shallow water equations, with the atmospheric circulation driven by a heating anomaly which consists of moisture convergence and SST anomalies.…”

Section: Methods Model and Datamentioning

confidence: 99%

“…The ocean domain is the tropical Pacific region (28.75°S to 28.75°N, 124°E to 80°W). The atmosphere and SST anomalies have a resolution of 5.625° lon × 2° lat (Zhao et al, 2019).…”

Section: Methods Model and Datamentioning

confidence: 99%

See 2 more Smart Citations

Controls of Spring Persistence Barrier Strength in Different ENSO Regimes and Implications for 21st Century Changes

Jin

Liu

2020

Geophysical Research Letters

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This paper investigates potential factors that control the El Niño–Southern Oscillation (ENSO) Spring Persistence Barrier (SPB) strength in two different ENSO regimes and apply it to explain the ENSO SPB strength modulation after the 21st century. In a damped, noise‐driven model, the theoretical solution of SPB strength illustrates that a weaker ENSO growth rate strengthens SPB. In the self‐sustained regime, as in the Cane‐Zebiak model (chaotic system), the strengthened thermodynamic damping and weakened thermocline positive feedback lead to a more negative ENSO growth rate and, in turn, a stronger SPB. Therefore, in both ENSO regimes, a weaker ENSO growth rate intensifies the SPB. The application of the theory to the real world suggests that a more negative ENSO growth rate, corresponding to a more damped feedback system, is responsible for the stronger SPB in recent decades than in 1980–2000.

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Section: Methods Model and Datasupporting

confidence: 89%

Section: Methods Model and Datamentioning

confidence: 99%

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Controls of Spring Persistence Barrier Strength in Different ENSO Regimes and Implications for 21st Century Changes

Jin

Liu

2020

Geophysical Research Letters

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“…Wu et al (2012) use an intermediate complexity coupled model to investigate a method which allows for a geographic-dependent parameter optimization, and find the geographic dependent technique superior to the single-value parameter estimation technique for both climate estimates and predictions. Recently, Zhao et al (2019) found that ensemble-based Kalman Filter parameter optimization in the Zebiak-Cane model (Zebiak & Cane, 1987) improves El Nino Southern Oscillation prediction skill. Other techniques for parameter estimation include Markov chain Monte Carlo algorithms (e.g., Posselt & Bishop, 2012), and gradient descent and genetic algorithms (e.g., Sumata et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Surface Radiative Flux Bias Reduction Through Regionally Varying Cloud Fraction Parameter Nudging in a Global Coupled Forecast System

Ridout¹,

Barton²,

Janiga³

et al. 2021

J Adv Model Earth Syst

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A simple parameter nudging procedure is described that systematically reduces near-analysis time errors in the surface net shortwave flux in the Navy ESPC (Earth System Prediction Capability) system, a global coupled forecast system that is the product of a continuing development effort at the U. S. Naval Research Laboratory. The procedure generates geographically varying perturbations to one of the cloud fraction parameters in the atmospheric model component of the system during the data assimilation cycle, resulting in large improvements in near-analysis time surface net shortwave flux biases. After a several week spin-up period, the global RMSE of the succeeding 10-day mean bias computed for lead times of 6-12 hours is reduced by 40 percent. Results from application of the approach in a series of 45-day integrations show that improvements are realized at longer forecast lead times as well. The global RMSE of the surface net shortwave flux averaged over these integrations improves by 37 percent for lead times from 1-5 days, decreasing to 18 percent for lead times from 31-45 days. The corresponding longwave flux errors are slightly degraded, ranging from a 2 percent increase for lead times from 1-5 days to a 0.5 percent increase for lead times from 31-45 days. Global-mean reductions in ground and sea surface temperature errors are obtained through most of the 45-day integration period due to improvements over ocean and polar regions. Potential steps for extension and operational application of the method are discussed.Plain Language Summary The net radiation budget at the earth's surface is of key importance for weather and climate, and clouds are a primary agent in regulating this budget. This study presents a simple but powerful method to gradually adjust the cloud cover in weather and seasonal forecast models during the process of forecast initiation in a way that brings the model surface radiation budget into better agreement with satellite observations. Improvements to ground and sea surface temperature forecasts are highlighted in addition to reductions in surface radiation budget errors.

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“…Subsequently, its prediction skill became comparable to the most advanced coupled general circulation models (CGCMs) after a bias-correction method was incorporated that reduced large systematic model biases (Chen et al 2000). This model has since become a benchmark for ENSO prediction models and has been intensively used in simulating and predicting ENSO in the past decades (Cheng et al 2010;Wu 2016;Peng et al 2018;Zhao et al 2019). Since its latest version LDEO5 was reported in the seminal paper by Chen et al (2004), this model-based prediction system has not been further developed.…”

Section: Introductionmentioning

confidence: 99%

An extension of LDEO5 model for ENSO ensemble predictions

et al. 2020

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This study provided an extension to the latest version of Lamont-Doherty Earth Observation (LDEO5) prediction system. First, an ensemble coupled data assimilation (CDA) system, based on the Ensemble Kalman Filter, was established. Both the Kaplan sea surface temperature (SST) data from January 1856 to December 2018 and the ECMWF twentieth century reanalysis (ERA-20C) wind data from January 1900 to February 2010 were assimilated for prediction initialization. Second, an ensemble prediction (EP) system was established using stochastic optimal perturbation that represented the uncertainty in the physical process. The assimilation experiments showed that assimilating multi-source data yielded better results than assimilating single-source data. The analyses of Niño3.4 SST anomalies and zonal wind stress (ZWS) anomalies were in good agreement with the observed counterparts, respectively. The root mean square errors of both Niño3.4 SST anomalies and ZWS anomalies were found to be significantly reduced, compared to the values obtained before assimilation. The modeled upper layer depth anomalies along the equator, and subsurface temperature anomalies in the Niño3.4 region were also found to be similar to the observed counterparts. A long-term ensemble hindcast was conducted using the EP system for the past 163 years, from January 1856 to December 2018. Results showed that the predictions initialized by assimilating multi-source data yielded best deterministic skill, reaching the international advanced level. A comparative analysis revealed that the EP system predicted the warm events well, followed by cold and neutral events.

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Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

Cited by 13 publications

References 29 publications

Controls of Spring Persistence Barrier Strength in Different ENSO Regimes and Implications for 21st Century Changes

Controls of Spring Persistence Barrier Strength in Different ENSO Regimes and Implications for 21st Century Changes

Surface Radiative Flux Bias Reduction Through Regionally Varying Cloud Fraction Parameter Nudging in a Global Coupled Forecast System

An extension of LDEO5 model for ENSO ensemble predictions

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