The Optimal Precursor of El Niño in the GFDL CM2p1 Model

By applying the principal component analysis‐based particle swarm optimization algorithm, the conditional nonlinear optimal perturbation is firstly calculated in the Geophysical Fluid Dynamics Laboratory Climate Model version 2p1 (GFDL CM2p1) to identify the optimal precursor (OPR) of El Niño. Speci...

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Veröffentlicht in:Journal of geophysical research. Oceans Jg. 125; H. 3
Hauptverfasser: Yang, Zeyun, Fang, Xianghui, Mu, Mu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Washington Blackwell Publishing Ltd 01.03.2020
Schlagworte:
Algorithms
Climate models
CNOP
Computational fluid dynamics
El Nino
El Nino phenomena
ENSO
Evolution
Fluid dynamics
fluid mechanics
Geophysical fluids
Geophysics
GFDL CM2p1
Hydrodynamics
Mathematical models
Mixed layer
Numerical models
optimal precursor
Optimization algorithms
Particle swarm optimization
Perturbation methods
Positive feedback
PPSO
Precursors
Principal components analysis
Sea surface
Sea surface temperature
Southern Oscillation
Surface temperature
surface water temperature
Surface wind
wind
ISSN:2169-9275, 2169-9291
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Zusammenfassung:By applying the principal component analysis‐based particle swarm optimization algorithm, the conditional nonlinear optimal perturbation is firstly calculated in the Geophysical Fluid Dynamics Laboratory Climate Model version 2p1 (GFDL CM2p1) to identify the optimal precursor (OPR) of El Niño. Specifically, through optimizing the initial perturbation, the OPRs that have the largest nonlinear evolution (i.e., mature state of El Niño) for two reference states are obtained, which are then confirmed according to the validation test. The results indicate that both OPRs show positive sea surface temperature perturbation in the west (2°N–2°S, 135.5–165.5°E). For the subsurface component, they exhibit positive subsurface temperature perturbation (STP) in the whole mixed layer of the west and negative STP in the upper layer of the east (i.e., 0‐ to 85‐m depth, 2°N–2°S, 79.5–109.5°W). Further analyses of the evolution of the sea surface temperature perturbation, STP, and surface wind perturbation suggest that the development of the OPRs in the model is consistent with the recognized mechanism for El Niño‐Southern Oscillation development, that is, through the Bjerknes positive feedback. The results indicate that the model can realistically capture the dominant processes for El Niño development, and the principal component analysis‐based particle swarm optimization algorithm is a practical solution for calculating the conditional nonlinear optimal perturbation in a complicated numerical model such as the GFDL CM2p1. They both shed a light on guiding the realistic observing systems. Key Points CNOP approach is applied to identify the optimal precursors of El Niño in the GFDL CM2p1 model The PPSO algorithm is firstly and successfully adopted in the GFDL CM2p1 model to calculate the CNOP Results suggest that the surface and subsurface temperature perturbations with specific patterns are crucial for El Niño development
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ISSN:2169-9275
2169-9291
DOI:10.1029/2019JC015797