A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

Zhang, Ronghua; Yu, Yongqiang; Song, Zhenya; Ren, Hong‐Li; Tang, Youmin; Qiao, Fangli; Wu, Tongwen; Gao, Chuan; Hu, Junya; Tian, Feng; Zhu, Yongxuan; Lin, Chunye; Liu, Hailong; Lin, Pengfei; Wu, Fang; Wang, Lin

doi:10.1007/s00343-020-0157-8

Cited by 72 publications

(31 citation statements)

References 163 publications

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“…For instance, Wang et al [19] constructed a physical based empirical model which is primarily built on SST anomalies, and found this model has good performance in predicting the western Pacific Subtropical High. Currently, the SST anomalies in the equatorial Pacific associated with the most prominent interannual variability-ENSO can be successfully predicted three seasons ahead [5,[20][21][22][23][24][25][26]. The SST anomalies in the equatorial Indian Ocean associated with the so-called Indian Ocean Dipole (IOD) can be predicted three to four months in advance [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Atlantic Niño/Niña Prediction Skills in NMME Models

Wang

Chen

et al. 2021

Atmosphere

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The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By diagnosing the recently released North American Multimodel Ensemble (NMME) models, we find that the Atlantic Niño/Niña prediction skills are improved, with the multi-model ensemble (MME) reaching five months. The prediction skills are season-dependent. Specifically, they show a marked dip in boreal spring, suggesting that the Atlantic Niño/Niña prediction suffers a “spring predictability barrier” like ENSO. The prediction skill is higher for Atlantic Niña than for Atlantic Niño, and better in the developing phase than in the decaying phase. The amplitude bias of the Atlantic Niño/Niña is primarily attributed to the amplitude bias in the annual cycle of the equatorial sea surface temperature (SST). The anomaly correlation coefficient scores of the Atlantic Niño/Niña, to a large extent, depend on the prediction skill of the Niño3.4 index in the preceding boreal winter, implying that the precedent ENSO may greatly affect the development of Atlantic Niño/Niña in the following boreal summer.

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Section: Introductionmentioning

confidence: 99%

Atlantic Niño/Niña Prediction Skills in NMME Models

Wang

Chen

et al. 2021

Atmosphere

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“…However, the posterior distribution p(θ | D) usually cannot be evaluated analytically. Variational inference is often used to approximate it, which uses a relatively simple distribution q(θ) to simulate p(θ | D) by minimizing the Kullback-Leibler (KL) divergence in equation (7). is process is shown in Figure 8.…”

Section: Stage 2: Ensemble Inference For Uncertainty Estimationmentioning

confidence: 99%

“…General circulation models (GCMs) are the traditional tools for simulating and investigating the various climate events [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events

Qin

Yuan

et al. 2020

Mathematical Problems in Engineering

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Climate downscaling is a way to provide finer resolution data at local scales, which has been widely used in meteorological research. The two main approaches for climate downscaling are dynamical and statistical. The traditional dynamical downscaling methods are quite time- and resource-consuming based on general circulation models (GCMs). Recently, more and more researchers construct a statistical deep learning model for climate downscaling motivated by the single-image superresolution (SISR) process in computer vision (CV). This is an approach that uses historical climate observations to learn a low-resolution to high-resolution mapping and produces great enhancements in terms of efficiency and effectiveness. Therefore, it has provided an appreciable new insight and successful downscaling solution to multiple climate phenomena. However, most existing models only make a simple analogy between climate downscaling and SISR and ignore the underlying dynamical mechanisms, which leads to the overaveraged downscaling results lacking crucial physical details. In this paper, we incorporate the a priori meteorological knowledge into a deep learning formalization for climate downscaling. More specifically, we consider the multiscale spatial correlations and the chaos in multiple climate events. Depending on two characteristics, we build up a two-stage deep learning model containing a stepwise reconstruction process and ensemble inference, which is named climate downscaling network (CDN). It can extract more local/remote spatial dependencies and provide more comprehensive captures of extreme conditions. We evaluate our model based on two datasets: climate science dataset (CSD) and benchmark image dataset (BID). The results demonstrate that our model shows the effectiveness and superiority in downscaling daily precipitation data from 2.5 degrees to 0.5 degrees over Asia and Europe. In addition, our model exhibits better performance than the other traditional approaches and state-of-the-art deep learning models.

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“…Although the ENSO phenomenon originates and develops in the tropical Pacific, it has global climatic, ecological, economic and societal impacts through ocean and atmospheric teleconnection (Rasmusson and Wallace 1983;Trenberth et al 1998;Ham et al 2014). Since the extreme 1982/83 El Niño event, considerable progress has been made in the development of observing systems, theories and numerical models related to ENSO (Wallace et al 1998;Wang 2018;Tang et al 2018;Zhang et al 2020). These endeavors have deepened our understanding of ENSO dynamics and improved the prediction skill of the ENSO, and it is skillfully predictable with a 1-year lead time in ENSO hindcast experiments (Zebiak and Cane 1987;Kirtman and Schopf 1998;Fedorov and Philander 2000;Yeh et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Optimally growing initial errors of El Niño events in the CESM

Chen

Duan

2021

Clim Dyn

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The optimally growing initial errors (OGEs) of El Niño events are found in the Community Earth System Model (CESM) by the conditional nonlinear optimal perturbation (CNOP) method. Based on the characteristics of low-dimensional attractors for ENSO (El Niño Southern Oscillation) systems, we apply singular vector decomposition (SVD) to reduce the dimensions of optimization problems and calculate the CNOP in a truncated phase space by the differential evolution (DE) algorithm. In the CESM, we obtain three types of OGEs of El Niño events with different intensities and diversities and call them type-1, type-2 and type-3 initial errors. Among them, the type-1 initial error is characterized by negative SSTA errors in the equatorial Pacific accompanied by a negative west–east slope of subsurface temperature from the subsurface to the surface in the equatorial central-eastern Pacific. The type-2 initial error is similar to the type-1 initial error but with the opposite sign. The type-3 initial error behaves as a basin-wide dipolar pattern of tropical sea temperature errors from the sea surface to the subsurface, with positive errors in the upper layers of the equatorial eastern Pacific and negative errors in the lower layers of the equatorial western Pacific. For the type-1 (type-2) initial error, the negative (positive) temperature errors in the eastern equatorial Pacific develop locally into a mature La Niña (El Niño)-like mode. For the type-3 initial error, the negative errors in the lower layers of the western equatorial Pacific propagate eastward with Kelvin waves and are intensified in the eastern equatorial Pacific. Although the type-1 and type-3 initial errors have different spatial patterns and dynamic growing mechanisms, both cause El Niño events to be underpredicted as neutral states or La Niña events. However, the type-2 initial error makes a moderate El Niño event to be predicted as an extremely strong event.

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A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

Cited by 72 publications

References 163 publications

Atlantic Niño/Niña Prediction Skills in NMME Models

Atlantic Niño/Niña Prediction Skills in NMME Models

A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events

Optimally growing initial errors of El Niño events in the CESM

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