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Dake Chen
and
Xiaojun Yuan

Abstract

A linear Markov model has been developed to simulated and predict the short-term climate change in the Antarctic, with particular emphasis on sea ice variability. Seven atmospheric variables along with sea ice were chosen to define the state of the Antarctic climate, and the multivariate empirical orthogonal functions of these variables were used as the building blocks of the model. The predictive skill of the model was evaluated in a cross-validated fashion, and a series of sensitivity experiments was carried out. In both hindcast and forecast experiments, the model showed considerable skill in predicting the anomalous Antarctic sea ice concentration up to 1 yr in advance, especially in austral winter and in the Antarctic dipole regions. The success of the model is attributed to the domination of the Antarctic climate variability by a few distinctive modes in the coupled air–sea–ice system and to the model's ability to detect these modes. This model is presently being used for the experimental seasonal forecasting of Antarctic sea ice, and a current prediction example is presented.

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Tao Lian
and
Dake Chen

Abstract

As an effective eigen method for phenomenon identification and space reduction, empirical orthogonal function (EOF) analysis is widely used in climate research. However, because of its orthorgonality constraint, EOF analysis has a tendency to produce unphysical modes. Previous studies have shown that the drawbacks of EOF analysis could be partly alleviated by rotated EOF (REOF) analysis, but such studies are always based on specific cases. This paper provides a thorough statistical evaluation of REOF analysis by comparing its ability with that of EOF analysis in reproducing a large number of randomly selected stationary modes of variability. The synthetic experiments indicate that REOF analysis is overwhelmingly a better choice in terms of accuracy and effectiveness, especially for picking up localized patterns. When applied to the tropical Pacific sea surface temperature variability, REOF and EOF analyses show obvious discrepancies, with the former making much better physical sense. This challenges the validity of the so-called sea surface temperature cooling mode and the spatial structure of “El Niño Modoki,” both of which are recently identified by EOF analysis. At any rate, one has to be cautious when claiming new discoveries of climate modes based on EOF analysis alone.

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Tao Lian
and
Dake Chen

Abstract

While both intrinsic low-frequency atmosphere–ocean interaction and multiplicative burst-like events affect the development of El Niño–Southern Oscillation (ENSO), the strong nonlinearity in ENSO dynamics has prevented us from separating their relative contributions. Here we propose an online filtering scheme to estimate the role of the westerly wind bursts (WWBs), a type of aperiodic burst-like atmospheric perturbation over the western-central tropical Pacific, in the genesis of the centennial extreme 1997/98 El Niño using the CESM coupled model. This scheme highlights the deterministic part of ENSO dynamics during model integration, and clearly demonstrates that the strong and long-lasting WWB in March 1997 was essential for generating the 1997/98 El Niño. Without this WWB, the intrinsic low-frequency coupling would have only produced a weak warm event in late 1997 similar to the 2014/15 El Niño.

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Xiaohui Xie
and
Dake Chen

Abstract

Two sets of mooring data were collected at two sites (MA and MB) along a cross-slope section on the northeastern continental slope in the South China Sea (SCS). These data are used to investigate evolution and energy decay of low-mode semidiurnal (M2) internal tides on a subcritical slope with respect to M2. At the deep portion of the slope (~1250 m; MA), the M2 internal tides show upward energy propagation, while vertically standing M2 internal tides are often observed at shallow MB (~845 m). A two-dimensional linear internal tide model with realistic topography and stratification reproduces the observations, suggesting that low-mode M2 internal tides incident on subcritical slopes evolve into vertically propagating internal waves due to topographic scattering, propagate upward to the boundary, and reflect from the sea surface. The reflection point largely depends on the phase between the modal components of the incoming flux. In the near-surface reflection region, two kinds of nonlinear effects are observed to decay energy of the incoming internal tides. One is the resonant parametric subharmonic instability which transfers M2 internal tides to diurnal subharmonics M1 (=M2/2), but the instability is found to mainly depend on the incident waves. The other one is the nonresonant wave–wave interaction, producing two higher-harmonic M4 (=2M2) rays with opposite vertical propagation. A strong westward mean flow is observed in the interacting region, with amplitude comparable to that of the incident waves. This mean flow also appears to be generated by the nonlinear reflection of the M2 internal tides.

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Chen Chen
,
Mark A. Cane
,
Andrew T. Wittenberg
, and
Dake Chen

Abstract

Focusing on ENSO seasonal phase locking, diversity in peak location, and propagation direction, as well as the El Niño–La Niña asymmetry in amplitude, duration, and transition, a set of empirical probabilistic diagnostics (EPD) is introduced to investigate how the ENSO behaviors reflected in SST may change in a warming climate.

EPD is first applied to estimate the natural variation of ENSO behaviors. In the observations El Niños and La Niñas mainly propagate westward and peak in boreal winter. El Niños occur more at the eastern Pacific whereas La Niñas prefer the central Pacific. In a preindustrial control simulation of the GFDL CM2.1 model, the El Niño–La Niña asymmetry is substantial. La Niña characteristics generally agree with observations but El Niño’s do not, typically propagating eastward and showing no obvious seasonal phase locking. So an alternative approach is using a stochastically forced simulation of a nonlinear data-driven model, which exhibits reasonably realistic ENSO behaviors and natural variation ranges.

EPD is then applied to assess the potential changes of ENSO behaviors in the twenty-first century using CMIP5 models. Other than the increasing SST climatology, projected changes in many aspects of ENSO reflected in SST anomalies are heavily model dependent and generally within the range of natural variation. Shifts favoring eastward-propagating El Niño and La Niña are the most robust. Given various model biases for the twentieth century and lack of sufficient model agreements for the twenty-first-century projection, whether the projected changes for ENSO behaviors would actually take place remains largely uncertain.

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Qiaoyan Wu
,
Ying Yan
, and
Dake Chen

Abstract

A linear Markov model has been developed to predict the short-term climate variability of the East Asian monsoon system, with emphasis on precipitation variability. Precipitation, sea level pressure, zonal and meridional winds at 850 mb, along with sea surface temperature and soil moisture, were chosen to define the state of the East Asian monsoon system, and the multivariate empirical orthogonal functions of these variables were used to construct the statistical Markov model. The forecast skill of the model was evaluated in a cross-validated fashion and a series of sensitivity experiments were conducted to further validate the model. In both hindcast and forecast experiments, the model showed considerable skill in predicting the precipitation anomaly a few months in advance, especially in boreal winter and spring. The prediction in boreal summer was relatively poor, though the model performance was better in an ENSO decaying summer than in an ENSO developing summer. Also, the prediction skill was better over the ocean than the land. The model's forecast ability is attributed to the domination of the East Asian monsoon climate variability by a few distinctive modes in the coupled atmosphere–ocean–land system, to the strong influence of ENSO on these modes, and to the Markov model's capability to capture these modes.

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Rong-Hua Zhang
,
Dake Chen
, and
Guihua Wang

Abstract

Satellite-based ocean color measurements indicate clear evidence for bioclimate interactions in the tropical Pacific associated with El Niño–Southern Oscillation (ENSO). Recent modeling studies have demonstrated that ocean biology can potentially affect the climate through the penetration depth of solar radiation in the upper ocean (Hp ), a primary parameter in coupling biology to physics in the ocean. At present, interannual variability in Hp and its related bioclimate feedback effects have not been adequately represented in coupled ocean–atmosphere models. In this work, chlorophyll (Chl) concentration data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), available since 1997, are used to characterize interannual Hp variability in the tropical Pacific and to quantify its relationships with physical fields, including sea surface temperature (SST) and sea level (SL). It is found that interannual Hp variability is dominated by ENSO signals, with the largest variability located in the central basin near the date line and a coherent relationship with SST. A singular value decomposition (SVD) analysis is adopted to extract interannual covariability patterns between Hp and SST during the period 1997–2007. Their close relationships are then utilized to construct an empirical anomaly model for Hp , allowing for its prognostic estimate in terms of SST anomalies without explicit involvement of a marine ecosystem model. Validation and sensitivity experiments indicate that the empirical model can reasonably well capture interannual Hp responses to SST anomalies in association with ENSO. The derived empirical Hp model offers a simple and an effective way to parameterize and represent the effects of Chl containing biomass on penetrative solar radiation in the tropical Pacific, demonstrating the dynamical implication of remotely sensed Chl data for bioclimate coupling studies. Further improvements and applications of the empirical Hp model to climate modeling are discussed.

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Dake Chen
,
Hsien Wang Ou
, and
Changming Dong

Abstract

Internal tides near a midlatitude shelf–slope front are studied using an idealized numerical model, with emphasis on their structure, energetics, and mixing effects. It is found that the properties of internal tides are highly dependent on frontal configuration and tidal frequency. At a winter front, energetic internal tides are generated and arrested in the frontal zone; the cross-shelf flow tends to be surface (bottom) intensified by a large internal circulation cell at the diurnal (semidiurnal) frequency. At a summer front, the diurnal internal tide is still trapped, but a semidiurnal internal tide propagates out of the frontal zone in the offshore direction while arrested at the inshore boundary. The presence of the shelf–slope front enhances the generation of internal tides, and it also causes an amplification of the semidiurnal internal tide by trapping its energy in the frontal zone. This amplification is most prominent at the offshore boundary of the winter front and the inshore boundary of the summer front, where strong tidal refraction takes place. Internal tides can cause significant mixing and dispersion in the frontal zone, with the semidiurnal internal tide being most effective toward the frontal boundaries, and the diurnal internal tide more effective near the site of generation.

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Guihua Wang
,
Dake Chen
, and
Jilan Su

Abstract

Generation of mesoscale eddies in the eastern South China Sea (SCS) in winters during August 1999 to July 2002 is studied with a reduced-gravity model. It is found that the orographic wind jets associated with the northeast winter monsoon and the gaps in the mountainous island chain along the eastern boundary of the SCS can spin up cyclonic and anticyclonic eddies over the SCS. Results suggest that direct wind forcing could be an important generation mechanism for the rich eddy activity in the SCS, and that to simulate this mechanism the resolution of the wind forcing has to be high enough to resolve the local wind jets induced by orographic effects.

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Shan He
,
Song Yang
, and
Dake Chen

Abstract

Using features based on correlation or noncausal dependence metrics can lead to false conclusions. However, recent research has shown that applying causal inference theory in conjunction with Bayesian networks to large-sample-size data can accurately attribute synoptic anomalies. Focusing on the East Asian summer monsoon (EASM), this study adopts a causal inference approach with model averaging to investigate causation of interannual climate variability. We attribute the EASM variability to five winter climate phenomena; our result shows that the eastern Pacific El Niño–Southern Oscillation has the largest causal effect. We also show that the causal precursors of the EASM variability are interpretable in terms of physics. Using linear regression, these precursors can predict the EASM one season ahead, outperforming correlation-based empirical models and three climate models. This study shows that even without large-sample-size data and substantial human intervention, even laymen can implement the causal inference approach to investigate the causes of climatic anomalies and construct reliable empirical models for prediction.

Significance Statement

We use causal inference theory to redesign the attribution procedure fundamentally and adjust a causal inference approach to commonly used climate research data. Our study shows that the causal inference approach can exhaustively reveal the causes of climatic anomalies with little human intervention, which is impossible for correlation-based studies. According to this attribution, one can construct models with a better predictive performance than the climate and correlation-based empirical models. Therefore, our causal inference approach will tremendously help both meteorologists and laymen (e.g., stakeholders and policymakers) accurately predict climate phenomena and reveal their interpretable causes. We recommend that it become a standard practice in attribution studies and operational prediction.

Open access