Fei Zheng

Chinese Academy of Sciences, Peping, Beijing, China

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Publications (12)29.15 Total impact

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    ABSTRACT: In this paper, interannual variations in the barrier layer thickness (BLT) are analyzed using Argo three-dimensional temperature and salinity data, with a focus on the effects of interannually varying salinity on the evolution of the El Niño-Southern Oscillation (ENSO). The interannually varying BLT exhibits a zonal seesaw pattern across the equatorial Pacific during ENSO cycles. This phenomenon has been attributed to two different physical processes. During El Niño (La Niña), the barrier layer (BL) is anomalously thin (thick) west of about 160°E, and thick (thin) to the east. In the western equatorial Pacific (the western part: 130°–160°E), interannual variations of the BLT indicate a lead of one year relative to those of the ENSO onset. The interannual variations of the BLT can be largely attributed to the interannual temperature variability, through its dominant effect on the isothermal layer depth (ILD). However, in the central equatorial Pacific (the eastern part: 160°E–170°W), interannual variations of the BL almost synchronously vary with ENSO, with a lead of about two months relative to those of the local SST. In this region, the interannual variations of the BL are significantly affected by the interannually varying salinity, mainly through its modulation effect on the mixed layer depth (MLD). As evaluated by a one-dimensional boundary layer ocean model, the BL around the dateline induced by interannual salinity anomalies can significantly affect the temperature fields in the upper ocean, indicating a positive feedback that acts to enhance ENSO.
    Advances in Atmospheric Sciences 05/2014; 31(3). · 1.34 Impact Factor
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    ABSTRACT: During 2010-11, a La Niña condition prevailed in the tropical Pacific. An intermediate coupled model (ICM) is used to demonstrate a real-time forecast of sea surface temperature (SST) evolution during the event. One of the ICM's unique features is an empirical parameterization of the temperature of subsurface water entrained into the mixed layer (T(e)). This model provided a good prediction, particularly of the "double dip" evolution of SST in 2011 that followed the La Niña event peak in October 2010. Thermocline feedback, explicitly represented by the relationship between T(e) and sea level in the ICM, is a crucial factor affecting the second cooling in 2011. Large negative T(e) anomalies were observed to persist in the central equatorial domain during 2010-11, inducing a cold SST anomaly to the east during July-August 2011 and leading to the development of a La Niña condition thereafter.
    Scientific Reports 01/2013; 3:1108. · 5.08 Impact Factor
  • Fei Zheng, Rong-Hua Zhang
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    ABSTRACT: Oceanic salinity and its related freshwater flux (FWF) forcing in the tropical Pacific have been of increased interest recently due to their roles in the El Niño-Southern Oscillation (ENSO), the global climate and water cycle. A comprehensive data analysis is performed to illustrate the significant effects of interannual salinity variability and FWF forcing during the 2007/08 La Niña event using three-dimensional temperature and salinity fields from Argo profiles, and some related fields derived from the Argo and satellite-based data, including the mixed layer depth (MLD), heat flux, freshwater flux, and buoyancy flux (QB). It is demonstrated that during the developing phase of 2007/08 La Niña, a negative FWF anomaly and its associated positive sea surface salinity (SSS) anomaly in the western-central basin act to increase oceanic density and de-stabilize the upper ocean. At the same time, the negative FWF anomaly tends to reduce a positive QB anomaly and deepen the mixed layer (ML). These related oceanic processes act to strengthen the vertical mixing and entrainment of subsurface water at the base of ML, which further enhance cold sea surface temperature (SST) anomalies associated with the La Niña event, a demonstration of a positive feedback induced by FWF forcing.
    Dynamics of Atmospheres and Oceans 09/2012; 57:45–57. · 1.73 Impact Factor
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    ABSTRACT: The El Niño-Southern Oscillation (ENSO) is modulated by many factors; most previous studies have emphasized the roles of wind stress and heat flux in the tropical Pacific. Freshwater flux (FWF) is another environmental forcing to the ocean; its effect and the related ocean salinity variability in the ENSO region have been of increased interest recently. Currently, accurate quantifications of the FWF roles in the climate remain challenging; the related observations and coupled ocean-atmosphere modeling involve large elements of uncertainty. In this study, we utilized satellite-based data to represent FWF-induced feedback in the tropical Pacific climate system; we then incorporated these data into a hybrid coupled ocean-atmosphere model (HCM) to quantify its effects on ENSO. A new mechanism was revealed by which interannual FWF forcing modulates ENSO in a significant way. As a direct forcing, FWF exerts a significant influence on the ocean through sea surface salinity (SSS) and buoyancy flux ( Q B) in the western-central tropical Pacific. The SSS perturbations directly induced by ENSO-related interannual FWF variability affect the stability and mixing in the upper ocean. At the same time, the ENSO-induced FWF has a compensating effect on heat flux, acting to reduce interannual Q B variability during ENSO cycles. These FWF-induced processes in the ocean tend to modulate the vertical mixing and entrainment in the upper ocean, enhancing cooling during La Niña and enhancing warming during El Niño, respectively. The interannual FWF forcing-induced positive feedback acts to enhance ENSO amplitude and lengthen its time scales in the tropical Pacific coupled climate system.
    Advances in Atmospheric Sciences 07/2012; 29(4):647-660. · 1.34 Impact Factor
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    Jiang Zhu, Fei Zheng, Xichen Li
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    ABSTRACT: Localization technique is commonly used in ensemble data assimilation of small-size ensemble members. It effectively eliminates the spurious correlations of the background and increases the rank of the system. However, one disadvantage in current localization schemes is that it is difficult to implement the assimilation of non-local observations. In this paper, we test a new localized implementation scheme that can directly assimilate non-local observations without pinpointing them. A classical local support correlation function matrix is first sampled by a set of local correlation function ensemble members (the size is M). Then, the dynamical ensemble (the size is N) is combined with the local correlation function ensemble to form an N×M ensemble by multiplying each dynamical member with each local correlation function member using the Schur product. The covariance matrix constructed by the N×M members is proved to approximate the Schur product of the local support correlation matrix and the dynamical covariance matrix. This scheme is verified through assimilating both local and non-local observations with a linear advection model and an intermediate coupled model. The analysis results show that this scheme is feasible and effective in providing reasonable and high-quality analysis fields with a relatively small dynamical ensemble size.
    Tellus 01/2011; 63:244-255. · 2.74 Impact Factor
  • Fei Zheng, Jiang Zhu
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    ABSTRACT: Based on an ENSO (El Niño-Southern Oscillation) ensemble prediction system (EPS), the seasonal variations in the predictability of ENSO are examined in both a deterministic and a probabilistic sense. For the deterministic prediction skills, the skills of the ensemble-mean are sensitive to the month in which the forecast was initiated. The anomaly correlations decrease rapidly during the Northern Hemisphere (NH) spring, and the root mean square (RMS) errors have the largest values and the fastest growth rates initialized before and during the NH spring. However, the probabilistic predictions based on the verification methods of the relative operating character (ROC) curve and area both show that there are no strong seasonal variations for the two extreme (warm and cold) ENSO events. For the near-normal events, the seasonal variations of the probabilistic skills are much more obvious, and the ROC areas of the ensemble forecasts made in the spring are clearly smaller than those of the ensemble forecasts that began during other seasons.At the same time, the probabilistic prediction skills of the EPS for all three events that only consider the initial perturbations are also clearly sensitive to the initial months. This was indicated by the fact that the most rapid decrease of the ROC area skill occurs as the hindcasts proceed through the spring season. A further signal-to-noise ratio analysis reveals that potential sources of the predictability barrier in the probabilistic skills for the EPS are namely that the spring is the period when stochastic initial error effects can be expected to strongly degrade forecast skill, and that small predicted signals can render the system noisier by further limiting the predictability. However, reasonable considerations of the model-error perturbations during the ensemble forecast process can alleviate the barrier caused by initial uncertainties through coordinately simulating the seasonal variations of the forecast uncertainty in order to significantly improve the probabilistic prediction skills and then to disorder the seasonal predictability related to the SPB.
    Global and Planetary Change 01/2010; · 3.16 Impact Factor
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    ABSTRACT: Based on an intermediate coupled model (ICM), a probabilistic ensemble prediction system (EPS) has been developed. The ensemble Kalman filter (EnKF) data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886–2005 using the EPS with 100 ensemble members and with initial conditions obtained by only assimilating historic SST anomaly observations. By examining the retrospective ensemble forecasts and available observations, the verification results show that the skill of the ensemble mean of the EPS is greater than that of a single deterministic forecast using the same ICM, with a distinct improvement of both the correlation and root mean square (RMS) error between the ensemble-mean hindcast and the deterministic scheme over the 12-month prediction period. The RMS error of the ensemble mean is almost 0.2°C smaller than that of the deterministic forecast at a lead time of 12 months. The probabilistic skill of the EPS is also high with the predicted ensemble following the SST observations well, and the areas under the relative operating characteristic (ROC) curves for three different ENSO states (warm events, cold events, and neutral events) are all above 0.55 out to 12 months lead time. However, both deterministic and probabilistic prediction skills of the EPS show an interdecadal variation. For the deterministic skill, there is high skill in the late 19th century and in the middle-late 20th century (which includes some artificial skill due to the model training period), and low skill during the period from 1906 to 1961. For probabilistic skill, for the three different ENSO states, there is still a similar interdecadal variation of ENSO probabilistic predictability during the period 1886–2005. There is high skill in the late 19th century from 1886 to 1905, and a decline to a minimum of skill around 1910–50s, beyond which skill rebounds and increases with time until the 2000s.
    Advances in Atmospheric Sciences 02/2009; 26(2):359-372. · 1.34 Impact Factor
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    Fei Zheng, Hui Wang, Jiang Zhu
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    ABSTRACT: Based on our developed ENSO (El Niño-Southern Oscillation) ensemble prediction system (EPS), the impacts of stochastic initial-error and model-error perturbations on ENSO ensemble predictions are examined and discussed by performing four sets of 14-a retrospective forecast experiments in both a deterministic and probabilistic sense. These forecast schemes are differentiated by whether they considered the initial or model stochastic perturbations. The comparison results suggest that the stochastic model-error perturbations, which are added into the modeled physical fields to mainly represent the uncertainties of the physical model, have significant, positive impacts on improving the ensemble prediction skills during the entire 12-month forecast process. However, the stochastic initial-error perturbations have relatively small impacts on the ensemble prediction system, and its impacts are mainly focusing on the first 3-month predictions.
    Chinese Science Bulletin 01/2009; 54(14):2516-2523. · 1.37 Impact Factor
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    ABSTRACT: The El Niño/Southern Oscillation (ENSO) predictions strongly depend on the accuracy and dynamical consistency of the coupled initial conditions. Based on the proposed ensemble Kalman filter (EnKF), a new initialization scheme for the ENSO ensemble prediction system (EPS) was designed and tested in an intermediate coupled model (ICM). The inclusion of this scheme in the ICM leads to substantial improvements in ENSO prediction skill via the successful assimilation of both observed sea surface temperature (SST) and TOPEX/Poseidon/Jason-1 (T/P/J) altimeter data into the initial ensemble conditions. Comparisons with the original ensemble hindcast experiment show that the ensemble prediction skills were significantly improved out to a 12-month lead time by improving sea level (SL) initial conditions for better parameterization of subsurface thermal effects. It is clearly demonstrated that improvement in forecast skill can result from the multivariate and multi-observational ensemble data assimilation.
    Geophysical Research Letters 01/2007; 34(13). · 3.98 Impact Factor
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    ABSTRACT: A simple method for initializing intermediate coupled models (ICMs) using only sea surface temperature (SST) anomaly data is comprehensively tested in two sets of hindcasts with a new ICM. In the initialization scheme, both the magnitude of the nudging parameter and the duration of the assimilation are considered, and initial conditions for both atmosphere and ocean are generated by running the coupled model with SST anomalies nudged to the observations. A comparison with the observations indicates that the scheme can generate realistic thermal fields and surface dynamic fields in the equatorial Pacific through hindcast experiments. An ideal experiment is performed to get the optimal nudging parameters which include the nudging intensity and nudging time length. Twelve-month-long hindcast experiments are performed with the model over the period 1984–2003 and the period 1997–2003. Compared with the original prediction results, the model prediction skills are significantly improved by the nudging method especially beyond a 6-month lead time during the two different periods. Potential problems and further improvements are discussed regarding the new coupled assimilation system.
    Advances in Atmospheric Sciences 11/2006; 23(4):615-624. · 1.34 Impact Factor
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    ABSTRACT: Ensemble hindcasts of sea surface temperature (SST) anomalies in the tropical Pacific are studied using an intermediate coupled model (ICM), in which an ensemble Kalman filter (EnKF) data assimilation system is implemented to provide the initial ensemble. A linear, first-order Markov stochastic model is adopted to represent model errors. Parameters in the stochastic model are estimated by comparing observation-minus-forecast values over 30 years. Twelve-month, 120 ensemble hindcasts are performed over the period 1995-2004, each with 100 ensemble members. This ensemble technique provides a simple method of extending the standard ICM forecasts to the probabilistic domain. The results show that the prediction skill of the ensemble mean is better than that of one single deterministic forecast using the same ICM. For the probabilistic perspective, those ensemble forecasts have their ensembles following observed SST anomaly variations well.
    Geophysical Research Letters 01/2006; 331(19). · 3.98 Impact Factor
  • Fei Zheng, Jiang Zhu
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    ABSTRACT: The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind–ocean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (1997–2008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Niña event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event. KeywordsCoupled data assimilation-ICM-ENSO prediction-EnKF
    Ocean Dynamics 60(5):1061-1073. · 1.76 Impact Factor

Publication Stats

58 Citations
29.15 Total Impact Points

Institutions

  • 2009–2012
    • Chinese Academy of Sciences
      • Institute of Atmospheric Physics
      Peping, Beijing, China
  • 2010
    • Northeast Institute of Geography and Agroecology
      • Institute of Atmospheric Physics
      Beijing, Beijing Shi, China
  • 2006–2009
    • Technical Institute of Physics and Chemistry
      Peping, Beijing, China