scholarly journals Bred Vector and ENSO Predictability in a Hybrid Coupled Model during the Period 1881–2000

2011 ◽  
Vol 24 (1) ◽  
pp. 298-314 ◽  
Author(s):  
Youmin Tang ◽  
Ziwang Deng
Keyword(s):  
Relative Entropy ◽  
Southern Oscillation ◽  
Heat Content ◽  
Coupled Model ◽  
Prediction Skill ◽  
Good Prediction ◽  
Initial State ◽  
Potential Predictability ◽  
Average Structure ◽  
Enso Predictability

Abstract In this study, a breeding analysis was conducted for a hybrid coupled El Niño–Southern Oscillation (ENSO) model that assimilated a historic dataset of sea surface temperature (SST) for the 120 yr between 1881 and 2000. Meanwhile, retrospective ENSO forecasts were performed for the same period. For a given initial state, 15 bred vectors (BVs) of both SST and upper-ocean heat content (HC) were derived. It was found that the average structure of the 15 BVs was insensitive to the initial states and independent of season and ENSO phase. The average structure of the BVs shared many features already seen in both the final patterns of leading singular vectors and the ENSO BVs of other models. However, individual BV patterns were quite different from case to case. The BV rate (the average cumulative growth rate of BVs) varied seasonally, and the maximum value appeared at the time when the model ran through the boreal spring and summer. It was also sensitive to the strength of the ENSO signal (i.e., the stronger ENSO signal, the smaller the BV rate). Furthermore, ENSO predictability was explored using BV analysis. Emphasis was placed on the relationship between BVs, which are able to characterize potential predictability without requiring observations, and actual prediction skills, which make use of real observations. The results showed that the relative entropy, defined using breeding vectors, was a good measure of potential predictability. Large relative entropy often leads to a good prediction skill; however, when the relative entropy was small, the prediction skill seemed much more variable. At decadal/interdecadal scales, the variations in prediction skills correlated with relative entropy.

scholarly journals Comparison of Information-Based Measures of Forecast Uncertainty in Ensemble ENSO Prediction

2008 ◽  
Vol 21 (2) ◽  
pp. 230-247 ◽  
Author(s):  
Youmin Tang ◽  
Richard Kleeman ◽  
Andrew M. Moore
Keyword(s):  
Southern Oscillation ◽  
A Priori ◽  
Prediction Skill ◽  
Coupled Models ◽  
Potential Predictability ◽  
Model Skill ◽  
Enso Predictability ◽  
Predictive Skill ◽  
Triangular Relationship ◽  
The Relationship

Abstract In this study, ensemble predictions of the El Niño–Southern Oscillation (ENSO) were conducted for the period 1981–98 using two hybrid coupled models. Several recently proposed information-based measures of predictability, including relative entropy (R), predictive information (PI), predictive power (PP), and mutual information (MI), were explored in terms of their ability of estimating a priori the predictive skill of the ENSO ensemble predictions. The emphasis was put on examining the relationship between the measures of predictability that do not use observations, and the model prediction skills of correlation and root-mean-square error (RMSE) that make use of observations. The relationship identified here offers a practical means of estimating the potential predictability and the confidence level of an individual prediction. It was found that the MI is a good indicator of overall skill. When it is large, the prediction system has high prediction skill, whereas small MI often corresponds to a low prediction skill. This suggests that MI is a good indicator of the actual skill of the models. The R and PI have a nearly identical average (over all predictions) as should be the case in theory. Comparing the different information-based measures reveals that R is a better predictor of prediction skill than PI and PP, especially when correlation-based metrics are used to evaluate model skill. A “triangular relationship” emerges between R and the model skill, namely, that when R is large, the prediction is likely to be reliable, whereas when R is small the prediction skill is quite variable. A small R is often accompanied by relatively weak ENSO variability. The possible reasons why R is superior to PI and PP as a measure of ENSO predictability will also be discussed.

scholarly journals Effects of Tropical Cyclones on ENSO

2019 ◽  
Vol 32 (19) ◽  
pp. 6423-6443 ◽  
Author(s):  
Tao Lian ◽  
Jun Ying ◽  
Hong-Li Ren ◽  
Chan Zhang ◽  
Ting Liu ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Heat Content ◽  
Coupled Model ◽  
Enso Event ◽  
Central Pacific ◽  
Final State ◽  
Enso Dynamics

AbstractNumerous studies have investigated the role of El Niño–Southern Oscillation (ENSO) in modulating the activity of tropical cyclones (TCs) in the western Pacific on interannual time scales, but the effects of TCs on ENSO are less discussed. Some studies have found that TCs sharply increase surface westerly anomalies over the equatorial western–central Pacific and maintain them there for a few days. Given the strong influence of equatorial surface westerly wind bursts on ENSO, as confirmed by much recent literature, the effects of TCs on ENSO may be much greater than previously expected. Using recently released observations and reanalysis datasets, it is found that the majority of near-equatorial TCs (simply TCs hereafter) are associated with strong westerly anomalies at the equator, and the number and longitude of TCs are significantly correlated with ENSO strength. When TC-related wind stresses are added into an intermediate coupled model, the simulated ENSO becomes more irregular, and both ENSO magnitude and skewness approach those of observations, as compared with simulations without TCs. Adding TCs into the model system does not break the linkage between the heat content anomaly and subsequent ENSO event in the model, which manifest the classic recharge–discharge ENSO dynamics. However, the influence of TCs on ENSO is so strong that ENSO magnitude and sometimes its final state—that is, either El Niño or La Niña—largely depend on the number and timing of TCs during the event year. Our findings suggest that TCs play a prominent role in ENSO dynamics, and their effects must be considered in ENSO forecast models.

The 3–4-Week MJO Prediction Skill in a GFDL Coupled Model

2015 ◽  
Vol 28 (13) ◽  
pp. 5351-5364 ◽  
Author(s):  
Baoqiang Xiang ◽  
Ming Zhao ◽  
Xianan Jiang ◽  
Shian-Jiann Lin ◽  
Tim Li ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Strong Dependence ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Prediction Skill ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Madden Julian Oscillation ◽  
Geophysical Fluid Dynamics ◽  
Potential Predictability ◽  
Anomaly Correlation Coefficient

Abstract Based on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden–Julian oscillation (MJO) prediction skill in boreal wintertime (November–April) is evaluated by analyzing 11 years (2003–13) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique toward observations. Using the real-time multivariate MJO (RMM) index as a predictand, it is demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 versus 7 days) than between initially strong and weak cases (29 versus 24 days). Meanwhile, a strong dependence on target phases is found, as opposed to relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during the Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (DYNAMO/CINDY) field campaign period. This model’s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.

scholarly journals Cold Tongue and Warm Pool ENSO Events in CMIP5: Mean State and Future Projections

2014 ◽  
Vol 27 (8) ◽  
pp. 2861-2885 ◽  
Author(s):  
Andréa S. Taschetto ◽  
Alexander Sen Gupta ◽  
Nicolas C. Jourdain ◽  
Agus Santoso ◽  
Caroline C. Ummenhofer ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Heat Content ◽  
Coupled Model ◽  
Warm Pool ◽  
Cmip5 Models ◽  
Cold Tongue ◽  
Future Projections ◽  
Enso Events ◽  
Almost All ◽  
Sst Anomalies

Abstract The representation of the El Niño–Southern Oscillation (ENSO) under historical forcing and future projections is analyzed in 34 models from the Coupled Model Intercomparison Project phase 5 (CMIP5). Most models realistically simulate the observed intensity and location of maximum sea surface temperature (SST) anomalies during ENSO events. However, there exist systematic biases in the westward extent of ENSO-related SST anomalies, driven by unrealistic westward displacement and enhancement of the equatorial wind stress in the western Pacific. Almost all CMIP5 models capture the observed asymmetry in magnitude between the warm and cold events (i.e., El Niños are stronger than La Niñas) and between the two types of El Niños: that is, cold tongue (CT) El Niños are stronger than warm pool (WP) El Niños. However, most models fail to reproduce the asymmetry between the two types of La Niñas, with CT stronger than WP events, which is opposite to observations. Most models capture the observed peak in ENSO amplitude around December; however, the seasonal evolution of ENSO has a large range of behavior across the models. The CMIP5 models generally reproduce the duration of CT El Niños but have biases in the evolution of the other types of events. The evolution of WP El Niños suggests that the decay of this event occurs through heat content discharge in the models rather than the advection of SST via anomalous zonal currents, as seems to occur in observations. No consistent changes are seen across the models in the location and magnitude of maximum SST anomalies, frequency, or temporal evolution of these events in a warmer world.

scholarly journals Interdecadal Variation of ENSO Predictability in Multiple Models

2008 ◽  
Vol 21 (18) ◽  
pp. 4811-4833 ◽  
Author(s):  
Youmin Tang ◽  
Ziwang Deng ◽  
Xiaobing Zhou ◽  
Yanjie Cheng ◽  
Dake Chen
Keyword(s):  
Interannual Variability ◽  
Southern Oscillation ◽  
Interdecadal Variation ◽  
Interdecadal Variability ◽  
Prediction Skill ◽  
Late Nineteenth Century ◽  
Enso Predictability ◽  
System A ◽  
Late Twentieth ◽  
High Predictability

Abstract In this study, El Niño–Southern Oscillation (ENSO) retrospective forecasts were performed for the 120 yr from 1881 to 2000 using three realistic models that assimilate the historic dataset of sea surface temperature (SST). By examining these retrospective forecasts and corresponding observations, as well as the oceanic analyses from which forecasts were initialized, several important issues related to ENSO predictability have been explored, including its interdecadal variability and the dominant factors that control the interdecadal variability. The prediction skill of the three models showed a very consistent interdecadal variation, with high skill in the late nineteenth century and in the middle–late twentieth century, and low skill during the period from 1900 to 1960. The interdecadal variation in ENSO predictability is in good agreement with that in the signal of interannual variability and in the degree of asymmetry of ENSO system. A good relationship was also identified between the degree of asymmetry and the signal of interannual variability, and the former is highly related to the latter. Generally, the high predictability is attained when ENSO signal strength and the degree of asymmetry are enhanced, and vice versa. The atmospheric noise generally degrades overall prediction skill, especially for the skill of mean square error, but is able to favor some individual prediction cases. The possible reasons why these factors control ENSO predictability were also discussed.

scholarly journals Improved ENSO Prediction by Singular Vector Analysis in a Hybrid Coupled Model

2009 ◽  
Vol 26 (3) ◽  
pp. 626-634
Author(s):  
Xiaobing Zhou ◽  
Youmin Tang ◽  
Yanjie Cheng ◽  
Ziwang Deng
Keyword(s):  
Prediction Model ◽  
Singular Vector ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Optimal Growth ◽  
Growth Patterns ◽  
Prediction Skill ◽  
Vector Analysis ◽  
Forecast Errors ◽  
Enso Prediction

Abstract In this study, a method based on singular vector analysis is proposed to improve El Niño–Southern Oscillation (ENSO) predictions. Its essential idea is that the initial errors are projected onto their optimal growth patterns, which are propagated by the tangent linear model (TLM) of the original prediction model. The forecast errors at a given lead time of predictions are obtained, and then removed from the raw predictions. This method is applied to a realistic ENSO prediction model for improving prediction skill for the period from 1980 to 1999. This correction method considerably improves the ENSO prediction skill, compared with the original predictions without the correction.

scholarly journals S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections

2021 ◽  
pp. 1-46
Author(s):  
Baoqiang Xiang ◽  
Lucas Harris ◽  
Thomas L. Delworth ◽  
Bin Wang ◽  
Guosen Chen ◽  
...  
Keyword(s):  
Coupled Model ◽  
Prediction Skill ◽  
Maritime Continent ◽  
Potential Predictability ◽  
Quasi Biennial Oscillation ◽  
The North ◽  
Anomaly Correlation Coefficient ◽  
Different Types ◽  
Biennial Oscillation ◽  
The North Pacific

AbstractA subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL SPEAR global coupled model. Based on 20-year hindcast results (2000-2019), the boreal wintertime (November-April) Madden-Julian Oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (15 days). The slow-propagating MJO detours southward when traversing the maritime continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases.The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

scholarly journals Revisiting ENSO Coupled Instability Theory and SST Error Growth in a Fully Coupled Model

2015 ◽  
Vol 28 (12) ◽  
pp. 4724-4742 ◽  
Author(s):  
Sarah M. Larson ◽  
Ben P. Kirtman
Keyword(s):  
Large Scale ◽  
Heat Content ◽  
Coupled Model ◽  
Enso Event ◽  
Error Growth ◽  
Model Framework ◽  
Modeling Framework ◽  
Enso Events ◽  
Enso Predictability ◽  
Strong Seasonality

Abstract A coupled model framework is presented to isolate coupled instability induced SST error growth in the ENSO region. The modeling framework using CCSM4 allows for seasonal ensembles of initialized simulations that are utilized to quantify the spatial and temporal behavior of coupled instabilities and the associated implications for ENSO predictability. The experimental design allows for unstable growth of initial perturbations that are not prescribed, and several cases exhibit sufficiently rapid growth to produce ENSO events that do not require a previous ENSO event, large-scale wind trigger, or subsurface heat content precursor. Without these precursors, however, ENSO amplitude is reduced. The initial error growth exhibits strong seasonality with fastest growth during spring and summer and also dependence on the initialization month with the fastest growth occurring in the July ensemble. Peak growth precedes the peak error, and evidence suggests that the final state error may be sensitive to a slight temperature bias in the initialized SST. The error growth displays a well-defined seasonal limit, with ensembles initialized prior to fall exhibiting a clear seasonal halt in error growth around September, consistent with increased background stability typical during fall. Overall, coupled instability error growth in CCSM4 is deemed best characterized by strong seasonality, dependence on the initialization month, and nonlinearity. The results pose real implications for predictability because the final error structure is ENSO-like and occurs without a subsurface precursor, which studies have shown to be essential to ENSO predictability. Despite the large error growth induced by coupled instabilities, analysis reveals that ENSO predictability is retained for most seasonal ensembles.

Effects of Tropical Cyclones on ENSO

2020 ◽  
Author(s):  
Tao Lian ◽  
Jun Ying ◽  
Hong-Li Ren
Keyword(s):  
Tropical Cyclones ◽  
Southern Oscillation ◽  
Heat Content ◽  
Coupled Model ◽  
Enso Event ◽  
Central Pacific ◽  
Final State ◽  
Enso Dynamics ◽  
Forecast Models

<p>Numerous studies have investigated the role of the El Niño–Southern Oscillation (ENSO) in modulating the activity of tropical cyclones (TCs) in the western Pacific on interannual timescales, but the effects of TCs on ENSO are less discussed. Some studies have found that TCs sharply increase surface westerly anomalies over the equatorial western–central Pacific and maintain them there for a few days. Given the strong influence of equatorial surface westerly wind bursts on ENSO, as confirmed by many recent literatures, the effects of TCs on ENSO may be much greater than previously expected.</p><p>Using recently released observations and reanalysis datasets, it is found that the majority of near-equatorial TCs (TCs hereafter) are associated with strong westerly anomalies at the equator, and the number and longitude of TCs are significantly correlated with ENSO strength. When TC-related wind stresses are added into an intermediate coupled model, the simulated ENSO becomes more irregular, and both ENSO magnitude and skewness approach those of observations, as compared with simulations without TCs. Adding TCs into the model system does not break the linkage between the heat content anomaly and subsequent ENSO event in the model, which manifest the classic recharge–discharge ENSO dynamics. However, the influence of TCs on ENSO is so strong that ENSO magnitude and sometimes its final state—i.e. either an El Niño or a La Niña—largely depend on the number and timing of TCs during the event year. Our findings suggest that TCs play a prominent role in ENSO dynamics, and their effects must be considered in ENSO forecast models.</p>

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

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

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