El Nino-Southern Oscillation Prediction Research Articles

Explainable deep learning for insights in El Niño and river flows

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

ENSO coupling to the equatorial Atlantic: Analysis with an extended improved recharge oscillator model

IntroductionObservational and modeling studies have examined the interactions between El Niño-Southern Oscillation (ENSO) and the equatorial Atlantic variability as incorporated into the classical charge-recharge oscillator model of ENSO. These studies included the role of the Atlantic in the predictability of ENSO but assumed stationarity in the relationships, i.e., that models’ coefficients do not change over time. A recent work by the authors has challenged the stationarity assumption in the ENSO framework but without considering the equatorial Atlantic influence on ENSO.MethodsThe present paper addresses the changing relationship between ENSO and the Atlantic El Niño using an extended version of the recharge oscillator model. The classical two-variable model of ENSO is extended by adding a linear coupling on the SST anomalies in the equatorial Atlantic. The model’s coefficients are computed for different periods. This calculation is done using two methods to fit the model to the data: (1) the traditional method (ReOsc), and (2) a novel method (ReOsc+) based on fitting the Fisher’s Z transform of the auto and cross-correlation functions.ResultsWe show that, during the 20th century, the characteristic damping rate of the SST and thermocline depth anomalies in the Pacific have decreased in time by a factor of 2 and 3, respectively. Moreover, the damping time of the ENSO fluctuations has doubled from 10 to 20 months, and the oscillation period of ENSO has decreased from 60-70 months before the 1960s to 50 months afterward. These two changes have contributed to enhancing ENSO amplitude. The results also show that correlations between ENSO and the Atlantic SST strengthened after the 70s and the way in which the impact of the equatorial Atlantic is added to the internal ENSO variability.ConclusionsThe remote effects of the equatorial Atlantic on ENSO must be considered in studies of ENSO dynamics and predictability during specific time-periods. Our results provide further insight into the evolution of the ENSO dynamics and its coupling to the equatorial Atlantic, as well as an improved tool to study the coupling of climatic and ecological variables.

Deep learning for skillful long-lead ENSO forecasts

El Niño-Southern Oscillation (ENSO) is one of the fundamental drivers of the Earth's climate variability. Thus, its skillful prediction at least a few months to years ahead is of utmost importance to society. Using both dynamical and statistical methods, several studies reported skillful ENSO predictions at various lead times. Predictions with long lead times, on the other hand, remain difficult. In this study, we propose a convolutional neural network (CNN)-based statistical ENSO prediction system with heterogeneous CNN parameters for each season with a modified loss function to predict ENSO at least 18–24 months ahead. The developed prediction system indicates that the CNN model is highly skillful in predicting ENSO at long lead times of 18–24 months with high skills in predicting extreme ENSO events compared with the Scale Interaction Experiment-Frontier ver. 2 (SINTEX-F2) dynamical system and several other statistical prediction systems. The analysis indicates that the CNN model can overcome the spring barrier, a major hindrance to dynamical prediction systems, in predicting ENSO at long lead times. The improvement in the prediction skill can partly be attributed to the heterogeneous parameters of seasonal CNN models used in this study and also to the use of a modified loss function in the CNN model. In this study, we also attempted to identify various precursors to ENSO events using CNN heatmap analysis.

Responses of GNSS ZTD Variations to ENSO Events and Prediction Model Based on FFT-LSTME

The El Niño-Southern Oscillation (ENSO) event often causes natural disasters in mainland China. Existing quantitative analysis of ENSO events effects on climate change in mainland China is insufficient. The monthly scale prediction effectiveness of ENSO events is still low. Global Navigation Satellite System (GNSS) can estimate zenith tropospheric delay (ZTD) with high accuracy, which can study ZTD responses to ENSO and improve the prediction accuracy of ENSO events. This study quantitatively analyzed the response patterns of GNSS ZTD time-frequency variation to ENSO events in mainland China. The monthly multivariate ENSO index (MEI) thresholds for GNSS ZTD anomaly response to ENSO events are (-1.12,1.92) for the tropical monsoon zone, (-1.12,1.61) for the subtropical monsoon zone, (-1.19,1.62) for the temperate monsoon zone, (-1.26,1.64) for the temperate continental zone, and (-1.22,1.72) for the mountain plateau zone. The ENSO event causes the amplitude of the 9-month variation period to decrease and the amplitude of the 0.8–3-month period to increase for the GNSS ZTD in mainland China. Furthermore, a forecasting model is proposed with integrating fast Fourier transform and long short-term memory extended (FFT-LSTME). The model uses monthly MEI as the primary input and the GNSS ZTD reconstruction sequence that responds to ENSO as the auxiliary input. It can predict ENSO events in the next 24 months with an index of agreement (IA) of 91.56% and a root mean square error (RMSE) of 0.25. The RMSE is optimized by 70.48%, 43.95%, and 11.6% when compared with radial basis function (RBF), LSTM, and FFT-LSTM.

ENSO analysis and prediction using deep learning: A review

El Niño/Southern Oscillation (ENSO) mainly occurs in the tropical Pacific Ocean every a few years. But it affects the climate around the world and has a dramatic impact on the development of ecology and agriculture. The analysis and prediction of ENSO become particularly important for meteorology and disaster management. However, due to insufficient data, spring predictability barrier (SPB), and model uncertainty, traditional analysis models face challenges. To address these issues, researchers begin to apply deep learning (DL) technologies to ENSO research, exploring the impact of ENSO on the world's extreme climate changes. In recent years, deep learning-based methods have obtained impressive progress with more accurate and effective predictions of ENSO. In this paper, we summarize the attempts of DL technologies in predicting ENSO. We first introduce the properties of ENSO, followed by the architecture introduction of DL technologies and their application to ENSO. We then investigate the potential of DL technologies for ENSO prediction from various aspects, including model evaluation metrics, prediction algorithms, overcoming SPB and prediction uncertainty. Finally, we provide discussions on the future trends and challenges of using DL technologies for ENSO prediction.

El Niño–Southern Oscillation (ENSO) predictability in equilibrated warmer climates

Abstract. Responses of El Niño–Southern Oscillation (ENSO) to global warming remain uncertain, which challenges ENSO forecasts in a warming climate. We investigate changes in ENSO characteristics and predictability in idealized simulations with quadrupled CO2 forcing from seven general circulation models. Comparing the warmer climate to control simulations, ENSO variability weakens, with the neutral state lasting longer, while active ENSO states last shorter and skew to favor the La Niña state. The 6-month persistence-assessed ENSO predictability slightly reduces in five models and increases in two models under the warming condition. While the overall changes in ENSO predictability are insignificant, we find significant relationships between changes in predictability and intensity, duration, and skewness of the three individual ENSO states. The maximal contribution to changes in the predictability of El Niño, La Niña and neutral states stems from changes in skewness and events' duration. Our findings show that a robust and significant decrease in ENSO characteristics does not imply a similar change in ENSO predictability in a warmer climate. This could be due to model deficiencies in ENSO dynamics and limitations in the persistence model when predicting ENSO.

The tropical Atlantic as a negative feedback on ENSO

The influence of the tropical Atlantic on El Niño-Southern Oscillation (ENSO) is examined using dedicated climate model experiments with sea-surface temperature (SST) restoring. Partial SST restoring to climatology in the tropical Atlantic leads to slower decay of ENSO events and to a shift of the power spectrum to longer periods. Perfect model hindcast experiments with and without restoring tropical Atlantic SST to climatology indicate that both the northern tropical and equatorial Atlantic have a very small influence on ENSO development. During decaying ENSO events, on the other hand, northern tropical Atlantic SST anomalies strongly accelerate the decay. Key to the Atlantic influence on ENSO decay are Atlantic SST anomalies just north of the equator (~ 5°N). These lead to local convection anomalies that change the Walker circulation so as to accelerate ENSO decay. Importantly, anomalous events in either the northern tropical or equatorial Atlantic fail to develop in the hindcast ensemble mean, when tropical Pacific SSTs are restored to climatology. This indicates that anomalous tropical Atlantic events in boreal spring and summer are strongly dependent on preceding ENSO events in boreal winter. Thus, the role of the tropical Atlantic is to mediate a negative feedback of ENSO on itself. Despite this passive role of the tropical Atlantic in the Pacific-Atlantic interaction, accurate simulation of the Atlantic feedback should play some role in ENSO prediction. Further model experiments will be required to evaluate model dependence of these findings and to quantify the impact of the Atlantic on ENSO prediction skill.

The Different Relationships between the ENSO Spring Persistence Barrier and Predictability Barrier

Abstract In this paper, we investigate the relationship between the El Niño–Southern Oscillation (ENSO) spring persistence barrier (PB) and predictability barrier (PD) and apply it to explain the interdecadal modulation of ENSO prediction skill using the anomaly correlation coefficient (ACC). Previous studies showed that a longer persistence (i.e., autocorrelation) tends to produce a higher prediction skill. Using the recharge oscillator model of ENSO, both analytical and numerical solutions suggest that the predictability (i.e., ACC) is related to the persistence of sea surface temperature (SST) and cross correlation between SST and subsurface ocean heat content in the tropical Pacific. In particular, a larger damping rate in SST anomalies will lead to a lower persistence and ACC and a stronger PD. However, a shortened ENSO period, which controls the cross correlation, will lead to a lower persistence but a higher ACC associated with a weaker PD. Finally, we apply our solutions to observations and suggest that a higher ACC associated with a weaker PD after 1960 is caused by the shortened ENSO period.

ENSO atmospheric feedbacks under global warming and their relation to mean-state changes

Two atmospheric feedbacks play an important role in the dynamics of the El Niño/Southern Oscillation (ENSO), namely the amplifying zonal wind feedback and the damping heat flux feedback. Here we investigate how and why both feedbacks change under global warming in climate models participating in the 5th and 6th phase of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) under the business-as-usual scenario (RCP8.5 and SSP5-8.5, respectively). The amplifying zonal wind feedback over the western equatorial Pacific (WEP) becomes significantly stronger in two third of the models, on average by 12 ± 7% in these models. The heat flux damping feedback over the eastern and central equatorial Pacific (EEP and CEP, respectively) increases as well in nearly all models, with the damping effect increasing on average by 18 ± 11%. The simultaneous strengthening of the two feedbacks can be explained by the stronger warming in the EEP relative to the WEP and the off-equatorial regions, which shifts the rising branch of the Pacific Walker Circulation to the east and increases the mean convection over the CEP. This in turn leads to a stronger vertical wind response during ENSO events over the CEP that strengthens both atmospheric feedbacks. We separate the climate models into sub-ensembles with STRONG and WEAK ENSO atmospheric feedbacks, as 2/3 of the models underestimate both feedbacks under present-day conditions by more than 40%, causing an error compensation in the ENSO dynamics. The biased mean state in WEAK in 20C constrains the ENSO atmospheric feedback projection in 21C, as the models of the WEAK sub-ensemble also have weaker ENSO atmospheric feedbacks in 21C. Further, due to the more realistic dynamics and teleconnections, we postulate that one should have more confidence in the ENSO predictions with models belonging to the STRONG sub-ensemble. Finally, we analyze the relation between ENSO amplitude change and ENSO atmospheric feedback change. We find that models simulating an eastward shift of the zonal wind feedback and increasing precipitation over the EEP during Eastern Pacific El Niño events tend to predict a larger ENSO amplitude in response to global warming.

Strengthening Amplitude and Impact of the Pacific Meridional Mode on ENSO in the Warming Climate Depicted by CMIP6 Models

Abstract The Pacific meridional mode (PMM) has been suggested to play an important role in modulating the development of El Niño–Southern Oscillation (ENSO). In this study, we examine the projected changes in the PMM and its impact on ENSO under greenhouse gas forcing using the models of phase 6 of the Coupled Model Intercomparison Project. These models can properly reproduce the characteristics of PMM patterns but reveal discrepant PMM–ENSO relationships owing to different wind–evaporation–sea surface temperature (SST) (WES) feedback efficiency and different magnitude of atmospheric convection response to SST anomalies. We select the models that show good performance in simulating the PMM and its impact on ENSO for investigation of future projections. Results show potential increases in both PMM amplitude and its impact on ENSO under the shared socioeconomic pathway (SSP) 585 (SSP585) warming scenario with great intermodel consensus. Diagnosis of the WES feedback indicates increasing sensitivity of latent heat flux to zonal wind speed in a warming climate, which seems to be the main reason for the projected strengthening PMM and its impact on ENSO. In addition, a slightly intensified response of atmospheric convection to SST anomalies in the subtropical Pacific may partially contribute to a stronger PMM–ENSO relationship. The results from this study highlight the increasing importance of the PMM for ENSO development, which calls for more attention to be paid to the PMM for ENSO prediction. Significance Statement Variability of the sea surface temperature in the equatorial Pacific related to El Niño–Southern Oscillation (ENSO) can exert a great impact on global climate. The development of ENSO is partially modulated by the dominant mode of ocean–atmospheric variation in the subtropical North Pacific, namely, the Pacific meridional mode (PMM). This study is aimed to understand the change in the amplitude of the PMM and its impact on ENSO due to climate change. Multimodel projections suggest that the PMM will likely become stronger and exert a greater impact on ENSO since the future warmer climate is favorable for the growth of the PMM. These results call for more attention to be paid to the PMM for ENSO prediction.

Long-term ENSO prediction with echo-state networks

The El Niño-Southern Oscillation (ENSO) is a climate phenomenon that profoundly impacts weather patterns and extreme events worldwide. Here we develop a method based on a recurrent neural network, called echo state network (ESN), which can be trained efficiently to predict different ENSO indices despite their relatively high noise levels. To achieve this, we train the ESN model on the low-frequency variability of ENSO indices and estimate the potential future high-frequency variability from specific samples of its past history. Our method reveals the importance of cross-scale interactions in the mechanisms underlying ENSO and skilfully predicts its variability and especially El Niño events at lead times up to 21 months. This study considers forecasts skillful if the correlation coefficients are above 0.5. Our results show that the low-frequency component of ENSO carries substantial predictive power, which can be exploited by training our model on single scalar time series. The proposed machine learning method for data-driven modeling can be readily applied to other time series, e.g. finance and physiology. However, it should be noted that our approach cannot straightforwardly be turned into a real-time operational forecast because of the decomposition of the original time series into the slow and fast components using low-pass filter techniques.

Improved ENSO and PDO Prediction Skill Resulting from Finer Parameterization Schemes in a CGCM

Coupled general circulation models (CGCMs), as tools of predicting climate variability, are constantly being improved due to their immense value in a host of theoretical and practical, real-world problems. Consequently, four new parameterization schemes are introduced in the First Institute of Oceanography Earth System Model (FIO-ESM), and a new climate prediction System (CPS) is built up based on modified and original FIO-ESM. Here, turbulence from the sea surface to deep ocean were fully described, and seasonal forecasts of El Niño-Southern Oscillation (ENSO) and year-to-year prediction of Pacific Decadal Oscillation (PDO) were made with both the modified and original FIO-ESM-CPS. The results illustrate that the anomaly correlation coefficient (ACC) of the Niño 3.4 index significantly increased, and the root mean square error (RMSE) significantly decreased, respectively, in the modified FIO-ESM-CPS as compared to the original. The RMSE is improved by over 20% at 4- and 5-month lead times. Over longer leads, and in the modified FIO-ESM-CPS, forecast ENSO amplitudes are far closer to observations than the original CGCM, which significantly overestimates amplitudes. PDO prediction skill is also improved in the modified FIO-ESM-CPS with ACC improving by 36% at the 4-year lead time and RMSE decreasing by 21% at the 3-year lead time.

The Signal‐to‐Noise Paradox in ENSO Prediction: Role of ENSO Growth Rate and Period

AbstractThis paper investigates the relationship between heterogeneous predictability and homogeneous predictability for El Niño‐Southern Oscillation (ENSO) prediction. Previous studies showed that a larger heterogeneous predictability than homogeneous predictability, referred as the signal‐to‐noise paradox, is caused by a higher persistence in the observations than in the model. By using the recharge oscillator model, it is found analytically and numerically that both ENSO growth rate and period contribute to this paradox. A larger ENSO growth rate in the observation can lead to a higher persistence and heterogeneous predictability. However, a larger ENSO growth rate associated with a shortened ENSO period can also lead to this paradox although the persistence of the observation is lower than forecast model. Finally, we apply it to explain the paradox after 1960 is caused by a larger ENSO growth rate and a shortened ENSO period.

The Diversity of ENSO Evolution during the Typical Decaying Periods Determined by an ENSO Developing Mode

AbstractThe evolution of El Niño–Southern Oscillation (ENSO) is an essential predictor for global climate anomalies, but ENSO’s effect is regulated by the considerable diversity of post-ENSO evolution. This study identified that there coexist a decaying mode and a developing mode of ENSO in the ENSO decaying periods (from September to August) through empirical orthogonal function (EOF) and extended EOF analyses. The diversity of ENSO evolution during the decaying periods is actually determined by the ENSO developing mode, which is unsteady and susceptible to other signals, whereas the ENSO decaying mode is quite steady. Using multiple datasets, segmented datasets, and sea surface temperature anomalies (SSTAs)-residual EOF, the key signals related to the diversity of ENSO evolution are identified and verified in the South Atlantic and equatorial South Pacific. Based on the decaying and developing modes, a prediction model was constructed to predict the boreal summer Niño-3 index. The ENSO developing mode, explaining very small variance, plays a decisive role in determining the prediction of the summer SSTAs in the Pacific. The forecasting skill of summer ENSO can also be greatly improved when continuous SSTAs and interbasin interactions in the whole tropics are considered. This prediction result indicates that identified precursory disturbance signals and interbasin interactions for ENSO development are crucial for boreal summer ENSO prediction.Significance StatementENSO often shows great diversity in the temporal evolution during post-ENSO periods, thus limiting the predictability of tropical SSTAs in the following spring and summer. Identifying the signal impacting the diversity of ENSO is crucial for predicting ENSO. Here, we offer a new understanding of the essence of ENSO’s temporal diversity from the perspective of interbasin interactions. We identified that the ENSO typical decaying period contains a steady decaying mode and an unsteady developing mode simultaneously, and the developing mode induces the great diversity in ENSO’s evolution during post-ENSO periods. Two signals located in the South Atlantic and equatorial South Pacific are recognized as the main sources influencing the diversity of ENSO evolution.

Toward an optimal observational array for improving two flavors of El Niño predictions in the whole Pacific

This paper investigates the optimal observational array for improving the El Niño-Southern Oscillation (ENSO) prediction by exploring sensitive areas for target observations of two types of El Niño events in the whole Pacific. A target observation method based on the particle filter and pre-industrial control runs from six coupled model outputs in Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments are used to quantify the relative importance of the initial accuracy of sea surface temperature (SST) in different Pacific areas. The initial accuracy of the tropical Pacific, subtropical Pacific, and extratropical Pacific can all exert influences on both types of El Niño predictions. The relative importance of different areas changes along with different lead times of predictions. Tropical Pacific observations are crucial for decreasing the root mean square error of predictions of all lead times. Subtropical and extratropical observations play an important role in decreasing the prediction uncertainty, especially when the prediction is made before and throughout the boreal spring. To consider different El Niño types and different start months for predictions, a quantitative frequency method based on frequency distribution is applied to determine the optimal observations of ENSO predictions. The final optimal observational array contains 31 grid points, including 21 grid points in the equatorial Pacific and 10 grid points in the North Pacific, suggesting the importance of the initial SST conditions for ENSO predictions not only in the tropical Pacific but also in the area outside the tropics. Furthermore, the predictions made by assimilating SST in sensitive areas have better prediction skills in the verification experiment, which can indicate the validity of the optimal observational array designed in this study.

Pacific subsurface temperature as a long‐range indicator of El Niño, regional precipitation, and fire

AbstractThe SubNiño4 index based on the subsurface potential temperature around the thermocline beneath the west Pacific warm pool, the Niño 4 region, is examined as a long‐range indicator of the surface El Niño–Southern Oscillation (ENSO) and ENSO‐driven atmospheric response. The SubNiño4 index captures the evolution of subsurface ocean heat content between the El Niño and La Niña phases of the ENSO cycle, allowing it to serve as a long‐range indicator of surface ENSO and hence also many ENSO‐driven atmospheric anomalies. The SubNiño4 index has more temporally stable correlations with Niño 3.4 than the widely used western equatorial Pacific warm‐water volume indicator. For a lead time of the order of 12 months, Niño 3.4 correlations afforded by the lead observed SubNiño4 index become similar to and can exceed those produced by typical dynamical ENSO predictions. The value and viability of the SubNiño4 index as a simple statistical long‐range indicator of ENSO‐driven atmospheric response is shown for regional precipitation anomalies throughout the Tropics and fires in Continental and Maritime Southeast Asia.

ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler

Abstract. The El Niño–Southern Oscillation (ENSO) is an extremely complicated ocean–atmosphere coupling event, the development and decay of which are usually modulated by the energy interactions between multiple physical variables. In this paper, we design a multivariate air–sea coupler (ASC) based on the graph using features of multiple physical variables. On the basis of this coupler, an ENSO deep learning forecast model (named ENSO-ASC) is proposed, whose structure is adapted to the characteristics of the ENSO dynamics, including the encoder and decoder for capturing and restoring the multi-scale spatial–temporal correlations, and two attention weights for grasping the different air–sea coupling strengths on different start calendar months and varied effects of physical variables in ENSO amplitudes. In addition, two datasets modulated to the same resolutions are used to train the model. We firstly tune the model performance to optimal and compare it with the other state-of-the-art ENSO deep learning forecast models. Then, we evaluate the ENSO forecast skill from the contributions of different predictors, the effective lead time with different start calendar months, and the forecast spatial uncertainties, to further analyze the underlying ENSO mechanisms. Finally, we make ENSO predictions over the validation period from 2014 to 2020. Experiment results demonstrate that ENSO-ASC outperforms the other models. Sea surface temperature (SST) and zonal wind are two crucial predictors. The correlation skill of the Niño 3.4 index is over 0.78, 0.65, and 0.5 within the lead time of 6, 12, and 18 months respectively. From two heat map analyses, we also discover the common challenges in ENSO predictability, such as the forecasting skills declining faster when making forecasts through June–July–August and the forecast errors being more likely to show up in the western and central tropical Pacific Ocean in longer-term forecasts. ENSO-ASC can simulate ENSO with different strengths, and the forecasted SST and wind patterns reflect an obvious Bjerknes positive feedback mechanism. These results indicate the effectiveness and superiority of our model with the multivariate air–sea coupler in predicting ENSO and analyzing the underlying dynamic mechanisms in a sophisticated way.

The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2

Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.

The Short-Term Climate Prediction System FIO-CPS v2.0 and its Prediction Skill in ENSO

The climate model is an important tool for simulating and predicting the mean state and variability of the climate system. The First Institute of Oceanography-Climate Prediction System (FIO-CPS), built on a climate model with the oceanic observation initialization, has been updated from version 1.0 to 2.0, with a finer resolution and more reasonable physical processes. Previous assessments show that the mean state was well simulated in version 2.0, and its influence on the prediction was further analyzed in this study. Hindcast experiments were conducted using FIO-CPS v1.0 and v2.0, and their prediction abilities based on 27 years (1993–2019) experiment data were analyzed. The results show that the sea surface temperature (SST) biases over the eastern Pacific and the Southern Ocean are improved in the initial condition of FIO-CPS v2.0. Moreover, this new system has a higher skill for predicting El Niño-Southern Oscillation (ENSO). The prediction skill represented by the anomaly correlation coefficient (ACC) of the Niño3.4 index is greater than 0.78 at the 6-month lead time, which increases by 11.09% compared to the value of 0.70 in FIO-CPS v1.0. The root mean square error (RMSE) decreases by 0.20, which accounts for 28.59% of the FIO-CPS v1.0 result. Furthermore, the improvement of the prediction skill changes seasonally, featured by the ACC significantly increasing in the boreal winter and early spring. The improvement in the annual mean SST prediction over the Equatorial Pacific mainly contributes to the enhanced ENSO prediction skill in FIO-CPS v2.0. These results indicate that a state-of-the-art climate model with a well-simulated mean state is critical in improving the prediction skill on the seasonal time scale.

Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction

Parameter estimation plays an important role in reducing model error and thus is of great significance to improve the simulation and prediction capabilities of the model. However, due to filtering divergence, parameter estimation by ensemble-based filters still faces great challenges. Previous studies have shown that a covariance inflation scheme could alleviate the filtering divergence problem by increasing the signal-to-noise ratio of the state-parameter covariance. In this study, we proposed a new inflation scheme based on a local ensemble transform Kalman filter (LETKF). With the new scheme, the Zebiak–Cane (Z-C) model parameters were estimated by assimilating the sea surface temperature anomaly (SSTA) data. The effectiveness of the parameter estimation and its influence on El Niño–Southern Oscillation (ENSO) prediction were evaluated in an observation system simulation experiments (OSSE) framework and real-world scenario, respectively. With the utilization of the OSSE framework, the results showed that the model parameters were successfully estimated. Parameter estimation reduced the model error when compared with only state estimation (onlySE); however, multiple parameter estimation (MPE) further improved the ENSO prediction skill by providing better initial conditions and parameter values than the single parameter estimation (SPE). Parameter estimation could thus alleviate the spring prediction barrier (SPB) phenomenon of ENSO to a certain extent. In real-world experiments, the optimized parameters significantly improved the ENSO forecasting skill, primarily in prediction of warm events. This study provides an effective parameter estimation strategy to improve climate models and further climate predictions in the real world.