Southern Oscillation Prediction Research Articles

Reducing Model Error Effects in El Niño–Southern Oscillation Prediction Using Ensemble Coupled Data Assimilation

Model error is an important source of uncertainty that significantly reduces the accuracy of El Niño–Southern Oscillation (ENSO) prediction. In this study, ensemble coupled data assimilation was employed to estimate the tendency error of the fifth-generation Lamont–Doherty Earth observation (LDEO5) model, which represented the comprehensive effect of different sources of errors. Then, the estimated tendency error was applied to an ensemble prediction system for ENSO prediction. Assimilation experiments showed that tendency error estimation yielded better analysis than state estimation only. With tendency error estimation, simulated state variables such as zonal wind stress anomalies and subsurface temperature anomalies in the Niño3.4 region and upper layer depth anomalies along the equator showed good agreement with their reanalyzed counterparts. The ensemble ENSO prediction system with tendency error estimation demonstrated significantly better prediction skill than the ensemble system without tendency error estimation or the original LDEO5 model, especially for long lead times. The tendency error estimation improved the prediction skill for El Niño more than for La Niña. This study provides a promising approach to further improve prediction skill by reducing model error effects in an ensemble prediction.

A most‐recognized principle to define El Niño and La Niña years based on the K‐line diagram technique

ABSTRACTDespite the many improvements that have been achieved in prediction methods, considerable uncertainties in the models and the observing systems remain in El Niño Southern Oscillation predictions. One of the uncertainties is the definition of El Niño and La Niña years. Because academia has accepted too many indexes, there is much confusion among scientists regarding whether El Niño and La Niña events have occurred. In the view of this author, the oceans on this planet can be vividly called ‘ocean stabilization machines’ (OSMs). When some part of an OSM loses control for unknown reasons, other parts of the OSM function stabilize the atmospheric circulation. As a result, El Niño or La Niña events occur. In this study, we use a most‐recognized principle to determine El Niño (La Niña) years based on a K‐line diagram technique. This technique not only focuses on the anomaly of five accepted indexes, but also aims at the signals of an on‐going crash downward or a dramatic upward tendency of the sea‐surface temperature. To achieve this purpose, a new terminology, ‘suspected El Niño (La Niña) years’ and an Ocean Stabilization Index (OSI) expressing the state of the Pacific are proposed. The results indicate that there were only 10 El Niño years and 12 La Niña years from 1950 to 2012. Significant statistical relationships between the annual OSI and 14 indexes are also found.

Empirical singular vector method for ensemble El Niño–Southern Oscillation prediction with a coupled general circulation model

[1] Optimal initial perturbation is an important issue related to the improvement of the current seasonal climate prediction. In this study, we have applied the empirical singular vector method to ensemble El Niño–Southern Oscillation (ENSO) prediction with the Seoul National University coupled general circulation model. It is found that from the empirical linear operator, the leading singular mode, which represents the fast growing error mode in the tropical Pacific, shows El Niño–like perturbation in the present coupled model. When the singular vector is used as an initial perturbation, the forecast skill of ENSO is significantly improved. Further, it is demonstrated that the predictions with the singular vector have a more reliable ensemble spread, suggesting a potential merit for a probabilistic forecast.

Precipitation Extremes Estimated by GPCP and TRMM: ENSO Relationships

Abstract Global monthly and daily precipitation extremes are examined in relation to the El Niño–Southern Oscillation phenomenon. For each month around the annual cycle and in each 2.5° grid block, extremes in the Global Precipitation Climatology Project dataset are defined as the top five (wet) and bottom five (dry) mean rain rates from 1979 to 2004. Over the tropical oceans El Niño–Southern Oscillation events result in a spatial redistribution and overall increase in extremes. Restricting the analysis to land shows that El Niño is associated with an increase in frequency of dry extremes and a decrease in wet extremes resulting in no change in net extreme months. During La Niña an increase in frequency of dry extremes and no change in wet extremes are observed. Thus, because of the juxtaposition of tropical land areas with the ascending branches of the global Walker Circulation, El Niño (La Niña) contributes to generally dry (wet) conditions in these land areas. In addition, daily rain rates computed from the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis are used to define extreme precipitation frequency locally as the number of days within a given season that exceeded the 95th percentile of daily rainfall for all seasons (1998–2005). During this period, the significant relationships between extreme daily precipitation frequency and Niño-3.4 in the Tropics are spatially similar to the significant relationships between seasonal mean rainfall and Niño-3.4. However, to address the shortness of the record extreme daily precipitation frequency is also related to seasonal rainfall for the purpose of identifying regions where positive seasonal rainfall anomalies can be used as proxies for extreme events. Finally, the longer (1979–2005) but coarser Global Precipitation Climatology Project analysis is reexamined to pinpoint regions likely to experience an increase in extreme precipitation during El Niño–Southern Oscillation events. Given the significance of El Niño–Southern Oscillation predictions, this information will enable the efficient use of resources in preparing for and mitigating the adverse effects of extreme precipitation.

El Niño southern-oscillation prediction using southern oscillation index and Niño3 as onset indicators: Application of artificial neural networks

El Niño southern-oscillation (ENSO) is known to be the strongest climatic variation on seasonal to inter-annual time scales. It causes severe droughts, floods, fires, and hurricanes leading to economical disasters. This study explores the use of relatively simple inputs in developing artificial neural network (ANN) models for predicting the onset of ENSO by forecasting some of its indicators. Two indicators, southern oscillation index (SOI) and Niño3, were used one at a time to model the ENSO occurrence using monthly averaged data. Both models performed well in forecasting and predicting ENSO occurrence up to 12 months in advance. Correlation coefficient values of more than 0.8 and 0.9 (one month lead time), and above 0.7 and 0.8 (12 month lead time) were obtained for SOI and Niño3, respectively. Both models apply the feed forward multilayer perceptron network trained with error back-propagation algorithm. The final models were compared with each other and found to be highly consistent with 75% agreement in their forecasting ability. Key words: climate anomalies, ENSO, El Niño, La Niña, artificial neural networks, southern oscillation index (SOI), Niño3, teleconnections.

An off‐line, numerically efficient initialization scheme in an oceanic general circulation model for El Niño–Southern Oscillation prediction

In this study a simplified initialization scheme, which is “off‐line,” is proposed and applied to an oceanic general circulation model (OGCM) for El Niño–Southern Oscillation (ENSO) prediction. The initialization scheme is based on the National Centers for Environmental Prediction ocean reanalysis and a two‐dimensional variational (2D‐Var) assimilation algorithm. It focuses on two basic issues in data assimilation: observed data and computational cost. Compared with a traditional assimilation system, this simplified scheme avoids model forward integration and the complications of acquiring and processing raw in situ temperature observations. The off‐line scheme only requires around 1/20 of the computational expense of a traditional algorithm. Two hybrid coupled models, an OGCM coupled to a statistical atmosphere, and the same ocean model coupled to a dynamical atmosphere, were used to examine the initialization scheme. A large ensemble of prediction experiments during the period from 1981 to 1998 shows that relative to just a wind forced initialization the off‐line scheme leads to a significant improvement in predictive skills of Niño3 sea surface temperature anomaly (SSTA) for all lead times. The prediction skills obtained by the scheme is as high as that attained by a more traditional “on‐line” assimilation scheme.