El Niño–Southern Oscillation Forecasts Research Articles

Improving ENSO prediction in a hybrid coupled model with an embedded entrainment temperature parameterisation

AbstractImproving El Niño/Southern Oscillation (ENSO) forecast remains a great challenge in the climate‐predicting community. Previously, an improved solution to sea surface temperature (SST) anomaly simulations in the tropical Pacific was obtained by explicitly embedding into an ocean general circulation model (OGCM) a separate SST anomaly submodel with an empirical parameterisation for the temperature of subsurface water entrained into the ocean mixed layer (Te). In the present work, the benefit of the approach is explored and demonstrated in terms of ENSO prediction. A hybrid coupled ocean‐atmosphere model (HCM) is utilized to perform two retrospective ENSO forecasts, differing in the way SST anomaly fields are taken for their coupling to the atmosphere, one directly from the OGCM (referred to as a standard coupling, HCMstd), and another from the embedded SST anomaly submodel with optimized Te parameterisation (referred to as an embedded coupling, HCMembed). The results indicate that ENSO forecasts can be effectively improved using the embedded approach; the predicted Niño‐3.4 SST anomaly correlation is higher by 0.1–0.2 at a 12‐month lead time in the HCMembed than in the HCMstd, and the corresponding root‐mean‐square (RMS) error is lower by 0.1–0.2 °C. Further improvements and applications are discussed. Copyright © 2012 Royal Meteorological Society

Stochastic reservoir optimization using El Niño information: case study of Daule Peripa, Ecuador

Reservoir optimization requires the ability to produce inflow scenarios that are consistent with the available climatic information. We approach stochastic inflow modelling with a Markov-switching model where inflow anomalies are described by a mixture of autoregressive models with exogenous input, each corresponding to a hidden climate state. Climatic information is used as exogenous input and to condition state transitions. We apply the model to the inflow of the Daule Peripa reservoir in western Ecuador, where El Niño events cause anomalously heavy rainfall. El Niño–Southern Oscillation (ENSO) indices constitute the climatic input of the inflow model. The Daule Peripa reservoir serves a hydropower plant and a downstream water supply facility. Based on ENSO forecasts, which are available with 9 month lead time, monthly inflow scenarios are generated to perform stochastic optimization of reservoir releases with monthly time-steps. To account for inflow uncertainty, we generate multiple synthetic inflow time series and apply a multi-objective genetic algorithm to evaluate the objective functions. The results highlight the advantages of using a climate-driven stochastic model to produce inflow scenarios and forecasts for reservoir optimization, and show significant potential improvements with respect to the current reservoir management.

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

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.

The Value of ENSO Forecast Information to Dual-Purpose Winter Wheat Production in the U.S. Southern High Plains

Abstract The value of El Niño–Southern Oscillation (ENSO) forecast information to southern high plains winter wheat and cattle-grazing production systems was estimated here by simulation. Although previous work has calculated average forecast value, the approach here was to estimate probabilities of the value of single forecasts from value distributions associated with categorical ENSO forecast conditions. A simple ENSO-phase forecast system’s value was compared with that of an ideal forecast method that exactly predicted the tercile category of regional November–March precipitation. Simulations were conducted for four price scenarios with wheat prices that randomly varied about a historical ($3.22 per bushel) and elevated ($7.00 per bushel) mean and with returns on live weight gain that are consistent with the grain producer leasing pasturage or owning cattle. In the simulations at $3.22 per bushel, the best practices for specific forecast conditions varied with cattle-ownership conditions. However, the ENSO-phase system’s value distributions were comparable to that of the perfect forecast system; thus more-accurate regional precipitation forecasts may not lead to more forecast value at the farm level. In the simulations at $7.00 per bushel, even perfect categorical forecasts produced only minor profit effects, a result that is attributed here to an increased profit margin rather than to increased wheat value. Under both wheat-price conditions, however, the best no-forecast baseline practices are also shown to have value relative to an arbitrarily chosen management practice. Thus, following practices optimized to climatic conditions and current price and cost conditions might increase profits when no forecast information is available.

The relationship between Pacific Decadal and Southern Oscillations: Implications for the climate of northwestern Baja California

The relationship between the Southern Oscillation (SO) and the Pacific Decadal Oscillation (PDO) is studied by means of forced and secondary forecast models of the PDO. These models are constructed with the same indices frequently associated to different aspects of the climate of northwestern Baja California, namely the Southern Oscillation Index and the Pacific Decadal Oscillation Index (SOI and PDOI). The secondary forecast model explains about 40% of the interannual variability of the PDO while the forced model explains about 60% of PDO interannual plus decadal variability. These results confirm that El Niño-Southern Oscillation (ENSO) forces the PDO. Thus interannual and decadal variabilities in the Pacific Ocean, and related climatological impacts in northwestern Baja California, may be mainly ENSO-generated. As ENSO forecasts improve, PDO forecast and climatological predictions in this region should also improve.

Exploring the initial errors that cause a significant “spring predictability barrier” for El Niño events

Within the Zebiak‐Cane model, we identify two types of initial errors that have significant season‐dependent evolutions related to the spring predictability barrier (SPB) for El Niño events. One type includes the sea surface temperature anomaly (SSTA) errors that have a zonal dipolar pattern with positive anomalies in the central equatorial Pacific and negative ones in the eastern equatorial Pacific; the other type consists of the SSTA errors with a spatial structure opposite to that of the former type, the zonal dipolar pattern shows negative anomalies in the central equatorial Pacific and positive anomalies in the eastern equatorial Pacific. The patterns of these two types of errors are nearly opposite of each other. The former causes the El Niño event to be underpredicted, and the latter causes the El Niño event to be overpredicted. For strong El Niño events the former tends to have a larger effect on the predictions than the latter, but for weak El Niño events, it is very difficult to determine which type of initial errors results in worse predictions. It is thought that strong (weak) El Niño events could be affected by strong (weak) nonlinearities. There are also other initial errors; however, they do not yield considerable season‐dependent evolutions nor can a common characteristic be extracted from their patterns. The two types of initial errors suggest two dynamical behaviors of error growth related to the SPB: in one case, the initial errors grow in a manner similar to the El Niño events; in the other case, the initial errors develop with a tendency opposite to the El Niño events. The two types of initial errors may capture the errors that exhibit significant season‐dependent evolutions related to the SPB. In addition, they may provide information regarding the “sensitive area” of ENSO predictions because of their localized regions. Therefore, if these types of initial errors exist in the realistic El Niño–Southern Oscillation (ENSO) predictions and if a data assimilation or a target method can filter them, the ENSO forecast skill may be improved. For ensemble forecast studies, different signs of prediction errors caused by the two types of initial errors could illustrate why the ensemble mean offers a better forecast than a single prediction.

El Niño prediction and predictability

El Niño-Southern Oscillation (ENSO) is by far the most energetic, and at present also the most predictable, short-term fluctuation in the Earth’s climate system, though the limits of its predictability are still a subject of considerable debate. As a result of over two-decades of intensive observational, theoretical and modeling efforts, ENSO’s basic dynamics is now well understood and its prediction has become a routine practice at application centers all over the world. The predictability of ENSO largely stems from the ocean–atmosphere interaction in the tropical Pacific and the low-dimensional nature of this coupled system. Present ENSO forecast models, in spite of their vast differences in complexity, exhibit comparable predictive skills, which seem to have hit a plateau at moderate level. However, mounting evidence suggests that there is still room for improvement. In particular, better model initialization and data assimilation, better simulation of surface heat and freshwater fluxes, and better representation of the relevant processes outside of the tropical Pacific, could all lead to improved ENSO forecasts.

A Hierarchy of Data-Based ENSO Models

AbstractGlobal sea surface temperature (SST) evolution is analyzed by constructing predictive models that best describe the dataset’s statistics. These inverse models assume that the system’s variability is driven by spatially coherent, additive noise that is white in time and are constructed in the phase space of the dataset’s leading empirical orthogonal functions. Multiple linear regression has been widely used to obtain inverse stochastic models; it is generalized here in two ways. First, the dynamics is allowed to be nonlinear by using polynomial regression. Second, a multilevel extension of classic regression allows the additive noise to be correlated in time; to do so, the residual stochastic forcing at a given level is modeled as a function of variables at this level and the preceding ones. The number of variables, as well as the order of nonlinearity, is determined by optimizing model performance.The two-level linear and quadratic models have a better El Niño–Southern Oscillation (ENSO) hindcast skill than their one-level counterparts. Estimates of skewness and kurtosis of the models’ simulated Niño-3 index reveal that the quadratic model reproduces better the observed asymmetry between the positive El Niño and negative La Niña events. The benefits of the quadratic model are less clear in terms of its overall, cross-validated hindcast skill; this model outperforms, however, the linear one in predicting the magnitude of extreme SST anomalies.Seasonal ENSO dependence is captured by incorporating additive, as well as multiplicative forcing with a 12-month period into the first level of each model. The quasi-quadrennial ENSO oscillatory mode is robustly simulated by all models. The “spring barrier” of ENSO forecast skill is explained by Floquet and singular vector analysis, which show that the leading ENSO mode becomes strongly damped in summer, while nonnormal optimum growth has a strong peak in December.

Integrating Ocean Subsurface Temperatures in Statistical ENSO Forecasts

Abstract Subsurface characteristics of oceans have recently become of interest to climate modelers. Here subsurface information has been linked to the evolution of the El Niño–Southern Oscillation (ENSO) in a simple statistical formulation. The hypothesis proposed is that the inclusion of subsurface ocean heat content in a persistence-based representation of ENSO results in an increase in prediction skill. The subsurface temperature field is represented by anomalies in the 20°C isotherm (Z20) in the Indian and Pacific Oceans. Using a cross-validation approach, the first two empirical orthogonal functions (EOFs) of the Z20 anomalies are derived, but only the second EOF is used as a predictor. The first EOF is found to be representative of the mature ENSO signal while the second EOF shows characteristics that are precursory to an ENSO event. When included in a persistence-based prediction scheme, the second EOF enhances the skill of ENSO hindcasts up to a lead time of 15 months. Results are compared with another model that uses the second EOF of the SST anomalies in the tropical Pacific Ocean and persistence as predictors. Cross-validated hindcasts from the isotherm-based scheme are generally more skillful than those obtained from the persistence and SST-based prediction schemes. Hindcasts of cold events are particularly close to the observed values even at long lags. Major improvements occur for predictions made during boreal winter and spring months when the addition of subsurface information resulted in predictions that are not greatly affected by the damping effect of the “spring barrier.”

Did the ECMWF Seasonal Forecast Model Outperform Statistical ENSO Forecast Models over the Last 15 Years?

Abstract The European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts since 1997 with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification. Seasonal predictability is to a large extent due to the El Niño–Southern Oscillation (ENSO) climate oscillations. ENSO predictions of the ECMWF models are compared with those of statistical models, some of which are used operationally. The relative skill depends strongly on the season. The dynamical models are better at forecasting the onset of El Niño or La Niña in boreal spring to summer. The statistical models are comparable at predicting the evolution of an event in boreal fall and winter.

Multi‐decadal variability of drought risk, eastern Australia

AbstractA number of previous studies have identified changes in the climate occurring on decadal to multi‐decadal time‐scales. Recent studies also have revealed multi‐decadal variability in the modulation of the magnitude of El Niño–Southern Oscillation (ENSO) impacts on rainfall and stream flow in Australia and other areas. This study investigates multi‐decadal variability of drought risk by analysing the performance of a water storage reservoir in New South Wales, Australia, during different climate epochs defined using the Inter‐decadal Pacific Oscillation (IPO) index. The performance of the reservoir is also analysed under three adaptive management techniques and these are compared with the reservoir performance using the current ‘reactive’ management practices. The results indicate that IPO modulation of both the magnitude and frequency of ENSO events has the effect of reducing and elevating drought risk on multi‐decadal time‐scales. The results also confirm that adaptive reservoir management techniques, based on ENSO forecasts, can improve drought security and become significantly more important during dry climate epochs. These results have marked implications for improving drought security for water storage reservoirs. Copyright © 2004 John Wiley & Sons, Ltd.

Combining ENSO Forecasts: A Feasibility Study

Numerical and statistical predictions of simplified models are linearly combined in a sensitivity study to identify the crucial parameters that are needed to enhance predictability of the El Niño–Southern Oscillation (ENSO) phenomenon. The results indicate that the ENSO prediction skill of the simplified models can be improved. The profit achieved strongly depends on the phase information that is utilized by the forecast combination and is inherent in predictions of a quasi-periodic process such as ENSO. The simplest forecast combination that still yields useful forecasts at longer lead times of about half of the ENSO period (18–24 months) is the combination of two persistence forecast schemes. For the prediction period 1982–2003, that is the persistence of a sea surface temperature anomaly (SSTA) index area at 60°S, 180°W and the Niño-3 index SSTA. The level of skill improvement critically depends on the prediction schemes and prediction period, as well as on the period from which the combination weights are derived. Differences between combination forecast and hindcast are minimized if the statistical weights are derived from a time period that is characterized by an ENSO statistic that is close to the prediction period. In this study the prediction period has simply been halved for the sake of simplicity to derive the statistical weights, which is sufficient for predicting the 1980s and 1990s (with each other), but not for predicting, for example, the 1970s. The suppressed 1976/77 El Niño event makes the periodic occurrence less regular compared to the other decades. However, this forecast combination technique can be applied in a much more elaborate way.

Evaluating Observing Requirements for ENSO Prediction: Experiments with an Intermediate Coupled Model

The Tropical Atmosphere Ocean (TAO) array of moored buoys in the tropical Pacific Ocean is a major source of data for understanding and predicting the El Niño–Southern Oscillation (ENSO). Despite the importance of the TAO array, limited work has been performed to date on the number and locations of observations required to predict ENSO effectively. To address this issue, this study performs a series of observing system simulation experiments (OSSEs) with a linearized intermediate coupled ENSO model, stochastically forced. ENSO forecasts are simulated for a number of observing network configurations, and forecast skill averaged over 1000 years of simulated ENSO events is compared. The experiments demonstrate that an OSSE framework can be used with a linear, stochastically forced ENSO model to provide useful information about requirements for ENSO prediction. To the extent that the simplified model dynamics represent ENSO dynamics accurately, the experiments also suggest which types of observations in which regions are most important for ENSO prediction. The results indicate that, using this model and experimental setup, subsurface ocean observations are relatively unimportant for ENSO prediction when good information about sea surface temperature (SST) is available; adding subsurface observations primarily improves forecasts initialized in late summer. For short lead-time (1–2 month) forecasts, observations within approximately 3° of the equator are most important for skillful forecasts, while for longer lead-time forecasts, forecast skill is increased by including information at higher latitudes. For forecasts longer than a few months, the most important region for observations is the eastern equatorial Pacific, south of the equator; a secondary region of importance is the western equatorial Pacific. These regions correspond to those where the leading singular vector for the ENSO model has a large amplitude. In a continuation of this study, these results will be used to develop efficient observing networks for forecasting ENSO in this system.

The El Niño southern oscillation and malaria epidemics in South America.

A better understanding of the relationship between the El Niño Southern Oscillation (ENSO), the climatic anomalies it engenders, and malaria epidemics could help mitigate the world-wide increase in incidence of this mosquito-transmitted disease. The purpose of this paper is to assess the possibility of using ENSO forecasts for improving malaria control. This paper analyses the relationship between ENSO events and malaria epidemics in a number of South American countries (Colombia, Ecuador, French Guiana, Guyana, Peru, Suriname, and Venezuela). A statistically significant relationship was found between El Niño and malaria epidemics in Colombia, Guyana, Peru, and Venezuela. We demonstrate that flooding engenders malaria epidemics in the dry coastal region of northern Peru, while droughts favor the development of epidemics in Colombia and Guyana, and epidemics lag a drought by 1 year in Venezuela. In Brazil, French Guiana, and Ecuador, where we did not detect an ENSO/malaria signal, non-climatic factors such as insecticide sprayings, variation in availability of anti-malaria drugs, and population migration are likely to play a stronger role in malaria epidemics than ENSO-generated climatic anomalies. In some South American countries, El Niño forecasts show strong potential for informing public health efforts to control malaria.

A linked-modeling framework to estimate maize production risk associated with ENSO-related climate variability in Argentina

A risk assessment framework is presented to characterize the vulnerability of agricultural production systems to El Niño-southern oscillation (ENSO)-related climate variability. The framework was applied to current maize production systems in two locations (Pergamino and Pilar) in the Pampas of central-eastern Argentina. Climatic, agronomic, and economic models were linked to produce probability distributions of farm-level yields and net returns by ENSO phase. Generally, an enhanced chance of higher (lower) simulated maize yields existed during warm (cold) ENSO events. However, regional differences existed: the effect of warm events on yields was more marked in Pilar, but Pergamino showed a proportionally stronger response to cold events. The modeling framework allowed the exploration of outcomes of high and low scenarios of soil water availability at planting time and ENSO phase. High initial soil water availability in Pilar offset increased yield risks from dry conditions associated with cold ENSO events. Fluctuations of output prices were shown to have considerable influence on the risk associated with ENSO-related climate variability. Despite these general results, there was considerable overlap in yields and net returns for the various ENSO phases. This overlap has significant implications for the adoption of ENSO forecasts in agriculture. The risk assessment framework developed here is a necessary precursor to risk management studies that prescribe or describe possible responses to expected climate scenarios.

Hydrological implications of the Southern Oscillation: variability of the rainfall-runoff relationship

A connection between El Niño Southern Oscillation (ENSO) and weather phenomena in eastern Australia has been recognized for several decades. However, little work has been devoted to addressing how this correlation affects hydrological system behaviour within regional-scale catchments. In this study, spatially distributed ENSO effects are evaluated in terms of monthly rainfall, evaporation, streamflow and runoff characteristics for a 1300 km2 catchment. The catchment is located in southeastern Australia where previous studies have indicated only modest ENSO influences on rainfall variability. Spatial and temporal analysis indicates that strongest ENSO-induced rainfall variability occurs during summer months. Additionally, the strength of the relationship is variable in space indicating that topographic controls may affect ENSO influences on rainfall totals and intensities. However, analysis of runoff shows substantially magnified ENSO-induced variability in comparison to the induced variability in rainfall. This may be attributable to the nonlinearity of runoff generation. Differences in antecedent moisture storage conditions will exist but may also be enhanced by complementary ENSO influences on daily rainfall intensities and mean monthly evaporation and temperature totals. The degree of the nonlinearity displayed by the hydrometeorological processes presented demonstrates that the significance of ENSO forecasts for surface water resource management should be assessed with direct regard to streamflow generation rather than on the basis of rainfall totals alone.

The singular vectors of a coupled ocean-atmosphere model of ENSO. I: Thermodynamics, energetics and error growth

The singular vectors of a dynamical system are the most rapidly growing perturbations that can exist in the system before nonlinearity becomes important. We have studied the singular vectors of an intermediate coupled ocean-atmosphere model of El Niño/Southern Oscillation (ENSO) in an effort to understand the dynamics responsible for the growth of small perturbations in the tropics. In particular, we have examined how the singular vectors of the observed seasonal cycle are influenced by various thermodynamic processes which operate in the upper-ocean mixed-layer, and which affect sea-surface temperature (SST). These processes include vertical movements of the main thermocline, zonal advection and vertical upwelling. the main findings are: the singular vector spectrum is dominated by the fastest growing member, regardless of which thermodynamic processes are active; the growth factors of the singular vectors exhibit a strong seasonal dependence; the western and central parts of the Pacific Ocean are the areas most often favoured for growth by the singular vectors, and the general criteria that must be met for singular-vector growth have been determined; at a given time of year, the growth factors of the singular vectors are sensitive to different combinations of thermodynamic processes, although the configuration of the dominant singular-vector wind field after optimal growth has been achieved is very similar in all cases. This suggests that in the tropics, the coupled system has a preferred response that the ocean thermodynamics controlling SST conspire to produce, regardless of which processes are operating in the mixed layer; the observed ENSO cycle is likely to influence singular-vector growth significantly. These results and ideas have important ramifications for the growth of errors and uncertainties in models during ENSO forecasts, and for the way this influences the predictability of ENSO. They also have important ramifications for the way perturbations grow in the real coupled ocean-atmosphere system.

The singular vectors of a coupled ocean‐atmosphere model of Enso. I: Thermodynamics, energetics and error growth

AbstractThe singular vectors of a dynamical system are the most rapidly growing perturbations that can exist in the system before nonlinearity becomes important. We have studied the singular vectors of an intermediate coupled ocean‐atmosphere model of El Niño/Southern Oscillation (ENSO) in an effort to understand the dynamics responsible for the growth of small perturbations in the tropics. In particular, we have examined how the singular vectors of the observed seasonal cycle are influenced by various thermodynamic processes which operate in the upper‐ocean mixed‐layer, and which affect sea‐surface temperature (SST). These processes include vertical movements of the main thermocline, zonal advection and vertical upwelling. the main findings are: the singular vector spectrum is dominated by the fastest growing member, regardless of which thermodynamic processes are active; the growth factors of the singular vectors exhibit a strong seasonal dependence; the western and central parts of the Pacific Ocean are the areas most often favoured for growth by the singular vectors, and the general criteria that must be met for singular‐vector growth have been determined; at a given time of year, the growth factors of the singular vectors are sensitive to different combinations of thermodynamic processes, although the configuration of the dominant singular‐vector wind field after optimal growth has been achieved is very similar in all cases. This suggests that in the tropics, the coupled system has a preferred response that the ocean thermodynamics controlling SST conspire to produce, regardless of which processes are operating in the mixed layer; the observed ENSO cycle is likely to influence singular‐vector growth significantly. These results and ideas have important ramifications for the growth of errors and uncertainties in models during ENSO forecasts, and for the way this influences the predictability of ENSO. They also have important ramifications for the way perturbations grow in the real coupled ocean‐atmosphere system.

VALUE OF IMPROVED LONG‐RANGE WEATHER INFORMATION

An important human welfare implication of climate involves effects of interannual variation in temperature and precipitation on agriculture. Year‐to‐year variations in U.S. climate result from El Niño‐Southern Oscillation (ENSO), a quasi‐periodic redistribution of heat and momentum in the tropical Pacific Ocean. The study described here represents a preliminary assessment of the value to the entire U.S. agricultural sector of improved ENSO forecasts in the southeastern United States. This interdisciplinary assessment combines data and models from meteorology, plant sciences, and economics under a value of information framework based on Bayesian decision theory. An economic model of the U.S. agricultural sector uses changes in yields for various ENSO phases to translate physical (yield) effects of ENSO changes into economic effects on producers and on domestic and foreign consumers. The value of perfect information to agriculture is approximately $145 million. The economic value of an imperfect forecast is $96 million. These results suggest that increases in forecast accuracy have substantial economic value to agriculture.