Development Of A European Multimodel Ensemble System For Seasonal-to-Interannual Prediction Research Articles

Has the prediction of the South China Sea summer monsoon improved since the late 1970s?

Based on the evaluation of state-of-the-art coupled ocean–atmosphere general circulation models (CGCMs) from the ENSEMBLES (Ensemble-based Predictions of Climate Changes and Their Impacts) and DEMETER (Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction) projects, it is found that the prediction of the South China Sea summer monsoon (SCSSM) has improved since the late 1970s. These CGCMs show better skills in prediction of the atmospheric circulation and precipitation within the SCSSM domain during 1979–2005 than that during 1960–1978. Possible reasons for this improvement are investigated. First, the relationship between the SSTs over the tropical Pacific, North Pacific and tropical Indian Ocean, and SCSSM has intensified since the late 1970s. Meanwhile, the SCSSM-related SSTs, with their larger amplitude of interannual variability, have been better predicted. Moreover, the larger amplitude of the interannual variability of the SCSSM and improved initializations for CGCMs after the late 1970s contribute to the better prediction of the SCSSM. In addition, considering that the CGCMs have certain limitations in SCSSM rainfall prediction, we applied the year-to-year increment approach to these CGCMs from the DEMETER and ENSEMBLES projects to improve the prediction of SCSSM rainfall before and after the late 1970s.

An Intensity Index for the East Asian Winter Monsoon

AbstractThe thermal contrast between the Asian continent and the adjacent oceans is the primary aspect of the East Asian winter monsoon (EAWM) that can be well represented in the sea level pressure (SLP) field. Based on this consideration, a new SLP-based index measuring the intensity of the EAWM is proposed by explicitly taking into account both the east–west and the north–south pressure gradients around East Asia. The new index can delineate the EAWM-related circulation anomalies well, including the deepened (shallow) midtropospheric East Asian trough, sharpened and accelerated (widened and decelerated) upper-tropospheric East Asian jet stream, and enhanced (weakened) lower-tropospheric northerly winds in strong (weak) EAWM winters. Compared with previous indices, the new index has a very good performance describing the winter-mean surface air temperature variations over East Asia, especially for the extreme warm or cold winters. The index is strongly correlated with several atmospheric teleconnections including the Arctic Oscillation, the Eurasian pattern, and the North Pacific Oscillation/western Pacific pattern, implying the possible internal dynamics of the EAWM variability. Meanwhile, the index is significantly linked to El Niño–Southern Oscillation (ENSO) and the sea surface temperature (SST) over the tropical Indian Ocean. Moreover, the SST anomalies over the tropical Indian Ocean are more closely related to the index than ENSO as an independent predictor. This adds further knowledge to the prediction potentials of the EAWM apart from ENSO. The predictability of the index is high in the hindcasts of the Centre National de Recherches Météorologiques (CNRM) model from Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER). Hence, it would be a good choice to use this index for the monitoring, prediction, and research of the EAWM.

An application of hybrid downscaling model to forecast summer precipitation at stations in China

A pattern prediction hybrid downscaling method was applied to predict summer (June–July–August) precipitation at China 160 stations. The predicted precipitation from the downscaling scheme is available one month before. Four predictors were chosen to establish the hybrid downscaling scheme. The 500-hPa geopotential height (GH5) and 850-hPa specific humidity (q85) were from the skillful predicted output of three DEMETER (Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction) general circulation models (GCMs). The 700-hPa geopotential height (GH7) and sea level pressure (SLP) were from reanalysis datasets. The hybrid downscaling scheme (HD-4P) has better prediction skill than a conventional statistical downscaling model (SD-2P) which contains two predictors derived from the output of GCMs, although two downscaling schemes were performed to improve the seasonal prediction of summer rainfall in comparison with the original output of the DEMETER GCMs. In particular, HD-4P downscaling predictions showed lower root mean square errors than those based on the SD-2P model. Furthermore, the HD-4P downscaling model reproduced the China summer precipitation anomaly centers more accurately than the scenario of the SD-2P model in 1998. A hybrid downscaling prediction should be effective to improve the prediction skill of summer rainfall at stations in China.

PNA Predictability at Various Time Scales

AbstractIn this study, the predictability of the Pacific–North American (PNA) pattern is evaluated on time scales from days to months using state-of-the-art dynamical multiple-model ensembles including the Canadian Historical Forecast Project (HFP2) ensemble, the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) ensemble, and the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES). Some interesting findings in this study include (i) multiple-model ensemble (MME) skill was better than most of the individual models; (ii) both actual prediction skill and potential predictability increased as the averaging time scale increased from days to months; (iii) there is no significant difference in actual skill between coupled and uncoupled models, in contrast with the potential predictability where coupled models performed better than uncoupled models; (iv) relative entropy (REA) is an effective measure in characterizing the potential predictability of individual prediction, whereas the mutual information (MI) is a reliable indicator of overall prediction skill; and (v) compared with conventional potential predictability measures of the signal-to-noise ratio, the MI-based measures characterized more potential predictability when the ensemble spread varied over initial conditions.Further analysis found that the signal component dominated the dispersion component in REA for PNA potential predictability from days to seasons. Also, the PNA predictability is highly related to the signal of the tropical sea surface temperature (SST), and SST–PNA correlation patterns resemble the typical ENSO structure, suggesting that ENSO is the main source of PNA seasonal predictability. The predictable component analysis (PrCA) of atmospheric variability further confirmed the above conclusion; that is, PNA is one of the most predictable patterns in the climate variability over the Northern Hemisphere, which originates mainly from the ENSO forcing.

Is the Interannual Variability of the Summer Asian–Pacific Oscillation Predictable?

Abstract The summer (June–August) Asian–Pacific Oscillation (APO) measures the interannual variability of large-scale atmospheric circulation over the Asian–North Pacific Ocean sector. In this study, the authors assess the predictability of the summer APO index interannual variability and the associated atmospheric circulation anomalies using the 1959–2001 hindcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Centre National de Recherches Météorologiques (CNRM), and the Met Office (UKMO) general circulation models from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project. The results show that these models predict the summer APO index interannual variability well and have higher skill for the North Pacific than for the Asian upper-tropospheric temperature. Meanwhile, the observed APO-related atmospheric circulation anomalies in the South Asian high, the tropical easterly wind jet over the Asian monsoon region in the upper troposphere, the subtropical anticyclone over the North Pacific, and the summer southwest monsoon over Asia in the lower troposphere are reasonably well predicted in their spatial patterns and intensities. Compared with the observations, however, these models display low skill in predicting the long-term varying trends of the upper-tropospheric temperature over the Asian–North Pacific sector or the APO index during 1959–2001.

Improving the Prediction of the East Asian Summer Monsoon: New Approaches

Abstract East Asian summer monsoon (EASM) prediction is difficult because of the summer monsoon’s weak and unstable linkage with El Niño–Southern Oscillation (ENSO) interdecadal variability and its complicated association with high-latitude processes. Two statistical prediction schemes were developed to include the interannual increment approach to improve the seasonal prediction of the EASM’s strength. The schemes were applied to three models [i.e., the Centre National de Recherches Météorologiques (CNRM), the Met Office (UKMO), and the European Centre for Medium-Range Weather Forecasts (ECMWF)] and the Multimodel Ensemble (MME) from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) results for 1961–2001. The inability of the three dynamical models to reproduce the weakened East Asian monsoon at the end of the 1970s leads to low prediction ability for the interannual variability of the EASM. Therefore, the interannual increment prediction approach was applied to overcome this issue. Scheme I contained the EASM in the form of year-to-year increments as a predictor that is derived from the direct outputs of the models. Scheme II contained two predictors: both the EASM and also the western North Pacific circulation in the form of year-to-year increments. Both the cross-validation test and the independent hindcast experiments showed that the two prediction schemes have a much better prediction ability for the EASM than does the original scheme. This study provides an efficient approach for predicting the EASM.

Systematic Comparison of ENSO Teleconnection Patterns between Models and Observations

Abstract This paper applies a new field significance test to establish the existence and consistency of ENSO teleconnection patterns across models and observations. An ENSO teleconnection pattern is defined as a field of regression coefficients between an index of the tropical Pacific sea surface temperature and a field of variables such as surface air temperature or precipitation. The test is applied to boreal winter and summer in six continents using observations and hindcasts from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) and the ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) projects. This comparison represents one of the most comprehensive and up-to-date assessments of the extent to which ENSO teleconnection patterns exist and can be reproduced by coupled models. Statistically significant ENSO teleconnection patterns are detected in both observations and models and in all continents and in both winter and summer seasons, except in two cases: 1) Europe (both seasons and variables), and 2) North America (both variables in boreal summer). Despite many ENSO teleconnection patterns being significant, however, the patterns do not necessarily agree between observations and models. The degree of agreement between models and observations is characterized as “robust,” “moderate,” or “low.” Only Australia and South America are found to have robust agreement between ENSO teleconnection patterns, and then only for limited seasons and variables. Although many of our conclusions regarding teleconnection patterns conform to previous studies, there are exceptions, including the fact that the teleconnection for boreal winter precipitation is generally accepted to exist in Africa but in fact has only low agreement with model simulations, while that in Asia is not widely recognized to exist but is found to be significant and in moderate agreement with model teleconnections.

The Skill of Seasonal Ensemble Prediction Systems to Forecast Wintertime Windstorm Frequency over the North Atlantic and Europe

The science of seasonal predictions has advanced considerably in the last decade. Today, operational predictions are generated by several institutions, especially for variables such as (sea) surface temperatures and precipitation. In contrast, few studies have been conducted on the seasonal predictability of extreme meteorological events such as European windstorms in winter. In this study, the predictive skill of extratropical wintertime windstorms in the North Atlantic/European region is explored in sets of seasonal hindcast ensembles from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) and the ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) projects.The observed temporal and spatial climatological distributions of these windstorms are reasonably well reproduced in the hindcast data. Using hindcasts starting on 1 November, significant predictive skill is found for the December–February windstorm frequency in the period 1980–2001, but also for the January–April storm frequency. Specifically, the model suite run at Météo France shows consistently high skill.Some aspects of the variability of skill are discussed. Predictive skill in the 1980–2001 period is usually higher than for the 1960–2001 period. Furthermore, the level of skill turns out to be related to the storm frequency of a given winter. Generally, winters with high storm frequency are better predicted than winters with medium storm frequency. Physical mechanisms potentially leading to such a variability of skill are discussed.

Evaluation of Probabilistic Quality and Value of the ENSEMBLES Multimodel Seasonal Forecasts: Comparison with DEMETER

Abstract The performance of the new multimodel seasonal prediction system developed in the framework of the European Commission FP7 project called ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) is compared with the results from the previous project [i.e., Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER)]. The comparison is carried out over the five seasonal prediction systems (SPSs) that participated in both projects. Since DEMETER, the contributing SPSs have improved in all aspects with the main advancements including the increase in resolution, the better representation of subgrid physical processes, land, sea ice, and greenhouse gas boundary forcing, and the more widespread use of assimilation for ocean initialization. The ENSEMBLES results show an overall enhancement for the prediction of anomalous surface temperature conditions. However, the improvement is quite small and with considerable space–time variations. In the tropics, ENSEMBLES systematically improves the sharpness and the discrimination attributes of the forecasts. Enhancements of the ENSEMBLES resolution attribute are also reported in the tropics for the forecasts started 1 February, 1 May, and 1 November. Our results indicate that, in ENSEMBLES, an increased portion of prediction signal from the single-models effectively contributes to amplify the multimodel forecasts skill. On the other hand, a worsening is shown for the multimodel calibration over the tropics compared to DEMETER. Significant changes are also shown in northern midlatitudes, where the ENSEMBLES multimodel discrimination, resolution, and reliability improve for February, May, and November starting dates. However, the ENSEMBLES multimodel decreases the capability to amplify the performance with respect to the contributing single models for the forecasts started in February, May, and August. This is at least partly due to the reduced overconfidence of the ENSEMBLES single models with respect to the DEMETER counterparts. Provided that they are suitably calibrated beforehand, it is shown that the ENSEMBLES multimodel forecasts represent a step forward for the potential economical value they can supply. A warning for all potential users concerns the need for calibration due to the degraded tropical reliability compared to DEMETER. In addition, the superiority of recalibrating the ENSEMBLES predictions through the discrimination information is shown. Concerning the forecasts started in August, ENSEMBLES exhibits mixed results over both tropics and northern midlatitudes. In this case, the increased potential predictability compared to DEMETER appears to be balanced by the reduction in the independence of the SPSs contributing to ENSEMBLES. Consequently, for the August start dates no clear advantage of using one multimodel system instead of the other can be evidenced.

Application and Validation of a Seasonal Ensemble Prediction System Using a Dynamic Malaria Model

Abstract Seasonal multimodel forecasts from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project are used to drive a malaria model and create reforecasts of malaria incidence for Botswana, in southern Africa, in a unique integration of a fully dynamic, process-based malaria model with an ensemble forecasting system. The forecasts are verified against a 20-yr malaria index and compared against reference simulations obtained by driving the malaria model with data from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). Performance assessment reveals skill in the DEMETER-driven malaria forecasts for prediction of low (below the lower tercile), above-average (above the median), and high (above the upper tercile) malaria events, with the best results obtained for low malaria events [relative operating characteristics (ROC) area = 0.84, 95% confidence interval = 0.63–1.0]. For high malaria events, the DEMETER-driven malaria forecasts are skillful, but the forecasting system performs poorly for those years that it predicts the highest probabilities of a high malaria event. Potential economic value analysis demonstrates the potential value for the DEMETER-driven malaria forecasts over a wide range of user cost-loss ratios, which is primarily due to the ability of the system to save on the cost of action in low malaria years.

Relationships between Upper-Level Circulation over South America and Rainfall over Southeastern South America: A Physical Base for Seasonal Predictions

Abstract The existence of a significant simultaneous correlation between bimonthly mean precipitation anomalies over southeastern South America (SESA) and either the first or the second (depending on season) leading mode of interannual variability of upper-level wind over South America (SA) is demonstrated during all seasons except winter. The pattern associated with these modes of variability is similar during all seasons and consists of a continental-scale vortex centered over the eastern coast of subtropical SA. The vortex has a quasi-barotropic structure during all seasons, and its variability modifies moisture transport from the South American low-level jet and the western tropical Atlantic to SESA thus creating precipitation anomalies in this region. During spring (October–November) and summer (January–February) the circulation creates a second center of precipitation anomalies over the South Atlantic convergence zone that are of opposite sign to those over SESA, while during fall (April–May) precipitation anomalies are primarily confined to SESA. On the basis of the correlation between upper-level winds and precipitation, an empirical method to produce long-range forecasts of bimonthly mean precipitation over SESA is developed. Method tests in hindcast mode for the period 1959–2001 show a potential for reliable predictions during the southern spring, summer, and fall. The method is further tested in an experimental mode by using Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) wind hindcasts. Forecasts obtained in this way are skillful during spring only, with highest skill during El Niño–Southern Oscillation years. During summer and fall, the DEMETER forecasts of wind anomalies limit the method’s ability to make reliable real predictions.

Assessing the Skill of Precipitation and Temperature Seasonal Forecasts in Spain: Windows of Opportunity Related to ENSO Events

Abstract The skill of state-of-the-art operational seasonal forecast models in extratropical latitudes is assessed using a multimodel ensemble from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project. In particular, probabilistic forecasts of surface precipitation and maximum temperature in Spain are analyzed using a high-resolution observation gridded dataset (Spain02). To this aim, a simple statistical test based on the observed and predicted tercile anomalies is used. First, the whole period 1960–2000 is considered and it is shown that the only significant skill is found for dry events in autumn. Then, the influence of ENSO events as a potential source of conditional predictability is studied and the validation to strong La Niña or El Niño periods is restricted. Skillful seasonal predictions are found in partial agreement with the observed teleconnections derived from the historical records. On the one hand, predictability is found in spring related to El Niño events for dry events over the south and the Mediterranean coast and for hot events in the southeast areas. In contrast, La Niña drives predictability in winter for dry events over the western part and for hot events in summer over the south and the Mediterranean coast. This study considers both the direct model outputs and the postprocessed predictions obtained using a statistical downscaling method based on analogs. In general, the use of the downscaling method outperforms the direct output for precipitation, whereas in the case of the temperature no improvement is obtained.

Estimating the Potential Economic Value of Seasonal Forecasts in West Africa: A Long-Term Ex-Ante Assessment in Senegal

Abstract Recent improvements in the capability of statistical or dynamic models to predict climate fluctuations several months in advance may be an opportunity to improve the management of climatic risk in rain-fed agriculture. The aim of this paper is to evaluate the potential benefits that seasonal climate predictions can bring to farmers in West Africa. The authors have developed an archetypal bioeconomic model of a smallholder farm in Nioro du Rip, a semiarid region of Senegal. The model is used to simulate the decisions of farmers who have access to a priori information on the quality of the next rainy season. First, the potential economic benefits of a perfect rainfall prediction scheme are evaluated, showing how these benefits are affected by forecast accuracy. Then, the potential benefits of several widely used rainfall prediction schemes are evaluated: one group of schemes based on the statistical relationship between rainfall and sea surface temperatures, and one group based on the predictions of coupled ocean–atmosphere models. The results show that forecasting a dryer than average rainy season would be the most useful to Nioro du Rip farmers if they interpret forecasts as deterministic. Indeed, because forecasts are imperfect, predicting a wetter than average rainy season exposes the farmers to a high risk of failure by favoring cash crops such as maize and peanut that are highly vulnerable to drought. On the other hand, the farmers’ response to a forecast of a dryer than average rainy season minimizes the climate risk by favoring robust crops such as millet and sorghum, which will tolerate higher rainfall in case the forecast is wrong. When either statistical or dynamic climate models are used for forecasting under the same lead time and the same 31-yr hindcast period (i.e., 1970–2000), similar skill and economic values at farm level are found. When a dryer than average rainy season is predicted, both methods yield an increase of the farmers’ income—13.8% for the statistical model and 9.6% for the bias-corrected Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel ensemble mean.

On the Weighting of Multimodel Ensembles in Seasonal and Short-Range Weather Forecasting

Abstract The performance of multimodel ensemble forecasting depends on the weights given to the different models of the ensemble in the postprocessing of the direct model forecasts. This paper compares the following different weighting methods with or without taking into account the single-model performance: equal weighting of models (EW), simple skill-based weighting (SW), using a simple model performance indicator, and weighting by Bayesian model averaging (BMA). These methods are tested for both short-range weather and seasonal temperature forecasts. The prototype seasonal multimodel ensemble is the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) system, with four different models and nine forecasts per model. The short-range multimodel prototype system is the European Meteorological Services (EUMETNET) Poor-Man’s Ensemble Prediction System (PEPS), with 14 models and one forecast per model. It is shown that despite the different forecast ranges and spatial scales, the impact of weighting is comparable for both forecast systems and is related to the same ensemble characteristics. In both cases the added value of ensemble forecasting over single-model forecasting increases considerably with the decreasing correlation of the models’ forecast errors, with a relation depending only on the number of models. Also, in both cases a larger spread in model performance increases the added value of combining model forecasts using the performance-based SW or BMA weighting instead of EW. Finally, the more complex BMA weighting adds value over SW only if the best model performs better than the ensemble with EW weighting.

Indian summer monsoon rainfall variability in global coupled ocean-atmospheric models

Performance of seven fully coupled models in simulating Indian summer monsoon climatology as well as the inter-annual variability was assessed using multi member 1 month lead hindcasts made by several European climate groups as part of the program called Development of a European multi-model ensemble system for seasonal-to-inter-annual prediction (DEMETER). Dependency of the model simulated Indian summer monsoon rainfall and global sea surface temperatures on model formulation and initial conditions have been studied in detail using the nine ensemble member simulations of the seven different coupled ocean–atmosphere models participated in the DEMETER program. It was found that the skills of the monsoon predictions in these hindcasts are generally positive though they are very modest. Model simulations of India summer monsoon rainfall for the earlier period (1959–1979) are closer to the ‘perfect model’ (attainable) score but, large differences are observed between ‘actual’ skill and ‘perfect model’ skill in the recent period (1980–2001). Spread among the ensemble members are found to be large in simulations of India summer monsoon rainfall (ISMR) and Indian ocean dipole mode (IODM), indicating strong dependency of model simulated Indian summer monsoon on initial conditions. Multi-model ensemble performs better than the individual models in simulating ENSO indices, but does not perform better than the individual models in simulating ISMR and IODM. Decreased skill of multi-model ensemble over the region indicates amplification of errors due to existence of similar errors in the individual models. It appears that large biases in predicted SSTs over Indian Ocean region and the not so perfect ENSO-monsoon (IODM-monsoon) tele-connections are some of the possible reasons for such lower than expected skills in the recent period. The low skill of multi-model ensemble, large spread among the ensemble members of individual models and the not so perfect monsoon tele-connection with global SSTs points towards the importance of improving individual models for better simulation of the Indian monsoon.

A New Way to Improve Seasonal Prediction by Diagnosing and Correcting the Intermodel Systematic Errors

Abstract Seasonal climate prediction, in general, can achieve excellent results with a multimodel system. A relevant calibration of individual models and an optimal combination of individual models are the key elements leading to this success. However, this commonly used approach appears to be insufficient to remove the intermodel systematic errors (IMSE), which represent similar error properties in individual models after their calibration. A new postprocessing method is proposed to correct the IMSE and to increase the prediction skill. The first step consists of carrying out a diagnosis on the calibrated errors before constructing the multimodel ensemble. In contrast to previous studies, the calibrated errors here are treated directly as the investigation target, and temporal correlation coefficients between the calibrated errors and other meteorological variables are calculated. In the second stage, mathematical and statistical tools are applied in an effort to forecast the IMSE in individual models. Then, the IMSE are removed from the calibrated results and the new corrected data are used to construct the multimodel ensemble. The hindcast of the European Union–funded Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel system is used to test the method. The simulated Southern Oscillation index is used to diagnose and to correct the calibrated errors of the simulated precipitation. The prediction qualities of the corrected data are assessed and compared with those of the uncorrected dataset. The results show that it is feasible to improve seasonal precipitation prediction by forecasting and correcting the IMSE. This improvement is visible not only for the individual models, but also for the multimodel ensemble.

Seasonal Ensemble Forecasts: Are Recalibrated Single Models Better than Multimodels?

Abstract Multimodel ensemble combination (MMEC) has become an accepted technique to improve probabilistic forecasts from short- to long-range time scales. MMEC techniques typically widen ensemble spread, thus improving the dispersion characteristics and the reliability of the forecasts. This raises the question as to whether the same effect could be achieved in a potentially cheaper way by rescaling single model ensemble forecasts a posteriori such that they become reliable. In this study a climate conserving recalibration (CCR) technique is derived and compared with MMEC. With a simple stochastic toy model it is shown that both CCR and MMEC successfully improve forecast reliability. The difference between these two methods is that CCR conserves resolution but inevitably dilutes the potentially predictable signal while MMEC is in the ideal case able to fully retain the predictable signal and to improve resolution. Therefore, MMEC is conceptually to be preferred, particularly since the effect of CCR depends on the length of the data record and on distributional assumptions. In reality, however, multimodels consist only of a finite number of participating single models, and the model errors are often correlated. Under such conditions, and depending on the skill metric applied, CCR-corrected single models can on average have comparable skill as multimodel ensembles, particularly when the potential model predictability is low. Using seasonal near-surface temperature and precipitation forecasts of three models of the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) dataset, it is shown that the conclusions drawn from the toy-model experiments hold equally in a real multimodel ensemble prediction system. All in all, it is not possible to make a general statement on whether CCR or MMEC is the better method. Rather it seems that optimum forecasts can be obtained by a combination of both methods, but only if first MMEC and then CCR is applied. The opposite order—first CCR, then MMEC—is shown to be of only little effect, at least in the context of seasonal forecasts.

Consolidation of Multimodel Forecasts by Ridge Regression: Application to Pacific Sea Surface Temperature

AbstractThe performance of ridge regression methods for consolidation of multiple seasonal ensemble prediction systems is analyzed. The methods are applied to predict SST in the tropical Pacific based on ensembles from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) models, plus two of NCEP’s operational models. Strategies to increase the ratio of the effective sample size of the training data to the number of coefficients to be fitted are proposed and tested. These strategies include objective selection of a smaller subset of models, pooling of information from neighboring grid points, and consolidating all ensemble members rather than each model’s ensemble average. In all variations of the ridge regression consolidation methods tested, increased effective sample size produces more stable weights and more skillful predictions on independent data. While the scores may not increase significantly as the effective sampling size is increased, the benefit is seen in terms of consistent improvements over the simple equal weight ensemble average. In the western tropical Pacific, most consolidation methods tested outperform the simple equal weight ensemble average; in other regions they have similar skill as measured by both the anomaly correlation and the relative operating curve. The main obstacles to progress are a short period of data and a lack of independent information among models.

Skill of Multimodel ENSO Probability Forecasts

The cross-validated hindcast skills of various multimodel ensemble combination strategies are compared for probabilistic predictions of monthly SST anomalies in the ENSO-related Niño-3.4 region of the tropical Pacific Ocean. Forecast data from seven individual models of the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project are used, spanning the 22-yr period of 1980–2001. Skill of the probabilistic forecasts is measured using the ranked probability skill score and rate of return, the latter being an information theory–based measure. Although skill is generally low during boreal summer relative to other times of the year, the advantage of the model forecasts over simple historical frequencies is greatest at this time. Multimodel ensemble predictions, even those using simple combination methods, generally have higher skill than single model predictions, and this advantage is greater than that expected as a result of an increase in ensemble size. Overall, slightly better performance was obtained using combination methods based on individual model skill relative to methods based on the complete joint behavior of the models. This finding is attributed to the comparatively large expected sampling error in the estimation of the relations between model errors based on the short history. A practical conclusion is that, unless some models have grossly low skill relative to the others, and until the history is much longer than two to three decades, equal, independent, or constrained joint weighting are reasonable courses.

Boreal Summer Intraseasonal Variability in Coupled Seasonal Hindcasts

Abstract The intraseasonal variability (ISV) of the Asian summer monsoon represented in seven coupled general circulation models (CGCMs) as part of the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project is analyzed and evaluated against observations. The focus is on the spatial and seasonal variations of ISV of outgoing longwave radiation (OLR). The large-scale organization of convection, the propagation characteristics, and the air–sea coupling related to the monsoon ISV are also evaluated. A multivariate local mode analysis (LMA) reveals that most models produce less organized convection and ISV events of shorter duration than observed. Compared to the real atmosphere, these simulated patterns of perturbations are poorly reproducible from one event to the other. Most models simulate too weak sea surface temperature (SST) perturbations and systematic phase quadrature between OLR, surface winds, and SST—indicative of a slab-ocean-like response of the SST to surface flux perturbations. The relatively coarse vertical resolution of the different ocean GCMs (OGCMs) limits their ability to represent intraseasonal processes, such as diurnal warm layer formation, which are important for realistic simulation of the SST perturbations at intraseasonal time scales. Models with the same atmospheric GCM (AGCM) and different OGCMs tend to have similar biases of the simulated ISV, indicating the dominant role of atmospheric models in fixing the nature of the intraseasonal variability. It is, therefore, implied that improvements in the representation of ISV in coupled models have to fundamentally arise from fixing problems in the large-scale organization of convection in AGCMs.