A multi-model superensemble algorithm for seasonal climate prediction using DEMETER forecasts

Abstract

A B S T R A C TIn this paper, a multi-model ensemble approach with statistical correction for seasonal precipitation forecasts using a coupled DEMETER model data set is presented. Despite the continuous improvement of coupled models, they have serious systematic errors in terms of the mean, the annual cycle and the interannual variability; consequently, the predictive skill of extended forecasts remains quite low. One of the approaches to the improvement of seasonal prediction is the empirical weighted multi-model ensemble, or superensemble, combination. In the superensemble approach, the different model forecasts are statistically combined during the training phase using multiple linear regression, with the skill of each ensemble member implicitly factored into the superensemble forecast. The skill of a superensemble relies strongly on the past performance of the individual member models used in its construction. The algorithm proposed here involves empirical orthogonal function (EOF) filtering of the actual data set prior to the construction of a multimodel ensemble or superensemble as an alternative solution for seasonal prediction. This algorithm generates a new data set from the input multi-model data set by finding a consistent spatial pattern between the observed analysis and the individual model forecast. This procedure is a multiple linear regression problem in the EOF space. The newly generated EOF-filtered data set is then used as an input data set for the construction of a multi-model ensemble and superensemble. The skill of forecast anomalies is assessed using statistics of categorical forecast, spatial anomaly correlation and root mean square (RMS) errors. The various verifications show that the unbiased multi-model ensemble of DEMETER forecasts improves the prediction of spatial patterns (i.e. the anomaly correlation), but it shows poor skill in categorical forecast. Due to the removal of seasonal mean biases of the different models, the forecast errors of the bias-corrected multi-model ensemble and superensemble are already quite small. Based on the anomaly correlation and RMS measures, the forecasts produced by the proposed method slightly outperform the other conventional forecasts.

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

“…In general, the MME prediction is superior to the predictions made by any single-model component for both two-tier systems (Krishnamurti et al 1999(Krishnamurti et al , 2000Palmer et al 2000;Shukla et al 2000;Barnston et al 2003) and one-tier systems Doblas-Reyes et al 2005;Yun et al 2005). A number of international projects have organized multi-model intercomparison and synthesis, among which the most comprehensive projects are the European Union-sponsored ''Development of a European Multi-model Ensemble System for Seasonal to InterAnnual Prediction (DEMETER; Palmer et al 2004) and the Climate Prediction and its Application to Society (CliPAS) project, sponsored by the Asian-Pacific Economic Cooperation (APEC) Climate Center (APCC).…”

“…Similarly, the combination of the worst models may not always yield the lowest skill. Many studies have been carried out to find the optimal combination of MME to improve forecast skill (Krishnamurti et al 1999(Krishnamurti et al , 2000Kang et al 2002;Yun et al 2005;Yoo and Kang 2005;Doblas-Reyes et al 2005;Kug et al 2008). The highest MME skill may be achievable by an optimal choice of a subgroup of models, drawing upon an individual model's skill and the mutual independence among the chosen models (Yoo and Kang 2005).…”

Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall

“…Because the AIR obtained by spatial averaging of the ISMR can increase the correlation relative to the ISMR relation at the grid points (Saha et al, 2006), the forecast quality of AIR interannual variability has been assessed in this section, on the basis of the basic measurement of verification metrics (Yun et al, 2005): anomaly correlation coefficient (ACC) and root mean square error (RMSE).…”

“…Finally, techniques for constructing optimal MME forecasting have been developed (Krishnamurti et al, 2000;Doblas-Reyes et al, 2005). These approaches of the MME have shown some prospect in improving the seasonal forecast quality of precipitation beyond the individual models (Yun et al, 2005).…”

Improvement of climate predictions and reduction of their uncertainties using learning algorithms

“…However, the simple multi-model average does not take into account the quality differences between the models; therefore, it is expected that a weighted average, with weights based on the past performances of the models, will provide better predictions than the simple average. As expected, it was shown that the weighted average of climate models can improve predictions when using ensembles of AGCMs (Rajagopalan et al, 2002;Robertson et al, 2004;Yun et al, 2003), AOGCMs (Yun et al, 2005;Pavan and Doblas-Reyes, 2000;Chakraborty and Krishnamurti, 2009) and regional climate models (Feng et al,…”