The COLA Anomaly Coupled Model: Ensemble ENSO Prediction

Ben P. Kirtman George Mason University, Fairfax, Virgina, and Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland

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Abstract

Results are described from a large sample of coupled ocean–atmosphere retrospective forecasts during 1980–99. The prediction system includes a global anomaly coupled general circulation model and a state-of-the-art ocean data assimilation system. The retrospective forecasts are initialized each January, April, July, and October of each year, and ensembles of six forecasts are run for each initial month, yielding a total of 480 1-yr predictions.

In generating the ensemble members, perturbations are added to the atmospheric initial state only. The skill of the prediction system is analyzed from both a deterministic and a probabilistic perspective. The probabilistic approach is used to quantify the uncertainty in any given forecast. The deterministic measures of skill for eastern tropical Pacific SST anomalies (SSTAs) suggest that the ensemble mean forecasts are useful up to lead times of 7–9 months. At somewhat shorter leads, the forecasts capture some aspects of the variability in the tropical Indian and Atlantic Oceans. The ensemble mean precipitation anomaly has disappointingly low correlation with observed rainfall. The probabilistic measures of skill (relative operating characteristics) indicate that the distribution of the ensemble provides useful forecast information that could not easily be gleaned from the ensemble mean. In particular, the prediction system has more skill at forecasting cold ENSO events compared to warm events. Despite the fact that the ensemble mean rainfall is not well correlated with the observed, the ensemble distribution does indicate significant regions where there is useful information in the forecast ensemble. In fact, it is possible to detect that droughts over land are more predictable than floods. It is argued that probabilistic verification is an important complement to any deterministic verification, and provides a useful and quantitative way to measure uncertainty.

Corresponding author address: Dr. Ben P. Kirtman, Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Road, Calverton, MD 20705. Email: kirtman@cola.iges.org

Abstract

Results are described from a large sample of coupled ocean–atmosphere retrospective forecasts during 1980–99. The prediction system includes a global anomaly coupled general circulation model and a state-of-the-art ocean data assimilation system. The retrospective forecasts are initialized each January, April, July, and October of each year, and ensembles of six forecasts are run for each initial month, yielding a total of 480 1-yr predictions.

In generating the ensemble members, perturbations are added to the atmospheric initial state only. The skill of the prediction system is analyzed from both a deterministic and a probabilistic perspective. The probabilistic approach is used to quantify the uncertainty in any given forecast. The deterministic measures of skill for eastern tropical Pacific SST anomalies (SSTAs) suggest that the ensemble mean forecasts are useful up to lead times of 7–9 months. At somewhat shorter leads, the forecasts capture some aspects of the variability in the tropical Indian and Atlantic Oceans. The ensemble mean precipitation anomaly has disappointingly low correlation with observed rainfall. The probabilistic measures of skill (relative operating characteristics) indicate that the distribution of the ensemble provides useful forecast information that could not easily be gleaned from the ensemble mean. In particular, the prediction system has more skill at forecasting cold ENSO events compared to warm events. Despite the fact that the ensemble mean rainfall is not well correlated with the observed, the ensemble distribution does indicate significant regions where there is useful information in the forecast ensemble. In fact, it is possible to detect that droughts over land are more predictable than floods. It is argued that probabilistic verification is an important complement to any deterministic verification, and provides a useful and quantitative way to measure uncertainty.

Corresponding author address: Dr. Ben P. Kirtman, Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Road, Calverton, MD 20705. Email: kirtman@cola.iges.org

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