Decadal climate predictions improved by ocean ensemble dispersion filtering

• Published in: Journal of advances in modeling earth systems Vol. 9; no. 2; pp. 1138 - 1149
• Main Authors: Kadow, C., Illing, S., Kröner, I., Ulbrich, U., Cubasch, U.
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 01.06.2017
• Subjects: atmosphere; Atmospheric precipitations; Boundary conditions; Capacity; Climate; climate modeling; Climate models; Climate prediction; Cyclones; decadal prediction; Dispersion; Earth; ensemble technique; forecasts; ocean; Ocean-atmosphere interaction; Precipitation; Resolution; Specific heat; Temperature; Temperature (air-sea); Weather forecasting; Winter
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1002/2016MS000787

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere‐ocean interaction plays an important role for such climate forecasts. While short‐term weather forecasts represent an initial value problem and long‐term climate projections represent a boundary condition problem, the decadal climate prediction falls in‐between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean. Plain Language Summary Decadal predictions aim to predict the climate several years in advance. Atmosphere‐ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its ensemble average, improves a prediction system. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Our study shows that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure applying the average during the model run, called ensemble dispersion filter, results in more accurate results than the standard prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Key Points Newly developed forecast method uses ocean ensemble dispersion filtering toward its ensemble mean Global and regional temperature as well as regional precipitation and cyclone predictions improve up to 5 years ahead It outperforms state‐of‐the‐art predictions even with higher resolution and larger ensembles for typical setups with ensemble members <=10