The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

• Published in: Climate dynamics Vol. 44; no. 9-10; pp. 2723 - 2735
• Main Authors: Baehr, J., Fröhlich, K., Botzet, M., Domeisen, D. I. V., Kornblueh, L., Notz, D., Piontek, R., Pohlmann, H., Tietsche, S., Müller, W. A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2015; Springer; Springer Nature B.V
• Subjects: Analysis; Climate change; Climate models; Climate prediction; Climatology; Earth and Environmental Science; Earth Sciences; El Nino; Geophysics/Geodesy; Global climate; Marine; Meteorology; Ocean temperature; Ocean-atmosphere interaction; Oceanography; Sea ice; Sea surface temperature; Simulation; Tropical environments; Weather forecasting; United States; Start Date; Ensemble Spread; Couple Climate Model; Root Mean Square Error; Predictive Skill
• ISSN: 0930-7575; 1432-0894
• DOI: 10.1007/s00382-014-2399-7

A seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2–4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.