Decadal climate predictions with a coupled OAGCM initialized with oceanic reanalyses - Bellucci, A.; Gualdi, S.; Masina, S.; Storto, A.; Scoccimarro, E.; Cagnazzo, C.; Fogli, P.; Manzini, E.; Navarra, A. (Journal of Geophysical Research, 2013)
We investigate the effects of realistic oceanic initial conditions on a set of decadal climate predictions performed with a state-of-the-art coupled ocean-atmosphere general circulation model. The decadal predictions are performed in both retrospective (hindcast) and forecast modes. Specifically, the full set of prediction experiments consists of 3-member ensembles of 30-year simulations, starting at 5-year intervals from 1960 to 2005, using historical radiative forcing conditions for the 1960-2005 period, followed by RCP4. 5 scenario settings for the 2006-2035 period. The ocean initial states are provided by ocean reanalyses differing by assimilation methods and assimilated data, but obtained with the same ocean model. The use of alternative ocean reanalyses yields the required perturbation of the full three-dimensional ocean state aimed at generating the ensemble members spread. A full-value initialization technique is adopted. The predictive skill of the system appears to be driven to large extent by trends in the radiative forcing. However, after detrending, a residual skill over specific regions of the ocean emerges in the near-term. Specifically, natural fluctuations in the North Atlantic sea-surface temperature (SST) associated with large-scale multi-decadal variability modes are predictable in the 2-5 year range. This is consistent with significant predictive skill found in the Atlantic meridional overturning circulation over a similar timescale. The dependency of forecast skill on ocean initialization is analysed, revealing a strong impact of details of ocean data assimilation products on the system predictive skill. This points to the need of reducing the large uncertainties that currently affect global ocean reanalyses, in the perspective of providing reliable near-term climate predictions. © 2012 Springer-Verlag.