Local decadal prediction according to statistical/dynamical approaches

Dynamical climate models present an initialization problem due to the poor availability of deep oceanic data, which is required for the model assimilation process. In this sense, teleconnection indices, defined from spatial and temporal patterns of climatic variables, are conceived as useful tools to complement them. In this work, the near-term climate predictability of 35 temperature and 36 precipitation time series of three cities (Barcelona, Bristol and Lisbon) was analysed using two approaches: (a) a statistical-dynamical combination of self-predictable teleconnection indices and long-term climate projections on a local scale and (b) dynamical model outputs obtained from drift-corrected decadal experiments. Fourier and wavelet analyses were used to assess the predictability of seven teleconnection indices thanks to a cross-validation process (with differentiated training and validation periods). The standardized absolute error of teleconnection-based prediction was compared with that obtained from a (9) multi-model ensemble based on the Coupled Model Intercomparison Project Phase 5. Results showed that decadal predictions at horizons between 20 and 30 years are adequate for temperature and precipitation if a teleconnection-based approach is used, while temperature is better predicted from a 5-year horizon using drift-corrected dynamical outputs.