A Demonstration of Long-Term Memory and Climate Predictability

Climate forecast skills are evaluated for surface temperature time series at grid points of a millennium control simulation from a state-of-the-art global circulation model [ECHAM5-Max Planck Institute Ocean Model (MPI-OM)]. First, climate predictability is diagnosed in terms of potentially predictable variance fractions and the fluctuation power-law exponent (using detrended fluctuation analysis). Long-term memory (LTM) with a fluctuation exponent (or Hurst exponent) close to 0.9 occurs mainly in high-latitude oceans, which are also characterized by high potential predictability. Next, explicit prediction experiments for various time steps are conducted on a gridpoint basis using an autocorrelation predictor. In regions with LTM, prediction skills are beyond that expected from red noise persistence-exceptions occur in some areas in the southern oceans and over the Northern Hemisphere continents. Extending the predictability analysis to the fully forced simulation shows a large improvement in prediction skills.