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Keywords:

  • bias correction;
  • decadal predictions;
  • skill;
  • statistical post-processing;
  • trend correction

[1] A method for post-processing decadal predictions from global climate models that accounts for model deficiencies in representing climate trends is proposed and applied to decadal predictions of annual global mean temperature from the Canadian Centre for Climate Modelling and Analysis climate model. The method, which provides a time-dependent trend adjustment, reduces residual drifts that remain after applying the standard time-independent bias correction when the modelled and observed long-term trends differ. Initialized predictions and uninitialized simulations that share common specified external forcing are analyzed. Trend adjustment substantially reduces forecast errors in both cases and initialization further enhances skill, particularly for the first forecast year.