Abstract
A triangular T21, three-level, quasigeostrophic global spectral model was used to investigate how precursors relate to the predictability of blocking onset when a conditional nonlinear optimal perturbation approach is used. Here the authors focused on links between the optimal precursor to blocking onset and the optimally growing initial error in onset prediction.
Numerical results have shown that during the prediction of blocking events, a type-1 optimally growing initial error, which causes an overprediction of blocking onset, bears the greatest resemblance to the optimal precursor, and both are distributed primarily over the blocking and its upstream regions. A type-2 optimally growing initial error is also characterized by a similar pattern, but with the opposite sign. Further analysis reveals that a type-1 optimally growing initial error has a similar growth behavior to that of the optimal precursor, and both develop into a dipole blocking anomaly pattern with a strong positive anomaly in the north and a weak negative anomaly to the south. The evolutionary mechanism of a type-1 optimally growing initial error during blocking onset can be explained in the same manner as that of an optimal precursor triggering blocking onset. This similarity between an optimal precursor and an optimally growing initial error also suggests that targeted observations over sensitive areas may be carried out in advance to eliminate optimally growing errors (as many as possible) in the prediction of blocking onset. Thus, the improved observation network will help to better capture the spatial structure of precursors that trigger blocking onset and will increase the ability to predict blocking events.
