Finite-time normal mode disturbances and error growth during southern hemisphere blocking

Mozheng Wei, Jorgen S. Frederiksen

Research output: Contribution to journalArticleOther

8 Citations (Scopus)


The structural organization of initially random errors evolving in a barotropic tangent linear model, with time-dependent basic states taken from analyses, is examined for cases of block development, maturation and decay in the Southern Hemisphere atmosphere during April, November, and December 1989. The statistics of 100 evolved errors are studied for six-day periods and compared with the growth and structures of fast growing normal modes and finite-time normal modes (FTNMs). The amplification factors of most initially random errors are slightly less than those of the fastest growing FTNM for the same time interval. During their evolution, the standard deviations of the error fields become concentrated in the regions of rapid dynamical development, particularly associated with developing and decaying blocks. We have calculated probability distributions and the mean and standard deviations of pattern correlations between each of the 100 evolved error fields and the five fastest growing FTNMs for the same time interval. The mean of the largest pattern correlation, taken over the five fastest growing FTNMs, increases with increasing time interval to a value close to 0.6 or larger after six days. FTNM 1 generally, but not always, gives the largest mean pattern correlation with error fields. Corresponding pattern correlations with the fast growing normal modes of the instantaneous basic state flow are significant but lower than with FTNMs. Mean pattern correlations with fast growing FTNMs increase further when the time interval is increased beyond six days.

Original languageEnglish
Pages (from-to)69-89
Number of pages21
JournalAdvances in Atmospheric Sciences
Issue number1
Publication statusPublished - Jan 2005
Externally publishedYes


  • Blocking
  • Finite-time normal modes
  • Normal modes
  • Pattern correlations
  • Tangent linear model

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