Error growth and dynamical vectors during southern hemisphere blocking

International audienceThe structural organization of initially random perturbations or "errors" evolving in a barotropic tangent linear model with time-dependent basic states taken from observations, is examined for cases of block development, maturation and decay in the Southern Hemisphere atmosphere during April, November and December 1989. We determine statistical results relating the structures of evolved errors to singular vectors (SVs), Lyapunov vectors (LVs) and finite-time normal modes (FTNMs). The statistics of 100 evolved error fields are studied for six day periods or longer and compared with the growth and structures of leading fast growing SVs, LVs and FTNMs. The SVs are studied in the kinetic energy (KE), enstrophy (EN) and streamfunction (SF) norms, while all FTNMs and the first LV are norm independent. The mean of the largest pattern correlations between the 100 error fields and dynamical vectors, taken over the five fastest growing SVs, in any of the three norms, or over the five fastest growing FTNMs, increases with increasing time interval to a value close to 0.6 after six days. Corresponding pattern correlations with the five fastest growing LVs are slightly lower. The leading dynamical vectors (SVs 1, FTNM1 or LV 1) generally, but not always, give the largest pattern correlations with the error fields. It is found that viscosity slightly increases the average correlations between the evolved errors and LV 1 and evolved SVs 1. Mean pattern correlations with fast growing dynamical vectors increase further for time intervals longer than six days. The properties of the dynamical vectors during Southern Hemisphere blocking are briefly outlined. After a few days integration, the structures of the leading evolved SVs in the KE, EN and SF norms, are in general quite similar and also similar to some of the dominant FTNMs that are norm independent. For optimization times of six days or less, the evolved SVs and FTNMs are, in general, different from the dominant LVs on the same day. Nevertheless, amplification factors of the first FTNMs and first LVs are very similar, and also similar to, but slightly larger than, the mean amplification factor of 100 initially random perturbations in the SF norm, while the amplification factors in the SF norm of KE SVs 1 and SF SV 1 are much higher. For longer optimization times, the first SVs and the first FTNM increasingly turn towards the leading LV with convergence achieved within a month