ENSO predictability of a fully coupled GCM model using singular vector analysis

Using a recently developed method of computing climatically relevant singular vectors (SVs), the error growth properties of ENSO in a fully coupled global climate model are investigated. In particular, the authors examine in detail how singular vectors are influenced by the phase of ENSO cycle - the physical variable under consideration as well as the error norm deployed. Previous work using SVs for studying ENSO predictability has been limited to intermediate or hybrid coupled models. The results show that the singular vectors share many of the pr operties already seen in simpler models. Thus, for example, the singular vector spectrum is dominated by one fastest growing member, regardless of the phase of ENSO cycle and the variable of perturbation or the error norm; in addition the growth rates of the singular vectors are very sensitive to the phase of the ENSO cycle, the variable of perturbation, and the error norm. This particular CGCM also displays some differences from simpler models; thus subsurface temperature optimal patterns are strongly sensitive to the phase of ENSO cycle, and at times an east-west dipole in the eastern tropical Pacific basin is seen. This optimal pattern also appears for SST when the error norm is defined using Niño-4. Simpler models consistently display a single-sign equatorial signature in the subsurface corresponding perhaps to the Wyrtki buildup of heat content before a warm event. Some deficiencies in the CGCM and their possible influences on SV growth are also discussed.