THE DYNAMICS OF ERROR GROWTH AND PREDICTABILITY IN A COUPLED MODEL OFENSO

Citation
Am. Moore et R. Kleeman, THE DYNAMICS OF ERROR GROWTH AND PREDICTABILITY IN A COUPLED MODEL OFENSO, Quarterly Journal of the Royal Meteorological Society, 122(534), 1996, pp. 1405-1446
Citations number
60
Categorie Soggetti
Metereology & Atmospheric Sciences
ISSN journal
00359009
Volume
122
Issue
534
Year of publication
1996
Part
B
Pages
1405 - 1446
Database
ISI
SICI code
0035-9009(1996)122:534<1405:TDOEGA>2.0.ZU;2-F
Abstract
Using new and emerging ideas about the growth of singular vectors ('op timal perturbations') in dynamical systems, the dynamics of error grow th and predictability in an intermediate coupled model of the El Nino Southern Oscillation (ENSO) is investigated. A mechanism is identified for error growth associated with penetrative-convection anomalies in the atmosphere. Conditions for error growth via this mechanism are mos t favourable in the central Pacific where sea surface temperatures (SS Ts) are relatively warm, and where changes in SST are moderately sensi tive to vertical movements of the main oceanic thermocline. The singul ar vectors of the coupled system were computed using the tangent-linea r coupled model and its adjoint. The singular-value spectrum was found to be dominated by one singular vector at all times of the year. The potential for error growth in the coupled model, measured in terms of the growth of energy of the dominant singular vector, is found to vary seasonally, being greatest during the boreal spring. These seasonal v ariations are associated with the seasonal cycle in SST. During boreal spring and early summer, the SST in the central Pacific is at its max imum, at which time conditions are most favourable for error growth. S pringtime is also the time of the 'predictability barrier' for ENSO. T he potential for error growth is also influenced by the ENSO cycle its elf. The results suggest that error growth will be enhanced during the onset of El Nino and suppressed during the onset of La Nina, which in dicates that El Nino may be less predictable than La Nina.