Open AccessENSO Amplitude Changes due to Climate Change Projections in Different Coupled Models
Author(s) -
SangWook Yeh,
Ben P. Kirtman
Publication year - 2007
Publication title -
journal of climate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.315
H-Index - 287
eISSN - 1520-0442
pISSN - 0894-8755
DOI - 10.1175/jcli4001.1
Subject(s) - climatology , amplitude , climate model , forcing (mathematics) , geophysical fluid dynamics , anomaly (physics) , environmental science , sea surface temperature , climate change , climate state , coupled model intercomparison project , multivariate enso index , atmospheric model , atmospheric noise , atmospheric sciences , physics , geology , global warming , meteorology , effects of global warming , oceanography , very low frequency , southern oscillation , condensed matter physics , quantum mechanics , astronomy
Four climate system models are chosen here for an analysis of ENSO amplitude changes in 4 × CO2 climate change projections. Despite the large changes in the tropical Pacific mean state, the changes in ENSO amplitude are highly model dependant. To investigate why similar mean state changes lead to very different ENSO amplitude changes, the characteristics of sea surface temperature anomaly (SSTA) variability simulated in two coupled general circulation models (CGCMs) are analyzed: the Meteorological Research Institute (MRI) and Geophysical Fluid Dynamics Laboratory (GFDL) models. The skewed distribution of tropical Pacific SSTA simulated in the MRI model suggests the importance of nonlinearities in the ENSO physics, whereas the GFDL model lies in the linear regime. Consistent with these differences in ENSO regime, the GFDL model is insensitive to the mean state changes, whereas the MRI model is sensitive to the mean state changes associated with the 4 × CO2 scenario. Similarly, the low-frequency modulation of ENSO amplitude in the GFDL model is related to atmospheric stochastic forcing, but in the MRI model the amplitude modulation is insensitive to the noise forcing. These results suggest that the understanding of changes in ENSO statistics among various climate change projections is highly dependent on whether the model ENSO is in the linear or nonlinear regime.