A Hierarchy of Data-Based ENSO Models
Kondrashov
D.
author
Kravtsov
S.
author
Robertson
Andrew W.
author
Columbia University. International Research Institute for Climate and Society
Ghil
Michael
author
Columbia University. International Research Institute for Climate and Society
originator
text
Articles
2005
English
Global sea surface temperature (SST) evolution is analyzed by constructing predictive models that best describe the dataset's statistics. These inverse models assume that the system's variability is driven by spatially coherent, additive noise that is white in time and are constructed in the phase space of the dataset's leading empirical orthogonal functions. Multiple linear regression has been widely used to obtain inverse stochastic models; it is generalized here in two ways. First, the dynamics is allowed to be nonlinear by using polynomial regression. Second, a multilevel extension of classic regression allows the additive noise to be correlated in time; to do so, the residual stochastic forcing at a given level is modeled as a function of variables at this level and the preceding ones. The number of variables, as well as the order of nonlinearity, is determined by optimizing model performance. The two-level linear and quadratic models have a better El Niņo–Southern Oscillation (ENSO) hindcast skill than their one-level counterparts. Estimates of skewness and kurtosis of the models' simulated Niņo-3 index reveal that the quadratic model reproduces better the observed asymmetry between the positive El Niņo and negative La Niņa events. The benefits of the quadratic model are less clear in terms of its overall, cross-validated hindcast skill; this model outperforms, however, the linear one in predicting the magnitude of extreme SST anomalies. Seasonal ENSO dependence is captured by incorporating additive, as well as multiplicative forcing with a 12-month period into the first level of each model. The quasi-quadrennial ENSO oscillatory mode is robustly simulated by all models. The "spring barrier" of ENSO forecast skill is explained by Floquet and singular vector analysis, which show that the leading ENSO mode becomes strongly damped in summer, while nonnormal optimum growth has a strong peak in December.
Atmospheric sciences
Climate change
Journal of Climate
18
21
4425
4444
2005-11
http://dx.doi.org/10.1175/JCLI3567.1
http://hdl.handle.net/10022/AC:P:14360
NNC
NNC
2012-08-14 10:03:16 -0400
2012-08-14 10:28:46 -0400
8380
eng