Abstract
Abstract. El Niño Southern Oscillation (ENSO) is the dominant mode of climate variability in the Pacific, having socio-economic impacts on surrounding regions. ENSO exhibits significant modulation on decadal to inter-decadal time scales which is related to changes in its characteristics (onset, amplitude, frequency, propagation, and predictability). Some of these characteristics tend to be overlooked in ENSO studies, such as its asymmetry (the number and amplitude of warm and cold events are not equal) and the deviation of its statistics from those of the Gaussian distribution. These properties could be related to the ability of the current generation of coupled models to predict ENSO and its modulation. Here, ENSO's non-Gaussian nature and asymmetry are diagnosed from in situ data and a variety of models (from intermediate complexity models to full-physics coupled general circulation models (CGCMs)) using robust statistical tools initially designed for financial mathematics studies. In particular α-stable laws are used as theoretical background material to measure (and quantify) the non-Gaussian character of ENSO time series and to estimate the skill of ``naïve'' statistical models in producing deviation from Gaussian laws and asymmetry. The former are based on non-stationary processes dominated by abrupt changes in mean state and empirical variance. It is shown that the α-stable character of ENSO may result from the presence of climate shifts in the time series. Also, cool (warm) periods are associated with ENSO statistics having a stronger (weaker) tendency towards Gaussianity and lower (greater) asymmetry. This supports the hypothesis of ENSO being rectified by changes in mean state through nonlinear processes. The relationship between changes in mean state and nonlinearity (skewness) is further investigated both in the Zebiak and Cane (1987)'s model and the models of the Intergovernmental Panel for Climate Change (IPCC). Whereas there is a clear relationship in all models between ENSO asymmetry (as measured by skewness or nonlinear advection) and changes in mean state, they exhibit a variety of behaviour with regard to α-stability. This suggests that the dynamics associated with climate shifts and the occurrence of extreme events involve higher-order statistical moments that cannot be accounted for solely by nonlinear advection.
Highlights
El Nino Southern Oscillation (ENSO, see the glossary for the acronyms list) is the dominant mode of climate variability in the Pacific (MacPhaden et al, 1998)
Most studies of ENSO implicitly assume that the Probability Density Function (PDF) of ENSO indices is undistinguishable from a Gaussian distribution, which basically leads to the representation of EEs being
We find that the Intergovernmental Panel for Climate Change (IPCC) models, all exhibiting a relationship between asymmetry and slowly varying mean state, behave differently with regard to nonlinearity as measured by α and slowly varying mean state
Summary
El Nino Southern Oscillation (ENSO, see the glossary for the acronyms list) is the dominant mode of climate variability in the Pacific (MacPhaden et al, 1998). It impacts many surrounding regions and has major socio-economic consequences. Our knowledge of the phenomenon has increased considerably in the last two decades, ENSO remains difficult to predict and its characteristics change in ways that are not yet understood by the scientific community. The difficulty in predicting ENSO and its evolution lies partly in the limited ability of Gaussian statistics to account for Extreme Events (EEs). Most studies of ENSO implicitly assume that the Probability Density Function (PDF) of ENSO indices is undistinguishable from a Gaussian distribution, which basically leads to the representation of EEs being
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