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• Abstract

Studying the response of a climate system to perturbations has practical significance. Standard methods in computing the trajectory-wise deviation caused by perturbations may suffer from the chaotic nature that makes the model error dominate the true response after a short lead time. Statistical response, which computes the return described by the statistics, provides a systematic way of reaching robust outcomes with an appropriate quantification of the uncertainty and extreme events. In this paper, information theory is applied to compute the statistical response and find the most sensitive perturbation direction of different El Niño-Southern Oscillation (ENSO) events to initial value and model parameter perturbations. Depending on the initial phase and the time horizon, different state variables contribute to the most sensitive perturbation direction. While initial perturbations in sea surface temperature (SST) and thermocline depth usually lead to the most significant response of SST at short- and long-range, respectively, initial adjustment of the zonal advection can be crucial to trigger strong statistical responses at medium-range around 5 to 7 months, especially at the transient phases between El Niño and La Niña. It is also shown that the response in the variance triggered by external random forcing perturbations, such as the wind bursts, often dominates the mean response, making the resulting most sensitive direction very different from the trajectory-wise methods. Finally, despite the strong non-Gaussian climatology distributions, using Gaussian approximations in the information theory is efficient and accurate for computing the statistical response, allowing the method to be applied to sophisticated operational systems.

• Schematic diagram of the framework

• Authors

1. Marios Andreou [Poster presenter], mandreou at math.wisc.edu

2. Nan Chen, chennan at math.wisc.edu

• Abstract

El Niño-Southern Oscillation (ENSO) has two major facets, namely the Eastern Pacific (EP) and the Central Pacific (CP) events, with irregular and quasi-periodic anomalies in wind currents and sea surface temperatures (SST) making it the most important climate phenomenon in the region. It also exhibits diverse characteristics in spatial pattern, peak intensity, and temporal evolution during its mature warming phase (El Niño) and mature cooling phase (La Niña), known as the ENSO diversity or complexity. Traditional methods for studying the sensitivity and response of ENSO to initial value and model parameter perturbations, are primarily based on trajectory-wise comparison. However, the intrinsic chaotic features and the model error impose significant challenges for accurately computing the statistical response in this manner. In this talk, we present a new approach to calculating the statistical response of ENSO diversity using information theory, by quantifying the intrinsic predictability and its response through a multiscale three-region stochastic model as a surrogate. It computes the response of the statistics, such as the mean and variance, to initial value or model parameter perturbations. We provide the most dangerous direction under initial and parameter perturbations for different ENSO events over the past 36 years (1982-2017). We also show that the uncertainty described by the variance and higher-order moments can have a significant response on certain perturbations, despite the insignificant change in the mean, which is a fundamental mechanism of the increment of extreme El Niño events and multi-year El Niño and La Niña, and that under a univariate SSTa regime for the probability densities, a Gaussian approximation captures most of the intricacies of intrinsic predictability and statistical response. This way of quantifying statistical response is more robust and physically meaningful, since it provides ways to inspect the response of ENSO diversity under the climate change scenario, increase or decrease of the Madden-Julian oscillation (MJO) or tropical cyclones, through model parameter perturbations, or to probe into the principal directions relating to forecast and prevention of extreme events, as well as impact on other climate variabilities, through initial value perturbations.