An efficient SPDE approach for El Ni\~no

TL;DR

This paper introduces a novel numerical framework for efficiently approximating the mean and covariance of SPDE-based models of El Niño, validated with real climate data, outperforming existing stochastic methods.

Contribution

It presents a deterministic PDE and operator differential equation approach for SPDEs modeling El Niño, offering a more efficient alternative to traditional stochastic methods.

Findings

The method accurately captures El Niño patterns using real data.

Numerical results show improved efficiency over existing stochastic approaches.

Validation with 2014-2015 El Niño data confirms practical effectiveness.

Abstract

We consider the numerical approximation of stochastic partial differential equations (SPDEs) based models for a quasi-periodic climate pattern in the tropical Pacific Ocean known as El Ni\~no phenomenon. We show that for these models the mean and the covariance are given by a deterministic partial differential equation and by an operator differential equation, respectively. In this context we provide a numerical framework to approximate these parameters directly. We compare this method to stochastic differential equations and SPDEs based models from the literature solved by Taylor methods and stochastic Galerkin methods, respectively. Numerical results for different scenarios taking as a reference measured data of the years 2014 and 2015 (last Ni\~no event) validate the efficiency of our approach.