Forecasting ENSO with a smooth transition autoregressive model

This study examines the benets of nonlinear time series modelling to improve forecast accuracy of the El Nino Southern Oscillation (ENSO) phenomenon. The paper adopts a smooth transition autoregressive (STAR) modelling framework to assess the potentially regime-dependent dynamics of sea surface temperature anomaly. The results reveal STAR-type nonlinearities in ENSO dynamics, resulting in superior out-of-sample forecast performance of STAR over the linear autoregressive models. The advantage of nonlinear models is especially apparent in the short- and intermediate-term forecasts. These results are of interest to researchers and policy makers in the elds of climate dynamics, agricultural production, and environmental management

Forecasting ENSO with a smooth transition autoregressive model

This study examines the benets of nonlinear time series modelling to improve forecast accuracy of the El Nino Southern Oscillation (ENSO) phenomenon. The paper adopts a smooth transition autoregressive (STAR) modelling framework to assess the potentially regime-dependent dynamics of sea surface temperature anomaly. The results reveal STAR-type nonlinearities in ENSO dynamics, resulting in superior out-of-sample forecast performance of STAR over the linear autoregressive models. The advantage of nonlinear models is especially apparent in the short- and intermediate-term forecasts. These results are of interest to researchers and policy makers in the elds of climate dynamics, agricultural production, and environmental management

The ENSO Impact on Predicting World Cocoa Prices

Cocoa beans are produced in equatorial and sub-equatorial regions of West Africa, Southeast Asia and South America. These are also the regions most affected by El Nino Southern Oscillation (ENSO) -- a climatic anomaly affecting temperature and precipitation in many parts of the world. Thus, ENSO, has a potential of affecting cocoa production and, subsequently, prices on the world market. This study investigates the benefits of using a measure of ENSO variable in world cocoa price forecasting through the application of a smooth transition autoregression (STAR) modeling framework to monthly data to examine potentially nonlinear dynamics of ENSO and cocoa prices. The results indicate that the nonlinear models appear to outperform linear models in terms of out-of-sample forecasting accuracy. Furthermore, the results of this study indicate evidence of Granger causality between ENSO and cocoa prices.Cocoa Prices, El Nino Southern Oscillation, Out-of-Sample Forecasting, Smooth Transition Autoregression, Demand and Price Analysis, Environmental Economics and Policy, Research Methods/ Statistical Methods, C32, Q11, Q54,

A Markov regime-switching framework to forecast El Niño Southern Oscillation patterns

International audienceThe El Niño-Southern Oscillation (ENSO) is an ocean-atmosphere phenomenon involving sustained sea surface temperature fluctuations in the Pacific Ocean, causing disruptions in the behavior of the ocean and atmosphere. We develop a Markov switching autoregressive model to describe the Southern Oscillation Index (SOI), a variable that explains ENSO, using two autoregressive processes to describe the time evolution of SOI, each of which associated with a specific phase of ENSO. The switching between these two models is governed by a discrete time Markov chain (DTMC), with time-varying transition probabilities. Then, we extend the model using sinusoidal functions to forecast future values of SOI. The results can be used as a decision-making tool in the process of risk mitigation against weather and climate related disasters

Essays on climate econometrics

This paper proposes a nonlinear cointegrating regression model based on the well-known energy balance climate model. Specifcally, I investigate the nonlinear cointegrating regression of mean of temperature anomaly distributions on total radiative forcing using estimated spatial distributions of temperature anomalies for Globe/Northern Hemisphere/Southern Hemisphere. Further, I provide two types of nonlinear response functions that map from the total radiative forcing level to mean temperature anomalies. Cointegration and specifcation tests are also provided that support the existence of nonlinear effects of total radiative forcing.Includes bibliographical reference

Statistical Modelling and Analysis of Pacific Sea Surface Temperatures

Sea surface temperature (SST) in the Pacific Ocean is a key component of many global climate models and of the El Ni~no Southern Oscillation (ENSO) phenomenon. We analyse SST for the period November 1981 { December 2014. To study the temporal variability of the ENSO phenomenon, we have selected a subregion of the tropical Pacific Ocean, namely the Ni~no 3.4 region, as it is thought to be the area where SST anomalies indicate most clearly ENSO\u27s in uence on the global atmosphere. SST anomalies, obtained by subtracting the appropriate monthly averages from the data, are the focus of the majority of previous analyses of the Pacific and other oceans\u27 SSTs. Preliminary data analysis showed that not only Ni~no 3.4 spatial means but also Ni~no 3.4 spatial variances varied with month of the year. In this thesis, we conduct an analysis of the raw SST data and introduce diagnostic plots (here, plots of variability versus central tendency). These plots show strong negative dependence between the spatial standard deviation and the spatial mean. Outliers are present, so we use robust regression to obtain intercept and slope estimates for the twelve individual months and for all-months-combined. Based on this meanstandard deviation relationship, we define a variance-stabilising transformation. The transformation we derive is logarithmic, monotonic, nonlinear, and it respects the variability seen in SSTs from month to-month during the year. On the raw SST and transformed scales, we describe the Ni~no 3.4 SST time series with statistical models that are linear, heteroskedastic, and dynamical. We also derive a back-transform to take our forecasts on the transformed scale back to degrees Celsius. We compare the two forecasting methods via in-sample forecasting the data the model was trained on, November 1981 { December 2014, and then out-of-sample forecasting from January 2015 { December 2017. Our results indicate that the forecasts on the transformed scale perform better when predicting up to and into boreal spring, while the forecasts on the original scale perform better when predicting across and from boreal spring into summer. We also provide visualisations of the forecast error bias and variance which can be used to better identify and understand the (boreal) spring barrier

Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data

Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep learning models have become more common, RNNs have been used to forecast increasingly complicated systems. Dynamical spatio-temporal processes represent a class of complex systems that can potentially benefit from these types of models. Although the RNN literature is expansive and highly developed, uncertainty quantification is often ignored. Even when considered, the uncertainty is generally quantified without the use of a rigorous framework, such as a fully Bayesian setting. Here we attempt to quantify uncertainty in a more formal framework while maintaining the forecast accuracy that makes these models appealing, by presenting a Bayesian RNN model for nonlinear spatio-temporal forecasting. Additionally, we make simple modifications to the basic RNN to help accommodate the unique nature of nonlinear spatio-temporal data. The proposed model is applied to a Lorenz simulation and two real-world nonlinear spatio-temporal forecasting applications