An Empirical Seasonal Rainfall Forecasting Model for the Northeast Region of Brazil

Rodrigo Lins Da Rocha Júnior,Fabrício Daniel Dos Santos Silva,Caio Augusto Dos Santos Coelho,Heliofábio Barros Gomes,Malaquías Peña,Helber Barros Gomes,Dirceu Luís Herdies,Marcos Paulo Santos Pereira,David Duarte Cavalcante Pinto,Rafaela Lisboa Costa

doi:10.3390/w13121613

Rodrigo Lins Da Rocha Júnior, Fabrício Daniel Dos Santos Silva + Show 8 more

Open Access

https://doi.org/10.3390/w13121613

Copy DOI

Abstract

The Northeast region of Brazil (NEB) is characterized by large climate variability that causes extreme and long unseasonal wet and dry periods. Despite significant model developments to improve seasonal forecasting for the NEB, the achievement of a satisfactory accuracy often remains a challenge, and forecasting methods aimed at reducing uncertainties regarding future climate are needed. In this work, we implement and assess the performance of an empirical model (EmpM) based on a decomposition of historical data into dominant modes of precipitation and seasonal forecast applied to the NEB domain. We analyzed the model’s performance for the February-March-April quarter and compared its results with forecasts based on data from the North American Multi-model Ensemble (NMME) project for the same period. We found that the first three leading precipitation modes obtained by empirical orthogonal functions (EOF) explained most of the rainfall variability for the season of interest. Thereby, this study focuses on them for the forecast evaluations. A teleconnection analysis shows that most of the variability in precipitation comes from sea surface temperature (SST) anomalies in various areas of the Pacific and the tropical Atlantic. The modes exhibit different spatial patterns across the NEB, with the first being concentrated in the northern half of the region and presenting remarkable associations with the El Niño-Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), both linked to the latitudinal migration of the intertropical convergence zone (ITCZ). As for the second mode, the correlations with oceanic regions and its loading pattern point to the influence of the incursion of frontal systems in the southern NEB. The time series of the third mode implies the influence of a lower frequency mode of variability, probably related to the Interdecadal Pacific Oscillation (IPO). The teleconnection patterns found in the analysis allowed for a reliable forecast of the time series of each mode, which, combined, result in the final rainfall prediction outputted by the model. Overall, the EmpM outperformed the post-processed NMME for most of the NEB, except for some areas along the northern region, where the NMME showed superiority.

Highlights

Climate prediction is an important tool for the preventive management of undesirable consequences of climate variability
For the precipitation forecast of the FMA quarter inby the Northeast region of Brazil (NEB), we focused on the firs is the reference model in this study, we used relative metrics to three leading modes obtained by empirical orthogonal functions (EOF)
For the precipitation forecast of the FMA quarter in the NEB, we focused on the first three leading modes obtained by EOF

Summary

Introduction

Climate prediction is an important tool for the preventive management of undesirable consequences of climate variability. Despite the strong chaotic component of the atmosphere, the oceanic phenomena that trigger an ocean-atmosphere energy transfer in relatively well-defined periodicities allow us to anticipate the climate with some skill [1]. The El Niño-Southern Oscillation (ENSO) is one of the most important cycles in that regard, considering that it influences the climate most around the globe [2,3,4,5]. In addition to ENSO, the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO) and the Atlantic Meridional Mode (AMM) are examples of forcings that modulate the climate of several regions of the planet [6,7,8]. The earliest climate predictions used empirical relations between sea surface temperature (SST) and lagged atmospheric variables. Dynamic modeling represented a successful evolution in seasonal forecasting, by using observed or predicted SST anomalies as a boundary condition [9,10]

Objectives

Methods

Results

Conclusion