DETECTION OF POINTS OF CLIMATIC CHANGES, A BAYESIAN APPROACH IN CLIMATIC DATA OF THE CITY OF SÃO PAULO

Emerson Barili; Jorge Alberto Achcar

doi:10.1590/SciELOPreprints.16244

##article.authors##

Emerson Barili State University of Maringá https://orcid.org/0000-0001-8210-6746
- Conceptualization
- Formal Analysis
- Methodology
- Writing – Original Draft Preparation
Jorge Alberto Achcar University of São Paulo https://orcid.org/0000-0002-9868-9453
- Conceptualization
- Formal Analysis
- Writing – Original Draft Preparation

DOI:

https://doi.org/10.1590/SciELOPreprints.16244

Keywords:

AR Models, Change-Points, Annual Mean Temperature, Annual Mean Rain Precipitation, Bayesian Approach

Abstract

In this study, we introduce a statistical model applied to climate change data (annual mean temperature and annual mean rain precipitation for a long period) obtained from a climate station in São Paulo City Brazil. The assumed model used in the data analysis consists of an autoregressive times series (AR) model which represents a type of random process. A Bayesian approach using MCMC (Markov Chain Monte Carlo) methods is considered to get the inferences of interest. The main goal of the study is to have a fitted statistical model to get good predictions for annual mean temperature and annual mean rain precipitation and also to be used to identify the time of possible climate change-points.