# A continuous spatio-temporal approach to estimate climate change

## TL;DR

This paper presents a novel continuous spatio-temporal method for analyzing climate data, effectively decomposing trends, cycles, and seasonal effects while accounting for spatial heterogeneity and missing data.

## Contribution

It introduces a new approach that models complex spatial dependencies in climate data, enabling detailed analysis of climate change in diverse regions.

## Key findings

- Detected temperature increase trends in Northeast Brazil
- Identified changes in climatic patterns over time
- Demonstrated effectiveness in handling missing data

## Abstract

We introduce a method for decomposition of trend, cycle and seasonal components in spatio-temporal models and apply it to investigate the existence of climate changes in temperature and rainfall series. The method incorporates critical features in the analysis of climatic problems - the importance of spatial heterogeneity, information from a large number of weather stations, and the presence of missing data. The spatial component is based on continuous projections of spatial covariance functions, allowing modeling the complex patterns of dependence observed in climatic data. We apply this method to study climate changes in the Northeast region of Brazil, characterized by a great wealth of climates and large amplitudes of temperatures and rainfall. The results show the presence of a tendency for temperature increases, indicating changes in the climatic patterns in this region.