Abstract

The quantification of climate change impacts on several human activities depends on reliable weather data series, without gaps and long enough to build up future climate. Based on that, this study aimed to evaluate the performance of temperature-based models for estimating global solar radiation and gridded databases (AgCFSR, AgMERRA, NASA/POWER, and XAVIER) as alternative ways for filling gaps in historical weather series (1980–2009) in Brazil and to project climate change scenarios based on measured and gridded weather data. Projections for mid- and end-of-century periods (2040–2069 and 2070–2099), using seven global climate models from CMIP5 under intermediate (RCP4.5) and high (RCP8.5) emission scenarios, were performed. The Bristow–Campbell model was the one that best estimated solar radiation, whereas the XAVIER gridded database was the closest to observed weather data. Future climate projections, under RCP4.5 and RCP8.5 scenarios, as expected, showed warmer conditions for all scenarios over Brazil. On the contrary, rainfall projections are more uncertain. Despite that, the rainfall amounts will be reduced in the North-Northeast region and increased in Southern Brazil. No significant differences between projections using the observed and XAVIER gridded database were observed; therefore, such a database showed to be reliable for both to fill gaps and to generate climate change scenarios.

Highlights

  • Given the projections of global climate changes, simulation models can be used to estimate the impact of historical and future climates on human activities, mainly in crop growth and yield and food availability [1]

  • The major difficulty regarding historical weather data in Brazil is the low density of weather stations, associated with the reduced number of measured variables and the large amount of missing data [3,4,5]

  • Once the historical data series have been filled, these can be used for generating future climate scenarios, derived from projections of climate models, which can be global (GCMs) or regional (RCMs)

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Summary

Introduction

Given the projections of global climate changes, simulation models can be used to estimate the impact of historical and future climates on human activities, mainly in crop growth and yield and food availability [1]. For proper simulations, these models require high-quality and long-term historical daily weather data [2]. Once the historical data series have been filled, these can be used for generating future climate scenarios, derived from projections of climate models, which can be global (GCMs) or regional (RCMs). Despite the finer resolution of RCMs, considering the continental dimension of Brazil, GCMs (which would provide the RCM boundary conditions) offer insight into the general characteristics of future climate [12, 13]

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