Analysis of future climate scenarios for northeastern Brazil and implications for human thermal comfort

Abstract

A thermal comfort index for the Northeast of Brazil was analyzed for two scenarios of climatic changes, A1B and A2, for 2021-2080, and compared with the reference period 1961-1990. A technique of regionalization was applied to rainfall, maximum and minimum temperature data from meteorological stations, obtained by statistical downscaling of projections from four global climate models. The results pointed to a signifi cant reduction of rainfall and an increase of temperature for three different climatically homogeneous subregions. Regarding the thermal comfort index, the results point to an increase in days with heat discomfort between 2021 and 2080. In the northern portion, the higher percentage of days with heat discomfort will be signifi cant since the fi rst half of the period under appreciation, i.e., from 2021 to 2050. Conversely, in the eastern of northeastern Brazil, the increase of days with heat discomfort should happen in the period from 2051 to 2080, whereas the central-western part of the region, which, in the reference period, had recorded less than 1% of days with heat discomfort, might see an elevation of that percentage to 7% between 2021 and 2050, potentially reaching 48% of its days made uncomfortable by heat between 2051 and 2080.

Gap Filling and Quality Control Applied to Meteorological Variables Measured in the Northeast Region of Brazil

“…The lower panel exhibits the RMSE, which also increased gradually for precipitation, ranging from 4 to 17 mm for daily values, from 12 to 37 mm for 10-day accumulated precipitation and from 22 to 72 mm for the monthly accumulated values. This behaviour is expected for precipitation, due to its accumulative nature, with similar results being observed in [4]. For all analysed variables, correlations increased with larger accumulation intervals, with the opposite being expected for RMSE [57].…”

“…These results showed the efficiency of the technique for filling time series of meteorological variables, as well as that of the QCS. In the case of precipitation and temperature, both the filled and control/comparison datasets from this research were successfully used in studies of analyses of climatic extremes indices [5], and for statistical downscaling of regionalized climate change scenarios [4,45,59]. In the field of seasonal and subseasonal climate forecasting, these series will compose a database of surface observations for the calibration and verification of the Brazilian Global Atmospheric Model (BAM) [67], which is the atmospheric module of the Brazilian Earth System Model (BESM), aiming to achieve a hybrid dynamic-statistic coupling for the observed surface data and to perform adjustments in the BAM's seasonal forecasting for the NEB.…”

“…This situation poses a hindrance to a more detailed, observation-based climate analysis, forcing the use of Reanalyses products, which constitute a synthetic database reconstructed by calibrating a regional climate model to observed historical conditions, in a grid format, with statistical properties, such as means and variances, that are very similar to those of the observations [3]. However, although useful for analysing long-term climate trends and studying future climate change scenarios [4], they are not reliable in terms of extreme events [5].…”

Avaliação das simulações de temperatura e precipitação de um subconjunto de modelos do CMIP6 para o Brasil

“…Os estudos de MARENGO et al (2020a) indicam que as áreas áridas e secas do nordeste devem se intensificar, caso o aquecimento global atinja 4 °C. Em um clima mais quente e seco, como mostra a tendência de aquecimento, a Região Nordeste, pode sofrer ainda mais com a escassez de água (ALVES et al 2016, MARENGO et al 2020a, COSTA et al 2021, impactando diretamente a população local.…”