Cenários Climáticos e Produtividade do Algodão no Nordeste do Brasil. Parte II: Simulação Para 2020 a 2080

Abstract

Resumo Este trabalho teve como objetivo principal gerar cenários climáticos futuros, e avaliar os impactos na produtividade do algodão herbáceo através de um modelo agrometeorólogico, quando comparada com a produtividade atual observada. Um downscaling estatístico foi empregado para obter as series futuras das variáveis meteorológicas necessárias para o cálculo da produtividade, obtido com um modelo agrometeorólogico devidamente calibrada para a realidade do algodão na região semiárida do Nordeste brasileiro. As series temporais das variáveis meteorológicas tiveram suas falhas devidamente preenchidas e passaram por um rigoroso sistema de controle de qualidade. As projeções futuras calculadas para os cenários climáticos A1B e A2, dos modelos ECHAM5-OM e HadGEM2-ES foram unanimes em apontar redução significativa das chuvas e aumento das temperaturas. A evapotranspiração, consequentemente, também apresenta tendência de aumento para o horizonte futuro estudado, de 2020 a 2080. Como impacto para a cultura do algodão, prevê-se, para 23 municípios estudados do semiárido nordestino, redução significativa da produtividade, que pode ser reduzida a aproximadamente metade da produtividade atual observada.

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

“…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.…”

“…The RMSE was selected as the "MICE" dexterity estimation measure because it has, among other advantages, the possibility of expressing the accuracy of the numerical results with error values in the same dimensions as the analysed variables, that is, millimetres for precipitation, degrees Celsius for temperature, percentage for relative humidity, hectoPascals for atmospheric pressure, metres per second for average wind speed and hours for insolation. The daily scale is important for analysing climate extreme indices [38][39][40][41][42][43]; the 10-day scale is important for application in agrometeorological studies, as this is the time step used in many crop growth simulation models [44][45][46], whereas the monthly scale is essential for studies that involve analyses of the influence of modes of variability on climate dynamics and also for research in the area of seasonal and subseasonal climate forecasts [47][48][49][50][51].…”

Application of Spatial Modeling for Upland Cotton Yield in the Semi-Arid of Paraíba State, Brazil

“…Silva et al (2012) verified climate monitoring and agricultural production in the states of Paraíba, Rio Grande do Norte, and Ceará over the last twenty years, comparing the interannual variability of agricultural production and rainfall, and obtained significant results. According to Silva et al (2020b), several agrometeorological models have pointed out significant reductions in rainfall and increases in temperature, and hence an increasing trend of evapotranspiration in a future projection .…”