Climate projections of precipitation and temperature in the Metropolitan Region of Vale do Paraíba-SP simulated by climate models from CMIP6

• Autor(a) principal: Bosco, Ricardo Brambila [UNESP]
• Data de Publicação: 2022
• Outros Autores: Valverde, María Cleofé
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.26848/rbgf.v15.3.p1621-1638; http://hdl.handle.net/11449/240299
• topic: Climate change; CMIP6; Extremes; Metropolitan Region of Vale do Paraíba

This work aimed to investigate the temperature and precipitation changes simulated by CMIP6 high-resolution climate models (CNRM-CM6-1-HR, EC-EARTH3 and MPI-ESM1-HR) for two emission scenarios (SSP2 4.5, SSP5 8.5) in the Metropolitan Region of Vale do Paraíba in the State of São Paulo. The region was divided into four sub-regions: Sub-region 1 – São José dos Campos, Sub-region 2 – Taubaté, Sub-region 3 – Guaratinguetá and Sub-region 4 – Cruzeiro. Based on the observed data (1961-2014), Sub-region 1 was the hottest and least rainy concerning the other sub-regions. According to the Man-Kendall test, the interannual variability of precipitation and temperature showed, for the four sub-regions, significant trends of decrease and increased, respectively. For future projections, there was consensus in the climate models used, for significant trends in temperature increase, considering the period 2015-2100, similar to that observed. Sub-region 3 – Guaratinguetá should present the most significant warming of 4.8°C, concerning the simulated climatology, for the most extreme scenario SSP5 8.5. About precipitation, was no consensus by the models, and only the MPI-ESM1-HR model showed a significant tendency to increase in the period 2041-2070, in Sub-regions 1 and 4 and for the SSP2 4.5 scenario. As for the SSP5 8.5 scenario, the significant increasing trends were only for 2071-2100 and sub-regions 1, 3, and 4. It is noteworthy that the simulation of precipitation is still a considerable challenge uncertainty is high, and validation of climate models is always recommended.