@Article{MaksicVSCPOCA:2022:PaFu,
               author = "Maksic, Jelena and Venancio, Igor Martins and Shimizu, 
                         Mar{\'{\i}}lia Harumi and Chiessi, C. M. and Piacsek, P. and 
                         Oliveira, Gilvan Sampaio de and Cruz, F. W. and Alexandre, Felipe 
                         Ferreira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade de S{\~a}o Paulo 
                         (USP)} and {Universidade Federal Fluminense (UFF)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade de 
                         S{\~a}o Paulo (USP)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Brazilian biomes distribution: Past and future",
              journal = "Palaeogeography, Palaeoclimatology, Palaeoecology",
                 year = "2022",
               volume = "585",
                pages = "e110717",
                month = "Jan.",
             keywords = "Biomes, Future scenario, Last Glacial Maximum, Palaeorecords, 
                         Simulations.",
             abstract = "The Last Glacial Maximum (LGM, 26.519 ka) was marked by 
                         atmospheric cooling, in contrast to the current warming climate, 
                         which will probably continue in the coming decades, according to 
                         climate models projections. The LGM to pre-industrial transition 
                         provides an opportunity to test the vegetation response to a very 
                         large temperature change that can then be applied to project 
                         pre-industrial to end-of-century changes. In order to explore the 
                         changes in Brazilian biomes due to temperature change, we 
                         projected potential vegetation for both past and future scenarios. 
                         We compared biome projections with a compilation of 149 published 
                         LGM reconstructions of climate and vegetation within Brazil and 
                         adjacent areas. In addition, we evaluated the particular effects 
                         that changes in precipitation, temperature and CO2 had on 
                         vegetation by performing sensitivity experiments. Our results 
                         suggest that biomes in the western and central portions of the 
                         Amazon forest remained largely unchanged during the LGM mainly due 
                         to negative temperature anomalies, while a decrease in past 
                         precipitation was responsible for the shift from tropical 
                         evergreen forest to tropical seasonal forest in the eastern 
                         portion of the Amazon. These results are consistent with proxy 
                         reconstructions. LGM model projections and proxy reconstructions 
                         suggest expansion of grassland in the southern Brazilian 
                         highlands. Under future warming scenarios, biome changes are 
                         mostly forced by decreasing precipitation and increasing 
                         temperatures, which counteract potential biomass gain from the 
                         positive CO2 fertilization effect. Under future warming, our 
                         simulations show an expansion of Savanna/Cerrado and a reduction 
                         of tropical seasonal forest and Caatinga, with potential large 
                         impacts over biodiversity and regional climate.",
                  doi = "10.1016/j.palaeo.2021.110717",
                  url = "http://dx.doi.org/10.1016/j.palaeo.2021.110717",
                 issn = "0031-0182",
             language = "en",
           targetfile = "maksic_2022.pdf",
        urlaccessdate = "06 jun. 2024"
}