@Article{GouveiaTorrMareAvil:2022:UnPrCl,
               author = "Gouveia, Carolina Daniel and Torres, Roger Rodrigues and Marengo, 
                         Jos{\'e} Antonio and Avila Diaz, Alvaro",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Itajub{\'a} (UNIFEI)} and {Centro de 
                         Monitoramento e Alerta de Desastres Naturais (CEMADEN)} and 
                         {Universidade Federal de Itajub{\'a} (UNIFEI)}",
                title = "Uncertainties in projections of climate extremes indices in South 
                         America via Bayesian inference",
              journal = "International Journal of Climatology",
                 year = "2022",
               volume = "42",
               number = "14",
                pages = "7362--7382",
                month = "Nov.",
             keywords = "Bayesian inference, climate extremes events, CMIP5, general 
                         circulation models.",
             abstract = "Historical simulations and projections of climate extremes indices 
                         of precipitation and temperature were analysed over South America 
                         until the end of the 21st century through 31 general circulation 
                         models (GCMs) under four Representative Concentration Pathways. 
                         Simulations were compared with reanalysis data, and a Bayesian 
                         inference method was used to assess the uncertainties involved in 
                         the multi-model climate projections. Regarding the precipitation 
                         extremes indices, the GCMs' simulations reasonably approached the 
                         reanalysis data, but with heterogeneous biases, both in sign and 
                         in the location of the highest values. The temperature extremes 
                         indices presented the smallest biases when compared to 
                         precipitation. Projections show a gradual growth of precipitation 
                         extremes events as the analysed radiative forcing scenario 
                         increases, both in magnitude and extent, over a large part of 
                         South America. Projections also indicate a decrease in cold days 
                         and nights and an increase in warm days and nights, more 
                         pronounced in the equatorial region. Bayesian inference method 
                         smoothed changes in precipitation extremes events, both in 
                         magnitude and extent, compared to the simple GCMs' ensemble mean. 
                         There was no considerable variation in the temperature indices 
                         when applying the Bayesian inference. Finally, the probability 
                         density functions resulted in a predominance of multimodal and 
                         wide curves for the precipitation indices, showing great 
                         uncertainties in the GCMs' results, differently from those for the 
                         temperature indices, where the GCMs presented good agreement 
                         represented through unimodal and narrow curves.",
                  doi = "10.1002/joc.7650",
                  url = "http://dx.doi.org/10.1002/joc.7650",
                 issn = "0899-8418",
             language = "en",
           targetfile = "Intl Journal of Climatology - 2022 - Gouveia - Uncertainties in 
                         projections of climate extremes indices in South America.pdf",
        urlaccessdate = "29 jun. 2024"
}