author = "Cavalcanti, Iracema Fonseca de Albuquerque and Silveira, Virginia 
                         Piccinini and Figueroa, Silvio Nilo and Kubota, Paulo Yoshio and 
                         Bonatti, Jos{\'e} Paulo and Souza, Dayana Castilho de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Climate variability over South America-regional and large scale 
                         features simulated by the Brazilian Atmospheric Model (BAM-v0)",
              journal = "International Journal of Climatology",
                 year = "2020",
               volume = "40",
               number = "5",
                pages = "2845--2869",
                month = "Apr.",
             keywords = "Atmospheric Global Circulation Model, BAM-v0, climate variability, 
                         South America, teleconnections.",
             abstract = "The reliability of climate prediction by a global model is 
                         directly related to the ability to simulate the observed climate 
                         variability and the main teleconnection patterns. Precipitation 
                         anomalies in certain regions are strongly affected by these 
                         features, and it is important to know if models are able to 
                         reproduce such patterns and influences. The main objective of this 
                         article is to analyse some global features of the Brazilian 
                         Atmospheric Model with simplified physics (BAM-v0), and to discuss 
                         several aspects of climate variability over South America. 
                         Especially, the ability of the model in simulating the main 
                         teleconnection patterns that affect South America and the 
                         precipitation variability in several regions of Brazil associated 
                         with the Pacific and Atlantic Sea Surface Temperature. The model 
                         is the atmospheric component of the Brazilian Earth System 
                         Model-OceanAtmosphere (BESM), which can be used to long 
                         integrations due to the simplified physics, considering computer 
                         limitations. Climate variability is investigated through analyses 
                         of variance and correlations, and teleconnections such as Southern 
                         Annular Mode (SAM) and Pacific South American (PSA) are obtained 
                         from EOF analyses. El Niņo Southern Oscillation (ENSO) features 
                         are analysed through the Southern Oscillation Index and 
                         precipitation anomalies. BAM-v0, even at coarse resolution, 
                         represents many climate variability features. It captures the 
                         influences of tropical Pacific and Atlantic Oceans on Northeast 
                         Brazil precipitation and reproduces the influences of ENSO over 
                         South America. SAM and PSA teleconnections are well simulated. 
                         Observed features of the South America Monsoon System are captured 
                         by the model, although the intensities of precipitation 
                         variability need to be improved. There are some deficiencies 
                         related to global budget, precipitation variance in some regions 
                         of the globe and precipitation anomalies in certain regions of 
                         South America. Identification of model deficiencies and 
                         variability analyses are important to model development and 
                         contribute to climate prediction improvements.",
                  doi = "10.1002/joc.6370",
                  url = "http://dx.doi.org/10.1002/joc.6370",
                 issn = "0899-8418",
             language = "en",
           targetfile = "cavalcanti_climate-compactado.pdf",
        urlaccessdate = "13 abr. 2021"