@InProceedings{FreireLonFreSilCoe:2018:EvImBi,
               author = "Freire, Julliana L M and Longo, Karla M. and Freitas, Saulo R. and 
                         Silva, Arlindo and Coelho, Caio A. S.",
                title = "Evaluation of the impacts of biomass burning emissions on the nasa 
                         geos seasonal climate forecast",
            booktitle = "Anais...",
                 year = "2018",
               editor = "Herdies, Dirceu Luis and Coelho, Simone Marilene Sievert da 
                         Costa",
         organization = "Encontro dos alunos de p{\'o}s-gradua{\c{c}}{\~a}o em 
                         meteorologia do CPTEC/INPE, 17. (EPGMET)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "African, Modeling, Aerosol Particles.",
             abstract = "Emission sources of trace gases and aerosol particles in the South 
                         American (SA) and African (Af) continents have a strong seasonal 
                         and space variability associated with the extensive vegetation 
                         fires activities. Smoke aerosols also act as cloud condensation 
                         nuclei affecting cloud microphysics properties and therefore, 
                         changing the radiation budget, hydrological cycle and global 
                         circulation patterns over disturbed areas (Andreae, et al. 2004; 
                         Randles et al. 2013). This study aims to evaluate and quantify the 
                         impact of including a comprehensive emission field of biomass 
                         burning aerosol on the performance of a seasonal climate forecast 
                         system, not only regarding the AOD itself but mainly on the 
                         meteorological state variable (e.g., precipitation and 
                         temperature). To address the questions put above, we designed two 
                         numerical experiments: 1- named AERO_CTL which applies the QFED 
                         emissions estimated with intra-diurnal variation (hereafter,BBE), 
                         and 2- named AERO_CLM where the source emission is based on a 
                         climatology of the QFED emissions, with only monthly variation 
                         (hereafter,BBCLIM). Hindcast simulations were produced using the 
                         GEOS5-S2S system with a nominal spatial resolution of 56km. In 
                         both experiments, the aerosol feedbacks from cloud developments 
                         and radiation interactions were accounted. The two experiments 
                         consisted of 4 members each and ran from June to November spanning 
                         over the years 2000 to 2015. Model performance was evaluated by 
                         calculating statistical metrics on the mean area of SA and Af. Our 
                         results demonstrated that the skill model in predicting AOD is 
                         significantly improve when BBE source emission is applied over SA, 
                         but not over the Af continent. Over SA, the correlation between 
                         the AERO_CTL model configuration and MERRA-2 is 0.93 (R2= 0.86, 
                         RMS=0.02, BIAS=0.01), while the AERO_CLM model presents a value of 
                         0.81 (R2= 0.65, RMS=0.04, BIAS=0.06). However, the AERO_CTL 
                         experiment better represents the inter-annual variability of the 
                         AOD in both regions. The gain of the skill in predicting the AOD 
                         by the AERO_CTL experiment is also seen in some meteorological 
                         variables. We observed an increase in the model skill in 
                         predicting the 2-meter temperature and precipitation of up to 0.3 
                         for the AERO_CTL experiment in comparison to the AERO_CLM. 
                         AERO_CLM. According to the analyzed hindcast, we inferred that 
                         representing the BBE more realistically implies in a significant 
                         gain of skills in the seasonal climate forecasting over SA and Af 
                         continents.",
  conference-location = "Cachoeira Paulista",
      conference-year = "22-26 out. 2018",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGPDW34R/3SR2CE8",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34R/3SR2CE8",
           targetfile = "MG3-06.pdf",
                 type = "Modelagem Geral",
        urlaccessdate = "19 maio 2024"
}