@Article{PendharkarFVKSKAVGNH:2023:AeMiIn,
               author = "Pendharkar, Jayant and Figueroa, Silvio Nilo and Vara-Vela, Angel 
                         and Krishna, R. Phani Murali and Schuch, Daniel and Kubota, Paulo 
                         Yoshio and Alvim, D{\'e}bora Souza and Vendrasco, {\'E}der Paulo 
                         and Gomes, Helber Barros and Nobre, Paulo and Herdies, Dirceu 
                         Luis",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Aarhus University} 
                         and {Indian Institute of Tropical Meteorology} and {Northeastern 
                         University} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Alagoas (UFAL)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Towards unified online-coupled aerosol parameterization for the 
                         Brazilian Global Atmospheric Model (BAM): aerosol-cloud 
                         microphysical-radiation interactions",
              journal = "Remote Sensing",
                 year = "2023",
               volume = "15",
               number = "1",
                pages = "e278",
                month = "Jan.",
             keywords = "aerosol-cloud microphysical-radiation interactions, aerosols, 
                         global model.",
             abstract = "In this work, we report the ongoing implementation of 
                         online-coupled aerosol-cloud microphysical-radiation interactions 
                         in the Brazilian global atmospheric model (BAM) and evaluate the 
                         initial results, using remote-sensing data for JFM 2014 and JAS 
                         2019. Rather than developing a new aerosol model, which incurs 
                         significant overheads in terms of fundamental research and 
                         workforce, a simplified aerosol module from a preexisting global 
                         aerosol-chemistry-climate model is adopted. The aerosol module is 
                         based on a modal representation and comprises a suite of aerosol 
                         microphysical processes. Mass and number mixing ratios, along with 
                         dry and wet radii, are predicted for black carbon, particulate 
                         organic matter, secondary organic aerosols, sulfate, dust, and sea 
                         salt aerosols. The module is extended further to include 
                         physically based parameterization for aerosol activation, vertical 
                         mixing, ice nucleation, and radiative optical properties 
                         computations. The simulated spatial patterns of surface mass and 
                         number concentrations are similar to those of other studies. The 
                         global means of simulated shortwave and longwave cloud radiative 
                         forcing are comparable with observations with normalized mean 
                         biases <= 11% and <= 30%, respectively. Large positive bias in BAM 
                         control simulation is enhanced with the inclusion of aerosols, 
                         resulting in strong overprediction of cloud optical properties. 
                         Simulated aerosol optical depths over biomass burning regions are 
                         moderately comparable. A case study simulating an intense biomass 
                         burning episode in the Amazon is able to reproduce the transport 
                         of smoke plumes towards the southeast, thus showing a potential 
                         for improved forecasts subject to using near-real-time 
                         remote-sensing fire products and a fire emission model. Here, we 
                         rely completely on remote-sensing data for the present evaluation 
                         and restrain from comparing our results with previous results 
                         until a complete representation of the aerosol lifecycle is 
                         implemented. A further step is to incorporate dry deposition, 
                         in-cloud and below-cloud scavenging, sedimentation, the sulfur 
                         cycle, and the treatment of fires.",
                  doi = "10.3390/rs15010278",
                  url = "http://dx.doi.org/10.3390/rs15010278",
                 issn = "2072-4292",
                label = "self-archiving-INPE-MCTIC-GOV-BR",
             language = "en",
           targetfile = "remotesensing-15-00278-v2.pdf",
        urlaccessdate = "16 jun. 2024"
}