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Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors.
Dadashazar, Hossein; Painemal, David; Alipanah, Majid; Brunke, Michael; Chellappan, Seethala; Corral, Andrea F; Crosbie, Ewan; Kirschler, Simon; Liu, Hongyu; Moore, Richard H; Robinson, Claire; Scarino, Amy Jo; Shook, Michael; Sinclair, Kenneth; Thornhill, K Lee; Voigt, Christiane; Wang, Hailong; Winstead, Edward; Zeng, Xubin; Ziemba, Luke; Zuidema, Paquita; Sorooshian, Armin.
Afiliação
  • Dadashazar H; Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA.
  • Painemal D; NASA Langley Research Center, Hampton, VA, USA.
  • Alipanah M; Science Systems and Applications, Inc., Hampton, VA, USA.
  • Brunke M; Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA.
  • Chellappan S; Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA.
  • Corral AF; Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA.
  • Crosbie E; Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA.
  • Kirschler S; NASA Langley Research Center, Hampton, VA, USA.
  • Liu H; Science Systems and Applications, Inc., Hampton, VA, USA.
  • Moore RH; Institute of Atmospheric Physics, German Aerospace Center, Oberpfaffenhofen, Germany.
  • Robinson C; National Institute of Aerospace, Hampton, VA, USA.
  • Scarino AJ; NASA Langley Research Center, Hampton, VA, USA.
  • Shook M; NASA Langley Research Center, Hampton, VA, USA.
  • Sinclair K; Science Systems and Applications, Inc., Hampton, VA, USA.
  • Thornhill KL; NASA Langley Research Center, Hampton, VA, USA.
  • Voigt C; Science Systems and Applications, Inc., Hampton, VA, USA.
  • Wang H; NASA Langley Research Center, Hampton, VA, USA.
  • Winstead E; NASA Goddard Institute for Space Studies, New York, NY, USA.
  • Zeng X; Universities Space Research Association, Columbia, MD, USA.
  • Ziemba L; NASA Langley Research Center, Hampton, VA, USA.
  • Zuidema P; Institute of Atmospheric Physics, German Aerospace Center, Oberpfaffenhofen, Germany.
  • Sorooshian A; Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA.
Atmos Chem Phys ; 21(13): 10499-10526, 2021 Jul.
Article em En | MEDLINE | ID: mdl-34377145
ABSTRACT
Cloud drop number concentrations (N d) over the western North Atlantic Ocean (WNAO) are generally highest during the winter (DJF) and lowest in summer (JJA), in contrast to aerosol proxy variables (aerosol optical depth, aerosol index, surface aerosol mass concentrations, surface cloud condensation nuclei (CCN) concentrations) that generally peak in spring (MAM) and JJA with minima in DJF. Using aircraft, satellite remote sensing, ground-based in situ measurement data, and reanalysis data, we characterize factors explaining the divergent seasonal cycles and furthermore probe into factors influencing N d on seasonal timescales. The results can be summarized well by features most pronounced in DJF, including features associated with cold-air outbreak (CAO) conditions such as enhanced values of CAO index, planetary boundary layer height (PBLH), low-level liquid cloud fraction, and cloud-top height, in addition to winds aligned with continental outflow. Data sorted into high- and low-N d days in each season, especially in DJF, revealed that all of these conditions were enhanced on the high-N d days, including reduced sea level pressure and stronger wind speeds. Although aerosols may be more abundant in MAM and JJA, the conditions needed to activate those particles into cloud droplets are weaker than in colder months, which is demonstrated by calculations of the strongest (weakest) aerosol indirect effects in DJF (JJA) based on comparing N d to perturbations in four different aerosol proxy variables (total and sulfate aerosol optical depth, aerosol index, surface mass concentration of sulfate). We used three machine learning models and up to 14 input variables to infer about most influential factors related to N d for DJF and JJA, with the best performance obtained with gradient-boosted regression tree (GBRT) analysis. The model results indicated that cloud fraction was the most important input variable, followed by some combination (depending on season) of CAO index and surface mass concentrations of sulfate and organic carbon. Future work is recommended to further understand aspects uncovered here such as impacts of free tropospheric aerosol entrainment on clouds, degree of boundary layer coupling, wet scavenging, and giant CCN effects on aerosol-N d relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite estimation of N d.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article