scholarly journals Cloud Drop Number Concentrations over the Western North Atlantic Ocean: Seasonal Cycle, Aerosol Interrelationships, and Other Influential Factors

2021 ◽  
Author(s):  
Hossein Dadashazar ◽  
David Painemal ◽  
Majid Alipanah ◽  
Michael Brunke ◽  
Seethala Chellappan ◽  
...  

Abstract. Cloud drop number concentrations (Nd) 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 measurements data as well as reanalysis data, we characterize factors explaining the divergent seasonal cycles and furthermore probe into factors influencing Nd on seasonal time scales. 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 Nd days in each season, especially in DJF, revealed that all of these conditions were enhanced on the high Nd 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 strongest (weakest) aerosol indirect effects in DJF (JJA) based on comparing Nd 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 12 input variables to infer about most influential factors related to Nd 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, wet scavenging and giant CCN effects on aerosol–Nd relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite estimation of Nd.

2021 ◽  
Vol 21 (13) ◽  
pp. 10499-10526
Author(s):  
Hossein Dadashazar ◽  
David Painemal ◽  
Majid Alipanah ◽  
Michael Brunke ◽  
Seethala Chellappan ◽  
...  

Abstract. Cloud drop number concentrations (Nd) 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 Nd 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-Nd days in each season, especially in DJF, revealed that all of these conditions were enhanced on the high-Nd 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 Nd 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 Nd 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–Nd relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite estimation of Nd.


Author(s):  
Deborah Steinberg

The structure of planktonic communities profoundly affects particle export and sequestration of organic material (the biological pump) and the chemical cycling of nutrients. This chapter describes the integral and multifaceted role zooplankton (both protozoan and metazoan) play in the export and cycling of elements in the ocean, with an emphasis on the North Atlantic Ocean and adjacent seas. Zooplankton consume a significant proportion of primary production across the world's oceans, and their metabolism plays a key role in recycling carbon, nitrogen, and other elements. The chapter also addresses how human or climate-influenced changes in North Atlantic zooplankton populations may in turn drive changes in zooplankton-mediated biogeochemical cycling.


2018 ◽  
Vol 612 ◽  
pp. 1141-1148 ◽  
Author(s):  
Min Zhang ◽  
Yuanling Zhang ◽  
Qi Shu ◽  
Chang Zhao ◽  
Gang Wang ◽  
...  

2021 ◽  
Vol 56 (7-8) ◽  
pp. 2027-2056
Author(s):  
Sandra M. Plecha ◽  
Pedro M. M. Soares ◽  
Susana M. Silva-Fernandes ◽  
William Cabos

Eos ◽  
1986 ◽  
Vol 67 (44) ◽  
pp. 835 ◽  
Author(s):  
W. E. Esaias ◽  
G. C. Feldman ◽  
C. R. McClain ◽  
J. A. Elrod

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