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Observed Relationships between Arctic Longwave Cloud Forcing and Cloud Parameters Using a Neural Network
 

Summary: Observed Relationships between Arctic Longwave Cloud Forcing and Cloud
Parameters Using a Neural Network
YONGHUA CHEN*
Institute of Marine and Coastal Sciences, Rutgers--The State University of New Jersey, New Brunswick, New Jersey
FILIPE AIRES
Laboratoire de Météorologie Dynamique du CNRS/Institut Pierre Simon Laplace, Université Pierre et Marie Curie, Paris, France
JENNIFER A. FRANCIS AND JAMES R. MILLER
Institute of Marine and Coastal Sciences, Rutgers--The State University of New Jersey, New Brunswick, New Jersey
(Manuscript received 20 June 2005, in final form 27 October 2005)
ABSTRACT
A neural network technique is used to quantify relationships involved in cloud­radiation feedbacks based
on observations from the Surface Heat Budget of the Arctic (SHEBA) project. Sensitivities of longwave
cloud forcing (CFL) to cloud parameters indicate that a bimodal distribution pattern dominates the histo-
gram of each sensitivity. Although the mean states of the relationships agree well with those derived in a
previous study, they do not often exist in reality. The sensitivity of CFL to cloud cover increases as the
cloudiness increases with a range of 0.1­0.9 W m 2
% 1
. There is a saturation effect of liquid water path
(LWP) on CFL. The highest sensitivity of CFL to LWP corresponds to clouds with low LWP, and sensitivity
decreases as LWP increases. The sensitivity of CFL to cloud-base height (CBH) depends on whether the

  

Source: Aires, Filipe - Laboratoire de Météorologie Dynamique du CNRS, Université Pierre-et-Marie-Curie, Paris 6
Fridlind, Ann - Earth Science Division, NASA Ames Research Center

 

Collections: Environmental Sciences and Ecology; Geosciences