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Observed and modeled relationships among Arctic climate variables Yonghua Chen, James R. Miller, and Jennifer A. Francis
 

Summary: Observed and modeled relationships among Arctic climate variables
Yonghua Chen, James R. Miller, and Jennifer A. Francis
Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey, USA
Gary L. Russell and Filipe Aires
NASA/Goddard Institute for Space Studies, New York, New York, USA
Received 2 June 2003; revised 4 September 2003; accepted 11 September 2003; published 31 December 2003.
[1] The complex interactions among climate variables in the Arctic have important
implications for potential climate change, both globally and locally. Because the Arctic is
a data-sparse region and because global climate models (GCMs) often represent Arctic
climate variables poorly, significant uncertainties remain in our understanding of these
processes. In addition to the traditional approach of validating individual variables with
observed fields, we demonstrate that a comparison of covariances among interrelated
parameters from observations and GCM output provides a tool to evaluate the realism of
modeled relationships between variables. We analyze and compare a combination of
conventional observations, satellite retrievals, and GCM simulations to examine some of
these relationships. The three climate variables considered in this study are surface
temperature, cloud cover, and downward longwave flux. Results show that the highest
correlations between daily changes in pairs of variables for all three data sets occur
between surface temperature and downward longwave flux, particularly in winter. There is
less variability in GCM output, in part, because there is greater spatial averaging.

  

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