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lmd_Dufresne2009_bib.html

lmd_Dufresne2009.bib

@comment{{This file has been generated by bib2bib 1.95}}
@comment{{Command line: /usr/bin/bib2bib --quiet -c 'not journal:"Discussions"' -c 'not journal:"Polymer Science"' -c '  author:"Dufresne"  ' -c year=2009 -c $type="ARTICLE" -oc lmd_Dufresne2009.txt -ob lmd_Dufresne2009.bib /home/WWW/LMD/public/Publis_LMDEMC3.link.bib}}
@article{2009JGRE..11411008E,
  author = {{Eymet}, V. and {Fournier}, R. and {Dufresne}, J.-L. and {Lebonnois}, S. and 
	{Hourdin}, F. and {Bullock}, M.~A.},
  title = {{Net exchange parameterization of thermal infrared radiative transfer in Venus' atmosphere}},
  journal = {Journal of Geophysical Research (Planets)},
  keywords = {Atmospheric Processes: Radiative processes, Atmospheric Composition and Structure: Radiation: transmission and scattering, Global Change: Global climate models (3337, 4928), Atmospheric Composition and Structure: Cloud/radiation interaction, Mineral Physics: Optical, infrared, and Raman spectroscopy},
  year = 2009,
  month = nov,
  volume = 114,
  number = e13,
  eid = {E11008},
  pages = {11008},
  abstract = {{Thermal radiation within Venus atmosphere is analyzed in close details.
Prominent features are identified, which are then used to design a
parameterization (a highly simplified and yet accurate enough model) to
be used in General Circulation Models. The analysis is based on a net
exchange formulation, using a set of gaseous and cloud optical data
chosen among available referenced data. The accuracy of the proposed
parameterization methodology is controlled against Monte Carlo
simulations, assuming that the optical data are exact. Then, the
accuracy level corresponding to our present optical data choice is
discussed by comparison with available observations, concentrating on
the most unknown aspects of Venus thermal radiation, namely the deep
atmosphere opacity and the cloud composition and structure.
}},
  doi = {10.1029/2008JE003276},
  adsurl = {http://adsabs.harvard.edu/abs/2009JGRE..11411008E},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{2009GeoRL..3616707H,
  author = {{Hannart}, A. and {Dufresne}, J.-L. and {Naveau}, P.},
  title = {{Why climate sensitivity may not be so unpredictable}},
  journal = {\grl},
  keywords = {Atmospheric Processes: Climate change and variability (1616, 1635, 3309, 4215, 4513), Mathematical Geophysics: Uncertainty quantification (1873), Global Change: Global climate models (3337, 4928)},
  year = 2009,
  month = aug,
  volume = 36,
  eid = {L16707},
  pages = {16707},
  abstract = {{Different explanations have been proposed as to why the range of climate
sensitivity predicted by GCMs has not lessened substantially in the last
decades, and subsequently if it can be reduced. One such study (Why is
climate sensitivity so unpredictable?) addressed these questions using
rather simple theoretical considerations and reached the conclusion that
reducing uncertainties on climate feedbacks and underlying climate
processes will not yield a large reduction in the envelope of climate
sensitivity. In this letter, we revisit the premises of this conclusion.
We show that it results from a mathematical artifact caused by a
peculiar definition of uncertainty used by these authors. Applying
standard concepts and definitions of descriptive statistics to the exact
same framework of analysis as Roe and Baker, we show that within this
simple framework, reducing inter-model spread on feedbacks does in fact
induce a reduction of uncertainty on climate sensitivity, almost
proportionally. Therefore, following Roe and Baker assumptions, climate
sensitivity is actually not so unpredictable.
}},
  doi = {10.1029/2009GL039640},
  adsurl = {http://adsabs.harvard.edu/abs/2009GeoRL..3616707H},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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