lmd_Dufresne2006.bib

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@article{2006ClDy...27..787H,
  author = {{Hourdin}, F. and {Musat}, I. and {Bony}, S. and {Braconnot}, P. and 
	{Codron}, F. and {Dufresne}, J.-L. and {Fairhead}, L. and {Filiberti}, M.-A. and 
	{Friedlingstein}, P. and {Grandpeix}, J.-Y. and {Krinner}, G. and 
	{Levan}, P. and {Li}, Z.-X. and {Lott}, F.},
  title = {{The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection}},
  journal = {Climate Dynamics},
  year = 2006,
  month = dec,
  volume = 27,
  pages = {787-813},
  abstract = {{The LMDZ4 general circulation model is the atmospheric component of the
IPSL CM4 coupled model which has been used to perform climate change
simulations for the 4th IPCC assessment report. The main aspects of the
model climatology (forced by observed sea surface temperature) are
documented here, as well as the major improvements with respect to the
previous versions, which mainly come form the parametrization of
tropical convection. A methodology is proposed to help analyse the
sensitivity of the tropical Hadley Walker circulation to the
parametrization of cumulus convection and clouds. The tropical
circulation is characterized using scalar potentials associated with the
horizontal wind and horizontal transport of geopotential (the Laplacian
of which is proportional to the total vertical momentum in the
atmospheric column). The effect of parametrized physics is analysed in a
regime sorted framework using the vertical velocity at 500 hPa as a
proxy for large scale vertical motion. Compared to Tiedtke{\rsquo}s
convection scheme, used in previous versions, the Emanuel{\rsquo}s scheme
improves the representation of the Hadley Walker circulation, with a
relatively stronger and deeper large scale vertical ascent over tropical
continents, and suppresses the marked patterns of concentrated rainfall
over oceans. Thanks to the regime sorted analyses, these differences are
attributed to intrinsic differences in the vertical distribution of
convective heating, and to the lack of self-inhibition by precipitating
downdraughts in Tiedtke{\rsquo}s parametrization. Both the convection and
cloud schemes are shown to control the relative importance of large
scale convection over land and ocean, an important point for the
behaviour of the coupled model.
}},
  doi = {10.1007/s00382-006-0158-0},
  adsurl = {http://adsabs.harvard.edu/abs/2006ClDy...27..787H},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{2006ClDy...26..145W,
  author = {{Williams}, K.~D. and {Ringer}, M.~A. and {Senior}, C.~A. and 
	{Webb}, M.~J. and {McAvaney}, B.~J. and {Andronova}, N. and 
	{Bony}, S. and {Dufresne}, J.-L. and {Emori}, S. and {Gudgel}, R. and 
	{Knutson}, T. and {Li}, B. and {Lo}, K. and {Musat}, I. and 
	{Wegner}, J. and {Slingo}, A. and {Mitchell}, J.~F.~B.},
  title = {{Evaluation of a component of the cloud response to climate change in an intercomparison of climate models}},
  journal = {Climate Dynamics},
  year = 2006,
  month = feb,
  volume = 26,
  pages = {145-165},
  abstract = {{Most of the uncertainty in the climate sensitivity of contemporary
general circulation models (GCMs) is believed to be connected with
differences in the simulated radiative feedback from clouds. Traditional
methods of evaluating clouds in GCMs compare time-mean geographical
cloud fields or aspects of present-day cloud variability, with
observational data. In both cases a hypothetical assumption is made that
the quantity evaluated is relevant for the mean climate change response.
Nine GCMs (atmosphere models coupled to mixed-layer ocean models) from
the CFMIP and CMIP model comparison projects are used in this study to
demonstrate a common relationship between the mean cloud response to
climate change and present-day variability. Although
atmosphere-mixed-layer ocean models are used here, the results are found
to be equally applicable to transient coupled model simulations. When
changes in cloud radiative forcing (CRF) are composited by changes in
vertical velocity and saturated lower tropospheric stability, a
component of the local mean climate change response can be related to
present-day variability in all of the GCMs. This suggests that the
relationship is not model specific and might be relevant in the real
world. In this case, evaluation within the proposed compositing
framework is a direct evaluation of a component of the cloud response to
climate change. None of the models studied are found to be clearly
superior or deficient when evaluated, but a couple appear to perform
well on several relevant metrics. Whilst some broad similarities can be
identified between the 60{\deg}N-60{\deg}S mean change in CRF to increased
CO$_{2}$ and that predicted from present-day variability, the two
cannot be quantitatively constrained based on changes in vertical
velocity and stability alone. Hence other processes also contribute to
the global mean cloud response to climate change.
}},
  doi = {10.1007/s00382-005-0067-7},
  adsurl = {http://adsabs.harvard.edu/abs/2006ClDy...26..145W},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{2006JCli...19.3445B,
  author = {{Bony}, S. and {Colman}, R. and {Kattsov}, V.~M. and {Allan}, R.~P. and 
	{Bretherton}, C.~S. and {Dufresne}, J.-L. and {Hall}, A. and 
	{Hallegatte}, S. and {Holland}, M.~M. and {Ingram}, W. and {Randall}, D.~A. and 
	{Soden}, B.~J. and {Tselioudis}, G. and {Webb}, M.~J.},
  title = {{How Well Do We Understand and Evaluate Climate Change Feedback Processes?}},
  journal = {Journal of Climate},
  year = 2006,
  volume = 19,
  pages = {3445},
  doi = {10.1175/JCLI3819.1},
  adsurl = {http://adsabs.harvard.edu/abs/2006JCli...19.3445B},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{2006JGRD..11114317C,
  author = {{Collins}, W.~D. and {Ramaswamy}, V. and {Schwarzkopf}, M.~D. and 
	{Sun}, Y. and {Portmann}, R.~W. and {Fu}, Q. and {Casanova}, S.~E.~B. and 
	{Dufresne}, J.-L. and {Fillmore}, D.~W. and {Forster}, P.~M.~D. and 
	{Galin}, V.~Y. and {Gohar}, L.~K. and {Ingram}, W.~J. and {Kratz}, D.~P. and 
	{Lefebvre}, M.-P. and {Li}, J. and {Marquet}, P. and {Oinas}, V. and 
	{Tsushima}, Y. and {Uchiyama}, T. and {Zhong}, W.~Y.},
  title = {{Radiative forcing by well-mixed greenhouse gases: Estimates from climate models in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4)}},
  journal = {Journal of Geophysical Research (Atmospheres)},
  keywords = {Atmospheric Processes: Global climate models (1626, 4928), Atmospheric Processes: Radiative processes, Global Change: Global climate models (3337, Global Change: Impacts of global change (1225), radiation, models, greenhouse gas},
  year = 2006,
  month = jul,
  volume = 111,
  number = d10,
  eid = {D14317},
  pages = {14317},
  abstract = {{The radiative effects from increased concentrations of well-mixed
greenhouse gases (WMGHGs) represent the most significant and best
understood anthropogenic forcing of the climate system. The most
comprehensive tools for simulating past and future climates influenced
by WMGHGs are fully coupled atmosphere-ocean general circulation models
(AOGCMs). Because of the importance of WMGHGs as forcing agents it is
essential that AOGCMs compute the radiative forcing by these gases as
accurately as possible. We present the results of a radiative transfer
model intercomparison between the forcings computed by the radiative
parameterizations of AOGCMs and by benchmark line-by-line (LBL) codes.
The comparison is focused on forcing by CO$_{2}$, CH$_{4}$,
N$_{2}$O, CFC-11, CFC-12, and the increased H$_{2}$O
expected in warmer climates. The models included in the intercomparison
include several LBL codes and most of the global models submitted to the
Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment
Report (AR4). In general, the LBL models are in excellent agreement with
each other. However, in many cases, there are substantial discrepancies
among the AOGCMs and between the AOGCMs and LBL codes. In some cases
this is because the AOGCMs neglect particular absorbers, in particular
the near-infrared effects of CH$_{4}$ and N$_{2}$O, while in
others it is due to the methods for modeling the radiative processes.
The biases in the AOGCM forcings are generally largest at the surface
level. We quantify these differences and discuss the implications for
interpreting variations in forcing and response across the multimodel
ensemble of AOGCM simulations assembled for the IPCC AR4.
}},
  doi = {10.1029/2005JD006713},
  adsurl = {http://adsabs.harvard.edu/abs/2006JGRD..11114317C},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}