lmd_Musat2006.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 = {https://ui.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 = {https://ui.adsabs.harvard.edu/abs/2006ClDy...26..145W},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{2006ClDy...27...17W,
  author = {{Webb}, M.~J. and {Senior}, C.~A. and {Sexton}, D.~M.~H. and 
	{Ingram}, W.~J. and {Williams}, K.~D. and {Ringer}, M.~A. and 
	{McAvaney}, B.~J. and {Colman}, R. and {Soden}, B.~J. and {Gudgel}, R. and 
	{Knutson}, T. and {Emori}, S. and {Ogura}, T. and {Tsushima}, Y. and 
	{Andronova}, N. and {Li}, B. and {Musat}, I. and {Bony}, S. and 
	{Taylor}, K.~E.},
  title = {{On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles}},
  journal = {Climate Dynamics},
  year = 2006,
  month = jul,
  volume = 27,
  pages = {17-38},
  abstract = {{Global and local feedback analysis techniques have been applied to two
ensembles of mixed layer equilibrium CO$_{2}$ doubling climate
change experiments, from the CFMIP (Cloud Feedback Model Intercomparison
Project) and QUMP (Quantifying Uncertainty in Model Predictions)
projects. Neither of these new ensembles shows evidence of a
statistically significant change in the ensemble mean or variance in
global mean climate sensitivity when compared with the results from the
mixed layer models quoted in the Third Assessment Report of the IPCC.
Global mean feedback analysis of these two ensembles confirms the large
contribution made by inter-model differences in cloud feedbacks to those
in climate sensitivity in earlier studies; net cloud feedbacks are
responsible for 66\% of the inter-model variance in the total feedback in
the CFMIP ensemble and 85\% in the QUMP ensemble. The ensemble mean
global feedback components are all statistically indistinguishable
between the two ensembles, except for the clear-sky shortwave feedback
which is stronger in the CFMIP ensemble. While ensemble variances of the
shortwave cloud feedback and both clear-sky feedback terms are larger in
CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP
spans 80\% or more of the CFMIP ranges in longwave and shortwave cloud
feedback. We introduce a local cloud feedback classification system
which distinguishes different types of cloud feedbacks on the basis of
the relative strengths of their longwave and shortwave components, and
interpret these in terms of responses of different cloud types diagnosed
by the International Satellite Cloud Climatology Project simulator. In
the CFMIP ensemble, areas where low-top cloud changes constitute the
largest cloud response are responsible for 59\% of the contribution from
cloud feedback to the variance in the total feedback. A similar figure
is found for the QUMP ensemble. Areas of positive low cloud feedback
(associated with reductions in low level cloud amount) contribute most
to this figure in the CFMIP ensemble, while areas of negative cloud
feedback (associated with increases in low level cloud amount and
optical thickness) contribute most in QUMP. Classes associated with
high-top cloud feedbacks are responsible for 33 and 20\% of the cloud
feedback contribution in CFMIP and QUMP, respectively, while classes
where no particular cloud type stands out are responsible for 8 and 21\%.
}},
  doi = {10.1007/s00382-006-0111-2},
  adsurl = {https://ui.adsabs.harvard.edu/abs/2006ClDy...27...17W},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}