lmd_Dufresne2015.bib

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@article{2015NatCC...5..280G,
  author = {{Good}, P. and {Lowe}, J.~A. and {Andrews}, T. and {Wiltshire}, A. and 
	{Chadwick}, R. and {Ridley}, J.-K. and {Menary}, M.~B. and {Bouttes}, N. and 
	{Dufresne}, J.~L. and {Gregory}, J.~M. and {Schaller}, N. and 
	{Shiogama}, H.},
  title = {{Corrigendum: Nonlinear regional warming with increasing CO$_{2}$ concentrations}},
  journal = {Nature Climate Change},
  year = 2015,
  month = mar,
  volume = 5,
  pages = {280},
  doi = {10.1038/nclimate2546},
  adsurl = {http://adsabs.harvard.edu/abs/2015NatCC...5..280G},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{2015JAtS...72.1022J,
  author = {{Jiang}, J.~H. and {Su}, H. and {Zhai}, C. and {Janice Shen}, T. and 
	{Wu}, T. and {Zhang}, J. and {Cole}, J.~N.~S. and {von Salzen}, K. and 
	{Donner}, L.~J. and {Seman}, C. and {Del Genio}, A. and {Nazarenko}, L.~S. and 
	{Dufresne}, J.-L. and {Watanabe}, M. and {Morcrette}, C. and 
	{Koshiro}, T. and {Kawai}, H. and {Gettelman}, A. and {Mill{\'a}n}, L. and 
	{Read}, W.~G. and {Livesey}, N.~J. and {Kasai}, Y. and {Shiotani}, M.
	},
  title = {{Evaluating the Diurnal Cycle of Upper-Tropospheric Ice Clouds in Climate Models Using SMILES Observations}},
  journal = {Journal of Atmospheric Sciences},
  year = 2015,
  month = mar,
  volume = 72,
  pages = {1022-1044},
  doi = {10.1175/JAS-D-14-0124.1},
  adsurl = {http://adsabs.harvard.edu/abs/2015JAtS...72.1022J},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{2015NatCC...5..138G,
  author = {{Good}, P. and {Lowe}, J.~A. and {Andrews}, T. and {Wiltshire}, A. and 
	{Chadwick}, R. and {Ridley}, J.~K. and {Menary}, M.~B. and {Bouttes}, N. and 
	{Dufresne}, J.~L. and {Gregory}, J.~M. and {Schaller}, N. and 
	{Shiogama}, H.},
  title = {{Nonlinear regional warming with increasing CO$_{2}$ concentrations}},
  journal = {Nature Climate Change},
  year = 2015,
  month = feb,
  volume = 5,
  pages = {138-142},
  abstract = {{When considering adaptation measures and global climate mitigation
goals, stakeholders need regional-scale climate projections, including
the range of plausible warming rates. To assist these stakeholders, it
is important to understand whether some locations may see
disproportionately high or low warming from additional forcing above
targets such as 2 K (ref. ). There is a need to narrow uncertainty in
this nonlinear warming, which requires understanding how climate changes
as forcings increase from medium to high levels. However, quantifying
and understanding regional nonlinear processes is challenging. Here we
show that regional-scale warming can be strongly superlinear to
successive CO$_{2}$ doublings, using five different climate
models. Ensemble-mean warming is superlinear over most land locations.
Further, the inter-model spread tends to be amplified at higher forcing
levels, as nonlinearities grow--especially when considering changes per
kelvin of global warming. Regional nonlinearities in surface warming
arise from nonlinearities in global-mean radiative balance, the Atlantic
meridional overturning circulation, surface snow/ice cover and
evapotranspiration. For robust adaptation and mitigation advice,
therefore, potentially avoidable climate change (the difference between
business-as-usual and mitigation scenarios) and unavoidable climate
change (change under strong mitigation scenarios) may need different
analysis methods.
}},
  doi = {10.1038/nclimate2498},
  adsurl = {http://adsabs.harvard.edu/abs/2015NatCC...5..138G},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}