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  author = {{Gaetani}, M. and {Flamant}, C. and {Bastin}, S. and {Janicot}, S. and 
	{Lavaysse}, C. and {Hourdin}, F. and {Braconnot}, P. and {Bony}, S.
  title = {{West African monsoon dynamics and precipitation: the competition between global SST warming and CO$_{2}$ increase in CMIP5 idealized simulations}},
  journal = {Climate Dynamics},
  year = 2017,
  month = feb,
  volume = 48,
  pages = {1353-1373},
  abstract = {{Climate variability associated with the West African monsoon (WAM) has
important environmental and socio-economic impacts in the region.
However, state-of-the-art climate models still struggle in producing
reliable climate predictions. An important cause of this low predictive
skill is the sensitivity of climate models to different forcings. In
this study, the mechanisms linking the WAM dynamics to the
CO$_{2}$ forcing are investigated, by comparing the effect of the
CO$_{2}$ direct radiative effect with its indirect effect mediated
by the global sea surface warming. The July-to-September WAM variability
is studied in climate simulations extracted from the Coupled Model
Intercomparison Project Phase 5 archive, driven by prescribed sea
surface temperature (SST). The individual roles of global SST warming
and CO$_{2}$ atmospheric concentration increase are investigated
through idealized experiments simulating a 4 K warmer SST and a
quadrupled CO$_{2}$ concentration, respectively. Results show
opposite and competing responses in the WAM dynamics and precipitation.
A dry response (-0.6 mm/day) to the SST warming is simulated in the
Sahel, with dryer conditions over western Sahel (-0.8 mm/day).
Conversely, the CO$_{2}$ increase produces wet conditions (+0.5
mm/day) in the Sahel, with the strongest response over central-eastern
Sahel (+0.7 mm/day). The associated responses in the atmospheric
dynamics are also analysed, showing that the SST warming affects the
Sahelian precipitation through modifications in the global tropical
atmospheric dynamics, reducing the importance of the regional drivers,
while the CO$_{2}$ increase reinforces the coupling between
precipitation and regional dynamics. A general agreement in model
responses demonstrates the robustness of the identified mechanisms
linking the WAM dynamics to the CO$_{2}$ direct and indirect
forcing, and indicates that these primary mechanisms are captured by
climate models. Results also suggest that the spread in future
projections may be caused by unbalanced model responses to the
CO$_{2}$ direct and indirect forcing.
  doi = {10.1007/s00382-016-3146-z},
  adsurl = {http://adsabs.harvard.edu/abs/2017ClDy...48.1353G},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
  author = {{Collins}, W.~J. and {Lamarque}, J.-F. and {Schulz}, M. and 
	{Boucher}, O. and {Eyring}, V. and {Hegglin}, M.~I. and {Maycock}, A. and 
	{Myhre}, G. and {Prather}, M. and {Shindell}, D. and {Smith}, S.~J.
  title = {{AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6}},
  journal = {Geoscientific Model Development},
  year = 2017,
  month = feb,
  volume = 10,
  pages = {585-607},
  abstract = {{The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is
endorsed by the Coupled-Model Intercomparison Project 6 (CMIP6) and is
designed to quantify the climate and air quality impacts of aerosols and
chemically reactive gases. These are specifically near-term climate
forcers (NTCFs: methane, tropospheric ozone and aerosols, and their
precursors), nitrous oxide and ozone-depleting halocarbons. The aim of
AerChemMIP is to answer four scientific questions. 

1. How have anthropogenic emissions contributed to global radiative forcing and affected regional climate over the historical period?

2. How might future policies (on climate, air quality and land use) affect the abundances of NTCFs and their climate impacts?

3.How do uncertainties in historical NTCF emissions affect radiative forcing estimates?

4. How important are climate feedbacks to natural NTCF emissions, atmospheric composition, and radiative effects?

These questions will be addressed through targeted simulations with CMIP6 climate models that include an interactive representation of tropospheric aerosols and atmospheric chemistry. These simulations build on the CMIP6 Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, the CMIP6 historical simulations, and future projections performed elsewhere in CMIP6, allowing the contributions from aerosols and/or chemistry to be quantified. Specific diagnostics are requested as part of the CMIP6 data request to highlight the chemical composition of the atmosphere, to evaluate the performance of the models, and to understand differences in behaviour between them. }}, doi = {10.5194/gmd-10-585-2017}, adsurl = {http://adsabs.harvard.edu/abs/2017GMD....10..585C}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }
  author = {{Marotzke}, J. and {Jakob}, C. and {Bony}, S. and {Dirmeyer}, P.~A. and 
	{O'Gorman}, P.~A. and {Hawkins}, E. and {Perkins-Kirkpatrick}, S. and 
	{Quéré}, C.~L. and {Nowicki}, S. and {Paulavets}, K. and 
	{Seneviratne}, S.~I. and {Stevens}, B. and {Tuma}, M.},
  title = {{Climate research must sharpen its view}},
  journal = {Nature Climate Change},
  year = 2017,
  month = jan,
  volume = 7,
  pages = {89-91},
  abstract = {{Human activity is changing Earth's climate. Now that this has been
acknowledged and accepted in international negotiations, climate
research needs to define its next frontiers.
  doi = {10.1038/nclimate3206},
  adsurl = {http://adsabs.harvard.edu/abs/2017NatCC...7...89M},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
  author = {{Webb}, M.~J. and {Andrews}, T. and {Bodas-Salcedo}, A. and 
	{Bony}, S. and {Bretherton}, C.~S. and {Chadwick}, R. and {Chepfer}, H. and 
	{Douville}, H. and {Good}, P. and {Kay}, J.~E. and {Klein}, S.~A. and 
	{Marchand}, R. and {Medeiros}, B. and {Pier Siebesma}, A. and 
	{Skinner}, C.~B. and {Stevens}, B. and {Tselioudis}, G. and 
	{Tsushima}, Y. and {Watanabe}, M.},
  title = {{The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6}},
  journal = {Geoscientific Model Development},
  year = 2017,
  month = jan,
  volume = 10,
  pages = {359-384},
  abstract = {{The primary objective of CFMIP is to inform future assessments of cloud
feedbacks through improved understanding of cloud-climate feedback
mechanisms and better evaluation of cloud processes and cloud feedbacks
in climate models. However, the CFMIP approach is also increasingly
being used to understand other aspects of climate change, and so a
second objective has now been introduced, to improve understanding of
circulation, regional-scale precipitation, and non-linear changes. CFMIP
is supporting ongoing model inter-comparison activities by coordinating
a hierarchy of targeted experiments for CMIP6, along with a set of
cloud-related output diagnostics. CFMIP contributes primarily to
addressing the CMIP6 questions $\lt$q$\gt$How does the Earth system
respond to forcing?$\lt$/q$\gt$ and $\lt$q$\gt$What are the origins and
consequences of systematic model biases?$\lt$/q$\gt$ and supports the
activities of the WCRP Grand Challenge on Clouds, Circulation and
Climate Sensitivity.

A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO$_{2}$, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO$_{2}$ doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO$_{2}$ forcing and sea surface warming?

CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. $\lt$ol class=``enumerate''$\gt$$\lt$li class=``item''$\gt$$\lt$div class=``para''$\gt$$\lt$p class=``p''$\gt$How well do clouds and other relevant variables simulated by models agree with observations?$\lt$/div$\gt$$\lt$/li$\gt$$\lt$li class=``item''$\gt$$\lt$div class=``para''$\gt$$\lt$p class=``p''$\gt$What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?$\lt$/div$\gt$$\lt$/li$\gt$$\lt$li class=``item''$\gt$$\lt$div class=``para''$\gt$$\lt$p class=``p''$\gt$Which models have the most credible representations of processes relevant to the simulation of clouds?$\lt$/div$\gt$$\lt$/li$\gt$$\lt$li class=``item''$\gt$$\lt$div class=``para''$\gt$$\lt$p class=``p''$\gt$How do clouds and their changes interact with other elements of the climate system?$\lt$/div$\gt$$\lt$/li$\gt$$\lt$/ol$\gt$ }}, doi = {10.5194/gmd-10-359-2017}, adsurl = {http://adsabs.harvard.edu/abs/2017GMD....10..359W}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }
  author = {{Gasser}, T. and {Ciais}, P. and {Boucher}, O. and {Quilcaille}, Y. and 
	{Tortora}, M. and {Bopp}, L. and {Hauglustaine}, D.},
  title = {{The compact Earth system model OSCAR v2.2: description and first results}},
  journal = {Geoscientific Model Development},
  year = 2017,
  month = jan,
  volume = 10,
  pages = {271-319},
  abstract = {{This paper provides a comprehensive description of OSCAR v2.2, a simple
Earth system model. The general philosophy of development is first
explained, followed by a complete description of the model's drivers and
various modules. All components of the Earth system necessary to
simulate future climate change are represented in the model: the oceanic
and terrestrial carbon cycles - including a book-keeping module to
endogenously estimate land-use change emissions - so as to simulate the
change in atmospheric carbon dioxide; the tropospheric chemistry and the
natural wetlands, to simulate that of methane; the stratospheric
chemistry, for nitrous oxide; 37 halogenated compounds; changing
tropospheric and stratospheric ozone; the direct and indirect effects of
aerosols; changes in surface albedo caused by black carbon deposition on
snow and land-cover change; and the global and regional response of
climate - in terms of temperature and precipitation - to all these
climate forcers. Following the probabilistic framework of the model, an
ensemble of simulations is made over the historical period (1750-2010).
We show that the model performs well in reproducing observed past
changes in the Earth system such as increased atmospheric concentration
of greenhouse gases or increased global mean surface temperature.
  doi = {10.5194/gmd-10-271-2017},
  adsurl = {http://adsabs.harvard.edu/abs/2017GMD....10..271G},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
  author = {{Genthon}, C. and {Piard}, L. and {Vignon}, E. and {Madeleine}, J.-B. and 
	{Casado}, M. and {Gallée}, H.},
  title = {{Atmospheric moisture supersaturation in the near-surface atmosphere at Dome C, Antarctic Plateau}},
  journal = {Atmospheric Chemistry \& Physics},
  year = 2017,
  month = jan,
  volume = 17,
  pages = {691-704},
  abstract = {{Supersaturation often occurs at the top of the troposphere where cirrus
clouds form, but is comparatively unusual near the surface where the air
is generally warmer and laden with liquid and/or ice condensation
nuclei. One exception is the surface of the high Antarctic Plateau. One
year of atmospheric moisture measurement at the surface of Dome C on the
East Antarctic Plateau is presented. The measurements are obtained using
commercial hygrometry sensors modified to allow air sampling without
affecting the moisture content, even in the case of supersaturation.
Supersaturation is found to be very frequent. Common unadapted
hygrometry sensors generally fail to report supersaturation, and most
reports of atmospheric moisture on the Antarctic Plateau are thus likely
biased low. The measurements are compared with results from two models
implementing cold microphysics parameterizations: the European Center
for Medium-range Weather Forecasts through its operational analyses, and
the Model Atmosphérique Régional. As in the observations,
supersaturation is frequent in the models but the statistical
distribution differs both between models and observations and between
the two models, leaving much room for model improvement. This is
unlikely to strongly affect estimations of surface sublimation because
supersaturation is more frequent as temperature is lower, and moisture
quantities and thus water fluxes are small anyway. Ignoring
supersaturation may be a more serious issue when considering water
isotopes, a tracer of phase change and temperature, largely used to
reconstruct past climates and environments from ice cores. Because
observations are easier in the surface atmosphere, longer and more
continuous in situ observation series of atmospheric supersaturation can
be obtained than higher in the atmosphere to test parameterizations of
cold microphysics, such as those used in the formation of high-altitude
cirrus clouds in meteorological and climate models.
  doi = {10.5194/acp-17-691-2017},
  adsurl = {http://adsabs.harvard.edu/abs/2017ACP....17..691G},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}