Low‐Level Marine Tropical Clouds in Six CMIP6 Models Are Too Few, Too Bright but Also Too Compact and Too Homogeneous
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A. Bodas‐Salcedo | H. Chepfer | J. Dufresne | J. Vial | T. Ogura | T. Koshiro | R. Roehrig | D. Konsta | M. Watanabe | Hideaki Kawai | Masahiro Watanabe
[1] B. Stevens,et al. Optically thin clouds in the trades , 2021, Atmospheric Chemistry and Physics.
[2] W. Gong,et al. Satellite retrieval of cloud base height and geometric thickness of low-level cloud based on CALIPSO , 2021 .
[3] S. Klein,et al. Observed Sensitivity of Low-Cloud Radiative Effects to Meteorological Perturbations over the Global Oceans , 2020, Journal of Climate.
[4] Peter J. Minnett,et al. Accuracy Assessment of MERRA-2 and ERA-Interim Sea Surface Temperature, Air Temperature, and Humidity Profiles over the Atlantic Ocean Using AEROSE Measurements , 2020, Journal of Climate.
[5] S. Bony,et al. Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model , 2020, Journal of Advances in Modeling Earth Systems.
[6] H. Douville,et al. The CNRM Global Atmosphere Model ARPEGE‐Climat 6.3: Description and Evaluation , 2020, Journal of Advances in Modeling Earth Systems.
[7] F. Chéruy,et al. LMDZ6A: The Atmospheric Component of the IPSL Climate Model With Improved and Better Tuned Physics , 2020, Journal of Advances in Modeling Earth Systems.
[8] M. Lebsock,et al. Climate Impact of Cloud Water Inhomogeneity through Microphysical Processes in a Global Climate Model , 2020, Journal of Climate.
[9] S. Bony,et al. Sugar, Gravel, Fish, and Flowers: Dependence of Mesoscale Patterns of Trade‐Wind Clouds on Environmental Conditions , 2020, Geophysical research letters.
[10] K. Taylor,et al. Causes of Higher Climate Sensitivity in CMIP6 Models , 2020, Geophysical Research Letters.
[11] S. Bony,et al. Sugar, gravel, fish and flowers: Mesoscale cloud patterns in the trade winds , 2019, Quarterly Journal of the Royal Meteorological Society.
[12] F. Hourdin,et al. Unified Parameterization of Convective Boundary Layer Transport and Clouds With the Thermal Plume Model , 2019, Journal of Advances in Modeling Earth Systems.
[13] S. Buehler,et al. The Dependence of Shallow Cumulus Macrophysical Properties on Large‐Scale Meteorology as Observed in ASTER Imagery , 2019, Journal of Geophysical Research: Atmospheres.
[14] S. Yukimoto,et al. Significant improvement of cloud representation in the global climate model MRI-ESM2 , 2019, Geoscientific Model Development.
[15] Ad Stoffelen,et al. Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT , 2019, Ocean Science.
[16] Mohamed Zerroukat,et al. The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations , 2011, Geoscientific Model Development.
[17] J. Dufresne,et al. Accounting for Vertical Subgrid‐Scale Heterogeneity in Low‐Level Cloud Fraction Parameterizations , 2018, Journal of Advances in Modeling Earth Systems.
[18] Dai Yamazaki,et al. Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6 , 2018, Geoscientific Model Development.
[19] R. Wood,et al. Deeper, Precipitating PBLs Associated With Optically Thin Veil Clouds in the Sc‐Cu Transition , 2018, Geophysical Research Letters.
[20] C. Bretherton,et al. Ultraclean Layers and Optically Thin Clouds in the Stratocumulus-to-Cumulus Transition. Part I: Observations , 2018 .
[21] J. R. Wilson,et al. The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 1. Simulation Characteristics With Prescribed SSTs , 2018 .
[22] J. R. Wilson,et al. The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies , 2018 .
[23] F. Martin Ralph,et al. An Intercomparison between Reanalysis and Dropsonde Observations of the Total Water Vapor Transport in Individual Atmospheric Rivers , 2017 .
[24] L. Oreopoulos,et al. New insights about cloud vertical structure from CloudSat and CALIPSO observations , 2017, Journal of geophysical research. Atmospheres : JGR.
[25] Andrew Gettelman,et al. The Art and Science of Climate Model Tuning , 2017 .
[26] S. Bony,et al. Shallowness of tropical low clouds as a predictor of climate models’ response to warming , 2016, Climate Dynamics.
[27] H. Chepfer,et al. Use of A-train satellite observations (CALIPSO–PARASOL) to evaluate tropical cloud properties in the LMDZ5 GCM , 2016, Climate Dynamics.
[28] Veronika Eyring,et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .
[29] I. Sandu,et al. Observed and modeled patterns of covariability between low‐level cloudiness and the structure of the trade‐wind layer , 2015 .
[30] Anthony J. Mannucci,et al. Assessing the performance of GPS radio occultation measurements in retrieving tropospheric humidity in cloudiness: A comparison study with radiosondes, ERA‐Interim, and AIRS data sets , 2014 .
[31] Heini Wernli,et al. Warm Conveyor Belts in the ERA-Interim Dataset (1979–2010). Part II: Moisture Origin and Relevance for Precipitation , 2014 .
[32] S. Bony,et al. On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates , 2013, Climate Dynamics.
[33] D. Winker,et al. On the nature and extent of optically thin marine low clouds , 2012 .
[34] S. Bony,et al. The ‘too few, too bright’ tropical low‐cloud problem in CMIP5 models , 2012 .
[35] H. Chepfer,et al. A process oriented characterization of tropical oceanic clouds for climate model evaluation, based on a statistical analysis of daytime A-train observations , 2012, Climate Dynamics.
[36] S. Klein,et al. Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator , 2012 .
[37] D. Klocke,et al. Tuning the climate of a global model , 2012 .
[38] A. Pier Siebesma,et al. Overlap statistics of cumuliform boundary-layer cloud fields in large-eddy simulations , 2011 .
[39] John M. Haynes,et al. COSP: Satellite simulation software for model assessment , 2011 .
[40] F. Bréon,et al. Remote sensing of aerosols by using polarized, directional and spectral measurements within the A-Train: the PARASOL mission , 2011 .
[41] J. Thepaut,et al. The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .
[42] S. Bony,et al. The GCM‐Oriented CALIPSO Cloud Product (CALIPSO‐GOCCP) , 2010 .
[43] S. Bony,et al. Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model , 2008 .
[44] Olivier Hagolle,et al. PARASOL in-flight calibration and performance. , 2007, Applied optics.
[45] C. Bretherton,et al. On the Relationship between Stratiform Low Cloud Cover and Lower-Tropospheric Stability , 2006 .
[46] I. Musat,et al. On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles , 2006 .
[47] S. Bony,et al. Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models , 2005 .
[48] S. Bony,et al. Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements , 2005 .
[49] R. Hogan,et al. Parameterizing the Difference in Cloud Fraction Defined by Area and by Volume as Observed with Radar and Lidar , 2005 .
[50] S. Bony,et al. Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models , 2001 .
[51] Anthony D. Del Genio,et al. A Prognostic Cloud Water Parameterization for Global Climate Models , 1996 .
[52] Annick Bricaud,et al. The POLDER mission: instrument characteristics and scientific objectives , 1994, IEEE Trans. Geosci. Remote. Sens..
[53] S. Klein,et al. The Seasonal Cycle of Low Stratiform Clouds , 1993 .
[54] E. Roeckner,et al. Cloud optical depth feedbacks and climate modelling , 1987, Nature.
[55] J. Slingo. A cloud parametrization scheme derived from GATE data for use with a numerical model , 1980 .