Cited by 375Atmospheric aerosols and cloudsClimate variability and modelsMeteorological Phenomena and Simulations

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models.

Open access

Nam, C., Bony, S., Dufresne, J.‐L., Chepfer, H.·2012·Geophysical Research Letters

DOI 10.1029/2012gl053421

Open this paper in CoChat — free account Open PDF

Abstract

Previous generations of climate models have been shown to under‐estimate the occurrence of tropical low‐level clouds and to over‐estimate their radiative effects. This study analyzes outputs from multiple climate models participating in the Fifth phase of the Coupled Model Intercomparison Project (CMIP5) using the Cloud Feedback Model Intercomparison Project Observations Simulator Package (COSP), and compares them with different satellite data sets. Those include CALIPSO lidar observations, PARASOL mono‐directional reflectances and CERES radiative fluxes at the top of the atmosphere. We show that current state‐of‐the‐art climate models predict overly bright low‐clouds, even for a correct low‐cloud cover. The impact of these biases on the Earth' radiation budget, however, is reduced by compensating errors. Those include the tendency of models to under‐estimate the low‐cloud cover and to over‐estimate the occurrence of mid‐ and high‐clouds above low‐clouds. Finally, we show that models poorly represent the dependence of the vertical structure of low‐clouds on large‐scale environmental conditions. The implications of this ‘too few, too bright low‐cloud problem’ for climate sensitivity and model development are discussed.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1029/2012gl053421

Cites

35 of 35 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The ERA‐Interim reanalysis: configuration and performance of the data assimilation system
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., et al. (2011) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.828 · Cited by 24,480
Climate change 2007 : the physical science basis : contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change
Solomon, S. (2007) · Cited by 17,554
An Overview of CMIP5 and the Experiment Design
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00094.1 · Cited by 14,052
Summarizing multiple aspects of model performance in a single diagram
Taylor, K. E. (2001) · Journal of Geophysical Research Atmospheres · doi:10.1029/2000jd900719 · Cited by 8,927
The NCEP Climate Forecast System Reanalysis
Saha, S., Moorthi, S., Pan, H., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R. E., Woollen, J. S., Behringer, D. W., et al. (2010) · Bulletin of the American Meteorological Society · doi:10.1175/2010bams3001.1 · Cited by 5,251
The Physical Science Basis : Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change
Solomon, S. (2007) · Medical Entomology and Zoology · Cited by 4,149
Tropospheric Aerosol Optical Thickness from the GOCART Model and Comparisons with Satellite and Sun Photometer Measurements
Chin, M., Ginoux, P. A., Kinne, S., Torres, O., HOLBEN, B. N., Duncan, B. N., Martin, R. V., Logan, J. A., Higurashi, A., & Nakajima, T. (2002) · Journal of the Atmospheric Sciences · doi:10.1175/1520-0469(2002)059 · Cited by 1,580
Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models
Bony, S., & Dufresne, J. (2005) · Geophysical Research Letters · doi:10.1029/2005gl023851 · Cited by 1,306
Self-citation
How Well Do We Understand and Evaluate Climate Change Feedback Processes?
Bony, S., Colman, R. A., Катцов, В. М., Allan, R. P., Bretherton, C. S., Dufresne, J., Hall, A. D., Stéphane Hallegatte, A., Holland, M. M., Ingram, W., et al. (2006) · Journal of Climate · doi:10.1175/jcli3819.1 · Cited by 1,068
Self-citation
Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere‐ocean climate models
Andrews, T., Gregory, J. M., Webb, M. J., & Taylor, K. E. (2012) · Geophysical Research Letters · doi:10.1029/2012gl051607 · Cited by 781

Flagged first, then by citations

Show all 35 references

Research works printed but not linked: 1

Jam A., 2012, Resolved versus parametrized boundary‐layer plumes. Part III: A diagnostic boundary‐layer cloud parameterization derived from large eddy simulations, Boundary Layer Meteorol.

Sorted by citations

Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO 2
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V. A., Claussen, M. A., Crueger, T., Esch, M., et al. (2019) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2018ms001400 · Cited by 1,630
Self-citation
Evaluation of CMIP6 DECK Experiments With CNRM‐CM6‐1
Voldoire, A., Saint‐Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J., Michou, M., Moine, M., et al. (2019) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001683 · Cited by 1,184
The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T. Y., Hosaka, M., et al. (2019) · Journal of the Meteorological Society of Japan Ser II · doi:10.2151/jmsj.2019-051 · Cited by 1,217
Bounding Global Aerosol Radiative Forcing of Climate Change
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson‐Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A. ‐., et al. (2020) · Reviews of Geophysics · doi:10.1029/2019rg000660 · Cited by 1,116
“GCMs are known to under-represent847 low-level cloudiness (Nam et al., 2012), so may underestimate cτ by simulating the wrong848 spatial patterns of C.849 [INSERT TABLE 3 HERE]850 In summary, the radiative forcing of ari, Fari, is computed as in Eq.”
Self-citation
The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation
Illingworth, A. J., Barker, H. W., Beljaars, A. C. M., Ceccaldi, M., Chepfer, H., Clerbaux, N., Cole, J. N. S., Delanoe͏̈, J., Doménech, C., Donovan, D. P., et al. (2014) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-12-00227.1 · Cited by 861
Self-citation
The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations
Williams, K. D., Copsey, D., Blockley, E., Bodas‐Salcedo, A., Calvert, D., Comer, R. E., Davis, P., Graham, T. R., Hewitt, H. T., Hill, R., et al. (2017) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2017ms001115 · Cited by 740
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations
Schneider, T., Lan, S., Stuart, A., & Teixeira, J. (2017) · Geophysical Research Letters · doi:10.1002/2017gl076101 · Cited by 391
The Met Office Global Coupled model 2.0 (GC2) configuration
Williams, K. D., Harris, C. M., Bodas‐Salcedo, A., Camp, J., Comer, R. E., Copsey, D., Fereday, D. R., Graham, T., Hill, R., Hinton, T. J., et al. (2015) · Geoscientific model development · doi:10.5194/gmd-8-1509-2015 · Cited by 322
The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6
Webb, M. J., Andrews, T., Bodas‐Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R. S., Chepfer, H., Douville, H., Good, P., Kay, J. E., et al. (2017) · Geoscientific model development · doi:10.5194/gmd-10-359-2017 · Cited by 310
Self-citation
ICON‐A, the Atmosphere Component of the ICON Earth System Model: I. Model Description
Giorgetta, M. A., Brokopf, R., Crueger, T., Esch, M., Fiedler, S., Helmert, J., Hohenegger, C., Kornblueh, L., Köhler, M., Manzini, E., et al. (2018) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2017ms001242 · Cited by 284
Self-citation
Rainforest-initiated wet season onset over the southern Amazon
Wright, J. S., Fu, R., Worden, J. R., Chakraborty, S., Clinton, N., Risi, C., Sun, Y., & Yin, L. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621516114 · Cited by 310
Possible climate transitions from breakup of stratocumulus decks under greenhouse warming
Schneider, T., Kaul, C., & Pressel, K. G. (2019) · Nature Geoscience · doi:10.1038/s41561-019-0310-1 · Cited by 299
Monsoons Climate Change Assessment
Wang, B., Biasutti, M., Byrne, M. P., Castro, C. L., Chang, C.-P., Cook, K. H., Fu, R., Grimm, A. M., Ha, K., Hendon, H. H., et al. (2020) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-19-0335.1 · Cited by 272
Projections of long-term changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems
Wild, M., Folini, D., Henschel, F., Fischer, N., & Müller, B. (2015) · Solar Energy · doi:10.1016/j.solener.2015.03.039 · Cited by 274
Understanding Future Change of Global Monsoons Projected by CMIP6 Models
Wang, B., Jin, C., & Liu, J. (2020) · Journal of Climate · doi:10.1175/jcli-d-19-0993.1 · Cited by 226
Constraining the aerosol influence on cloud fraction
Gryspeerdt, E., Quaas, J., & Bellouin, N. (2016) · Journal of Geophysical Research Atmospheres · doi:10.1002/2015jd023744 · Cited by 267
EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation
Bony, S., Stevens, B., Ament, F., Bigorre, S. P., Chazette, P., Crewell, S., Delanoe͏̈, J., Emanuel, K., Farrell, D., Flamant, C. N., et al. (2017) · Surveys in Geophysics · doi:10.1007/s10712-017-9428-0 · Cited by 247
Self-citation
Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator
Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R. L., Boyle, J., & Gleckler, P. J. (2013) · Journal of Geophysical Research Atmospheres · doi:10.1002/jgrd.50141 · Cited by 244
Assessment of CMIP5 climate models and projected temperature changes over Northern Eurasia
Miao, C., Duan, Q., Sun, Q., Huang, Y., Kong, D., Yang, T., Ye, A., Di, Z., & Gong, W. (2014) · Environmental Research Letters · doi:10.1088/1748-9326/9/5/055007 · Cited by 243
Rethinking the Lower Bound on Aerosol Radiative Forcing
Stevens, B. (2015) · Journal of Climate · doi:10.1175/jcli-d-14-00656.1 · Cited by 244
Understanding Cloud and Convective Characteristics in Version 1 of the E3SM Atmosphere Model
Xie, S., Lin, W., Rasch, P. J., Ma, P., Neale, R., Larson, V. E., Qian, Y., Bogenschutz, P., Caldwell, P., Cameron-Smith, P. J., et al. (2018) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2018ms001350 · Cited by 212
The Added Value of Large-eddy and Storm-resolving Models for Simulating Clouds and Precipitation
Stevens, B., Acquistapace, C., Hansen, A., Heinze, R., Klinger, C., Klocke, D., Rybka, H., Schubotz, W., Windmiller, J. M., Adamidis, P. A., et al. (2020) · Journal of the Meteorological Society of Japan Ser II · doi:10.2151/jmsj.2020-021 · Cited by 228
Self-citation
ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP
Eyring, V., Righi, M., Lauer, A., Evaldsson, M., Wenzel, S., Jones, C., Anav, A., Andrews, O., Cionni, I., Davin, É. L., et al. (2016) · Geoscientific model development · doi:10.5194/gmd-9-1747-2016 · Cited by 191
On the relationships among cloud cover, mixed‐phase partitioning, and planetary albedo in GCMs
McCoy, D. T., Tan, I., Hartmann, D. L., Zelinka, M. D., & Storelvmo, T. (2016) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2015ms000589 · Cited by 207
Spaceborne Cloud and Precipitation Radars: Status, Challenges, and Ways Forward
Battaglia, A., Kollias, P., Dhillon, R. S., Roy, R., Tanelli, S., Lamer, K., Grecu, M., Lebsock, M., Watters, D. C., Mróz, K., et al. (2020) · Reviews of Geophysics · doi:10.1029/2019rg000686 · Cited by 188
Challenges and Prospects for Reducing Coupled Climate Model SST Biases in the Eastern Tropical Atlantic and Pacific Oceans: The U.S. CLIVAR Eastern Tropical Oceans Synthesis Working Group
Zuidema, P., Chang, P., Medeiros, B., Kirtman, B. P., Mechoso, C. R., Schneider, E. K., Toniazzo, T., Richter, I., Small, J., Bellomo, K., et al. (2016) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-15-00274.1 · Cited by 181
Convection‐Permitting Simulations With the E3SM Global Atmosphere Model
Caldwell, P., Terai, C. R., Hillman, B., Keen, N. D., Bogenschutz, P., Lin, W., Beydoun, H., Taylor, M. A., Bertagna, L., Bradley, A., et al. (2021) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002544 · Cited by 182
Constraining the instantaneous aerosol influence on cloud albedo
Gryspeerdt, E., Quaas, J., Ferrachat, S., Gettelman, A., Ghan, S. J., Lohmann, U., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617765114 · Cited by 165
The CNRM Global Atmosphere Model ARPEGE‐Climat 6.3: Description and Evaluation
Roehrig, R., Beau, I., Saint‐Martin, D., Alias, A., Decharme, B., Guérémy, J., Voldoire, A., Abdel‐Lathif, A. Y., Bazile, É., Belamari, S., et al. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002075 · Cited by 158
Emergent constraints on future projections of the western North Pacific Subtropical High
Chen, X., Zhou, T., Wu, P., Guo, Z., & Wang, M. (2020) · Nature Communications · doi:10.1038/s41467-020-16631-9 · Cited by 156
The tropical atlantic observing system
Foltz, G. R., Brandt, P., Richter, I., Rodríguez-Fonseca, B., Hernandez, F., Dengler, M., Rodrigues, R. R., Schmidt, J. O., Yu, L., Lefevre, N., et al. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00206 · Cited by 150
The ICON Earth System Model Version 1.0
Jungclaus, J. H., Lorenz, S. J., Schmidt, H., Brovkin, V. A., Brüggemann, N., Chegini, F., Crüger, T., de Vrese, P., Gayler, V., Giorgetta, M. A., et al. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002813 · Cited by 143
Direct and semi-direct radiative forcing of biomass-burning aerosols over the southeast Atlantic (SEA) and its sensitivity to absorbing properties: a regional climate modeling study
Mallet, M., Solmon, F., Nabat, P., Elguindi, N., Waquet, F., Bouniol, D., Sayer, A. M., Meyer, K., Roehrig, R., Michou, M., et al. (2020) · Atmospheric chemistry and physics · doi:10.5194/acp-20-13191-2020 · Cited by 153
On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models
Ma, H., Xie, S., Klein, S. A., Williams, K. D., Boyle, J. S., Bony, S., Douville, H., Fermepin, S., Medeiros, B., Tytéca, S., et al. (2013) · Journal of Climate · doi:10.1175/jcli-d-13-00474.1 · Cited by 141
Self-citation
Evaluating and improving cloud phase in the Community Atmosphere Model version 5 using spaceborne lidar observations
Kay, J. E., Bourdages, L., Miller, N. B., Morrison, A. L., Yettella, V., Chepfer, H., & Eaton, B. (2016) · Journal of Geophysical Research Atmospheres · doi:10.1002/2015jd024699 · Cited by 139
Self-citation
Significant improvement of cloud representation in the global climate model MRI-ESM2
Kawai, H., Yukimoto, S., Koshiro, T., Oshima, N., Tanaka, T. Y., Yoshimura, H., & Nagasawa, R. (2019) · Geoscientific model development · doi:10.5194/gmd-12-2875-2019 · Cited by 129
Mechanisms and Impacts of Earth System Tipping Elements
Wang, S., Foster, A. C., Lenz, E. A., Kessler, J. D., Stroeve, J. C., Anderson, L. O., Turetsky, M. R., Betts, R., Zou, S., Liu, W., et al. (2023) · Reviews of Geophysics · doi:10.1029/2021rg000757 · Cited by 145
The global aerosol–climate model ECHAM6.3–HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing, and climate sensitivity
Neubauer, D., Ferrachat, S., Drian, C. S., Stier, P., Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., & Lohmann, U. (2019) · Geoscientific model development · doi:10.5194/gmd-12-3609-2019 · Cited by 125
Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF
Regayre, L. A., Johnson, J. S., Yoshioka, M., Pringle, K. J., Sexton, D. M. H., Booth, B. B. B., Lee, L. A., Bellouin, N., & Carslaw, K. S. (2018) · Atmospheric chemistry and physics · doi:10.5194/acp-18-9975-2018 · Cited by 122
Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement
Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., et al. (2021) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002217 · Cited by 121
The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2
Swales, D. J., Pincus, R. L., & Bodas‐Salcedo, A. (2018) · Geoscientific model development · doi:10.5194/gmd-11-77-2018 · Cited by 117
LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2
Zhu, J., Otto‐Bliesner, B. L., Brady, E. C., Gettelman, A., Bacmeister, J. T., Neale, R., Poulsen, C. J., Shaw, J., McGraw, Z. S., & Kay, J. E. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002776 · Cited by 119
The Influence of Atmospheric Cloud Radiative Effects on the Large-Scale Atmospheric Circulation
Li, Y., Thompson, D. W. J., & Bony, S. (2015) · Journal of Climate · doi:10.1175/jcli-d-14-00825.1 · Cited by 109
Self-citation
Amplification of El Niño by cloud longwave coupling to atmospheric circulation
Rädel, G., Mauritsen, T., Stevens, B., Dommenget, D., Matei, D., Bellomo, K., & Clement, A. (2016) · Nature Geoscience · doi:10.1038/ngeo2630 · Cited by 109
Evaluation of Clouds in Version 1 of the E3SM Atmosphere Model With Satellite Simulators
Zhang, Y., Xie, S., Lin, W., Klein, S. A., Zelinka, M. D., Ma, P., Rasch, P. J., Qian, Y., Tang, Q., & Ma, H. (2019) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2018ms001562 · Cited by 103
The Southern Ocean Radiative Bias, Cloud Compensating Errors, and Equilibrium Climate Sensitivity in CMIP6 Models
Schuddeboom, A. J., & McDonald, A. (2021) · Journal of Geophysical Research Atmospheres · doi:10.1029/2021jd035310 · Cited by 103
Using aquaplanets to understand the robust responses of comprehensive climate models to forcing
Medeiros, B., Stevens, B., & Bony, S. (2014) · Climate Dynamics · doi:10.1007/s00382-014-2138-0 · Cited by 101
Self-citation
Evaluating cloud tuning in a climate model with satellite observations
Suzuki, K., Golaz, J., & Stephens, G. L. (2013) · Geophysical Research Letters · doi:10.1002/grl.50874 · Cited by 95
Future Changes and Controlling Factors of the Eight Regional Monsoons Projected by CMIP6 Models
Jin, C., Wang, B., & Liu, J. (2020) · Journal of Climate · doi:10.1175/jcli-d-20-0236.1 · Cited by 95
Where and when will we observe cloud changes due to climate warming?
Chepfer, H., Noël, V., Winker, D. M., & Chiriaco, M. (2014) · Geophysical Research Letters · doi:10.1002/2014gl061792 · Cited by 93
Self-citation
ICON‐A, The Atmosphere Component of the ICON Earth System Model: II. Model Evaluation
Crueger, T., Giorgetta, M. A., Brokopf, R., Esch, M., Fiedler, S., Hohenegger, C., Kornblueh, L., Mauritsen, T., Nam, C., Naumann, A. K., et al. (2018) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2017ms001233 · Cited by 91
Self-citation
Diurnal cloud cycle biases in climate models
Yin, J., & Porporato, A. M. (2017) · Nature Communications · doi:10.1038/s41467-017-02369-4 · Cited by 90
Direct atmosphere opacity observations from CALIPSO provide new constraints on cloud‐radiation interactions
Guzman, R., Chepfer, H., Noël, V., de Guélis, T. V., Kay, J. E., Raberanto, P., Cesana, G., Vaughan, M. A., & Winker, D. M. M. (2017) · Journal of Geophysical Research Atmospheres · doi:10.1002/2016jd025946 · Cited by 85
Self-citation
Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic
Doherty, S. J., Saide, P. E., Zuidema, P., Shinozuka, Y., Ferrada, G. A., Gordon, H., Mallet, M., Meyer, K., Painemal, D., Howell, S. G., et al. (2022) · Atmospheric chemistry and physics · doi:10.5194/acp-22-1-2022 · Cited by 84
An LES model study of the influence of the free tropospheric thermodynamic conditions on the stratocumulus response to a climate perturbation
van der Dussen, J. J., de Roode, S. R., Gesso, S. D., & Siebesma, A. P. (2015) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2014ms000380 · Cited by 83
Why Do Global Climate Models Struggle to Represent Low-Level Clouds in the West African Summer Monsoon?
Hannak, L., Knippertz, P., Fink, A. H., Kniffka, A., & Pante, G. (2016) · Journal of Climate · doi:10.1175/jcli-d-16-0451.1 · Cited by 81
The behavior of trade‐wind cloudiness in observations and models: The major cloud components and their variability
Nuijens, L. A., Medeiros, B., Sandu, I., & Ahlgrimm, M. (2015) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2014ms000390 · Cited by 80
Relation of the double‐ITCZ bias to the atmospheric energy budget in climate models
Adam, O., Schneider, T., Brient, F., & Bischoff, T. (2016) · Geophysical Research Letters · doi:10.1002/2016gl069465 · Cited by 77
Response to marine cloud brightening in a multi-model ensemble
Stjern, C. W., Muri, H., Ahlm, L., Boucher, O., Cole, J. N. S., Ji, D., Jones, A., Haywood, J., Kravitz, B., Lenton, A. A., et al. (2018) · Atmospheric chemistry and physics · doi:10.5194/acp-18-621-2018 · Cited by 77
Prognostic Precipitation in the MIROC6‐SPRINTARS GCM: Description and Evaluation Against Satellite Observations
Michibata, T., Suzuki, K., Sekiguchi, M., & Takemura, T. (2019) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2018ms001596 · Cited by 78
Evaluating models' response of tropical low clouds to SST forcings using CALIPSO observations
Cesana, G., C, A. D. D. G., Ackerman, A. S., Kelley, M., Elsaesser, G. S., Fridlind, A. M., Cheng, Y., & Yao, M. (2019) · Atmospheric chemistry and physics · doi:10.5194/acp-19-2813-2019 · Cited by 74
Ultraclean Layers and Optically Thin Clouds in the Stratocumulus-to-Cumulus Transition. Part I: Observations
Wood, R., Kuan‐Ting, O., Bretherton, C. S., Mohrmann, J., Albrecht, B. A., Zuidema, P., Ghate, V. P., Schwartz, C., Eloranta, E., Glienke, S., et al. (2018) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-17-0213.1 · Cited by 73
Discussion on climate oscillations: CMIP5 general circulation models versus a semi-empirical harmonic model based on astronomical cycles
Nicola Scafetta, A. (2013) · Earth-Science Reviews · doi:10.1016/j.earscirev.2013.08.008 · Cited by 72
Cloud, precipitation and radiation responses to large perturbations in global dimethyl sulfide
Fiddes, S. L., Woodhouse, M. T., Nicholls, Z., Lane, T. P., & Schofield, R. (2018) · Atmospheric chemistry and physics · doi:10.5194/acp-18-10177-2018 · Cited by 78
Evaluation of high-resolution atmospheric and oceanic simulations of the California Current System
Renault, L., McWilliams, J. C., Kessouri, F., Jousse, A., Frenzel, H., Chen, R., & Deutsch, C. A. (2020) · Progress In Oceanography · doi:10.1016/j.pocean.2021.102564 · Cited by 73
Quantifying variations in shortwave aerosol–cloud–radiation interactions using local meteorology and cloud state constraints
Douglas, A., & L’Ecuyer, T. (2019) · Atmospheric chemistry and physics · doi:10.5194/acp-19-6251-2019 · Cited by 69
Influence of low‐cloud radiative effects on tropical circulation and precipitation
Fermepin, S., & Bony, S. (2014) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2013ms000288 · Cited by 66
Self-citation
Estimation of prediction interval in ANN-based multi-GCMs downscaling of hydro-climatologic parameters
Nourani, V., Paknezhad, N. J., Sharghi, E., & Khosravi, A. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.124226 · Cited by 66
Stratospheric dynamics and midlatitude jets under geoengineering with space mirrors and sulfate and titania aerosols
Ferraro, A. J., Charlton-Perez, A. J., & Highwood, E. J. (2014) · Journal of Geophysical Research Atmospheres · doi:10.1002/2014jd022734 · Cited by 73
Southern Ocean Solar Reflection Biases in CMIP6 Models Linked to Cloud Phase and Vertical Structure Representations
Cesana, G., Khadir, T., Chepfer, H., & Chiriaco, M. (2022) · Geophysical Research Letters · doi:10.1029/2022gl099777 · Cited by 69
Self-citation
The efficacy of aerosol–cloud radiative perturbations from near-surface emissions in deep open-cell stratocumuli
Possner, A., Wang, H., Wood, R., Caldeira, K., & Ackerman, T. P. (2018) · Atmospheric Chemistry and Physics · doi:10.5194/acp-18-17475-2018 · Cited by 63
Low‐Level Marine Tropical Clouds in Six CMIP6 Models Are Too Few, Too Bright but Also Too Compact and Too Homogeneous
Konsta, D., Dufresne, J., Chepfer, H., Vial, J., Koshiro, T., Kawai, H., Bodas‐Salcedo, A., Roehrig, R., Watanabe, M., & Ogura, T. (2022) · Geophysical Research Letters · doi:10.1029/2021gl097593 · Cited by 63
Self-citation
Strong cloud–circulation coupling explains weak trade cumulus feedback
Vogel, R., Albright, A. L., Vial, J., George, G., Stevens, B., & Bony, S. (2022) · Nature · doi:10.1038/s41586-022-05364-y · Cited by 70
Self-citation
Numerical framework and performance of the new multiple-phase cloud microphysics scheme in RegCM4.5: precipitation, cloud microphysics, and cloud radiative effects
Nogherotto, R., Tompkins, A. M., Giuliani, G., Coppola, E., & Giorgi, F. (2016) · Geoscientific model development · doi:10.5194/gmd-9-2533-2016 · Cited by 63
The light-deficient climates of western Central African evergreen forests
Philippon, N., Cornu, G., Monteil, L., Gond, V., Moron, V., Pergaud, J., Seze, G. M., Bigot, S., Camberlin, P., Doumenge, C., et al. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aaf5d8 · Cited by 63
Simulation of the transport, vertical distribution, optical properties and radiative impact of smoke aerosols with the ALADIN regional climate model during the ORACLES-2016 and LASIC experiments
Mallet, M., Nabat, P., Zuidema, P., Redemann, J., Sayer, A. M., Stengel, M., Schmidt, K. S., Cochrane, S., Burton, S. P., Ferrare, R. A., et al. (2019) · Atmospheric chemistry and physics · doi:10.5194/acp-19-4963-2019 · Cited by 61
Evaluation of autoconversion schemes in a single model framework with satellite observations
Michibata, T., & Takemura, T. (2015) · Journal of Geophysical Research Atmospheres · doi:10.1002/2015jd023818 · Cited by 68
Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models
Medeiros, B., & Nuijens, L. A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1521494113 · Cited by 59
Low clouds link equilibrium climate sensitivity to hydrological sensitivity
Watanabe, M., Kamae, Y., Shiogama, H., DeAngelis, A. M., & Suzuki, K. (2018) · Nature Climate Change · doi:10.1038/s41558-018-0272-0 · Cited by 59
Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations
Kuma, P., McDonald, A., Morgenstern, O., Alexander, S. P., Cassano, J. J., Garrett, S., Halla, J. D., Hartery, S., Harvey, M. J., Parsons, S., et al. (2020) · Atmospheric chemistry and physics · doi:10.5194/acp-20-6607-2020 · Cited by 59
Quantifying the Contribution of Different Cloud Types to the Radiation Budget in Southern West Africa
Hill, P. G., Allan, R. P., Chiu, J.-Y. C., Bodas‐Salcedo, A., & Knippertz, P. (2018) · Journal of Climate · doi:10.1175/jcli-d-17-0586.1 · Cited by 56
Observational constraints on atmospheric and oceanic cross-equatorial heat transports: revisiting the precipitation asymmetry problem in climate models
Loeb, N. G., Wang, H., Cheng, A., Kato, S., Fasullo, J., Xu, K., & Allan, R. P. (2015) · Climate Dynamics · doi:10.1007/s00382-015-2766-z · Cited by 56
A global survey of the instantaneous linkages between cloud vertical structure and large‐scale climate
Li, Y., Thompson, D. W. J., Stephens, G. L., & Bony, S. (2014) · Journal of Geophysical Research Atmospheres · doi:10.1002/2013jd020669 · Cited by 54
Self-citation
Characterizing and understanding systematic biases in the vertical structure of clouds in CMIP5/CFMIP2 models
Cesana, G., & Waliser, D. E. (2016) · Geophysical Research Letters · doi:10.1002/2016gl070515 · Cited by 54
Anthropogenic aerosol forcing – insights from multiple estimates from aerosol-climate models with reduced complexity
Fiedler, S., Kinne, S., Huang, W. T. K., Räisänen, P., O’Donnell, D., Bellouin, N., Stier, P., Merikanto, J., van Noije, T., Carslaw, K. S., et al. (2019) · Atmospheric chemistry and physics · doi:10.5194/acp-19-6821-2019 · Cited by 53
A Climatology of Marine Boundary Layer Cloud and Drizzle Properties Derived from Ground-Based Observations over the Azores
Wu, P., Dong, X., & Xi, B. (2020) · Journal of Climate · doi:10.1175/jcli-d-20-0272.1 · Cited by 53
Instantaneous Linkages between Clouds and Large-Scale Meteorology over the Southern Ocean in Observations and a Climate Model
Wall, C. J., Hartmann, D. L., & Ma, P. (2017) · Journal of Climate · doi:10.1175/jcli-d-17-0156.1 · Cited by 52
Single‐Column Model Simulations of Subtropical Marine Boundary‐Layer Cloud Transitions Under Weakening Inversions
Neggers, R., Ackerman, A. S., Angevine, W. M., Bazile, É., Beau, I., Blossey, P. N., Boutle, I., de Bruijn, E. I. F., Cheng, A., van der Dussen, J. J., et al. (2017) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2017ms001064 · Cited by 52
On the sensitivity of anthropogenic aerosol forcing to model‐internal variability and parameterizing a T womey effect
Fiedler, S., Stevens, B., & Mauritsen, T. (2017) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2017ms000932 · Cited by 51
Improved Representation of Clouds in the Atmospheric Component LMDZ6A of the IPSL‐CM6A Earth System Model
Madeleine, J., Hourdin, F., Grandpeix, J., Rio, C., Dufresne, J., Vignon, É., Boucher, O., Konsta, D., Cheruy, F., Musat, I., et al. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002046 · Cited by 49
Self-citation
Evaluation of Hemispheric Asymmetries in Marine Cloud Radiative Properties
Bender, F. A. ‐., Engström, A., Wood, R., & Charlson, R. J. (2017) · Journal of Climate · doi:10.1175/jcli-d-16-0263.1 · Cited by 48
Object‐Oriented Identification of Coherent Structures in Large Eddy Simulations: Importance of Downdrafts in Stratocumulus
Brient, F., Couvreux, F., Villefranque, N., Rio, C., & Honnert, R. (2019) · Geophysical Research Letters · doi:10.1029/2018gl081499 · Cited by 48
Cloud radiative forcing intercomparison between fully coupled CMIP5 models and CERES satellite data
Calisto, M., Folini, D., Wild, M., & Bengtsson, L. O. (2014) · Annales Geophysicae · doi:10.5194/angeo-32-793-2014 · Cited by 46
Unified Parameterization of Convective Boundary Layer Transport and Clouds With the Thermal Plume Model
Hourdin, F., Jam, A., Rio, C., Couvreux, F., Sandu, I., Lefebvre, M., Brient, F., & Idelkadi, A. (2019) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001666 · Cited by 46
Mind the gap – Part 1: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars
Lamer, K., Kollias, P., Battaglia, A., & Preval, S. P. (2020) · Atmospheric measurement techniques · doi:10.5194/amt-13-2363-2020 · Cited by 45
The nonlinear relationship between albedo and cloud fraction on near‐global, monthly mean scale in observations and in the CMIP5 model ensemble
Engström, Å., Bender, F. A. ‐., Charlson, R. J., & Wood, R. (2015) · Geophysical Research Letters · doi:10.1002/2015gl066275 · Cited by 44
Evaluating Arctic cloud radiative effects simulated by NICAM with A‐train
Hashino, T., Satoh, M., Hagihara, Y., Kato, S., Kubota, T., Matsui, T., Nasuno, T., Okamoto, H., & Sekiguchi, M. (2016) · Journal of Geophysical Research Atmospheres · doi:10.1002/2016jd024775 · Cited by 44
Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors
Winker, D. M. M., Chepfer, H., Noël, V., & Cai, X. (2017) · Surveys in Geophysics · doi:10.1007/s10712-017-9452-0 · Cited by 44
Self-citation
The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback
McCoy, I. L., McCoy, D. T., Wood, R., Zuidema, P., & Bender, F. A. ‐. (2023) · Geophysical Research Letters · doi:10.1029/2022gl101042 · Cited by 42
Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool
Lauer, A., Eyring, V., Righi, M., Buchwitz, M., Defourny, P., Evaldsson, M., Friedlingstein, P., de Jeu, R., de Leeuw, G., Loew, A., et al. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.01.007 · Cited by 41

Authors: Nam, C., Bony, S., Dufresne, J. ‐., & Chepfer, H.
Affiliations: Centre National de la Recherche Scientifique (FR)
Corresponding author: Nam, C.
ORCID: Nam, C.
Bony, S.
Dufresne, J.‐L.
Chepfer, H.
Venue: Geophysical Research Letters
Publisher: American Geophysical Union (AGU)
Published: 2012-11
Volume: 39
Issue: 21
Pages: L21801
ISSN: 0094-8276 (ISSN-L)
1944-8007
DOI: 10.1029/2012gl053421
OpenAlex: W1925502865
Cited by: 375
References: 35
Citations by year: 2026: 10 · 2025: 22 · 2024: 26 · 2023: 38 · 2022: 33 · 2021: 41 · 2020: 38 · 2019: 27 · 2018: 32 · 2017: 31 · 2016: 26 · 2015: 29 · 2014: 15 · 2013: 6
FWCI: 13.30
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Geophysical Research Letters (publishedVersion)
HAL (submittedVersion, other-oa)
Funders: European Commission
Topics: Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
Keywords: cloud radiative effects, cloud vertical structure, cloud feedback, Earth radiation budget, cloud model evaluation, CALIPSO lidar, climate sensitivity, CMIP5

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models.

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks