Articles | Volume 26, issue 1
https://doi.org/10.5194/acp-26-117-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-117-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GCM clouds and actual clouds as seen from different space lidars: towards a long-term assessment of cloud representation in GCMs using lidar simulators
Marie-Laure Roussel
CORRESPONDING AUTHOR
Laboratoire de Météorologie Dynamique, Palaiseau, France
Hélène Chepfer
Laboratoire de Météorologie Dynamique, Palaiseau, France
Zacharie Titus
Laboratoire de Météorologie Dynamique, Palaiseau, France
Marine Bonazzola
Laboratoire de Météorologie Dynamique, Palaiseau, France
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Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem G. Feofilov, Marie-Laure Roussel, Benjamin Witschas, and Sophie Bastin
Atmos. Chem. Phys., 26, 443–475, https://doi.org/10.5194/acp-26-443-2026, https://doi.org/10.5194/acp-26-443-2026, 2026
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Aeolus spaceborne Doppler Wind Lidar observes perfectly co-located vertical profiles of clouds and vertical profiles of horizontal wind that can be used to study cloud-wind interactions. At regional scale, we show that over the Indian Ocean, high cloud fractions increase when the Tropical Easterly Jet is active. At a smaller scale, we observe for the first time from space differences in the wind profiles within the cloud and its surrounding clear sky, that can be imputed to convective motions.
Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem Feofilov, and Marie-Laure Roussel
EGUsphere, https://doi.org/10.5194/egusphere-2025-5335, https://doi.org/10.5194/egusphere-2025-5335, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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In this paper, we use satellite observations to study the relationships between profiles of clouds and profiles of wind from June to October around India. We found that thin clouds between 14 and 17 km are transported over the Arabian Sea by fast westward winds. We also show that the wind can be modified by 3 m/s because of the presence of deep convective clouds that spread out between 14 and 17 km of altitude. We discuss the implications of these interactions in a warming climate.
Jean Lac, Hélène Chepfer, Matthew D. Shupe, and Hannes Griesche
EGUsphere, https://doi.org/10.5194/egusphere-2025-3549, https://doi.org/10.5194/egusphere-2025-3549, 2025
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Satellite observations show that Arctic spring experiences a rapid increase in liquid-containing clouds over sea ice. Our study shows that this transition is mostly driven by warmer temperatures in early spring than in late spring, favoring more liquid clouds formation, rather than a limited moisture source in early spring. It suggests that, in the future, this transition is likely to occur earlier in spring considering the rapid Arctic warming.
Zacharie Titus, Amélie Cuynet, Elodie Salmon, and Catherine Ottlé
The Cryosphere, 19, 2105–2114, https://doi.org/10.5194/tc-19-2105-2025, https://doi.org/10.5194/tc-19-2105-2025, 2025
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The representation of lake ice dynamics is key to model water–atmosphere energy and mass transfers in cold environments. The use of albedo satellite products to constrain the modeling of ice coverage appears to be very suitable and valuable. In this work, we show how the modeling of lake albedo and ice phenology in the land surface model ORCHIDEE was improved by accounting for fractional ice cover calibrated against lake surface albedo data.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
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We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
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Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
Michael R. Gallagher, Matthew D. Shupe, Hélène Chepfer, and Tristan L'Ecuyer
The Cryosphere, 16, 435–450, https://doi.org/10.5194/tc-16-435-2022, https://doi.org/10.5194/tc-16-435-2022, 2022
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By using direct observations of snowfall and mass changes, the variability of daily snowfall mass input to the Greenland ice sheet is quantified for the first time. With new methods we conclude that cyclones west of Greenland in summer contribute the most snowfall, with 1.66 Gt per occurrence. These cyclones are contextualized in the broader Greenland climate, and snowfall is validated against mass changes to verify the results. Snowfall and mass change observations are shown to agree well.
Cited articles
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: Satellite simulation software for model assessment, Bulletin of the American Meteorological Society, 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Bonazzola, M., Chepfer, H., Ma, P.-L., Quaas, J., Winker, D. M., Feofilov, A., and Schutgens, N.: Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation, Geoscientific Model Development, 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, 2023. a, b
Bony, S. and Dufresne, J.-L.: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models, Geophysical Research Letters, 32, https://doi.org/10.1029/2005GL023851, 2005. a
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, Lionel, E., Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the IPSL-CM6A-LR Climate Model, Journal of Advances in Modeling Earth Systems, 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020. a
Cesana, G. and Chepfer, H.: Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP, Journal of Geophysical Research: Atmospheres, 118, 7922–7937, https://doi.org/10.1002/jgrd.50376, 2013. a, b, c
Cesana, G. V., Khadir, T., Chepfer, H., and Chiriaco, M.: Southern Ocean Solar Reflection Biases in CMIP6 Models Linked to Cloud Phase and Vertical Structure Representations, Geophysical Research Letters, 49, e2022GL099777, https://doi.org/10.1029/2022GL099777, 2022. a, b
Cesana, G. V., Ackerman, A. S., Fridlind, A. M., Silber, I., Del, A. D., Zelinka, M. D., Chepfer, H., Khadir, T., and Roehrig, R.: Observational constraint on a feedback from supercooled clouds reduces projected warming uncertainty, Communications Earth & Environment, 5, https://doi.org/10.1038/s43247-024-01339-1, 2024. a, b
Chepfer, H., Chiriaco, M., Vautard, R., and Spinhirne, J.: Evaluation of MM5 Optically Thin Clouds over Europe in Fall Using ICESat Lidar Spaceborne Observations, Monthly Weather Review, 135, 2737–2753, https://doi.org/10.1175/MWR3413.1, 2007. a
Chepfer, H., Bony, S., Winker, D., Chiriaco, M., Dufresne, J.-L., and Sèze, G.: Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model, Geophysical Research Letters, 35, https://doi.org/10.1029/2008GL034207, 2008. a, b
Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J. L., Minnis, P., Stubenrauch, C. J., and Zeng, S.: The GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP), Journal of Geophysical Research: Atmospheres, 115, https://doi.org/10.1029/2009JD012251, 2010. a, b
Chepfer, H., Cesana, G., Winker, D., Getzewich, B., Vaughan, M., and Liu, Z.: Comparison of two different cloud climatologies derived from CALIOP-attenuated backscattered measurements (level 1): The calipso-st and the calipso-GOCCP, Journal of Atmospheric and Oceanic Technology, 30, 725–744, https://doi.org/10.1175/jtech-d-12-00057.1, 2013. a
Chepfer, H., Brogniez, H., and Noel, V.: Diurnal variations of cloud and relative humidity profiles across the tropics, Scientific Reports, 9, https://doi.org/10.1038/s41598-019-52437-6, 2019. a
Chepfer, H., Chomette, O., Arouf, A., Noel, V., Winker, D., Feofilov, A., and Alava Baldazo, A.: Variability and Trends in Cloud Properties Over 17 Years From CALIPSO Space Lidar Observations, Journal of Geophysical Research: Atmospheres, 130, e2025JD043764, https://doi.org/10.1029/2025JD043764, 2025. a, b, c
Chiriaco, M., Vautard, R., Chepfer, H., Haeffelin, M., Dudhia, J., Wanherdrick, Y., Morille, Y., and Protat, A.: The Ability of MM5 to Simulate Ice Clouds: Systematic Comparison between Simulated and Measured Fluxes and Lidar/Radar Profiles at the SIRTA Atmospheric Observatory, Monthly Weather Review, 134, 897–918, https://doi.org/10.1175/MWR3102.1, 2006. a
Donovan, D. P., van Zadelhoff, G.-J., and Wang, P.: The EarthCARE lidar cloud and aerosol profile processor (A-PRO): the A-AER, A-EBD, A-TC, and A-ICE products, Atmospheric Measurement Techniques, 17, 5301–5340, https://doi.org/10.5194/amt-17-5301-2024, 2024. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geoscientific Model Development, 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Feofilov, A., Chepfer, H., Noël, V., and Hajiaghazadeh-Roodsari, M.: Towards Establishing a Long-Term Cloud Record from Space-Borne Lidar Observations, Springer aerospace technology, 57–72, https://doi.org/10.1007/978-3-031-53618-2_6, 2024. a, b, c
Feofilov, A. G., Chepfer, H., Noël, V., Guzman, R., Gindre, C., Ma, P.-L., and Chiriaco, M.: Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles, Atmospheric Measurement Techniques, 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, 2022. a, b
Feofilov, A. G., Chepfer, H., Noël, V., and Szczap, F.: Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product, Atmospheric Measurement Techniques, 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, 2023. a, b
Flamant, P., Cuesta, J., Denneulin, M.-L., Dabas, A., and Huber, D.: ADM-Aeolus retrieval algorithms for aerosol and cloud products, Tellus A, 60, 273–288, https://doi.org/10.1111/j.1600-0870.2007.00287.x, 2008. a
Garnier, A., Pelon, J., Vaughan, M. A., Winker, D. M., Trepte, C. R., and Dubuisson, P.: Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans, Atmospheric Measurement Techniques, 8, 2759–2774, https://doi.org/10.5194/amt-8-2759-2015, 2015. a
Guzman, R., Chepfer, H., Noel, V., Vaillant de Guélis, T., Kay, J. E., Raberanto, P., Cesana, G., Vaughan, M. A., and Winker, D. M.: Direct atmosphere opacity observations from CALIPSO provide new constraints on cloud-radiation interactions, Journal of Geophysical Research: Atmospheres, 122, 1066–1085, https://doi.org/10.1002/2016JD025946, 2017. a, b
Hourdin, F., Rio, C., Grandpeix, J.-Y., Madeleine, J.-B., Cheruy, F., Rochetin, N., Jam, A., Musat, I., Idelkadi, A., Fairhead, L., Foujols, M.-A., Mellul, L., Traore, A.-K., Dufresne, J.-L., Boucher, O., Lefebvre, M.-P., Millour, E., Vignon, E., Jouhaud, J., Diallo, F. B., Lott, F., Gastineau, G., Caubel, A., Meurdesoif, Y., and Ghattas, J.: LMDZ6A: The Atmospheric Component of the IPSL Climate Model With Improved and Better Tuned Physics, Journal of Advances in Modeling Earth Systems, 12, e2019MS001892, https://doi.org/10.1029/2019MS001892, 2020. a
Hunt, W. H., Winker, D. M., Vaughan, M. A., Powell, K. A., Lucker, P. L., and Weimer, C.: CALIPSO Lidar Description and Performance Assessment, Journal of Atmospheric and Oceanic Technology, 26, 1214–1228, https://doi.org/10.1175/2009JTECHA1223.1, 2009. a, b, c
Kay, J. E., Hillman, B. R., Klein, S. A., Zhang, Y., Medeiros, B., Pincus, R., Gettelman, A., Eaton, B., Boyle, J., Marchand, R., and Ackerman, T. P.: Exposing Global Cloud Biases in the Community Atmosphere Model (CAM) Using Satellite Observations and Their Corresponding Instrument Simulators, Journal of Climate, 25, 5190–5207, https://doi.org/10.1175/JCLI-D-11-00469.1, 2012. a
Konsta, D., Dufresne, J.-L., Chepfer, H., Vial, J., Koshiro, T., Kawai, H., Bodas-Salcedo, A., Roehrig, R., Watanabe, M., and Ogura, T.: Low-Level Marine Tropical Clouds in Six CMIP6 Models Are Too Few, Too Bright but Also Too Compact and Too Homogeneous, Geophysical Research Letters, 49, e2021GL097593, https://doi.org/10.1029/2021GL097593, 2022. a, b
Madeleine, J.-B., Hourdin, F., Grandpeix, J.-Y., Rio, C., Dufresne, J.-L., Vignon, E., Boucher, O., Konsta, D., Cheruy, F., Musat, I., Idelkadi, A., Fairhead, L., Millour, E., Lefebvre, M.-P., Mellul, L., Rochetin, N., Lemonnier, F., Touzé-Peiffer, L., and Bonazzola, M.: Improved Representation of Clouds in the Atmospheric Component LMDZ6A of the IPSL-CM6A Earth System Model, Journal of Advances in Modeling Earth Systems, 12, e2020MS002046, https://doi.org/10.1029/2020MS002046, 2020. a, b
Morrison, A. L., Kay, J. E., Frey, W. R., Chepfer, H., and Guzman, R.: Cloud Response to Arctic Sea Ice Loss and Implications for Future Feedback in the CESM1 Climate Model, Journal of Geophysical Research: Atmospheres, 124, 1003–1020, https://doi.org/10.1029/2018JD029142, 2019. a
Nam, C. C. W., Quaas, J., Neggers, R., Siegenthaler-Le Drian, C., and Isotta, F.: Evaluation of boundary layer cloud parameterizations in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data, Journal of Advances in Modeling Earth Systems, 6, 300–314, https://doi.org/10.1002/2013MS000277, 2014. a
Noel, V., Chepfer, H., Hoareau, C., Reverdy, M., and Cesana, G.: Effects of solar activity on noise in CALIOP profiles above the South Atlantic Anomaly, Atmospheric Measurement Techniques, 7, 1597–1603, https://doi.org/10.5194/amt-7-1597-2014, 2014. a
Noel, V., Chepfer, H., Chiriaco, M., and Yorks, J.: The diurnal cycle of cloud profiles over land and ocean between 51° S and 51° N, seen by the CATS spaceborne lidar from the International Space Station, Atmospheric Chemistry and Physics, 18, 9457–9473, https://doi.org/10.5194/acp-18-9457-2018, 2018. a
Reverdy, M., Chepfer, H., Donovan, D., Noel, V., Cesana, G., Hoareau, C., Chiriaco, M., and Bastin, S.: An EarthCARE/ATLID simulator to evaluate cloud description in climate models, Journal of Geophysical Research: Atmospheres, 120, 11090–11113, https://doi.org/10.1002/2015JD023919, 2015. a, b, c, d, e
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence, Reviews of Geophysics, 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a
Swales, D. J., Pincus, R., and Bodas-Salcedo, A.: The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2, Geoscientific Model Development, 11, 77–81, https://doi.org/10.5194/gmd-11-77-2018, 2018. a, b
Titus, Z.: ALADIN/Aeolus – Wind-cloud interactions dataset, AERIS [data set], https://doi.org/10.25326/746, 2024. a, b, c, d
Titus, Z., Bonazzola, M., Chepfer, H., Feofilov, A., Roussel, M.-L., Witschas, B., and Bastin, S.: Wind-cloud interactions observed with Aeolus spaceborne Doppler Wind Lidar, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-2065, 2025. a, b, c, d
Vial, J., Dufresne, J.-L., and Bony, S.: On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates, Climate Dynamics, 41, 3339–3362, https://doi.org/10.1007/s00382-013-1725-9, 2013. a
Wang, P., Donovan, D. P., van Zadelhoff, G.-J., de Kloe, J., Huber, D., and Reissig, K.: Evaluation of Aeolus feature mask and particle extinction coefficient profile products using CALIPSO data, Atmospheric Measurement Techniques, 17, 5935–5955, https://doi.org/10.5194/amt-17-5935-2024, 2024. a, b
Wehr, T., Kubota, T., Tzeremes, G., Wallace, K., Nakatsuka, H., Ohno, Y., Koopman, R., Rusli, S., Kikuchi, M., Eisinger, M., Tanaka, T., Taga, M., Deghaye, P., Tomita, E., and Bernaerts, D.: The EarthCARE mission – science and system overview, Atmospheric Measurement Techniques, 16, 3581–3608, https://doi.org/10.5194/amt-16-3581-2023, 2023. a, b
Williams, K. D. and Bodas-Salcedo, A.: A multi-diagnostic approach to cloud evaluation, Geoscientific Model Development, 10, 2547–2566, https://doi.org/10.5194/gmd-10-2547-2017, 2017. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophysical Research Letters, 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a
Short summary
Clouds are crucial for understanding and predicting climate, yet their processes remain uncertain in global models. We reproduced detailed observations from a lidar and a wind-lidar using an advanced instrument simulator to evaluate how well the model represents clouds. A dedicated module was developed for the wind-lidar. Both instruments reveal comparable cloud biases, confirming its value for improving model evaluation and enabling consistent multi-decade assessments with more recent sensors.
Clouds are crucial for understanding and predicting climate, yet their processes remain...
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