Articles | Volume 15, issue 8
https://doi.org/10.5194/gmd-15-3347-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3347-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
22 Apr 2022
Model evaluation paper |
| 22 Apr 2022
| 22 Apr 2022
Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration
CNRM, University of Toulouse, Météo-France, CNRS, Toulouse,
France
Romain Roehrig
CNRM, University of Toulouse, Météo-France, CNRS, Toulouse,
France
Hervé Giordani
CNRM, University of Toulouse, Météo-France, CNRS, Toulouse,
France
Robin Waldman
CNRM, University of Toulouse, Météo-France, CNRS, Toulouse,
France
Yunyan Zhang
Lawrence Livermore National Laboratory, Livermore, California, USA
Shaocheng Xie
Lawrence Livermore National Laboratory, Livermore, California, USA
Marie-Nöelle Bouin
CNRM, University of Toulouse, Météo-France, CNRS, Toulouse,
France
Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique
et Spatiale (LOPS), IUEM, 29840 Brest, France
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Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024, https://doi.org/10.5194/gmd-17-117-2024, 2024
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In numerical models, the turbulent exchanges of heat and momentum at the air–sea interface are not represented explicitly but with parameterisations depending on the surface parameters. A new parameterisation of turbulent fluxes (WASP) has been implemented in the surface model SURFEX v8.1 and validated on four case studies. It combines a close fit to observations including cyclonic winds, a dependency on the wave growth rate, and the possibility of being used in atmosphere–wave coupled models.
Alisée A. Chaigneau, Stéphane Law-Chune, Angélique Melet, Aurore Voldoire, Guillaume Reffray, and Lotfi Aouf
Ocean Sci., 19, 1123–1143, https://doi.org/10.5194/os-19-1123-2023, https://doi.org/10.5194/os-19-1123-2023, 2023
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Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Thibault Guinaldo, Aurore Voldoire, Robin Waldman, Stéphane Saux Picart, and Hervé Roquet
Ocean Sci., 19, 629–647, https://doi.org/10.5194/os-19-629-2023, https://doi.org/10.5194/os-19-629-2023, 2023
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In the summer of 2022, France experienced a series of unprecedented heatwaves. This study is the first to examine the response of sea surface temperatures to these events, using spatial operational data and attributing the observed abnormally warm SSTs to atmospheric forcings. The findings of this study underscore the critical need for an efficient and sustainable operational system to monitor alterations that threaten the oceans in the context of climate change.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
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Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
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Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
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.
Alisée A. Chaigneau, Guillaume Reffray, Aurore Voldoire, and Angélique Melet
Geosci. Model Dev., 15, 2035–2062, https://doi.org/10.5194/gmd-15-2035-2022, https://doi.org/10.5194/gmd-15-2035-2022, 2022
Short summary
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Climate-change-induced sea level rise is a major threat for coastal and low-lying regions. Projections of coastal sea level changes are thus of great interest for coastal risk assessment and have significantly developed in recent years. In this paper, the objective is to provide high-resolution (6 km) projections of sea level changes in the northeastern Atlantic region bordering western Europe. For that purpose, a regional model is used to refine existing coarse global projections.
César Sauvage, Cindy Lebeaupin Brossier, and Marie-Noëlle Bouin
Atmos. Chem. Phys., 21, 11857–11887, https://doi.org/10.5194/acp-21-11857-2021, https://doi.org/10.5194/acp-21-11857-2021, 2021
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Air–sea processes are key elements during Mediterranean heavy precipitation events. We aim to progress in their representation in high-resolution weather forecast. Using coupled ocean–air–wave simulations, we investigated air–sea mechanisms modulated by ocean and waves during a case that occurred in southern France. Results showed significant impact of the forecast on low-level dynamics and air–sea fluxes and illustrated potential benefits of coupled numerical weather prediction systems.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
David L. A. Flack, Gwendal Rivière, Ionela Musat, Romain Roehrig, Sandrine Bony, Julien Delanoë, Quitterie Cazenave, and Jacques Pelon
Weather Clim. Dynam., 2, 233–253, https://doi.org/10.5194/wcd-2-233-2021, https://doi.org/10.5194/wcd-2-233-2021, 2021
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The representation of an extratropical cyclone in simulations of two climate models is studied by comparing them to observations of the international field campaign NAWDEX. We show that the current resolution used to run climate model projections (more than 100 km) is not enough to represent the life cycle accurately, but the use of 50 km resolution is good enough. Despite these encouraging results, cloud properties (partitioning liquid and solid) are found to be far from the observations.
Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma
Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, https://doi.org/10.5194/gmd-14-1575-2021, 2021
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A stochastic deep convection parameterization is implemented into the US Department of Energy Energy Exascale Earth System Model Atmosphere Model version 1 (EAMv1). Compared to the default model, the well-known problem of
too much light rain and too little heavy rainis largely alleviated over the tropics with the stochastic scheme. Results from this study provide important insights into the model performance of EAMv1 when stochasticity is included in the deep convective parameterization.
Qi Tang, Michael J. Prather, Juno Hsu, Daniel J. Ruiz, Philip J. Cameron-Smith, Shaocheng Xie, and Jean-Christophe Golaz
Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, https://doi.org/10.5194/gmd-14-1219-2021, 2021
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
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We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Florian Lemarié, Guillaume Samson, Jean-Luc Redelsperger, Hervé Giordani, Théo Brivoal, and Gurvan Madec
Geosci. Model Dev., 14, 543–572, https://doi.org/10.5194/gmd-14-543-2021, https://doi.org/10.5194/gmd-14-543-2021, 2021
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A simplified model of the atmospheric boundary layer (ABL) of intermediate complexity between a bulk parameterization and a full three-dimensional atmospheric model has been developed and integrated to the NEMO ocean model.
An objective in the derivation of such a simplified model is to reach an apt representation of ocean-only numerical simulations of some of the key processes associated with air–sea interactions at the characteristic scales of the oceanic mesoscale.
Hsi-Yen Ma, Chen Zhou, Yunyan Zhang, Stephen A. Klein, Mark D. Zelinka, Xue Zheng, Shaocheng Xie, Wei-Ting Chen, and Chien-Ming Wu
Geosci. Model Dev., 14, 73–90, https://doi.org/10.5194/gmd-14-73-2021, https://doi.org/10.5194/gmd-14-73-2021, 2021
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We propose an experimental design of a suite of multi-year, short-term hindcasts and compare them with corresponding observations or measurements for periods based on different weather and climate phenomena. This atypical way of evaluating model performance is particularly useful and beneficial, as these hindcasts can give scientists a robust picture of modeled precipitation, and cloud and radiation processes from their diurnal variation to year-to-year variability.
Marc Mallet, Fabien Solmon, Pierre Nabat, Nellie Elguindi, Fabien Waquet, Dominique Bouniol, Andrew Mark Sayer, Kerry Meyer, Romain Roehrig, Martine Michou, Paquita Zuidema, Cyrille Flamant, Jens Redemann, and Paola Formenti
Atmos. Chem. Phys., 20, 13191–13216, https://doi.org/10.5194/acp-20-13191-2020, https://doi.org/10.5194/acp-20-13191-2020, 2020
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This paper presents numerical simulations using two regional climate models to study the impact of biomass fire plumes from central Africa on the radiative balance of this region. The results indicate that biomass fires can either warm the regional climate when they are located above low clouds or cool it when they are located above land. They can also alter sea and land surface temperatures by decreasing solar radiation at the surface. Finally, they can also modify the atmospheric dynamics.
Peter A. Bogenschutz, Shuaiqi Tang, Peter M. Caldwell, Shaocheng Xie, Wuyin Lin, and Yao-Sheng Chen
Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020, https://doi.org/10.5194/gmd-13-4443-2020, 2020
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This paper documents a tool that has been developed that can be used to accelerate the development and understanding of climate models. This version of the model, known as a the single-column model, is much faster to run than the full climate model, and we demonstrate that this tool can be used to quickly exploit model biases that arise due to physical processes. We show examples of how this single-column model can directly benefit the field.
Marie-Noëlle Bouin and Cindy Lebeaupin Brossier
Ocean Sci., 16, 1125–1142, https://doi.org/10.5194/os-16-1125-2020, https://doi.org/10.5194/os-16-1125-2020, 2020
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A kilometre-scale coupled ocean–atmosphere simulation is used to study the impact of a medicane on the oceanic upper layer. The processes responsible for the surface cooling are comparable to those of weak tropical cyclones. The oceanic response is influenced by the dynamics of the central Mediterranean. In particular, a cyclonic eddy leads to weaker cooling. Heavy rain occuring early in the event creates a salinity barrier layer, reinforcing the effects of the surface fluxes on the cooling.
Cited articles
Abdel-Lathif, A. Y., Roehrig, R., Beau, I., and Douville, H.: Single-Column
Modeling of Convection During the CINDY2011/DYNAMO Field Campaign With the
CNRM Climate Model Version 6, J. Adv. Model. Earth Sy., 10, 578–602,
https://doi.org/10.1002/2017MS001077, 2018.
Acreman, D. M. and Jeffery, C. D.: The use of Argo for validation and tuning
of mixed layer models, Ocean Model., 19, 53–69,
https://doi.org/10.1016/j.ocemod.2007.06.005, 2007.
Barnier, B., Siefridt, L., and Marchesiello, P.: Thermal forcing for a
global ocean circulation model using a three-year climatology of ECMWF
analyses, J. Mar. Syst., 6, 363–380,
https://doi.org/10.1016/0924-7963(94)00034-9, 1995.
Bechtold, P., Krueger, S. K., Lewellen, W. S., Meijgaard, E. van, Moeng,
C.-H., Randall, D. A., Ulden, A. van, and Wang, S.: Modeling a
Stratocumulus-Topped PBL: Intercomparison among Different One-Dimensional
Codes and with Large Eddy Simulation, B. Am. Meteorol. Soc., 77,
2033–2042, https://doi.org/10.1175/1520-0477-77.9.2033, 1996.
Bellenger, H., Drushka, K., Asher, W., Reverdin, G., Katsumata, M., and
Watanabe, M.: Extension of the prognostic model of sea surface temperature
to rain-induced cool and fresh lenses, J. Geophys. Res.-Oceans, 122,
484–507, https://doi.org/10.1002/2016JC012429, 2017.
Bernie, D. J., Woolnough, S. J., Slingo, J. M., and Guilyardi, E.: Modeling
diurnal and intraseasonal variability of the ocean mixed layer, J. Climate,
18, 1190–1202, https://doi.org/10.1175/JCLI3319.1, 2005.
Bernie, D. J., Guilyardi, E., Madec, G., Slingo, J. M., Woolnough, S. J.,
and Cole, J.: Impact of resolving the diurnal cycle in an ocean-atmosphere
GCM. Part 2: A diurnally coupled CGCM, Clim. Dynam., 31, 909–925,
https://doi.org/10.1007/s00382-008-0429-z, 2008.
Betts, A. K. and Miller, M. J.: A new convective adjustment scheme. Part II:
Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data
sets, Q. J. Roy. Meteor. Soc., 112, 693–709,
https://doi.org/10.1002/qj.49711247308, 1986.
Bhattacharya, R., Bordoni, S., Suselj, K., and Teixeira, J.:
Parameterization Interactions in Global Aquaplanet Simulations, J. Adv.
Model. Earth Sy., 10, 403–420, https://doi.org/10.1002/2017MS000991,
2018.
Blanke, B. and Delecluse, P.: Variability of the Tropical Atlantic Ocean
Simulated by a General Circulation Model with Two Different Mixed-Layer
Physics, J. Phys. Oceanogr., 23, 1363–1388,
https://doi.org/10.1175/1520-0485(1993)023<1363:VOTTAO>2.0.CO;2, 1993.
Bosveld, F. C., Baas, P., Steeneveld, G. J., Holtslag, A. A. M., Angevine,
W. M., Bazile, E., de Bruijn, E. I. F., Deacu, D., Edwards, J. M., Ek, M.,
Larson, V. E., Pleim, J. E., Raschendorfer, M., and Svensson, G.: The Third
GABLS Intercomparison Case for Evaluation Studies of Boundary-Layer Models.
Part B: Results and Process Understanding, Bound.-Lay. Meteorol., 152,
157–187, https://doi.org/10.1007/S10546-014-9919-1, 2014.
Brilouet, P. E., Redelsperger, J. L., Bouin, M. N., Couvreux, F., and
Lebeaupin Brossier, C.: A case-study of the coupled ocean–atmosphere
response to an oceanic diurnal warm layer, Q. J. Roy. Meteor. Soc., 147,
2008–2032, https://doi.org/10.1002/qj.4007, 2021.
Chlond, A., Müller, F., and Sednev, I.: Numerical simulation of the diurnal cycle
of marine stratocumulus during FIRE—An LES and SCM modelling study, Q. J. R.
Meteorol. Soc., 130, 3297–3321, https://doi.org/10.1256/qj.03.128,
2004.
Ciesielski, P. E., Yu, H., Johnson, R. H., Yoneyama, K., Katsumata, M.,
Long, C. N., Wang, J., Loehrer, S. M., Young, K., Williams, S. F., Brown,
W., Braun, J., and Hove, T. V.: Quality-Controlled Upper-Air Sounding
Dataset for DYNAMO/CINDY/AMIE: Development and Corrections, J. Atmos.
Ocean. Tech., 31, 741–764, https://doi.org/10.1175/JTECH-D-13-00165.1,
2014.
Clayson, C. A. and Chen, A.: Sensitivity of a coupled single-column model in
the tropics to treatment of the interfacial parameterizations, J. Climate, 15,
1805–1831, https://doi.org/10.1175/1520-0442(2002)015<1805:SOACSC>2.0.CO;2, 2002.
COESA: U.S. Standard Atmosphere, US Government Printing Office,
NOAA, Washington, DC, 1976.
Couvreux, F., Roehrig, R., Rio, C., Lefebvre, M.-P., Caian, M., Komori, T.,
Derbyshire, S., Guichard, F., Favot, F., D'andrea, F., Bechtold, P., and
Gentine, P.: Representation of daytime moist convection over the semi-arid
Tropics by parametrizations used in climate and meteorological models, Q. J.
Roy. Meteor. Soc., 141, 2220–2236,
https://doi.org/10.1002/qj.2517, 2015.
Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V.,
Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., Brient, F.,
Favot, F., Honnert, R., Lefebvre, M. P., Madeleine, J. B., Rodier, Q., and
Xu, W.: Process-Based Climate Model Development Harnessing Machine Learning:
I. A Calibration Tool for Parameterization Improvement, J. Adv. Model. Earth
Sy., 13, e2020MS002217, https://doi.org/10.1029/2020MS002217, 2021.
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017.
Cuxart, J., Holtslag, A. A. M., Beare, R. J., Bazile, E., Beljaars, A.,
Cheng, A., Conangla, L., Ek, M., Freedman, F., Hamdi, R., Kerstein, A.,
Kitagawa, H., Lenderink, G., Lewellen, D., Mailhot, J., Mauritsen, T.,
Perov, V., Schayes, G., Steeneveld, G.-J., Svensson, G., Taylor, P., Weng,
W., Wunsch, S., and Xu, K.-M.: Single-Column Model Intercomparison for a
Stably Stratified Atmospheric Boundary Layer, Bound.-Lay. Meteorol., 118,
273–303, https://doi.org/10.1007/s10546-005-3780-1, 2006.
Damerell, G. M., Heywood, K. J., Calvert, D., Grant, A. L. M., Bell, M. J.,
and Belcher, S. E.: A comparison of five surface mixed layer models with a
year of observations in the North Atlantic, Prog. Oceanogr., 187, 102316,
https://doi.org/10.1016/j.pocean.2020.102316, 2020.
Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S.,
van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C.,
Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J.,
Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E.,
Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System
Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916,
https://doi.org/10.1029/2019MS001916, 2020.
Davies, L., Jakob, C., Cheung, K., Genio, A. D., Hill, A., Hume, T., Keane,
R. J., Komori, T., Larson, V. E., Lin, Y., Liu, X., Nielsen, B. J., Petch,
J., Plant, R. S., Singh, M. S., Shi, X., Song, X., Wang, W., Whitall, M. A.,
Wolf, A., Xie, S., and Zhang, G.: A single-column model ensemble approach
applied to the TWP-ICE experiment, J. Geophys. Res.-Atmos., 118,
6544–6563, https://doi.org/10.1002/jgrd.50450, 2013.
Decharme, B., Delire, C., Minvielle, M., Colin, J., Vergnes, J., Alias, A.,
Saint-Martin, D., Séférian, R., Sénési, S., and Voldoire,
A.: Recent changes in the ISBA-CTRIP land surface system for use in the
CNRM-CM6 climate model and in global off-line hydrological applications, J.
Adv. Model. Earth Sy., 11, 1207–1252, https://doi.org/10.1029/2018MS001545,
2019.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M.,
Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis:
Configuration and performance of the data assimilation system, Q. J. Roy.
Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Deppenmeier, A. L., Haarsma, R. J., van Heerwaarden, C., and Hazeleger, W.:
The southeastern tropical atlantic sst bias investigated with a coupled
atmosphere-ocean single-column model at a pirata mooring site, J. Climate, 33,
6255–6271, https://doi.org/10.1175/JCLI-D-19-0608.1, 2020.
de Szoeke, S. P., Edson, J. B., Marion, J. R., Fairall, C. W., and Bariteau,
L.: The MJO and air-sea interaction in TOGA COARE and DYNAMO, J. Climate, 28,
597–622, https://doi.org/10.1175/JCLI-D-14-00477.1, 2015.
Edson, J. B., Fairall, C. W., and De Szoeke, S.: R/V Roger Revelle Flux,
Near-Surface Meteorology, and Navigation Data, Version 3.0, 347.177 [data set],
https://doi.org/10.5065/D6KP80J9, 2016.
Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A., and Edson, J.
B.: Bulk parameterization of air-sea fluxes: Updates and verification for
the COARE algorithm, J. Climate, 16, 571–591,
https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2, 2003.
Ferry, N., Parent, L., Garric, G., Bricaud, C., Testut, C. E., Galloudec, O.
L., Lellouche, J. M., Drevillon, M., Greiner, E., Barnier, B., Molines, J.
M., Jourdain, N., Guinehut, S., Cabanes, C., and Zawadzki, L.: GLORYS2V1
global ocean reanalysis of the altimetric era (1992–2009) at meso scale,
Mercat. Ocean Quaterly Newsl., 44, 29–39, 2012.
Gaspar, P., Grégoris, Y., and Lefevre, J.-M.: A simple eddy kinetic
energy model for simulations of the oceanic vertical mixing: Tests at
station Papa and Long-Term Upper Ocean Study site, J. Geophys. Res.-Oceans,
95, 16179–16193, 1990.
Ge, X., Wang, W., Kumar, A., and Zhang, Y.: Importance of the vertical
resolution in simulating SST diurnal and intraseasonal variability in an
oceanic general circulation model, J. Climate, 30, 3963–3978,
https://doi.org/10.1175/JCLI-D-16-0689.1, 2017.
Gentemann, C. L., Minnett, P. J., and Ward, B.: Profiles of ocean surface
heating (POSH): A new model of upper ocean diurnal warming, J. Geophys. Res.-Oceans, 114, C07017, https://doi.org/10.1029/2008JC004825, 2009.
Giordani, H., Noilhan, J., Lacarrère, P., Bessemelin, P., and Mascart,
P.: Modelling the surface processes and the atmospheric boundary layer for
semi-arid conditions, Agr. Forest Meteorol., 80, 263–296, 1996.
Giordani, H., Bourdallé-Badie, R., and Madec, G.: An Eddy-Diffusivity
Mass-Flux Parameterization for Modeling Oceanic Convection, J. Adv. Model.
Earth Sy., 12, e2020MS002078, https://doi.org/10.1029/2020MS002078, 2020.
Godfrey, J. S. and Beljaars, A. C. M.: On the turbulent fluxes of buoyancy,
heat and moisture at the air-sea interface at low wind speeds, J. Geophys.
Res.-Oceans, 96, 22043–22048, https://doi.org/10.1029/91JC02015, 1991.
Guichard, F., Petch, J. C., Redelsperger, J. L., Bechtold, P., Chaboureau,
J. P., Cheinet, S., Grabowski, W., Grenier, H., Jones, C. G., Köhler,
M., Piriou, J. M., Tailleux, R., and Tomasini, M.: Modelling the diurnal
cycle of deep precipitating convection over land with cloud-resolving models
and single-column models, Q. J. Roy. Meteor. Soc., 130 C, 3139–3172,
https://doi.org/10.1256/qj.03.145, 2004.
Hartung, K., Svensson, G., Struthers, H., Deppenmeier, A.-L., and Hazeleger, W.: An EC-Earth coupled atmosphere–ocean single-column model (AOSCM.v1_EC-Earth3) for studying coupled marine and polar processes, Geosci. Model Dev., 11, 4117–4137, https://doi.org/10.5194/gmd-11-4117-2018, 2018.
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan,
Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C.,
Tomassini, L., Watanabe, M., and Williamson, D.: The Art and Science of
Climate Model Tuning, B. Am. Meteorol. Soc., 98, 589–602,
https://doi.org/10.1175/BAMS-D-15-00135.1, 2017.
Hsu, J. Y., Hendon, H., Feng, M., and Zhou, X.: Magnitude and Phase of
Diurnal SST Variations in the ACCESS-S1 Model During the Suppressed Phase of
the MJOs, J. Geophys. Res.-Oceans, 124, 9553–9571,
https://doi.org/10.1029/2019JC015458, 2019.
Itterly, K., Taylor, P., and Roberts, J. B.: Satellite Perspectives of Sea
Surface Temperature Diurnal Warming on Atmospheric Moistening and Radiative
Heating during MJO, J. Climate, 34, 1203–1226,
https://doi.org/10.1175/JCLI-D-20-0350.1, 2021.
Kawai, Y. and Wada, A.: Diurnal sea surface temperature variation and its
impact on the atmosphere and ocean: A review, J. Oceanogr., 63, 721–744,
https://doi.org/10.1007/S10872-007-0063-0, 2007.
Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A.,
Boer, G. de, Chen, M., Cole, J. N. S., Genio, A. D. D., Falk, M., Foster, M.
J., Fridlind, A., Golaz, J.-C., Hashino, T., Harrington, J. Y., Hoose, C.,
Khairoutdinov, M. F., Larson, V. E., Liu, X., Luo, Y., McFarquhar, G. M.,
Menon, S., Neggers, R. A. J., Park, S., Poellot, M. R., Schmidt, J. M.,
Sednev, I., Shipway, B. J., Shupe, M. D., Spangenberg, D. A., Sud, Y. C.,
Turner, D. D., Veron, D. E., Salzen, K. von, Walker, G. K., Wang, Z., Wolf,
A. B., Xie, S., Xu, K.-M., Yang, F., and Zhang, G.: Intercomparison of model
simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic
Cloud Experiment. I: single-layer cloud, Q. J. Roy. Meteor. Soc., 135,
979–1002, https://doi.org/10.1002/qj.416, 2009.
Lazar, A., Madec, G., and Delecluse, P.: The Deep Interior Downwelling, the
Veronis Effect, and Mesoscale Tracer Transport Parameterizations in an OGCM,
J. Phys. Oceanogr., 29, 2945–2961,
https://doi.org/10.1175/1520-0485(1999)029<2945:TDIDTV>2.0.CO;2, 1999.
Lenderink, G., Siebesma, A. P., Cheinet, S., Irons, S., Jones, C. G.,
Marquet, P., Müller, F., Olmeda, D., Calvo, J., Sánchez, E., and
Soares, P. M. M.: The diurnal cycle of shallow cumulus clouds over land: A
single-column model intercomparison study, Q. J. Roy. Meteor. Soc., 130 C,
3339–3364, https://doi.org/10.1256/qj.03.122, 2004.
Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., Dong, L., Zhou, T.,
Liu, L., Wang, L., Pu, Y., Chen, X., Chen, L., Xie, Z., Liu, H., Zhang, L.,
Huang, X., Feng, T., Zheng, W., Xia, K., Liu, H., Liu, J., Wang, Y., Wang,
L., Jia, B., Xie, F., Wang, B., Zhao, S., Yu, Z., Zhao, B., and Wei, J.: The
Flexible Global Ocean-Atmosphere-Land System Model Grid-Point Version 3
(FGOALS-g3): Description and Evaluation, J. Adv. Model. Earth Sy., 12,
e2019MS002012, https://doi.org/10.1029/2019MS002012, 2020.
Ma, L. and Jiang, Z.: Reevaluating the impacts of oceanic vertical
resolution on the simulation of Madden–Julian Oscillation eastward
propagation in a climate system model, Clim. Dynam., 56, 2259–2278,
https://doi.org/10.1007/s00382-020-05587-7, 2021.
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C.,
Bruciaferri, D., Calvert, D., Chanut, J., Clementi, E., Coward, A.,
Delrosso, D., Ethé, C., Flavoni, S., Graham, T., Harle, J., Iovino, D.,
Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S.,
Paul, J., Rousset, C., Storkey, D., Storto, A., and Vancoppenolle, M.: NEMO
ocean engine, Zenodo, https://doi.org/10.5281/ZENODO.1472492, 2017.
Marion, J. R.: Providing the best turbulent heat flux estimates from
eddy correlation and bulk methods using DYNAMO data, PhD Thesis, Ocean
Earth, and Atmospheric Science, Oregon State University, 161 pp., 2014.
Marti, O., Nguyen, S., Braconnot, P., Valcke, S., Lemarié, F., and Blayo, E.: A Schwarz iterative method to evaluate ocean–atmosphere coupling schemes: implementation and diagnostics in IPSL-CM6-SW-VLR, Geosci. Model Dev., 14, 2959–2975, https://doi.org/10.5194/gmd-14-2959-2021, 2021.
Matthews, A. J., Baranowski, D. B., Heywood, K. J., Flatau, P. J., and
Schmidtko, S.: The Surface Diurnal Warm Layer in the Indian Ocean during
CINDY/DYNAMO, J. Climate, 27, 9101–9122,
https://doi.org/10.1175/JCLI-D-14-00222.1, 2014.
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H.,
Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T.,
Jimenéz-de-la-Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S.,
Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke,
J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B.,
Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.
S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp,
M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T.,
Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein,
L., Stemmler, I., Stevens, B., von Storch, J. S., Tian, F., Voigt, A.,
Vrese, P., Wieners, K. H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.:
Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and
Its Response to Increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038,
https://doi.org/10.1029/2018MS001400, 2019.
McPhaden, M. J. and Foltz, G. R.: Intraseasonal variations in the surface
layer heat balance of the central equatorial Indian Ocean: The importance of
zonal advection and vertical mixing, Geophys. Res. Lett., 40, 2737–2741,
https://doi.org/10.1002/grl.50536, 2013.
Moulin, A. J., Moum, J. N., and Shroyer, E. L.: Evolution of turbulence in
the diurnal warm layer, J. Phys. Oceanogr., 48, 383–396,
https://doi.org/10.1175/JPO-D-17-0170.1, 2018.
Moum, J.: R/V Roger Revelle CTD Data, Version 1.0. UCAR/NCAR – Earth
Observing Laboratory, https://data.eol.ucar.edu/dataset/347.177 (last access: 6 September 2018), 2016.
Nabat, P., Somot, S., Cassou, C., Mallet, M., Michou, M., Bouniol, D., Decharme, B., Drugé, T., Roehrig, R., and Saint-Martin, D.: Modulation of radiative aerosols effects by atmospheric circulation over the Euro-Mediterranean region, Atmos. Chem. Phys., 20, 8315–8349, https://doi.org/10.5194/acp-20-8315-2020, 2020.
Neggers, R. A. J., Ackerman, A. S., Angevine, W. M., Bazile, E., Beau, I.,
Blossey, P. N., Boutle, I. A., de Bruijn, C., Cheng, A., van der Dussen, J.,
Fletcher, J., Dal Gesso, S., Jam, A., Kawai, H., Cheedela, S. K., Larson, V.
E., Lefebvre, M. P., Lock, A. P., Meyer, N. R., de Roode, S. R., de Rooy,
W., Sandu, I., Xiao, H., and Xu, K. M.: Single-Column Model Simulations of
Subtropical Marine Boundary-Layer Cloud Transitions Under Weakening
Inversions, J. Adv. Model. Earth Sy., 9, 2385–2412,
https://doi.org/10.1002/2017MS001064, 2017.
Price, J. F., Weller, R. A., and Pinkel, R.: Diurnal Cycling: observations
and models of the upper ocean response to diurnal heating, cooling, and wind
mixing, J. Geophys. Res.-Oceans, 91, 8411–8427, https://doi.org/10.1029/jc091ic07p08411, 1986.
Randall, D. A. and Cripe, D. G.: Alternative methods for specification of
observed forcing in single-column models and cloud system models, J.
Geophys. Res., 104, 24527–24545,
https://doi.org/10.1029/1999JD900765, 1999.
Randall, D. A., Xu, K.-M., Somerville, R. J., and Iacobellis, S.:
Single-column models and cloud ensemble models as links between observations
and climate models, J. Climate, 9, 1683–1697, 1996.
Reffray, G., Bourdalle-Badie, R., and Calone, C.: Modelling turbulent vertical mixing sensitivity using a 1-D version of NEMO, Geosci. Model Dev., 8, 69–86, https://doi.org/10.5194/gmd-8-69-2015, 2015.
Roehrig, R., Beau, I., Saint-Martin, D., Alias, A., Decharme, B.,
Guérémy, J.-F., Voldoire, A., Abdel-Lathif, A. Y., Bazile, E.,
Belamari, S., Blein, S., Bouniol, D., Bouteloup, Y., Cattiaux, J., Chauvin,
F., Chevallier, M., Colin, J., Douville, H., Marquet, P., Michou, M., Nabat,
P., Oudar, T., Peyrillé, P., Piriou, J.-M., Salas y Mélia, D.,
Séférian, R., and Sénési, S.: The CNRM Global Atmosphere
Model ARPEGE-Climat 6.3: Description and Evaluation, J. Adv. Model. Earth
Sy., 12, e2020MS002075, https://doi.org/10.1029/2020MS002075, 2020.
Scanlon, B., Wick, G. A., and Ward, B.: Near-surface diurnal warming simulations: validation with high resolution profile measurements, Ocean Sci., 9, 977–986, https://doi.org/10.5194/os-9-977-2013, 2013.
Sellar, A. A., Walton, J., Jones, C. G., Wood, R., Abraham, N. L.,
Andrejczuk, M., Andrews, M. B., Andrews, T., Archibald, A. T., de Mora, L.,
Dyson, H., Elkington, M., Ellis, R., Florek, P., Good, P., Gohar, L.,
Haddad, S., Hardiman, S. C., Hogan, E., Iwi, A., Jones, C. D., Johnson, B.,
Kelley, D. I., Kettleborough, J., Knight, J. R., Köhler, M. O.,
Kuhlbrodt, T., Liddicoat, S., Linova-Pavlova, I., Mizielinski, M. S.,
Morgenstern, O., Mulcahy, J., Neininger, E., O'Connor, F. M., Petrie, R.,
Ridley, J., Rioual, J. C., Roberts, M., Robertson, E., Rumbold, S., Seddon,
J., Shepherd, H., Shim, S., Stephens, A., Teixiera, J. C., Tang, Y.,
Williams, J., Wiltshire, A., and Griffiths, P. T.: Implementation of U.K.
Earth System Models for CMIP6, J. Adv. Model. Earth Sy., 12, e2019MS001946,
https://doi.org/10.1029/2019MS001946, 2020.
Seo, H., Subramanian, A. C., Miller, A. J., and Cavanaugh, N. R.: Coupled Impacts of the Diurnal Cycle of Sea Surface Temperature on the
Madden-Julian Oscillation, J. Climate, 27, 8422–8443, https://doi.org/10.1175/JCLI-D-14-00141.1, 2014.
Tian, F., von Storch, J. S., and Hertwig, E.: Impact of SST diurnal cycle on
ENSO asymmetry, Clim. Dynam., 52, 2399–2411,
https://doi.org/10.1007/s00382-018-4271-7, 2019.
Voldoire, A.: Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration – model outputs (Version v1), Zenodo [data set and code], https://doi.org/10.5281/zenodo.5815165, 2021.
Voldoire, A., Decharme, B., Pianezze, J., Lebeaupin Brossier, C., Sevault, F., Seyfried, L., Garnier, V., Bielli, S., Valcke, S., Alias, A., Accensi, M., Ardhuin, F., Bouin, M.-N., Ducrocq, V., Faroux, S., Giordani, H., Léger, F., Marsaleix, P., Rainaud, R., Redelsperger, J.-L., Richard, E., and Riette, S.: SURFEX v8.0 interface with OASIS3-MCT to couple atmosphere with hydrology, ocean, waves and sea-ice models, from coastal to global scales, Geosci. Model Dev., 10, 4207–4227, https://doi.org/10.5194/gmd-10-4207-2017, 2017.
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A.,
Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P.,
Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke,
S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes,
J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O.,
Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A.,
Sanchez-Gomez, E., Terray, L., and Waldman, R.: Evaluation of CMIP6 DECK
Experiments With CNRM-CM6-1, J. Adv. Model. Earth Sy., 11, 2177–2213,
https://doi.org/10.1029/2019MS001683, 2019.
Ward, B.: Near-surface ocean temperature, J. Geophys. Res.-Oceans, 111, C02005,
https://doi.org/10.1029/2004JC002689, 2006.
Wick, G. A. and Castro, S. L.: Assessment of extreme diurnal warming in
operational geosynchronous satellite sea surface temperature products,
Remote Sens., 12, 1–23, https://doi.org/10.3390/rs12223771, 2020.
Xie, S., Cederwall, R. T., and Zhang, M.: Developing long-term single-column
model/cloud system–resolving model forcing data using numerical weather
prediction products constrained by surface and top of the atmosphere
observations, J. Geophys. Res.-Atmos., 109, D01104,
https://doi.org/10.1029/2003JD004045, 2004.
Yang, X., Song, Z., Tseng, Y.-H., Qiao, F., and Shu, Q.: Evaluation of three
temperature profiles of a sublayer scheme to simulate SST diurnal cycle in a
global ocean general circulation model, J. Adv. Model. Earth Sy., 9,
1994–2006, https://doi.org/10.1002/2017MS000927, 2017.
Yoneyama, K., Zhang, C., and Long, C. N.: Tracking Pulses of the
Madden–Julian Oscillation, B. Am. Meteorol. Soc., 94, 1871–1891,
https://doi.org/10.1175/BAMS-D-12-00157.1, 2013.
Zeng, X. and Beljaars, A.: A prognostic scheme of sea surface skin
temperature for modeling and data assimilation, Geophys. Res. Lett., 32,
1–4, https://doi.org/10.1029/2005GL023030, 2005.
Zhang, M. H. and Lin, J. L.: Constrained Variational Analysis of Sounding
Data Based on Column-Integrated Budgets of Mass, Heat, Moisture, and
Momentum: Approach and Application to ARM Measurements, J. Atmos. Sci.,
54, 1503–1524, https://doi.org/10.1175/1520-0469(1997)054<1503:CVAOSD>2.0.CO;2, 1997.
Zhang, M. H., Lin, J. L., Cederwall, R. T., Yio, J. J., and Xie, S. C.:
Objective Analysis of ARM IOP Data: Method and Sensitivity, Mon. Weather
Rev., 129, 295–311, https://doi.org/10.1175/1520-0493(2001)129<0295:OAOAID>2.0.CO;2, 2001.
Zhao, N. and Nasuno, T.: How Does the Air-Sea Coupling Frequency Affect
Convection During the MJO Passage?, J. Adv. Model. Earth Sy., 12,
e2020MS002058, https://doi.org/10.1029/2020MS002058, 2020.
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019.
Short summary
A single-column version of the global climate model CNRM-CM6-1 has been designed to ease development and validation of the model physics at the air–sea interface in a simplified environment. This model is then used to assess the ability to represent the sea surface temperature diurnal cycle. We conclude that the sea surface temperature diurnal variability is reasonably well represented in CNRM-CM6-1 with a 1 h coupling time step and the upper-ocean model resolution of 1 m.
A single-column version of the global climate model CNRM-CM6-1 has been designed to ease...
