Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1575-2021
© Author(s) 2021. 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-14-1575-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
18 Mar 2021
Development and technical paper |
| 18 Mar 2021
| 18 Mar 2021
Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model
Yong Wang
Ministry of Education Key Laboratory for Earth System Modeling &
Department of Earth System Science, Tsinghua University, Beijing, 100084,
China
Guang J. Zhang
CORRESPONDING AUTHOR
Scripps Institution of Oceanography, La Jolla, CA, USA
Shaocheng Xie
Lawrence Livermore National Laboratory, Livermore, CA, USA
Wuyin Lin
Brookhaven National Laboratory, Upton, NY, USA
George C. Craig
Meteorologisches Institut, Ludwig-Maximilians-Universität, Munich,
Germany
Lawrence Livermore National Laboratory, Livermore, CA, USA
Hsi-Yen Ma
Lawrence Livermore National Laboratory, Livermore, CA, USA
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Revised manuscript not accepted
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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
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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.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
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Hyein Jeong, Adrian K. Turner, Andrew F. Roberts, Milena Veneziani, Stephen F. Price, Xylar S. Asay-Davis, Luke P. Van Roekel, Wuyin Lin, Peter M. Caldwell, Hyo-Seok Park, Jonathan D. Wolfe, and Azamat Mametjanov
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Peter A. Bogenschutz, Hsiang-He Lee, Qi Tang, and Takanobu Yamaguchi
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Ming Yin, Yilun Han, Yong Wang, Wenqi Sun, Jianbo Deng, Daoming Wei, Ying Kong, and Bin Wang
Geosci. Model Dev., 16, 135–156, https://doi.org/10.5194/gmd-16-135-2023, https://doi.org/10.5194/gmd-16-135-2023, 2023
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Konstantin Krüger, Andreas Schäfler, Martin Wirth, Martin Weissmann, and George C. Craig
Atmos. Chem. Phys., 22, 15559–15577, https://doi.org/10.5194/acp-22-15559-2022, https://doi.org/10.5194/acp-22-15559-2022, 2022
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A comprehensive data set of airborne lidar water vapour profiles is compared with ERA5 reanalyses for a robust characterization of the vertical structure of the mid-latitude lower-stratospheric moist bias. We confirm a moist bias of up to 55 % at 1.3 km altitude above the tropopause and uncover a decreasing bias beyond. Collocated O3 and H2O observations reveal a particularly strong bias in the mixing layer, indicating insufficiently modelled transport processes fostering the bias.
Andreas Alexander Beckert, Lea Eisenstein, Annika Oertel, Timothy Hewson, George C. Craig, and Marc Rautenhaus
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2022-36, https://doi.org/10.5194/wcd-2022-36, 2022
Preprint withdrawn
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This study revises and extends a previously presented 3-D objective front detection method and demonstrates its benefits to analyse weather dynamics in numerical simulation data. Based on two case studies of extratropical cyclones, we demonstrate the evaluation of conceptual models from dynamic meteorology, illustrate the benefits of our interactive analysis approach by comparing fronts in data with different model resolutions, and study the impact of convection on fronts.
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.
Xin Wang, Yilun Han, Wei Xue, Guangwen Yang, and Guang J. Zhang
Geosci. Model Dev., 15, 3923–3940, https://doi.org/10.5194/gmd-15-3923-2022, https://doi.org/10.5194/gmd-15-3923-2022, 2022
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This study uses a set of deep neural networks to learn a parameterization scheme from a superparameterized general circulation model (GCM). After being embedded in a realistically configurated GCM, the parameterization scheme performs stably in long-term climate simulations and reproduces reasonable climatology and climate variability. This success is the first for long-term stable climate simulations using machine learning parameterization under real geographical boundary conditions.
Xue Zheng, Qing Li, Tian Zhou, Qi Tang, Luke P. Van Roekel, Jean-Christophe Golaz, Hailong Wang, and Philip Cameron-Smith
Geosci. Model Dev., 15, 3941–3967, https://doi.org/10.5194/gmd-15-3941-2022, https://doi.org/10.5194/gmd-15-3941-2022, 2022
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Aurore Voldoire, Romain Roehrig, Hervé Giordani, Robin Waldman, Yunyan Zhang, Shaocheng Xie, and Marie-Nöelle Bouin
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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.
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.
Yong Wang, Wenwen Xia, and Guang J. Zhang
Atmos. Chem. Phys., 21, 16797–16816, https://doi.org/10.5194/acp-21-16797-2021, https://doi.org/10.5194/acp-21-16797-2021, 2021
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This study developed a novel approach to detect what rainfall rates climatologically are most efficient for wet removal of different aerosol types and applied it to a global climate model (GCM). Results show that light rain has disproportionate control on aerosol wet scavenging, with distinct rain rates for different aerosol sizes. The approach can be applied to other GCMs to better understand the aerosol wet scavenging by rainfall, which is important to better simulate aerosols.
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.
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.
Jingyu Wang, Jiwen Fan, Robert A. Houze Jr., Stella R. Brodzik, Kai Zhang, Guang J. Zhang, and Po-Lun Ma
Geosci. Model Dev., 14, 719–734, https://doi.org/10.5194/gmd-14-719-2021, https://doi.org/10.5194/gmd-14-719-2021, 2021
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This paper presents an evaluation of the E3SM model against NEXRAD radar observations for the warm seasons during 2014–2016. The COSP forward simulator package is implemented in the model to generate radar reflectivity, and the NEXRAD observations are coarsened to the model resolution for comparison. The model severely underestimates the reflectivity above 4 km. Sensitivity tests on the parameters from cumulus parameterization and cloud microphysics do not improve this model bias.
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.
Landon A. Rieger, Jason N. S. Cole, John C. Fyfe, Stephen Po-Chedley, Philip J. Cameron-Smith, Paul J. Durack, Nathan P. Gillett, and Qi Tang
Geosci. Model Dev., 13, 4831–4843, https://doi.org/10.5194/gmd-13-4831-2020, https://doi.org/10.5194/gmd-13-4831-2020, 2020
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Recently, the stratospheric aerosol forcing dataset used as an input to the Coupled Model Intercomparison Project phase 6 was updated. This work explores the impact of those changes on the modelled historical climates in the CanESM5 and EAMv1 models. Temperature differences in the stratosphere shortly after the Pinatubo eruption are found to be significant, but surface temperatures and precipitation do not show a significant change.
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.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak,
J., Rudolf, B., Schneider, U., Curtis, S., and Bolvin, D.: The version-2
global precipitation climatology project (GPCP) monthly precipitation
analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, 2003.
Bentamy, A., Queffeulou, P., Quilfen, Y., and Katsaros, K.: Ocean surface
wind fields estimated from satellite active and passive microwave
instruments, IEEE T. Geosci. R., 37,
2469–2486, 1999.
Caldwell, P. M., Mametjanov, A., Tang, Q., Van Roekel, L. P., Golaz, J.-C.,
Lin, W., Bader, D. C., Keen, N. D., Feng, Y., Jacob, R., Maltrud, M. E.,
Roberts, A. F., Taylor, M. A., Veneziani, M., Wang, H., Wolfe, J. D.,
Balaguru, K., Cameron-Smith, P., Dong, L., Klein, S. A., Leung, L. R., Li,
H.-Y., Li, Q., Liu, X., Neale, R. B., Pinheiro, M., Qian, Y., Ullrich, P.
A., Xie, S., Yang, Y., Zhang, Y., Zhang, K., and Zhou, T.: The DOE E3SM
Coupled Model Version 1: Description and Results at High Resolution, J.
Adv. Model. Earth Sy., 11, 4095–4146, https://doi.org/10.1029/2019ms001870,
2019.
Cohen, B. G. and Craig, G. C.: Fluctuations in an Equilibrium Convective
Ensemble. Part II: Numerical Experiments, J. Atmos.
Sci., 63, 2005–2015, https://doi.org/10.1175/JAS3710.1, 2006.
Craig, G. C. and Cohen, B. G.: Fluctuations in an Equilibrium Convective
Ensemble. Part I: Theoretical Formulation, J. Atmos.
Sci., 63, 1996–2004, https://doi.org/10.1175/JAS3709.1, 2006.
Dai, A.: Precipitation Characteristics in Eighteen Coupled Climate Models,
J. Climate, 19, 4605–4630, https://doi.org/10.1175/JCLI3884.1, 2006.
Davies, L., Jakob, C., May, P., Kumar, V. V., and Xie, S.: Relationships
between the large-scale atmosphere and the small-scale convective state for
Darwin, Australia, J. Geophys. Res.-Atmos., 118,
11534–511545, https://doi.org/10.1002/jgrd.50645, 2013.
Gettelman, A., Morrison, H., Santos, S., Bogenschutz, P., and Caldwell, P.:
Advanced two-moment bulk microphysics for global models. Part II: Global
model solutions and aerosol-cloud interactions, J. Climate, 28,
1288–1307, 2015.
Golaz, J.-C., Larson, V. E., and Cotton, W. R.: A PDF-based model for
boundary layer clouds. Part I: Method and model description, J.
Atmos. Sci., 59, 3540–3551, 2002.
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C.,
Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M.
A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S.,
Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J.
G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J.,
Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W.,
Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W.,
Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A.,
McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch,
P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F.,
Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J.,
Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang,
H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang,
Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang,
K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model
Version 1: Overview and Evaluation at Standard Resolution, J.
Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018ms001603,
2019.
Goswami, B., Khouider, B., Phani, R., Mukhopadhyay, P., and Majda, A.:
Improving synoptic and intraseasonal variability in CFSv2 via stochastic
representation of organized convection, Geophys. Res. Lett., 44,
1104–1113, 2017.
Groenemeijer, P. and Craig, G. C.: Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics, Atmos. Chem. Phys., 12, 4555–4565, https://doi.org/10.5194/acp-12-4555-2012, 2012.
Hsu, J. and Prather, M. J.: Stratospheric variability and tropospheric
ozone, J. Geophys. Res.-Atmos., 114, D06102, https://doi.org/10.1029/2008JD010942, 2009.
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S.,
Joyce, R., McGavock, B., and Susskind, J.: lobal precipitation at
one-degree daily resolution from multisatellite observations, J.
Hydrometeorol., 2, 36–50, 2001.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F.,
Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM multisatellite
precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor
precipitation estimates at fine scales, J. Hydrometeorol., 8,
38–55, 2007.
Huffman, G. J., Adler, R. F., Bolvin, D. T., and Gu, G.: Improving the global precipitation record: GPCP Version 2.1, Geophys. Res. Lett., 36, L17808, https://doi.org/10.1029/2009GL040000, 2009.
Huffman, G. J., Bolvin, D., and Adler, R.: GPCP version 1.2 1-degree daily
(1DD) precipitation data set, World Data Center A, National Climatic Data
Center, Asheville, USA, available at ftp://rsd.gsfc.nasa.gov/pub/1dd-v1.2/ (last access: 8 November 2015), 2012.
Huffman, G. J., Bolvin, D. T., and Nelkin, E. J.: Integrated Multi-satellite Retrievals for GPM (IMERG) technical documentation, available at:
https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf (last access: 8 July 2019), 2017.
Jiang, X., Waliser, D. E., Xavier, P. K., Petch, J., Klingaman, N. P., Woolnough, S. J., Guan, B., Bellon, G., Crueger, T., DeMott, C., Hannay, C., Lin, H., Hu, W., Kim, D., Lappen, C.-L., Lu, M.-M., Ma, H.-Y., Miyakawa, T., Ridout, J. A., Schubert, S. D., Scinocca, J., Seo, K.-H., Shindo, E., Song, X., Stan, C., Tseng, W.-L., Wang, W., Wu, T., Wu, X., Wyser, K., Zhang, G. J., and Zhu, H.: Vertical structure and physical processes of the Madden-Julian oscillation: Exploring key model physics in climate simulations, J. Geophys. Res.-Atmos., 120, 4718–4748, https://doi.org/10.1002/2014JD022375, 2015.
Jones, T. R. and Randall, D. A.: Quantifying the limits of convective
parameterizations, J. Geophys. Res.-Atmos., 116, D08210,
https://doi.org/10.1029/2010jd014913, 2011.
Keane, R. J., Craig, G. C., Keil, C., and Zängl, G.: The Plant-Craig
Stochastic Convection Scheme in ICON and Its Scale Adaptivity, J.
Atmos. Sci., 71, 3404–3415, https://doi.org/10.1175/JAS-D-13-0331.1, 2014.
Keane, R. J., Plant, R. S., and Tennant, W. J.: Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3), Geosci. Model Dev., 9, 1921–1935, https://doi.org/10.5194/gmd-9-1921-2016, 2016.
Khouider, B., Biello, J., and Majda, A. J.: A stochastic multicloud model
for tropical convection, Commun. Math. Sci., 8, 187–216, 2010.
Kim, D., Sobel, A. H., Maloney, E. D., Frierson, D. M., and Kang, I.-S.: A
systematic relationship between intraseasonal variability and mean state
bias in AGCM simulations, J. Climate, 24, 5506–5520, 2011.
Klingaman, N. P. and Demott, C. A.: Mean State Biases and Interannual
Variability Affect Perceived Sensitivities of the Madden-Julian Oscillation
to Air-Sea Coupling, J. Adv. Model. Earth Sy., 12,
e2019MS001799, https://doi.org/10.1029/2019ms001799, 2020.
Kooperman, G. J., Pritchard, M. S., Burt, M. A., Branson, M. D., and
Randall, D. A.: Robust effects of cloud superparameterization on simulated
daily rainfall intensity statistics across multiple versions of the Community Earth System Model, J. Adv. Model. Earth
Sy., 8, 140–165, 2016.
Kooperman, G. J., Pritchard, M. S., O'Brien, T. A., and Timmermans, B. W.:
Rainfall From Resolved Rather Than Parameterized Processes Better Represents
the Present-Day and Climate Change Response of Moderate Rates in the
Community Atmosphere Model, J. Adv. Model. Earth Sy.,
10, 971–988, 2018.
Larson, V. E. and Golaz, J.-C.: Using probability density functions to
derive consistent closure relationships among higher-order moments, Mon.
Weather Rev., 133, 1023–1042, 2005.
Lin, J. W. B. and Neelin, J. D.: Influence of a stochastic moist convective
parameterization on tropical climate variability, Geophys. Res.
Lett., 27, 3691–3694, 2000.
Lin, J. W.-B. and Neelin, J. D.: Considerations for stochastic convective
parameterization, J. Atmos. Sci., 59, 959–975, 2002.
Liu, X., Ma, P.-L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., and Rasch, P. J.: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, 2016.
Loeb, N. G., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F.,
Kato, S., Manalo-Smith, N., and Wong, T.: Toward optimal closure of the
Earth's top-of-atmosphere radiation budget, J. Climate, 22, 748–766,
2009.
Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F., Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M. J., Fan, J., Fisch, G., Goldstein, A. H., Guenther, A., Jimenez, J. L., Pöschl, U., Silva Dias, M. A., Smith, J. N., and Wendisch, M.: Introduction: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16, 4785–4797, https://doi.org/10.5194/acp-16-4785-2016, 2016.
McLinden, C., Olsen, S., Hannegan, B., Wild, O., Prather, M., and Sundet,
J.: Stratospheric ozone in 3-D models: A simple chemistry and the
cross-tropopause flux, J. Geophys. Res.-Atmos., 105,
14653–14665, 2000.
Morrison, H. and Gettelman, A.: A New Two-Moment Bulk Stratiform Cloud
Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3).
Part I: Description and Numerical Tests, J. Climate, 21, 3642–3659,
https://doi.org/10.1175/2008JCLI2105.1, 2008.
O'Gorman, P. A. and Schneider, T.: The physical basis for increases in
precipitation extremes in simulations of 21st-century climate change,
P. Natl. Acad. Sci. USA, 106, 14773–14777, 2009.
Palmer, T. N.: A nonlinear dynamical perspective on model error: A proposal
for non-local stochastic-dynamic parametrization in weather and climate
prediction models, Q. J. Roy. Meteor. Soc.,
127, 279–304, 2001.
Palmer, T. N.: Towards the probabilistic Earth-system simulator: a vision
for the future of climate and weather prediction, Q.
J. Roy. Meteor. Soc., 138, 841–861, 2012.
Pendergrass, A., Coleman, D., Deser, C., Lehner, F., Rosenbloom, N., and
Simpson, I.: Nonlinear response of extreme precipitation to warming in
CESM1, Geophys. Res. Lett., 46, 10551–10560, 2019.
Peters, K., Jakob, C., Davies, L., Khouider, B., and Majda, A. J.:
Stochastic Behavior of Tropical Convection in Observations and a Multicloud
Model, J. Atmos. Sci., 70, 3556–3575, 2013.
Peters, K., Crueger, T., Jakob, C., and Mobis, B.: Improved MJO-simulation
in ECHAM6.3 by coupling a Stochastic Multicloud Model to the convection
scheme, J. Adv. Model. Earth Sy., 9, 193–219, 2017.
Plant, R. S. and Craig, G. C.: A Stochastic Parameterization for Deep
Convection Based on Equilibrium Statistics, J. Atmos.
Sci., 65, 87–105, https://doi.org/10.1175/2007JAS2263.1, 2008.
Rasch, P., Xie, S., Ma, P. L., Lin, W., Wang, H., Tang, Q., Burrows, S.,
Caldwell, P., Zhang, K., and Easter, R.: An overview of the atmospheric
component of the Energy Exascale Earth System Model, J. Adv.
Model. Earth Sy., 11, 2377–2411, 2019.
Rauscher, S. A., O'Brien, T. A., Piani, C., Coppola, E., Giorgi, F.,
Collins, W. D., and Lawston, P. M.: A multimodel intercomparison of
resolution effects on precipitation: simulations and theory, Clim. Dynam., 47,
2205–2218, https://doi.org/10.1007/s00382-015-2959-5, 2016.
Roca, R.: Estimation of extreme daily precipitation thermodynamic scaling
using gridded satellite precipitation products over tropical land,
Environ. Res. Lett., 14, 095009, https://doi.org/10.1088/1748-9326/ab35c6, 2019.
Roca, R., Alexander, L. V., Potter, G., Bador, M., Jucá, R., Contractor, S., Bosilovich, M. G., and Cloché, S.: FROGS: a daily 1∘ × 1∘ gridded precipitation database of rain gauge, satellite and reanalysis products, Earth Syst. Sci. Data, 11, 1017–1035, https://doi.org/10.5194/essd-11-1017-2019, 2019.
Sakradzija, M., Seifert, A., and Heus, T.: Fluctuations in a quasi-stationary shallow cumulus cloud ensemble, Nonlin. Processes Geophys., 22, 65–85, https://doi.org/10.5194/npg-22-65-2015, 2015.
Simmons, A., Uppala, S., Dee, D., and Kobayashi, S.: ERA-Interim: New ECMWF
reanalysis products from 1989 onwards, ECMWF Newsl., 110, 1–11, 2007.
Stone, D., Risser, M. D., Angelil, O., Wehner, M., Cholia, S., Keen, N.,
Krishnan, H., Obrien, T. A., and Collins, W. D.: A basis set for exploration
of sensitivity to prescribed ocean conditions for estimating human
contributions to extreme weather in CAM5.1–1degree, Weather Clim.
Extremes, 19, 10–19, 2018.
Tang, Q., Klein, S. A., Xie, S., Lin, W., Golaz, J.-C., Roesler, E. L., Taylor, M. A., Rasch, P. J., Bader, D. C., Berg, L. K., Caldwell, P., Giangrande, S. E., Neale, R. B., Qian, Y., Riihimaki, L. D., Zender, C. S., Zhang, Y., and Zheng, X.: Regionally refined test bed in E3SM atmosphere model version 1 (EAMv1) and applications for high-resolution modeling, Geosci. Model Dev., 12, 2679–2706, https://doi.org/10.5194/gmd-12-2679-2019, 2019.
Tang, S., Xie, S., Zhang, Y., Zhang, M., Schumacher, C., Upton, H., Jensen, M. P., Johnson, K. L., Wang, M., Ahlgrimm, M., Feng, Z., Minnis, P., and Thieman, M.: Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment, Atmos. Chem. Phys., 16, 14249–14264, https://doi.org/10.5194/acp-16-14249-2016, 2016.
Trenberth, K. E., Zhang, Y., and Gehne, M.: Intermittency in Precipitation:
Duration, Frequency, Intensity, and Amounts Using Hourly Data, J.
Hydrometeorol., 18, 1393–1412, https://doi.org/10.1175/jhm-d-16-0263.1, 2017.
Wang, Y.: A mapping file for the EAMv1 simulation output, Zenodo, https://doi.org/10.5281/zenodo.4543233, 2021.
Wang, Y. and Zhang, G. J.: Global climate impacts of stochastic deep
convection parameterization in the NCAR CAM5, J. Adv.
Model. Earth Sy., 8, 1641–1656, https://doi.org/10.1002/2016MS000756, 2016.
Wang, Y., Liu, X., Hoose, C., and Wang, B.: Different contact angle distributions for heterogeneous ice nucleation in the Community Atmospheric Model version 5, Atmos. Chem. Phys., 14, 10411–10430, https://doi.org/10.5194/acp-14-10411-2014, 2014.
Wang, Y., Zhang, G. J., and Craig, G. C.: Stochastic convective
parameterization improving the simulation of tropical precipitation
variability in the NCAR CAM5, Geophys. Res. Lett., 43, 6612–6619,
https://doi.org/10.1002/2016GL069818, 2016.
Wang, Y., Zhang, G. J., and He, Y. J.: Simulation of precipitation extremes using a stochastic convective parameterization in the NCAR CAM5 under different resolutions, J. Geophys. Res.-Atmos., 122, 12875–12891, 2017.
Wang, Y., Zhang, G. J., and Jiang, Y.: Linking Stochasticity of Convection
to Large-Scale Vertical Velocity to Improve Indian Summer Monsoon Simulation
in the NCAR CAM5, J. Climate, 31, 6985–7002,
https://doi.org/10.1175/jcli-d-17-0785.1, 2018.
Wang, Y., Zhang, G. J., Xie, S., Lin, W., Craig, G. C., Tang, Q., and Ma, H.-Y.: The EAMv1 simulation datasets for the manuscript, Zenodo, https://doi.org/10.5281/zenodo.3902998, 2020.
Wang, Y., Xia, W., Liu, X., Xie, S., Lin, W., Tang, Q., Ma, H.-Y., Jiang, Y., Wang, B., and Zhang, G. J.: Disproportionate control on aerosol burden by light
rain, Nat. Geosci., 14, 72–76, https://doi.org/10.1038/s41561-020-00675-z, 2021a.
Wang, Y., Zhang, G. J., and Craig, G. C.: Stochastic convection code based on the DOE EAMv1, Zenodo, https://doi.org/10.5281/zenodo.4543261, 2021b.
Watson, P. A. G., Berner, J., Corti, S., Davini, P., Von Hardenberg, J.,
Sanchez, C., Weisheimer, A., and Palmer, T. N.: The impact of stochastic
physics on tropical rainfall variability in global climate models on daily
to weekly time scales, J. Geophys. Res., 122, 5738–5762,
2017.
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017.
Willmott, C. J. and Matsuura, K.: Smart interpolation of annually averaged
air temperature in the United States, J. Appl. Meteorol., 34,
2577–2586, 1995.
Xie, P. and Arkin, P. A.: Analyses of Global Monthly Precipitation Using
Gauge Observations, Satellite Estimates, and Numerical Model Predictions,
J. Climate, 9, 840–858, https://doi.org/10.1175/1520-0442(1996)009<0840:AOGMPU>2.0.CO;2, 1996.
Xie, S., Cederwall, R. T., and Zhang, M. H.: Developing long-term
single-column model/cloud system-resolving model forcing using numerical
weather prediction products constrained by surface and top of the atmosphere
observations, J. Geophys. Res., 109, D01104, https://doi.org/10.1029/2003JD004045, 2004.
Xie, S., Lin, W., Rasch, P. J., Ma, P. L., Neale, R., Larson, V. E., Qian,
Y., Bogenschutz, P. A., Caldwell, P., and Cameron-Smith, P.: Understanding
cloud and convective characteristics in version 1 of the E3SM atmosphere
model, J. Adv. Model. Earth Sy., 10, 2618–2644, 2018.
Xie, S., Wang, Y., Lin, W., Ma, H., Tang, Q., Tang, S., Zheng, X., Golaz,
J., Zhang, G. J., and Zhang, M.: Improved Diurnal Cycle of Precipitation in
E3SM With a Revised Convective Triggering Function, J. Adv.
Model. Earth Sy., 11, 2290–2310, 2019.
Zhang, G. J. and McFarlane, N. A.: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model, Atmosphere-Ocean, 33, 407–446, 1995.
Zhang, G. J. and Mu, M.: Simulation of the Madden – Julian Oscillation in
the NCAR CCM3 Using a Revised Zhang-McFarlane Convection Parameterization
Scheme, J. Climate, 18, 4046–4064, 2005a.
Zhang, G. J. and Mu, M.: Effects of modifications to the Zhang-McFarlane
convection parameterization on the simulation of the tropical precipitation
in the National Center for Atmospheric Research Community Climate Model,
version 3, J. Geophys. Res.-Atmos., 110, D09109,
https://doi.org/10.1029/2004JD005617, 2005b.
Zhang, G. J. and Wang, H.: Toward mitigating the double ITCZ problem in
NCAR CCSM3, Geophys. Res. Lett., 33, L06709, https://doi.org/10.1029/2005GL025229, 2006.
Zhang, G. J., Song, X., and Wang, Y.: The double ITCZ syndrome in GCMs: A
coupled feedback problem among convection, clouds, atmospheric and ocean
circulations, Atmos. Res., 229, 255–268, https://doi.org/10.1016/j.atmosres.2019.06.023, 2019.
Short summary
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.
A stochastic deep convection parameterization is implemented into the US Department of Energy...
