Articles | Volume 8, issue 9
https://doi.org/10.5194/gmd-8-2829-2015
https://doi.org/10.5194/gmd-8-2829-2015
Development and technical paper
 | 
09 Sep 2015
Development and technical paper |  | 09 Sep 2015

A new ensemble-based consistency test for the Community Earth System Model (pyCECT v1.0)

A. H. Baker, D. M. Hammerling, M. N. Levy, H. Xu, J. M. Dennis, B. E. Eaton, J. Edwards, C. Hannay, S. A. Mickelson, R. B. Neale, D. Nychka, J. Shollenberger, J. Tribbia, M. Vertenstein, and D. Williamson

Related authors

Optimizing high-resolution Community Earth System Model on a heterogeneous many-core supercomputing platform
Shaoqing Zhang, Haohuan Fu, Lixin Wu, Yuxuan Li, Hong Wang, Yunhui Zeng, Xiaohui Duan, Wubing Wan, Li Wang, Yuan Zhuang, Hongsong Meng, Kai Xu, Ping Xu, Lin Gan, Zhao Liu, Sihai Wu, Yuhu Chen, Haining Yu, Shupeng Shi, Lanning Wang, Shiming Xu, Wei Xue, Weiguo Liu, Qiang Guo, Jie Zhang, Guanghui Zhu, Yang Tu, Jim Edwards, Allison Baker, Jianlin Yong, Man Yuan, Yangyang Yu, Qiuying Zhang, Zedong Liu, Mingkui Li, Dongning Jia, Guangwen Yang, Zhiqiang Wei, Jingshan Pan, Ping Chang, Gokhan Danabasoglu, Stephen Yeager, Nan Rosenbloom, and Ying Guo
Geosci. Model Dev., 13, 4809–4829, https://doi.org/10.5194/gmd-13-4809-2020,https://doi.org/10.5194/gmd-13-4809-2020, 2020
Short summary
Nine time steps: ultra-fast statistical consistency testing of the Community Earth System Model (pyCECT v3.0)
Daniel J. Milroy, Allison H. Baker, Dorit M. Hammerling, and Elizabeth R. Jessup
Geosci. Model Dev., 11, 697–711, https://doi.org/10.5194/gmd-11-697-2018,https://doi.org/10.5194/gmd-11-697-2018, 2018
Short summary
Evaluating lossy data compression on climate simulation data within a large ensemble
Allison H. Baker, Dorit M. Hammerling, Sheri A. Mickelson, Haiying Xu, Martin B. Stolpe, Phillipe Naveau, Ben Sanderson, Imme Ebert-Uphoff, Savini Samarasinghe, Francesco De Simone, Francesco Carbone, Christian N. Gencarelli, John M. Dennis, Jennifer E. Kay, and Peter Lindstrom
Geosci. Model Dev., 9, 4381–4403, https://doi.org/10.5194/gmd-9-4381-2016,https://doi.org/10.5194/gmd-9-4381-2016, 2016
Short summary
P-CSI v1.0, an accelerated barotropic solver for the high-resolution ocean model component in the Community Earth System Model v2.0
Xiaomeng Huang, Qiang Tang, Yuheng Tseng, Yong Hu, Allison H. Baker, Frank O. Bryan, John Dennis, Haohuan Fu, and Guangwen Yang
Geosci. Model Dev., 9, 4209–4225, https://doi.org/10.5194/gmd-9-4209-2016,https://doi.org/10.5194/gmd-9-4209-2016, 2016
Short summary
Evaluating statistical consistency in the ocean model component of the Community Earth System Model (pyCECT v2.0)
Allison H. Baker, Yong Hu, Dorit M. Hammerling, Yu-heng Tseng, Haiying Xu, Xiaomeng Huang, Frank O. Bryan, and Guangwen Yang
Geosci. Model Dev., 9, 2391–2406, https://doi.org/10.5194/gmd-9-2391-2016,https://doi.org/10.5194/gmd-9-2391-2016, 2016
Short summary

Related subject area

Climate and Earth system modeling
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024,https://doi.org/10.5194/gmd-17-3111-2024, 2024
Short summary
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024,https://doi.org/10.5194/gmd-17-3081-2024, 2024
Short summary
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024,https://doi.org/10.5194/gmd-17-3099-2024, 2024
Short summary
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024,https://doi.org/10.5194/gmd-17-3063-2024, 2024
Short summary
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024,https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary

Cited articles

Baker, A. H., Xu, H., Dennis, J. M., Levy, M. N., Nychka, D., Mickelson, S. A., Edwards, J., Vertenstein, M., and Wegener, A.: A methodology for evaluating the impact of data compression on climate simulation data, in: Proceedings of the 23rd international symposium on High-Performance Parallel and Distributed Computing, HPDC '14, 203–214, 2014.
Carson II, J. S.: Model verification and validation, in: Proceedings of the 2002 Winter Simulation Conference, 52–58, 2002.
Clune, T. and Rood, R.: Software testing and verification in climate model development, IEEE Softw., 28, 49–55, https://doi.org/10.1109/MS.2011.117, 2011.
Dai, A., Meehl, G., Washington, W., Wigley, T., and Arblaster, J. M.: Ensemble simulation of 21st century climate changes: business as usual vs. CO2 stabilization, B. Am. Meteor. Soc., 82, 2377–2388, 2001.
Easterbrook, S. M. and Johns, T. C.: Engineering the software for understanding climate change, Comput. Sci. Eng., 11, 65–74, https://doi.org/10.1109/MCSE.2009.193, 2009.
Download
Short summary
Climate simulation codes are especially complex, and their ongoing state of development requires frequent software quality assurance to both preserve code quality and instil model confidence. To formalize and simplify this previously subjective and expensive process, we have developed a new tool for evaluating climate consistency. The tool has proven its utility in detecting errors in software and hardware environments and providing rapid feedback to model developers.