Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5567-2020
https://doi.org/10.5194/gmd-13-5567-2020
Development and technical paper
 | 
13 Nov 2020
Development and technical paper |  | 13 Nov 2020

A new end-to-end workflow for the Community Earth System Model (version 2.0) for the Coupled Model Intercomparison Project Phase 6 (CMIP6)

Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein

Related authors

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
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
Geosci. Model Dev., 8, 2829–2840, https://doi.org/10.5194/gmd-8-2829-2015,https://doi.org/10.5194/gmd-8-2829-2015, 2015
Short summary

Related subject area

Earth and space science informatics
Machine learning for numerical weather and climate modelling: a review
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023,https://doi.org/10.5194/gmd-16-6433-2023, 2023
Short summary
Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023,https://doi.org/10.5194/gmd-16-5979-2023, 2023
Short summary
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023,https://doi.org/10.5194/gmd-16-5825-2023, 2023
Short summary
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023,https://doi.org/10.5194/gmd-16-5113-2023, 2023
Short summary
A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation
Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du
Geosci. Model Dev., 16, 2777–2794, https://doi.org/10.5194/gmd-16-2777-2023,https://doi.org/10.5194/gmd-16-2777-2023, 2023
Short summary

Cited articles

Abdulla, G.: Annual Earth System Grid Federation 2019 Progress Report, available at: https://esgf.llnl.gov/esgf-media/pdf/2019-ESGF-Progress-Report.pdf (last access: November 2020), 2019. a
Atmospheric Diagnostics Results: Atmospheric Diagnostics, available at: http://webext.cgd.ucar.edu/B1850/PMIP4/atm/b.e21.B1850.f09_g17.PMIP4-midHolo.001.90_109-b.e21.B1850.f09_g17.CMIP6-piControl.001.614_633/ (last access: November 2020), 2019. a
Bertini, A. and Mickelson, S.: CESM Postprocessing (verison 2.2.1), https://doi.org/10.5065/4XV0-FG55, 2019. a, b
CESM Diagnostics Results: CESM Diagnostics, available at: http://webext.cgd.ucar.edu/ (last access: November 2020), 2019. a, b
Cheyenne: Computational and Information Systems Laboratory, Cheyenne: HPE/SGI ICE XA System (Climate Simulation Laboratory), National Center for Atmospheric Research, Boulder, CO, https://doi.org/10.5065/D6RX99HX, 2017. a
Download
Short summary
Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.