Articles | Volume 9, issue 7
https://doi.org/10.5194/gmd-9-2391-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-2391-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
12 Jul 2016
Development and technical paper |
| 12 Jul 2016
Evaluating statistical consistency in the ocean model component of the Community Earth System Model (pyCECT v2.0)
The National Center for Atmospheric Research, Boulder, CO, USA
Yong Hu
Center for Earth System Science,Tsinghua University, Beijing 100084, China
Joint Center for Global Change Studies, Beijing 100875, China
Dorit M. Hammerling
The National Center for Atmospheric Research, Boulder, CO, USA
Yu-heng Tseng
The National Center for Atmospheric Research, Boulder, CO, USA
Haiying Xu
The National Center for Atmospheric Research, Boulder, CO, USA
Xiaomeng Huang
Center for Earth System Science,Tsinghua University, Beijing 100084, China
Joint Center for Global Change Studies, Beijing 100875, China
Frank O. Bryan
The National Center for Atmospheric Research, Boulder, CO, USA
Guangwen Yang
Center for Earth System Science,Tsinghua University, Beijing 100084, China
Joint Center for Global Change Studies, Beijing 100875, China
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Cited
8 citations as recorded by crossref.
- An ensemble-based statistical methodology to detect differences in weather and climate model executables C. Zeman & C. Schär 10.5194/gmd-15-3183-2022
- Ensemble-based statistical verification of INM RAS Earth system model M. Tarasevich et al. 10.1515/rnam-2023-0014
- A deep-learning-based consistency test approach for Earth system models on HPC systems Y. Yu et al. 10.1016/j.isci.2024.111574
- Nine time steps: ultra-fast statistical consistency testing of the Community Earth System Model (pyCECT v3.0) D. Milroy et al. 10.5194/gmd-11-697-2018
- swNEMO_v4.0: an ocean model based on NEMO4 for the new-generation Sunway supercomputer Y. Ye et al. 10.5194/gmd-15-5739-2022
- On Preserving Scientific Integrity for Climate Model Data in the HPC Era A. Baker 10.1109/MCSE.2021.3119509
- A comparison of model validation approaches for echo state networks using climate model replicates K. McClernon et al. 10.1016/j.spasta.2024.100813
- A new and inexpensive non-bit-for-bit solution reproducibility test based on time step convergence (TSC1.0) H. Wan et al. 10.5194/gmd-10-537-2017
8 citations as recorded by crossref.
- An ensemble-based statistical methodology to detect differences in weather and climate model executables C. Zeman & C. Schär 10.5194/gmd-15-3183-2022
- Ensemble-based statistical verification of INM RAS Earth system model M. Tarasevich et al. 10.1515/rnam-2023-0014
- A deep-learning-based consistency test approach for Earth system models on HPC systems Y. Yu et al. 10.1016/j.isci.2024.111574
- Nine time steps: ultra-fast statistical consistency testing of the Community Earth System Model (pyCECT v3.0) D. Milroy et al. 10.5194/gmd-11-697-2018
- swNEMO_v4.0: an ocean model based on NEMO4 for the new-generation Sunway supercomputer Y. Ye et al. 10.5194/gmd-15-5739-2022
- On Preserving Scientific Integrity for Climate Model Data in the HPC Era A. Baker 10.1109/MCSE.2021.3119509
- A comparison of model validation approaches for echo state networks using climate model replicates K. McClernon et al. 10.1016/j.spasta.2024.100813
- A new and inexpensive non-bit-for-bit solution reproducibility test based on time step convergence (TSC1.0) H. Wan et al. 10.5194/gmd-10-537-2017
Latest update: 30 Dec 2024
Short summary
Software quality assurance is critical to detecting errors in large, complex climate simulation codes. We focus on ocean model simulation data in the context of an ensemble-based statistical consistency testing approach developed for atmospheric data. Because ocean and atmosphere models have differing characteristics, we develop a new statistical tool to evaluate ocean model simulation data that provide a simple, subjective, and systematic way to detect errors and instil model confidence.
Software quality assurance is critical to detecting errors in large, complex climate simulation...
