Articles | Volume 14, issue 4
https://doi.org/10.5194/gmd-14-1921-2021
© 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-14-1921-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
12 Apr 2021
Model evaluation paper |
| 12 Apr 2021
| 12 Apr 2021
Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Shixuan Zhang
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Philip J. Rasch
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Vincent E. Larson
Department of Mathematical Sciences, University of Wisconsin – Milwaukee, Milwaukee, Wisconsin, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Xubin Zeng
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, Arizona, USA
Huiping Yan
School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
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Cited
12 citations as recorded by crossref.
- Cloud Process Coupling and Time Integration in the E3SM Atmosphere Model S. Santos et al. 10.1029/2020MS002359
- Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model H. Schmidt et al. 10.5194/gmd-17-1563-2024
- Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 2: A semi-discrete error analysis framework for assessing coupling schemes C. Vogl et al. 10.5194/gmd-17-1409-2024
- Effective radiative forcing of anthropogenic aerosols in E3SM version 1: historical changes, causality, decomposition, and parameterization sensitivities K. Zhang et al. 10.5194/acp-22-9129-2022
- Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects S. Zhang et al. 10.5194/gmd-15-6787-2022
- CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM) H. Wan et al. 10.5194/gmd-15-3205-2022
- Sowing Storms: How Model Timestep Can Control Tropical Cyclone Frequency in a GCM C. Zarzycki 10.1029/2021MS002791
- Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling X. Li et al. 10.5194/gmd-16-2975-2023
- The Energy Exascale Earth System Model Simulations With High Vertical Resolution in the Lower Troposphere P. Bogenschutz et al. 10.1029/2020MS002239
- Removing Numerical Pathologies in a Turbulence Parameterization Through Convergence Testing S. Zhang et al. 10.1029/2023MS003633
- Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach J. Li et al. 10.5194/gmd-17-1327-2024
- Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme H. Wan et al. 10.5194/gmd-17-1387-2024
12 citations as recorded by crossref.
- Cloud Process Coupling and Time Integration in the E3SM Atmosphere Model S. Santos et al. 10.1029/2020MS002359
- Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model H. Schmidt et al. 10.5194/gmd-17-1563-2024
- Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 2: A semi-discrete error analysis framework for assessing coupling schemes C. Vogl et al. 10.5194/gmd-17-1409-2024
- Effective radiative forcing of anthropogenic aerosols in E3SM version 1: historical changes, causality, decomposition, and parameterization sensitivities K. Zhang et al. 10.5194/acp-22-9129-2022
- Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects S. Zhang et al. 10.5194/gmd-15-6787-2022
- CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM) H. Wan et al. 10.5194/gmd-15-3205-2022
- Sowing Storms: How Model Timestep Can Control Tropical Cyclone Frequency in a GCM C. Zarzycki 10.1029/2021MS002791
- Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling X. Li et al. 10.5194/gmd-16-2975-2023
- The Energy Exascale Earth System Model Simulations With High Vertical Resolution in the Lower Troposphere P. Bogenschutz et al. 10.1029/2020MS002239
- Removing Numerical Pathologies in a Turbulence Parameterization Through Convergence Testing S. Zhang et al. 10.1029/2023MS003633
- Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach J. Li et al. 10.5194/gmd-17-1327-2024
- Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme H. Wan et al. 10.5194/gmd-17-1387-2024
Latest update: 25 Apr 2024
Short summary
Numerical models used in weather and climate research and prediction unavoidably contain numerical errors resulting from temporal discretization, and the impact of such errors can be substantial. Complex process interactions often make it difficult to pinpoint the exact sources of such errors. This study uses a series of sensitivity experiments to identify components in a global atmosphere model that are responsible for time step sensitivities in various cloud regimes.
Numerical models used in weather and climate research and prediction unavoidably contain...
