the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Nov 2024
UA-ICON with NWP physics package (version: ua-icon-2.1): mean state and variability of the middle atmosphere
Abstract. The Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension (UA-ICON) in the configuration with the physics package for numerical weather prediction (NWP) is presented with optimized parameter settings for the non-orographic and orographic gravity wave drag parameterizations (GWD). In this paper, we present UA-ICON(NWP) (version: ua-icon-2.1) in which we implemented optimized parameter settings for the GWD parameterizations to achieve more realistic MLT temperatures and zonal winds. The parameter optimization is based on perpetual January simulations targeting the thermal and dynamic state of the MLT and the Northern Hemisphere stratosphere. The climatology and variability of the Northern Hemisphere stratospheric winter circulation widely improve when applying UA-ICON with the NWP physics package compared to UA-ICON with ECHAM physics. Likewise improves the thermal and dynamic state of the MLT of the re-tuned UA-ICON(NWP) compared with the UA-ICON(NWP) using default settings. For UA-ICON(NWP), a statistical evaluation reveals a slight improvement in the stratosphere/mesosphere coupling compared to UA-ICON(ECHAM). The cold summer mesopause, the warm winter stratopause, and the related wind reversals are reasonably simulated. The GWD parameter optimization further significantly improves the frequency of major sudden stratospheric warmings (SSWs). However, the seasonal distribution needs improvement and the relative frequency of split vortex SSWs is underestimated compared to reanalyses, as is the zonal wavenumber 2 preconditioning of SSWs. This indicates that zonal wavenumber 2 forcing in UA-ICON(NWP) is underrepresented. The analysis of migrating diurnal and semidiurnal tides in temperature shows a good agreement of UA-ICON(NWP) with SABER-derived tides and the enhancement of the migrating semidiurnal tide during SSWs is well represented in UA-ICON(NWP).
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RC1: 'Comment on gmd-2024-191', Michael García Rodríguez, 09 Dec 2024
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The work presented in the article on the implementation and optimisation of the UA-ICON(NWP) model stands out for its comprehensive approach in improving the representation of the middle and upper atmosphere. Its contribution is particularly valuable in effectively addressing the parameterisation of complex phenomena such as gravity wave drag and atmospheric tides, achieving a notable improvement in the simulation of key climatic conditions and phenomena like sudden stratospheric warmings. It is evident that the team has made a significant effort to balance scientific accuracy with computational efficiency, which is crucial in the context of general circulation models that require intensive resources. Moreover, this advancement represents an important step in the understanding and simulation of atmospheric dynamics on a global scale, making science reproducible by using public repositories and open-access journals.
From a software engineering perspective applied to climate models, it would be interesting to gain a deeper understanding of the architectural decisions behind the development of the model's code. Therefore, we pose the following questions:
- For the new configuration, were specific design patterns such as Facade or Dependency Injection used to structure the model’s code? This would ensure the separation between physical parameterisations, dynamic processes, and the model logic.
- To coordinate the interactions between the different physical and dynamic parameterisations, do you use patterns such as Observer or Mediator? If so, what benefits have you observed in terms of performance, maintainability, or scalability?
- To manage complex parameterisations (such as orographic gravity waves), have specific techniques based on patterns like Strategy been implemented?
- To ensure the reliability of both the simulations and the implemented code, have automated testing frameworks or static code analysis tools, such as pFUnit and FortranAnalyser, been employed in the development process? If so, how have they contributed to identifying and addressing potential issues in the codebase? The use of tools such as FortranAnalyser would be interesting to mention in order to be able to verify that the quality of the developed code is maintained or improved with the development of new versions of the software.
Citation: https://doi.org/10.5194/gmd-2024-191-RC1
Model code and software
Supplementary information on - UA-ICON with NWP physics package (version: ua-icon-2.1): mean state and variability of the middle atmosphere Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt https://doi.org/10.5281/zenodo.13927891
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