Over the years, global climate modelling has advanced, aiming for realistic and precise models by increasing their complexity. An integral component of climate models, the physics parameterizations, are a major limitation, but are required due to limited computational power. Grid adaptivity is an avenue that is being explored to mitigate these challenges, but comes with its own difficulties. For example, the question of whether the physics should be ``scale-aware’’, by adjusting according to the resolution and the fact that parameterizations are optimized for specific grid ranges. To research these challenges, test cases that work in both the adaptive and non-adaptive cases are required. This thesis concentrates on physics parameterizations of Atmospheric Global Climate Models (AGCMs) presenting the current hierarchy of idealized physics parameterizations found in the literature. It focuses on and provides a comprehensive explanation of a simplified dry physics model for AGCMs, exploring where it is situated in the current hierarchy and its steady states in the uncoupled case. A coupling of the physics model to the adaptive dynamical core wavetrisk is explained and explored. This includes characterizing the results in the non-adaptive case for time convergence, grid convergence, and the effects of the soil, while also benchmarking the climatology of the coupling. The simplified dry physics model introduces another level of complexity in the current dry physics hierarchy and is stable in the coupled and uncoupled cases. A decreasing temperature trend with height is observed, however warmer surface temperatures and cooler upper atmosphere temperatures, than that of Earth, are produced in the steady states. Additionally a linear rate of convergence in space is noted and an improvement in parallel efficiency with resolution is required. Overall these results can be used as a benchmark for future coupling in the adaptive case. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29079 |
| Date | January 2023 |
| Creators | Ching-Johnson, Gabrielle |
| Contributors | Kevlahan, Nicholas, Computational Engineering and Science |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0026 seconds