[A-AS08] Weather, Climate, and Environmental Science Studies using High-Performance Computing

[AAS08-05] Importance of Minor-Looking Treatments in Global Climate Models

*Hideaki Kawai¹, Kohei Yoshida¹, Tsuyoshi Koshiro¹, Seiji Yukimoto¹ (1.Meteorological Research Institute)

Keywords:climate model, numerical prediction model, parameterization

Modelers know very well that parameter tuning can drastically control the performance of climate models. However, parameter tuning is not the only implementation detail that can drastically affect model performance and the representation of various phenomena in models. For example, lower limits (sometimes upper limits) of parameters often control the model performance critically. Not only lower limits but also thresholds of variables that control the enabling or disabling of a specific process sometimes exert a large influence on the performance. In addition, whether two schemes can work together or only one scheme of them exclusively works affects significantly the results. The importance of these treatments is often overlooked and not discussed in the literature. However, the impacts of such minor-looking treatments are often even much larger than introducing advanced parameterizations based on theory or observation. We would like to show a number of examples of various minor-looking treatments that can considerably affect model performance (Kawai et al. 2022, JAMES). Many of them are important not only for GCMs but also for global numerical weather prediction models and even for regional scale models.