A Guiding Principles for Choosing Numerical Precision in Atmospheric Model based on CESM

The next generation of weather and climate models will have an unprecedented level of resolution and model complexity, while also increasing the requirements for calculation and memory speed. Reducing the accuracy of certain variables and using mixed precision methods in atmospheric models can greatly improve Computing and memory speed. However, in order to ensure the accuracy of the results, most models have over-designed numerical accuracy, which results in that occupied resources have being much larger than the required resources. Previous studies have shown that the necessary precision for an accurate weather model has clear scale dependence, with large spatial scales requiring higher precision than small scales. Even at large scales the necessary precision is far below that of double precision. However, it is difficult to find a guided method to assign different precisions to different variables, so that it can save unnecessary waste. This paper will take CESM1.2.1 as a research object to conduct a large number of tests to reduce accuracy, and propose a new discrimination method similar to the CFL criterion. This method can realize the correlation verification of a single variable, thereby determining which variables can use a lower level of precision without degrading the accuracy of the results.