Reduced floating-point precision in regional climate simulations: An ensemble-based statistical verification

Abstract.

The use of single precision in floating-point representation has become increasingly common in operational weather prediction. Meanwhile, climate simulations are still typically run in double precision. The reasons for this are likely manifold and range from concerns about compliance to conservation laws to the unknown effect of single precision on slow processes, or simply the less frequent opportunity and higher computational costs of validation.

Using an ensemble-based statistical methodology, Zeman and Schär (2022) could detect differences between double- and single-precision simulations from the regional weather and climate model COSMO. However, these differences are minimal and often only detectable during the first few hours or days of the simulation. To evaluate whether these differences are relevant for regional climate simulations, we have conducted 10-year-long ensemble simulations over the EURO-CORDEX domain in single and double precision with 100 ensemble members.

By applying the statistical testing at a grid-cell level for 47 output variables every 12 or 24 hours, we only detected a marginally increased rejection rate for the single-precision climate simulations compared to the double-precision reference. This increase in the rejection rate is much smaller than that arising from minor variations of the horizontal diffusion coefficient in the model. Therefore, we deem it negligible.

To our knowledge, this study represents the most comprehensive analysis so far on the effects of reduced precision in a climate simulation for a realistic setting, namely with a fully-fledged regional climate model in a configuration that has already been used for climate change impact and adaptation studies. The ensemble-based verification of model output at a grid-cell level and high temporal resolution is very sensitive and suitable for verifying climate models. Furthermore, the verification methodology is model agnostic, meaning it can be applied to any model. Our findings encourage exploiting the reduction of computational costs ( ∼ 30 % for COSMO) obtained from reduced precision for regional climate simulations.